ReferenceIntegration

workflowr

• Summary
• Checks
• Past versions

Last updated: 2021-07-05

Checks: 6 1

Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it's best to always run the code in an empty environment.

Seed: set.seed(20200804)

The command set.seed(20200804) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: absolute

Using absolute paths to the files within your workflowr project makes it difficult for you and others to run your code on a different machine. Change the absolute path(s) below to the suggested relative path(s) to make your code more reproducible.

absolute	relative
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/mergedObjects/Harmony.Batchindividual.rds	../output/mergedObjects/Harmony.Batchindividual.rds
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/hESC1.rawdge.txt.gz	../data/dge/hESC1.rawdge.txt.gz
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/iPS-to-EB_20Day_dge.txt.gz	../data/dge/iPS-to-EB_20Day_dge.txt.gz
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds	../output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/TranferredAnnotations_ReferenceInt_JustNeuron.csv	../output/TranferredAnnotations_ReferenceInt_JustNeuron.csv

Repository version: f17b081

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version f17b081. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/.Rhistory
    Ignored:    output/.Rhistory

Untracked files:
    Untracked:  GSE122380_raw_counts.txt.gz
    Untracked:  UTF1_plots.Rmd
    Untracked:  analysis/OLD/
    Untracked:  analysis/child/
    Untracked:  build_refint_scale.R
    Untracked:  build_refint_sct.R
    Untracked:  build_stuff.R
    Untracked:  build_varpart_sc.R
    Untracked:  code/.ipynb_checkpoints/
    Untracked:  code/CellRangerPreprocess.Rmd
    Untracked:  code/GEO_processed_data.Rmd
    Untracked:  code/PowerAnalysis_NoiseRatio.ipynb
    Untracked:  code/Untitled.ipynb
    Untracked:  code/Untitled1.ipynb
    Untracked:  data/HCL_Fig1_adata.h5ad
    Untracked:  data/HCL_Fig1_adata.h5seurat
    Untracked:  data/dge/
    Untracked:  data/dge_raw_data.tar.gz
    Untracked:  data/ref.expr.rda
    Untracked:  figure/
    Untracked:  output/CR_sampleQCrds/
    Untracked:  output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds
    Untracked:  output/CaoEtAl.Obj.rds
    Untracked:  output/ClusterInfo_res0.1.csv
    Untracked:  output/DGELists/
    Untracked:  output/DownSampleVarPart.rds
    Untracked:  output/Frequency.MostCommonAnnotation.FiveNearestRefCells.csv
    Untracked:  output/GEOsubmissionProcessedFiles/
    Untracked:  output/GeneLists_by_minPCT/
    Untracked:  output/MostCommonAnnotation.FiveNearestRefCells.csv
    Untracked:  output/NearestReferenceCell.Cao.hESC.EuclideanDistanceinHarmonySpace.csv
    Untracked:  output/NearestReferenceCell.Cao.hESC.FrequencyofEachAnnotation.csv
    Untracked:  output/NearestReferenceCell.SCTregressRNAassay.Cao.hESC.EuclideanDistanceinHarmonySpace.csv
    Untracked:  output/NearestReferenceCell.SCTregressRNAassay.Cao.hESC.FrequencyofEachAnnotation.csv
    Untracked:  output/Pseudobulk_Limma_res0.1_OnevAllTopTables.csv
    Untracked:  output/Pseudobulk_Limma_res0.1_OnevAll_top10Upregby_adjP.csv
    Untracked:  output/Pseudobulk_Limma_res0.1_OnevAll_top10Upregby_logFC.csv
    Untracked:  output/Pseudobulk_Limma_res0.5_OnevAllTopTables.csv
    Untracked:  output/Pseudobulk_Limma_res0.8_OnevAllTopTables.csv
    Untracked:  output/Pseudobulk_Limma_res1_OnevAllTopTables.csv
    Untracked:  output/Pseudobulk_VarPart.ByCluster.Res0.1.rds
    Untracked:  output/ResidualVariances_fromDownSampAnalysis.csv
    Untracked:  output/SingleCell_VariancePartition_RNA_Res0.1_minPCT0.2.rds
    Untracked:  output/SingleCell_VariancePartition_Res0.1_minPCT0.2.rds
    Untracked:  output/SingleCell_VariancePartition_SCT_Res0.1_minPCT0.2.rds
    Untracked:  output/TopicModelling_k10_top10drivergenes.byBeta.csv
    Untracked:  output/TopicModelling_k6_top10drivergenes.byBeta.csv
    Untracked:  output/TopicModelling_k6_top15drivergenes.byZ.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustEarlyEcto.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustEndoderm.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustMeso.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustNeuralCrest.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustNeuron.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustPluripotent.csv
    Untracked:  output/VarPart.ByCluster.Res0.1.rds
    Untracked:  output/azimuth/
    Untracked:  output/downsamp_10800cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/downsamp_16200cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/downsamp_21600cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/downsamp_2700cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/downsamp_2700cells_10subreps_medianexplainedbyresiduals_varpart_scres.rds
    Untracked:  output/downsamp_5400cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/downsamp_7200cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/fasttopics/
    Untracked:  output/figs/
    Untracked:  output/merge.Cao.SCTwRegressOrigIdent.rds
    Untracked:  output/merge.all.SCTwRegressOrigIdent.Harmony.rds
    Untracked:  output/merged.SCT.counts.matrix.rds
    Untracked:  output/merged.raw.counts.matrix.rds
    Untracked:  output/mergedObjects/
    Untracked:  output/pdfs/
    Untracked:  output/sampleQCrds/
    Untracked:  output/splitgpm_gsea_results/
    Untracked:  slurm-12005914.out
    Untracked:  slurm-12005923.out

Unstaged changes:
    Deleted:    analysis/IntegrateAnalysis.afterFilter.HarmonyBatch.Rmd
    Deleted:    analysis/IntegrateAnalysis.afterFilter.HarmonyBatchSampleIDindividual.Rmd
    Deleted:    analysis/IntegrateAnalysis.afterFilter.NOHARMONYjustmerge.Rmd
    Deleted:    analysis/IntegrateAnalysis.afterFilter.SCTregressBatchIndividual.Rmd
    Deleted:    analysis/IntegrateAnalysis.afterFilter.SCTregressBatchIndividualHarmonyBatchindividual.Rmd
    Deleted:    analysis/RunscHCL_HarmonyBatchInd.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/IntegrateReference_SCTregressCaoPlusScHCL_JustNeuron.Rmd) and HTML (docs/IntegrateReference_SCTregressCaoPlusScHCL_JustNeuron.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	1ff079e	KLRhodes	2021-07-05	wflow_git_commit(c("analysis/IntegrateReference_SCTregressCaoPlusScHCL_Just*",
html	1ff079e	KLRhodes	2021-07-05	wflow_git_commit(c("analysis/IntegrateReference_SCTregressCaoPlusScHCL_Just*",

---

library(Seurat)
library(edgeR)

Loading required package: limma

library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(tidyr)
library(tibble)
library(purrr)
library(harmony)

Loading required package: Rcpp

library(ggplot2)

loading data

first, my data

merged<- readRDS("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/mergedObjects/Harmony.Batchindividual.rds")

subset to only neurons (cluster 5, res 0.1)

merged<- subset(merged, idents = 5)

DimPlot(merged)

loading in hESC and iPS-to-EB raw dges from scHCL reference set

hESC<- read.table("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/hESC1.rawdge.txt.gz", header=T, row.names = 1)

iPStoEBday20<- read.table("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/iPS-to-EB_20Day_dge.txt.gz", header=T, row.names = 1)

Note: there is no available metadata for these iPS to EB differentiations (no cell annotations online)

make a seurat objects with all of the data

hESC.obj<- CreateSeuratObject(hESC)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

EB20.obj<- CreateSeuratObject(iPStoEBday20)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

#normalizing each dataset
hESC.obj<- suppressWarnings(SCTransform(hESC.obj, variable.features.n=5000,verbose=F))

EB20.obj<-suppressWarnings(SCTransform(EB20.obj, variable.features.n=5000,verbose=F))

Cao.obj<-readRDS("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds")

Cao.obj<- RunPCA(Cao.obj, npcs= 100, verbose = F)

Cao.obj<- FindNeighbors(Cao.obj, dims = 1:30, verbose = F)
Cao.obj<- RunUMAP(Cao.obj, dims=1:30, verbose = F)

Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session

#rename Cao metadata so none match with EB (just need to replace Batch)
colnames(Cao.obj@meta.data)

 [1] "orig.ident"        "nCount_RNA"        "nFeature_RNA"     
 [4] "Assay"             "Batch"             "Experiment_batch" 
 [7] "Main_cluster_name" "Fetus_id"          "Sex"              
[10] "nCount_SCT"        "nFeature_SCT"     

colnames(Cao.obj@meta.data)[5]<-"Batch_week"

#rename orig.idents
hESC.obj$orig.ident<- "scHCL.hESC"
EB20.obj$orig.ident<- "scHCL.EB20"
merged$orig.ident<- "EB.Pilot"
Cao.obj$orig.ident<- "Cao.EtAl"
#merge objects
obj.list<- list(Cao.obj, merged, hESC.obj, EB20.obj)
merge.all<- merge(x=obj.list[[1]], y=c(obj.list[[2]], obj.list[[3]], obj.list[[4]]), merge.data=T)

FeatureScatter(merge.all, feature1 = "nCount_SCT", feature2 = "nFeature_SCT", group.by = "orig.ident")

FeatureScatter(merge.all, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "orig.ident")

merge.all<- SCTransform(merge.all, variable.features.n = 5000, vars.to.regress = c("orig.ident"), assay= "SCT")

  |                                                                            
  |                                                                      |   0%

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |=========                                                             |  12%

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |==================                                                    |  25%

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |==========================                                            |  38%

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |===================================                                   |  50%

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |============================================                          |  62%

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |====================================================                  |  75%

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |=============================================================         |  88%

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in sqrt(1/i): NaNs produced

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |=                                                                     |   1%
  |                                                                            
  |==                                                                    |   2%
  |                                                                            
  |==                                                                    |   3%
  |                                                                            
  |===                                                                   |   5%
  |                                                                            
  |====                                                                  |   6%
  |                                                                            
  |=====                                                                 |   7%
  |                                                                            
  |======                                                                |   8%
  |                                                                            
  |======                                                                |   9%
  |                                                                            
  |=======                                                               |  10%
  |                                                                            
  |========                                                              |  11%
  |                                                                            
  |=========                                                             |  12%
  |                                                                            
  |==========                                                            |  14%
  |                                                                            
  |==========                                                            |  15%
  |                                                                            
  |===========                                                           |  16%
  |                                                                            
  |============                                                          |  17%
  |                                                                            
  |=============                                                         |  18%
  |                                                                            
  |==============                                                        |  19%
  |                                                                            
  |==============                                                        |  20%
  |                                                                            
  |===============                                                       |  22%
  |                                                                            
  |================                                                      |  23%
  |                                                                            
  |=================                                                     |  24%
  |                                                                            
  |==================                                                    |  25%
  |                                                                            
  |==================                                                    |  26%
  |                                                                            
  |===================                                                   |  27%
  |                                                                            
  |====================                                                  |  28%
  |                                                                            
  |=====================                                                 |  30%
  |                                                                            
  |=====================                                                 |  31%
  |                                                                            
  |======================                                                |  32%
  |                                                                            
  |=======================                                               |  33%
  |                                                                            
  |========================                                              |  34%
  |                                                                            
  |=========================                                             |  35%
  |                                                                            
  |=========================                                             |  36%
  |                                                                            
  |==========================                                            |  38%
  |                                                                            
  |===========================                                           |  39%
  |                                                                            
  |============================                                          |  40%
  |                                                                            
  |=============================                                         |  41%
  |                                                                            
  |=============================                                         |  42%
  |                                                                            
  |==============================                                        |  43%
  |                                                                            
  |===============================                                       |  44%
  |                                                                            
  |================================                                      |  45%
  |                                                                            
  |=================================                                     |  47%
  |                                                                            
  |=================================                                     |  48%
  |                                                                            
  |==================================                                    |  49%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |====================================                                  |  51%
  |                                                                            
  |=====================================                                 |  52%
  |                                                                            
  |=====================================                                 |  53%
  |                                                                            
  |======================================                                |  55%
  |                                                                            
  |=======================================                               |  56%
  |                                                                            
  |========================================                              |  57%
  |                                                                            
  |=========================================                             |  58%
  |                                                                            
  |=========================================                             |  59%
  |                                                                            
  |==========================================                            |  60%
  |                                                                            
  |===========================================                           |  61%
  |                                                                            
  |============================================                          |  62%
  |                                                                            
  |=============================================                         |  64%
  |                                                                            
  |=============================================                         |  65%
  |                                                                            
  |==============================================                        |  66%
  |                                                                            
  |===============================================                       |  67%
  |                                                                            
  |================================================                      |  68%
  |                                                                            
  |=================================================                     |  69%
  |                                                                            
  |=================================================                     |  70%
  |                                                                            
  |==================================================                    |  72%
  |                                                                            
  |===================================================                   |  73%
  |                                                                            
  |====================================================                  |  74%
  |                                                                            
  |====================================================                  |  75%
  |                                                                            
  |=====================================================                 |  76%
  |                                                                            
  |======================================================                |  77%
  |                                                                            
  |=======================================================               |  78%
  |                                                                            
  |========================================================              |  80%
  |                                                                            
  |========================================================              |  81%
  |                                                                            
  |=========================================================             |  82%
  |                                                                            
  |==========================================================            |  83%
  |                                                                            
  |===========================================================           |  84%
  |                                                                            
  |============================================================          |  85%
  |                                                                            
  |============================================================          |  86%
  |                                                                            
  |=============================================================         |  88%
  |                                                                            
  |==============================================================        |  89%
  |                                                                            
  |===============================================================       |  90%
  |                                                                            
  |================================================================      |  91%
  |                                                                            
  |================================================================      |  92%
  |                                                                            
  |=================================================================     |  93%
  |                                                                            
  |==================================================================    |  94%
  |                                                                            
  |===================================================================   |  95%
  |                                                                            
  |====================================================================  |  97%
  |                                                                            
  |====================================================================  |  98%
  |                                                                            
  |===================================================================== |  99%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |=                                                                     |   1%
  |                                                                            
  |==                                                                    |   2%
  |                                                                            
  |==                                                                    |   3%
  |                                                                            
  |===                                                                   |   5%
  |                                                                            
  |====                                                                  |   6%
  |                                                                            
  |=====                                                                 |   7%
  |                                                                            
  |======                                                                |   8%
  |                                                                            
  |======                                                                |   9%
  |                                                                            
  |=======                                                               |  10%
  |                                                                            
  |========                                                              |  11%
  |                                                                            
  |=========                                                             |  12%
  |                                                                            
  |==========                                                            |  14%
  |                                                                            
  |==========                                                            |  15%
  |                                                                            
  |===========                                                           |  16%
  |                                                                            
  |============                                                          |  17%
  |                                                                            
  |=============                                                         |  18%
  |                                                                            
  |==============                                                        |  19%
  |                                                                            
  |==============                                                        |  20%
  |                                                                            
  |===============                                                       |  22%
  |                                                                            
  |================                                                      |  23%
  |                                                                            
  |=================                                                     |  24%
  |                                                                            
  |==================                                                    |  25%
  |                                                                            
  |==================                                                    |  26%
  |                                                                            
  |===================                                                   |  27%
  |                                                                            
  |====================                                                  |  28%
  |                                                                            
  |=====================                                                 |  30%
  |                                                                            
  |=====================                                                 |  31%
  |                                                                            
  |======================                                                |  32%
  |                                                                            
  |=======================                                               |  33%
  |                                                                            
  |========================                                              |  34%
  |                                                                            
  |=========================                                             |  35%
  |                                                                            
  |=========================                                             |  36%
  |                                                                            
  |==========================                                            |  38%
  |                                                                            
  |===========================                                           |  39%
  |                                                                            
  |============================                                          |  40%
  |                                                                            
  |=============================                                         |  41%
  |                                                                            
  |=============================                                         |  42%
  |                                                                            
  |==============================                                        |  43%
  |                                                                            
  |===============================                                       |  44%
  |                                                                            
  |================================                                      |  45%
  |                                                                            
  |=================================                                     |  47%
  |                                                                            
  |=================================                                     |  48%
  |                                                                            
  |==================================                                    |  49%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |====================================                                  |  51%
  |                                                                            
  |=====================================                                 |  52%
  |                                                                            
  |=====================================                                 |  53%
  |                                                                            
  |======================================                                |  55%
  |                                                                            
  |=======================================                               |  56%
  |                                                                            
  |========================================                              |  57%
  |                                                                            
  |=========================================                             |  58%
  |                                                                            
  |=========================================                             |  59%
  |                                                                            
  |==========================================                            |  60%
  |                                                                            
  |===========================================                           |  61%
  |                                                                            
  |============================================                          |  62%
  |                                                                            
  |=============================================                         |  64%
  |                                                                            
  |=============================================                         |  65%
  |                                                                            
  |==============================================                        |  66%
  |                                                                            
  |===============================================                       |  67%
  |                                                                            
  |================================================                      |  68%
  |                                                                            
  |=================================================                     |  69%
  |                                                                            
  |=================================================                     |  70%
  |                                                                            
  |==================================================                    |  72%
  |                                                                            
  |===================================================                   |  73%
  |                                                                            
  |====================================================                  |  74%
  |                                                                            
  |====================================================                  |  75%
  |                                                                            
  |=====================================================                 |  76%
  |                                                                            
  |======================================================                |  77%
  |                                                                            
  |=======================================================               |  78%
  |                                                                            
  |========================================================              |  80%
  |                                                                            
  |========================================================              |  81%
  |                                                                            
  |=========================================================             |  82%
  |                                                                            
  |==========================================================            |  83%
  |                                                                            
  |===========================================================           |  84%
  |                                                                            
  |============================================================          |  85%
  |                                                                            
  |============================================================          |  86%
  |                                                                            
  |=============================================================         |  88%
  |                                                                            
  |==============================================================        |  89%
  |                                                                            
  |===============================================================       |  90%
  |                                                                            
  |================================================================      |  91%
  |                                                                            
  |================================================================      |  92%
  |                                                                            
  |=================================================================     |  93%
  |                                                                            
  |==================================================================    |  94%
  |                                                                            
  |===================================================================   |  95%
  |                                                                            
  |====================================================================  |  97%
  |                                                                            
  |====================================================================  |  98%
  |                                                                            
  |===================================================================== |  99%
  |                                                                            
  |======================================================================| 100%

all.genes= rownames(merge.all)
merge.all<-FindVariableFeatures(merge.all,selection.method="vst", nfeatures = 5000)
merge.all<- ScaleData(merge.all, features = all.genes, assay = "SCT")

Centering and scaling data matrix

merge.all<-RunPCA(merge.all, npcs = 100, verbose=F, Assay="SCT")

DimPlot(merge.all, reduction = "pca", group.by = "orig.ident")

merge.all<- RunHarmony(merge.all, c("orig.ident", "individual", "Batch"), theta = c(3,1,1), plot_convergence = T, assay.use = "SCT")

Harmony 1/10

Harmony 2/10

Harmony 3/10

Harmony 4/10

Harmony 5/10

Harmony converged after 5 iterations

Warning: Invalid name supplied, making object name syntactically valid. New
object name is Seurat..ProjectDim.SCT.harmony; see ?make.names for more details
on syntax validity

DimPlot(merge.all, group.by= "orig.ident", reduction= "harmony")

merge.all<- RunUMAP(merge.all,dims=1:100, reduction="harmony")

23:47:38 UMAP embedding parameters a = 0.9922 b = 1.112

23:47:38 Read 48238 rows and found 100 numeric columns

23:47:38 Using Annoy for neighbor search, n_neighbors = 30

23:47:38 Building Annoy index with metric = cosine, n_trees = 50

0%   10   20   30   40   50   60   70   80   90   100%

[----|----|----|----|----|----|----|----|----|----|

**************************************************|
23:47:55 Writing NN index file to temp file /tmp/jobs/12040951/RtmpWoOReb/file1e2d5229e2a5b
23:47:55 Searching Annoy index using 1 thread, search_k = 3000
23:48:20 Annoy recall = 97.24%
23:48:20 Commencing smooth kNN distance calibration using 1 thread
23:48:23 Initializing from normalized Laplacian + noise
23:48:29 Commencing optimization for 200 epochs, with 2209836 positive edges
23:49:28 Optimization finished

V<- DimPlot(merge.all, group.by = "Main_cluster_name", label = T, label.size = 2.5, repel = T)+NoLegend()

Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.

V

Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

Add new metadata to include Main_cluster_name as well as cluster labels from EB Pilot DE results, and orig ident of the HCL data

merge.all<- AddMetaData(merge.all, col.name = "all.labels", metadata = merge.all@meta.data$Main_cluster_name)

#for cells with NA for main_cluster_name, replace label from SCT_snn_res 0.1
for (i in (1:nrow(merge.all@meta.data))){
if (is.na(merge.all@meta.data$all.labels[i]) == T){
  merge.all@meta.data$all.labels[i]<- merge.all@meta.data$SCT_snn_res.0.1[i]
}
}

#and now replace remaining NAs with orig.ident
for (i in (1:nrow(merge.all@meta.data))){
if (is.na(merge.all@meta.data$all.labels[i]) == T){
  merge.all@meta.data$all.labels[i]<- merge.all@meta.data$orig.ident[i]
}
}

V<- DimPlot(merge.all, group.by = "all.labels", label = T, label.size = 2.5, repel = T)+NoLegend()

V

Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

V<- DimPlot(merge.all, group.by = "orig.ident", label = F, order= c("scHCL.hESC","scHCL.EB20", "EB.Pilot","Cao.EtAl"))

V

DimPlot(merge.all, split.by = "orig.ident", label = F, order= c("scHCL.hESC","scHCL.EB20", "EB.Pilot","Cao.EtAl"))

options(ggrepel.max.overlaps = Inf)

#subset object to just my data and Cao reference, plot UMAP
Idents(merge.all)<- "orig.ident"
sub<- subset(merge.all, idents= c("Cao.EtAl", "EB.Pilot"))
V<- DimPlot(sub, group.by = "all.labels", label = T, repel = T, label.size = 2.5)+NoLegend()
V

V<- DimPlot(sub, group.by = "Main_cluster_name", label = T, repel = T, label.size = 2.5)+NoLegend()
V

V<- DimPlot(sub, group.by = "SCT_snn_res.0.1", label = T, repel = T, label.size = 2.5)+NoLegend()
V

DimPlot(sub, group.by = "orig.ident")

DimPlot(sub, group.by = "orig.ident", order="Cao.EtAl")

#Subset object to just my data and HCL references, plot UMAP
sub<- subset(merge.all, idents= c("scHCL.EB20", "EB.Pilot"))
V<- DimPlot(sub, group.by = "orig.ident", label = F)
V

sub<- subset(merge.all, idents= c("scHCL.EB20", "EB.Pilot", "Cao.EtAl"))
DimPlot(sub, group.by="orig.ident", pt.size = 0.2, label=F) 

DimPlot(merge.all, split.by="orig.ident",group.by = "all.labels", pt.size = 0.2, label=F) +NoLegend()

Now, will transfer labels for Cao Et Al and hESC onto my data.

#subset to remove scHCL.EB20
sub<- subset(merge.all, idents= c("Cao.EtAl", "EB.Pilot", "scHCL.hESC"))
#compute distance matrix based on harmony embeddings, dims 1:100
har_embeds<- sub@reductions$harmony@cell.embeddings

har_distmat<- as.matrix(dist(har_embeds, method="euclidean", upper=TRUE))

#vectors with cell ids from Cao.EtAl, EB.pilot, and scHCL.hESC
EB.pilot.ids<-rownames(merge.all@meta.data[merge.all@meta.data$orig.ident == "EB.Pilot",])

#subset rows to only cells in EB.pilot
sub_har_distmat<- har_distmat[rownames(har_distmat) %in% EB.pilot.ids,]

#subset cols to only cells not in EB.pilot
'%notin%'<- Negate('%in%')
sub_har_distmat<- sub_har_distmat[,colnames(sub_har_distmat) %notin% EB.pilot.ids]

nearest.ref.cell.id<- NULL
nearest.ref.cell.dist<- NULL
#for loop, loop through each row
for (i in 1:nrow(sub_har_distmat)){
  nearest.ref.cell.dist[i]<- min(sub_har_distmat[i,])
  nearest.ref.cell.id[i]<- names(which.min(sub_har_distmat[i,]))
}

nearest.ref.table<- cbind(rownames(sub_har_distmat), nearest.ref.cell.id,nearest.ref.cell.dist)

colnames(nearest.ref.table)<- c("EB.cell.id", "nearest.ref.cell.id", "harmony.dist.to.nearest.ref.cell")

#add annotation
ann<- as.data.frame(merge.all@meta.data$all.labels)
ann<- cbind(rownames(merge.all@meta.data), ann)
colnames(ann)<- c("nearest.ref.cell.id", "annotation")

nearest.ref.table<- as.data.frame(nearest.ref.table)
nearest.ann<- left_join(nearest.ref.table, ann, by=c("nearest.ref.cell.id"))

a<- as.data.frame(table(nearest.ann$annotation))
a<- a[a$Var1 != "scHCL.EB20",]
a<- a[a$Var1 != "0",]
a<- a[a$Var1 != "1",]
a<- a[a$Var1 != "2",]
a<- a[a$Var1 != "3",]
a<- a[a$Var1 != "4",]
a<- a[a$Var1 != "5",]
a<- a[a$Var1 != "6",]
a<- a[a$Var1 != "7",]
colnames(a)<- c("reference.cell.type", "Frequency")

a

                                 reference.cell.type Frequency
2                             AFP_ALB positive cells         1
3                                       Acinar cells         0
4                               Adrenocortical cells         0
5                                     Amacrine cells         0
6                           Antigen presenting cells         0
7                                         Astrocytes         5
8                                      Bipolar cells        66
9          Bronchiolar and alveolar epithelial cells         0
10                        CCL19_CCL21 positive cells         0
11                          CLC_IL5RA positive cells         0
12                          CSH1_CSH2 positive cells         0
13                                    Cardiomyocytes         0
14                                  Chromaffin cells         0
15                         Ciliated epithelial cells       135
16         Corneal and conjunctival epithelial cells         0
17                                      Ductal cells         0
18                         ELF3_AGBL2 positive cells         1
19                                          ENS glia        42
20                                       ENS neurons         0
21                                 Endocardial cells         0
22                              Epicardial fat cells         0
23                                     Erythroblasts         0
24                                Excitatory neurons         0
25                         Extravillous trophoblasts        19
26                                    Ganglion cells        13
27                                      Goblet cells         0
28                                   Granule neurons         0
29                          Hematopoietic stem cells         0
30                                      Hepatoblasts       177
31                                  Horizontal cells         0
32                        IGFBP1_DKK1 positive cells       111
33                           Inhibitory interneurons         6
34                                Inhibitory neurons        51
35                       Intestinal epithelial cells         5
36                             Islet endocrine cells         3
37                                  Lens fibre cells         1
38                             Limbic system neurons        65
39                       Lymphatic endothelial cells         0
40                                    Lymphoid cells         0
41                        MUC13_DMBT1 positive cells         0
42                                    Megakaryocytes         1
43                                   Mesangial cells         0
44                                 Mesothelial cells         0
45                                 Metanephric cells         5
46                                         Microglia         1
47                                     Myeloid cells         0
48                              Neuroendocrine cells         7
49                                  Oligodendrocytes       197
50                         PAEP_MECOM positive cells         0
51                     PDE11A_FAM19A2 positive cells        39
52                        PDE1C_ACSM3 positive cells         0
53                          Parietal and chief cells         0
54                               Photoreceptor cells         0
55                                  Purkinje neurons       102
56                             Retinal pigment cells         0
57               Retinal progenitors and Muller glia         0
58                        SATB2_LRRC7 positive cells         7
59                        SKOR2_NPSR1 positive cells        30
60                      SLC24A4_PEX5L positive cells        12
61                       SLC26A4_PAEP positive cells         0
62                          STC2_TLX1 positive cells         0
63                                   Satellite cells         0
64                                     Schwann cells         8
65                             Skeletal muscle cells        32
66                               Smooth muscle cells         0
67                         Squamous epithelial cells         0
68                                    Stellate cells         0
69                                     Stromal cells         0
70                                    Sympathoblasts         0
71 Syncytiotrophoblasts and villous cytotrophoblasts         0
72                           Thymic epithelial cells         0
73                                        Thymocytes         0
74                           Trophoblast giant cells        96
75                              Unipolar brush cells         0
76                                Ureteric bud cells         0
77                        Vascular endothelial cells         0
78                                  Visceral neurons         2
80                                        scHCL.hESC       750

sub<- subset(merge.all, idents= c("EB.Pilot"))
EB.cell.id<- rownames(sub@meta.data)
sub@meta.data<- cbind(sub@meta.data, EB.cell.id)
sub@meta.data<- full_join(sub@meta.data, nearest.ann, by= c("EB.cell.id"))
rownames(sub@meta.data)<- EB.cell.id

V<- DimPlot(sub, group.by="annotation", pt.size = 0.2, label.size = 2.5,label=T, repel=T) +NoLegend()

V

mostcommon.ann<- NULL
maxann.FIVEnearest<- NULL

#for loop, loop through each row
for (i in 1:nrow(sub_har_distmat)){
  cell<- sub_har_distmat[i,]
  cell<- cell[order(cell)]
  topfive<- names(cell[1:5])
  
  #get the annotations of the nearest 5 reference cells
  topfiveann<- merge.all@meta.data$all.labels[rownames(merge.all@meta.data) %in% topfive]
  
  #if/else at least 3/5 match annotations
  maxann<- max(table(topfiveann))
  finalann<- names(which.max(table(topfiveann)))
  
  maxann.FIVEnearest[i]<- maxann
  
  if(maxann >= 3){
    mostcommon.ann[i]<- finalann
  } else {
    mostcommon.ann[i]<- "uncertain"
  }
  
}

CommonAnnDF<- as.data.frame(cbind(rownames(sub_har_distmat), mostcommon.ann, maxann.FIVEnearest))
colnames(CommonAnnDF)<- c("EB.cell.id", "Annotation", "NoutofFIVErefneighborsWithSameAnnotation")

write.csv(CommonAnnDF,"/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/TranferredAnnotations_ReferenceInt_JustNeuron.csv")

b<- table(CommonAnnDF$Annotation)

b

       AFP_ALB positive cells     Ciliated epithelial cells 
                            1                           134 
                     ENS glia     Extravillous trophoblasts 
                           28                             8 
               Ganglion cells                  Hepatoblasts 
                            2                           198 
   IGFBP1_DKK1 positive cells            Inhibitory neurons 
                          105                            34 
        Limbic system neurons             Metanephric cells 
                            1                             1 
             Oligodendrocytes PDE11A_FAM19A2 positive cells 
                          197                            13 
             Purkinje neurons         Retinal pigment cells 
                           65                             1 
   SKOR2_NPSR1 positive cells               Satellite cells 
                            3                             8 
                Schwann cells         Skeletal muscle cells 
                            1                            29 
      Trophoblast giant cells              Visceral neurons 
                           20                             1 
                   scHCL.hESC                     uncertain 
                          650                           490 

#print fetal celltypes not present in EB data
unique(merge.all@meta.data$Main_cluster_name)[unique(merge.all@meta.data$Main_cluster_name) %notin% names(b)] 

 [1] "Retinal progenitors and Muller glia"              
 [2] "Horizontal cells"                                 
 [3] "Photoreceptor cells"                              
 [4] "Lens fibre cells"                                 
 [5] "Amacrine cells"                                   
 [6] "Corneal and conjunctival epithelial cells"        
 [7] "Bipolar cells"                                    
 [8] "Stromal cells"                                    
 [9] "Microglia"                                        
[10] "Vascular endothelial cells"                       
[11] "Smooth muscle cells"                              
[12] "Adrenocortical cells"                             
[13] "Chromaffin cells"                                 
[14] "Myeloid cells"                                    
[15] "Megakaryocytes"                                   
[16] "Lymphoid cells"                                   
[17] "Sympathoblasts"                                   
[18] "Erythroblasts"                                    
[19] "SLC26A4_PAEP positive cells"                      
[20] "CSH1_CSH2 positive cells"                         
[21] "Inhibitory interneurons"                          
[22] "SLC24A4_PEX5L positive cells"                     
[23] "Astrocytes"                                       
[24] "Granule neurons"                                  
[25] "Unipolar brush cells"                             
[26] "Excitatory neurons"                               
[27] "CLC_IL5RA positive cells"                         
[28] "SATB2_LRRC7 positive cells"                       
[29] "Epicardial fat cells"                             
[30] "Cardiomyocytes"                                   
[31] "ELF3_AGBL2 positive cells"                        
[32] "Endocardial cells"                                
[33] "Lymphatic endothelial cells"                      
[34] "Mesothelial cells"                                
[35] "ENS neurons"                                      
[36] "Intestinal epithelial cells"                      
[37] "Mesangial cells"                                  
[38] "Ureteric bud cells"                               
[39] "Stellate cells"                                   
[40] "Hematopoietic stem cells"                         
[41] "Neuroendocrine cells"                             
[42] "Bronchiolar and alveolar epithelial cells"        
[43] "Squamous epithelial cells"                        
[44] "CCL19_CCL21 positive cells"                       
[45] "Ductal cells"                                     
[46] "Islet endocrine cells"                            
[47] "Acinar cells"                                     
[48] "PAEP_MECOM positive cells"                        
[49] "Syncytiotrophoblasts and villous cytotrophoblasts"
[50] "STC2_TLX1 positive cells"                         
[51] "PDE1C_ACSM3 positive cells"                       
[52] "MUC13_DMBT1 positive cells"                       
[53] "Parietal and chief cells"                         
[54] "Goblet cells"                                     
[55] "Antigen presenting cells"                         
[56] "Thymic epithelial cells"                          
[57] "Thymocytes"                                       
[58] NA                                                 

sub<- subset(merge.all, idents= c("EB.Pilot"))
EB.cell.id<- rownames(sub@meta.data)
sub@meta.data<- cbind(sub@meta.data, EB.cell.id)
sub@meta.data<- full_join(sub@meta.data, CommonAnnDF, by= c("EB.cell.id"))
rownames(sub@meta.data)<- EB.cell.id

V<- DimPlot(sub, group.by="Annotation", pt.size = 0.2, label.size = 2.5,label=T, repel=T) +NoLegend()

V

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
[1] C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggplot2_3.3.3   harmony_1.0     Rcpp_1.0.6      purrr_0.3.4    
 [5] tibble_3.0.4    tidyr_1.1.0     dplyr_1.0.2     edgeR_3.28.1   
 [9] limma_3.42.2    Seurat_3.2.0    workflowr_1.6.2

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_2.0-0      deldir_0.1-28        
  [4] ellipsis_0.3.1        ggridges_0.5.2        rprojroot_2.0.2      
  [7] fs_1.4.2              spatstat.data_1.4-3   farver_2.0.3         
 [10] leiden_0.3.3          listenv_0.8.0         npsurv_0.4-0         
 [13] ggrepel_0.9.0         RSpectra_0.16-0       codetools_0.2-16     
 [16] splines_3.6.1         lsei_1.2-0            knitr_1.29           
 [19] polyclip_1.10-0       jsonlite_1.7.2        ica_1.0-2            
 [22] cluster_2.1.0         png_0.1-7             uwot_0.1.10          
 [25] shiny_1.5.0           sctransform_0.2.1     compiler_3.6.1       
 [28] httr_1.4.2            Matrix_1.2-18         fastmap_1.0.1        
 [31] lazyeval_0.2.2        later_1.1.0.1         htmltools_0.5.0      
 [34] tools_3.6.1           rsvd_1.0.3            igraph_1.2.6         
 [37] gtable_0.3.0          glue_1.4.2            RANN_2.6.1           
 [40] reshape2_1.4.4        rappdirs_0.3.3        spatstat_1.64-1      
 [43] vctrs_0.3.6           gdata_2.18.0          ape_5.4-1            
 [46] nlme_3.1-140          lmtest_0.9-37         xfun_0.16            
 [49] stringr_1.4.0         globals_0.12.5        mime_0.9             
 [52] miniUI_0.1.1.1        lifecycle_0.2.0       irlba_2.3.3          
 [55] gtools_3.8.2          goftest_1.2-2         future_1.18.0        
 [58] MASS_7.3-51.4         zoo_1.8-8             scales_1.1.1         
 [61] promises_1.1.1        spatstat.utils_1.17-0 parallel_3.6.1       
 [64] RColorBrewer_1.1-2    yaml_2.2.1            reticulate_1.20      
 [67] pbapply_1.4-2         gridExtra_2.3         rpart_4.1-15         
 [70] stringi_1.5.3         highr_0.8             caTools_1.18.0       
 [73] rlang_0.4.10          pkgconfig_2.0.3       bitops_1.0-6         
 [76] evaluate_0.14         lattice_0.20-38       ROCR_1.0-7           
 [79] tensor_1.5            labeling_0.4.2        patchwork_1.1.1      
 [82] htmlwidgets_1.5.1     cowplot_1.1.1         tidyselect_1.1.0     
 [85] RcppAnnoy_0.0.18      plyr_1.8.6            magrittr_2.0.1       
 [88] R6_2.5.0              gplots_3.0.4          generics_0.1.0       
 [91] withr_2.4.2           pillar_1.4.7          whisker_0.4          
 [94] mgcv_1.8-28           fitdistrplus_1.0-14   survival_3.2-3       
 [97] abind_1.4-5           future.apply_1.6.0    crayon_1.3.4         
[100] KernSmooth_2.23-15    plotly_4.9.2.1        rmarkdown_2.3        
[103] locfit_1.5-9.4        grid_3.6.1            data.table_1.13.4    
[106] git2r_0.26.1          digest_0.6.27         xtable_1.8-4         
[109] httpuv_1.5.4          munsell_0.5.0         viridisLite_0.3.0    

Session information

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
[1] C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggplot2_3.3.3   harmony_1.0     Rcpp_1.0.6      purrr_0.3.4    
 [5] tibble_3.0.4    tidyr_1.1.0     dplyr_1.0.2     edgeR_3.28.1   
 [9] limma_3.42.2    Seurat_3.2.0    workflowr_1.6.2

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_2.0-0      deldir_0.1-28        
  [4] ellipsis_0.3.1        ggridges_0.5.2        rprojroot_2.0.2      
  [7] fs_1.4.2              spatstat.data_1.4-3   farver_2.0.3         
 [10] leiden_0.3.3          listenv_0.8.0         npsurv_0.4-0         
 [13] ggrepel_0.9.0         RSpectra_0.16-0       codetools_0.2-16     
 [16] splines_3.6.1         lsei_1.2-0            knitr_1.29           
 [19] polyclip_1.10-0       jsonlite_1.7.2        ica_1.0-2            
 [22] cluster_2.1.0         png_0.1-7             uwot_0.1.10          
 [25] shiny_1.5.0           sctransform_0.2.1     compiler_3.6.1       
 [28] httr_1.4.2            Matrix_1.2-18         fastmap_1.0.1        
 [31] lazyeval_0.2.2        later_1.1.0.1         htmltools_0.5.0      
 [34] tools_3.6.1           rsvd_1.0.3            igraph_1.2.6         
 [37] gtable_0.3.0          glue_1.4.2            RANN_2.6.1           
 [40] reshape2_1.4.4        rappdirs_0.3.3        spatstat_1.64-1      
 [43] vctrs_0.3.6           gdata_2.18.0          ape_5.4-1            
 [46] nlme_3.1-140          lmtest_0.9-37         xfun_0.16            
 [49] stringr_1.4.0         globals_0.12.5        mime_0.9             
 [52] miniUI_0.1.1.1        lifecycle_0.2.0       irlba_2.3.3          
 [55] gtools_3.8.2          goftest_1.2-2         future_1.18.0        
 [58] MASS_7.3-51.4         zoo_1.8-8             scales_1.1.1         
 [61] promises_1.1.1        spatstat.utils_1.17-0 parallel_3.6.1       
 [64] RColorBrewer_1.1-2    yaml_2.2.1            reticulate_1.20      
 [67] pbapply_1.4-2         gridExtra_2.3         rpart_4.1-15         
 [70] stringi_1.5.3         highr_0.8             caTools_1.18.0       
 [73] rlang_0.4.10          pkgconfig_2.0.3       bitops_1.0-6         
 [76] evaluate_0.14         lattice_0.20-38       ROCR_1.0-7           
 [79] tensor_1.5            labeling_0.4.2        patchwork_1.1.1      
 [82] htmlwidgets_1.5.1     cowplot_1.1.1         tidyselect_1.1.0     
 [85] RcppAnnoy_0.0.18      plyr_1.8.6            magrittr_2.0.1       
 [88] R6_2.5.0              gplots_3.0.4          generics_0.1.0       
 [91] withr_2.4.2           pillar_1.4.7          whisker_0.4          
 [94] mgcv_1.8-28           fitdistrplus_1.0-14   survival_3.2-3       
 [97] abind_1.4-5           future.apply_1.6.0    crayon_1.3.4         
[100] KernSmooth_2.23-15    plotly_4.9.2.1        rmarkdown_2.3        
[103] locfit_1.5-9.4        grid_3.6.1            data.table_1.13.4    
[106] git2r_0.26.1          digest_0.6.27         xtable_1.8-4         
[109] httpuv_1.5.4          munsell_0.5.0         viridisLite_0.3.0