Package: batchmix 2.2.1

batchmix: Semi-Supervised Bayesian Mixture Models Incorporating Batch Correction

Semi-supervised and unsupervised Bayesian mixture models that simultaneously infer the cluster/class structure and a batch correction. Densities available are the multivariate normal and the multivariate t. The model sampler is implemented in C++. This package is aimed at analysis of low-dimensional data generated across several batches. See Coleman et al. (2022) <doi:10.1101/2022.01.14.476352> for details of the model.

Authors:Stephen Coleman [aut, cre], Paul Kirk [aut], Chris Wallace [aut]

batchmix_2.2.1.tar.gz
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batchmix_2.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
batchmix/json (API)

# Install 'batchmix' in R:

install.packages('batchmix', repos = c('https://stcolema.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/stcolema/batchmix/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

On CRAN:

openblas cpp openmp

3.70 score 1 stars 4 scripts 545 downloads 43 exports 32 dependencies

Last updated from:bdd3e8ab1b. Checks:11 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	NOTE	260
linux-devel-x86_64	NOTE	221
source / vignettes	OK	374
linux-release-arm64	NOTE	241
linux-release-x86_64	NOTE	218
macos-release-arm64	NOTE	221
macos-release-x86_64	NOTE	628
macos-oldrel-arm64	NOTE	227
macos-oldrel-x86_64	NOTE	373
windows-devel	NOTE	228
windows-release	NOTE	263
windows-oldrel	NOTE	197
wasm-release	OK	241

Exports:batchSemiSupervisedMixtureModel calcAllocProb checkDataGenerationInputs checkProposalWindows collectAcceptanceRates continueChain continueChains createSimilarityMat gammaLogLikelihood generateBatchData generateBatchDataLogPoisson generateBatchDataMVT generateBatchDataVaryingRepresentation generateGroupIDsInSimulator generateInitialLabels getLikelihood getSampledBatchScale getSampledBatchShift getSampledClusterMeans invGammaLogLikelihood invWishartLogLikelihood minVI plotAcceptanceRates plotLikelihoods plotSampledBatchMeans plotSampledBatchScales plotSampledClusterMeans plotSampledParameter predictClass predictFromMultipleChains prepareInitialParameters processMCMCChain processMCMCChains rStickBreakingPrior runBatchMix runMCMCChains sampleMVN sampleMVT samplePriorLabels sampleSemisupervisedMVN sampleSemisupervisedMVT VI.lb wishartLogLikelihood

Dependencies:cli cpp11 dplyr farver generics ggplot2 glue gtable isoband labeling lifecycle magrittr pillar pkgconfig purrr R6 RColorBrewer Rcpp RcppArmadillo rlang S7 salso scales stringi stringr tibble tidyr tidyselect utf8 vctrs viridisLite withr

batchmix workflow

Introduction | Data generation | Modelling | Process chains | Visualisation

Rendered frombatchmix_workflow.Rmdusingknitr::rmarkdown

Last update: 2024-02-15
Started: 2022-06-14

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Bayesian Mixture Modelling for Joint Model-Based Clustering/Classification and Batch Correction	batchmix-package batchmix
Batch semisupervised mixture model	batchSemiSupervisedMixtureModel
Calculate allocation probabilities	calcAllocProb
Check data generation inputs	checkDataGenerationInputs
Check proposal windows	checkProposalWindows
Collect acceptance rate	collectAcceptanceRates
Continue chain	continueChain
Continue chains	continueChains
Create Similarity Matrix	createSimilarityMat
Gamma log-likelihood	gammaLogLikelihood
Generate batch data	generateBatchData
Generate batch data	generateBatchDataLogPoisson
Generate batch data from a multivariate t distribution	generateBatchDataMVT
Generate batch data	generateBatchDataVaryingRepresentation
Generate group IDs	generateGroupIDsInSimulator
Generate initial labels	generateInitialLabels
Get likelihood	getLikelihood
Get sampled batch shift	getSampledBatchScale
Get sampled batch shift	getSampledBatchShift
Get sampled cluster means	getSampledClusterMeans
Inverse gamma log-likelihood	invGammaLogLikelihood
Inverse-Wishart log-likelihood	invWishartLogLikelihood
Minimium VI	minVI
Plot acceptance rates	plotAcceptanceRates
Plot likelihoods	plotLikelihoods
Plot sampled batch means	plotSampledBatchMeans
Plot sampled batch scales	plotSampledBatchScales
Plot sampled cluster means	plotSampledClusterMeans
Plot sampled vector parameter	plotSampledParameter
Predict class	predictClass
Predict from multiple MCMC chains	predictFromMultipleChains
Prepare initial values	prepareInitialParameters
Process MCMC chain	processMCMCChain
Process MCMC chains	processMCMCChains
Random Draw From Stick Breaking Prior	rStickBreakingPrior
Run Batch Mixture Model	runBatchMix
Run MCMC Chains	runMCMCChains
Sample mixture of multivariate normal distributions with batch effects	sampleMVN
Sample mixture of multivariate t-distributions with batch effects	sampleMVT
Sample prior labels	samplePriorLabels
Sample semi-supervised MVN Mixture model	sampleSemisupervisedMVN
Sample semi-supervised MVT Mixture model	sampleSemisupervisedMVT
Minimum VI lower bound	VI.lb
Wishart log-likelihood	wishartLogLikelihood