Package: MultiModalR 1.0.0

MultiModalR: Fast Bayesian Probability Estimation for Multimodal Categorical Data

Fast Bayesian probability estimation for multimodal categorical data using speed-optimized MCMC implementation (Metropolis-Hastings-within-partial-Gibbs). The package provides efficient algorithms for detecting subpopulations, estimating mixture components, and assigning observations to subgroups with probability estimates.

Authors:Gergo Dioszegi [aut, cre]

MultiModalR_1.0.0.tar.gz
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MultiModalR_1.0.0.tgz(r-4.6-x86_64)MultiModalR_1.0.0.tgz(r-4.6-arm64)MultiModalR_1.0.0.tgz(r-4.5-x86_64)MultiModalR_1.0.0.tgz(r-4.5-arm64)
MultiModalR_1.0.0.tar.gz(r-4.7-arm64)MultiModalR_1.0.0.tar.gz(r-4.7-x86_64)MultiModalR_1.0.0.tar.gz(r-4.6-arm64)MultiModalR_1.0.0.tar.gz(r-4.6-x86_64)
MultiModalR_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MultiModalR/json (API)

# Install 'MultiModalR' in R:

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

Bug tracker:https://github.com/dijog/multimodalr/issues

Uses libs:

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

Datasets:

multimodal_dummy - Multimodal Dummy Dataset

On CRAN:

cpp openmp

3.65 score 9 exports 46 dependencies

Last updated from:d235608b9f. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	164
linux-devel-x86_64	OK	203
source / vignettes	OK	197
linux-release-arm64	OK	196
linux-release-x86_64	OK	174
macos-release-arm64	OK	159
macos-release-x86_64	OK	298
macos-oldrel-arm64	OK	248
macos-oldrel-x86_64	OK	392
windows-devel	OK	117
windows-release	OK	100
windows-oldrel	OK	122
wasm-release	OK	171

Exports:check_PACKS create_MM_output create_multimodal_dummy fuss_PARALLEL_mcmc get_MODES_enhanced group_MODES_enhanced MM_MH MM_MH_dirichlet plot_VALIDATION

Dependencies:bit bit64 cli clipr codetools cpp11 crayon digest dplyr farver furrr future generics ggplot2 globals glue gtable hms isoband labeling lifecycle listenv magrittr parallelly pillar pkgconfig prettyunits progress purrr R6 RColorBrewer Rcpp RcppArmadillo readr rlang S7 scales tibble tidyselect truncnorm tzdb utf8 vctrs viridisLite vroom withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Check and install required packages	check_PACKS
Create output data frame	create_MM_output
Create multimodal dummy dataset	create_multimodal_dummy
Parallel Bayesian mixture modeling using Markov Chain Monte Carlo (MCMC)	fuss_PARALLEL_mcmc
Density height-aware mode detection	get_MODES_enhanced
Density height-aware mode grouping	group_MODES_enhanced
Fast MCMC for mixture models (Metropolis-Hastings-within-partial-Gibbs)	MM_MH
Dirichlet MCMC (identical interface to MM_MH)	MM_MH_dirichlet
Multimodal Dummy Dataset	multimodal_dummy
Plot validation of subgroup assignments (handles both balanced and imbalanced data)	plot_VALIDATION