Package: miniLNM 0.1.0

miniLNM: Miniature Logistic-Normal Multinomial Models

Logistic-normal Multinomial (LNM) models are common in problems with multivariate count data. This package gives a simple implementation with a 30 line 'Stan' script. This lightweight implementation makes it an easy starting point for other projects, in particular for downstream tasks that require analysis of "compositional" data. It can be applied whenever a multinomial probability parameter is thought to depend linearly on inputs in a transformed, log ratio space. Additional utilities make it easy to inspect, create predictions, and draw samples using the fitted models. More about the LNM can be found in Xia et al. (2013) "A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis" <doi:10.1111/biom.12079> and Sankaran and Holmes (2023) "Generative Models: An Interdisciplinary Perspective" <doi:10.1146/annurev-statistics-033121-110134>.

Authors:Kris Sankaran [aut, cre]

miniLNM_0.1.0.tar.gz
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miniLNM.pdf |miniLNM.html
miniLNM/json (API)

# Install 'miniLNM' in R:
install.packages('miniLNM', repos = c('https://krisrs1128.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/krisrs1128/minilnm/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

9 exports 1.24 score 57 dependencies 5 scripts

Last updated 7 days agofrom:57571dad4a. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 14 2024
R-4.5-win-x86_64NOTESep 14 2024
R-4.5-linux-x86_64NOTESep 14 2024
R-4.4-win-x86_64NOTESep 14 2024
R-4.4-mac-x86_64NOTESep 14 2024
R-4.4-mac-aarch64NOTESep 14 2024
R-4.3-win-x86_64NOTESep 14 2024
R-4.3-mac-x86_64NOTESep 14 2024
R-4.3-mac-aarch64NOTESep 14 2024

Exports:ansi_aware_handlerbeta_meanbeta_sampleslnmlnm_dataphi_inversepredictprepare_newdatasample

Dependencies:abindbackportsBHcallrcheckmateclicolorspacedescdistributionaldplyrfansifarverformula.toolsgenericsggplot2gluegridExtragtableinlineisobandlabelinglatticelifecycleloomagrittrMASSMatrixmatrixStatsmgcvmunsellnlmenumDerivoperator.toolspillarpkgbuildpkgconfigposteriorprocessxpsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelrlangrstanrstantoolsscalesStanHeaderstensorAtibbletidyselectutf8vctrsviridisLitewithr

Example on Simulated Data

Rendered fromdemo.Rmdusingknitr::rmarkdownon Sep 14 2024.

Last update: 2024-09-07
Started: 2023-12-16