Package: miniLNM
Title: Miniature Logistic-Normal Multinomial Models
Version: 0.1.2
Authors@R: 
    person("Kris", "Sankaran", , "ksankaran@wisc.edu", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-9415-1971"))
Description: 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>.
License: CC0
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), cli, dplyr, fansi,
        formula.tools, glue, methods, posterior, rstan (>= 2.18.1),
        rstantools (>= 2.4.0), tidyselect
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
Biarch: true
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0),
        RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>=
        2.18.0)
SystemRequirements: GNU make
BugReports: https://github.com/krisrs1128/miniLNM/issues/
URL: https://github.com/krisrs1128/miniLNM/,
        https://krisrs1128.github.io/miniLNM/
Config/testthat/edition: 3
Config/roxygen2/version: 8.0.0
NeedsCompilation: yes
Packaged: 2026-07-21 00:06:36 UTC; krissankaran
Author: Kris Sankaran [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-9415-1971>)
Maintainer: Kris Sankaran <ksankaran@wisc.edu>
Repository: CRAN
Date/Publication: 2026-07-21 02:40:02 UTC
Built: R 4.7.0; x86_64-w64-mingw32; 2026-07-21 23:52:14 UTC; windows
Archs: x64
