ddtlcm: Latent Class Analysis with Dirichlet Diffusion Tree Process Prior

Implements a Bayesian algorithm for overcoming weak separation in Bayesian latent class analysis. Reference: Li et al. (2023) <doi:10.48550/arXiv.2306.04700>.

Version:	0.2.1
Depends:	R (≥ 4.3)
Imports:	ape (≥ 5.6-2), data.table (≥ 1.14.4), extraDistr (≥ 1.9.1), ggplot2 (≥ 3.4.0), ggpubr (≥ 0.6.0), ggtext (≥ 0.1.2), ggtree (≥ 3.4.0), label.switching (≥ 1.8), matrixStats (≥ 0.62.0), methods (≥ 4.2.3), phylobase (≥ 0.8.10), poLCA (≥ 1.6.0.1), testthat (≥ 3.1.7), truncnorm (≥ 1.0-8), BayesLogit (≥ 2.1), Matrix (≥ 1.5-1), Rdpack (≥ 2.5), R.utils (≥ 2.12.2)
Suggests:	knitr, parallel, rmarkdown, xfun
Published:	2024-04-04
Author:	Mengbing Li [cre, aut], Briana Stephenson [ctb], Zhenke Wu [ctb]
Maintainer:	Mengbing Li <mengbing at umich.edu>
BugReports:	https://github.com/limengbinggz/ddtlcm/issues
License:	MIT + file LICENSE
URL:	https://github.com/limengbinggz/ddtlcm
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	ddtlcm results

Documentation:

Reference manual:	ddtlcm.pdf
Vignettes:	Vignettes for ddtlcm: An R package for fitting tree-regularized Bayesian latent class models

Downloads:

Package source:	ddtlcm_0.2.1.tar.gz
Windows binaries:	r-devel: ddtlcm_0.2.1.zip, r-release: ddtlcm_0.2.1.zip, r-oldrel: ddtlcm_0.2.1.zip
macOS binaries:	r-release (arm64): ddtlcm_0.2.1.tgz, r-oldrel (arm64): ddtlcm_0.2.1.tgz, r-release (x86_64): ddtlcm_0.2.1.tgz, r-oldrel (x86_64): ddtlcm_0.2.1.tgz
Old sources:	ddtlcm archive

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