This package is considered a duplicate. The official version of this package is found at:https://diogoferrari.r-universe.dev/hdpGLM

Package: hdpGLM 1.0.5

Diogo Ferrari

hdpGLM: Hierarchical Dirichlet Process Generalized Linear Models

Implementation of MCMC algorithms to estimate the Hierarchical Dirichlet Process Generalized Linear Model (hdpGLM) presented in the paper Ferrari (2020) Modeling Context-Dependent Latent Heterogeneity, Political Analysis <doi:10.1017/pan.2019.13> and <doi:10.18637/jss.v107.i10>.

Authors:Diogo Ferrari [aut, cre]

hdpGLM_1.0.5.tar.gz
hdpGLM_1.0.5.tar.gz(r-4.7-arm64)hdpGLM_1.0.5.tar.gz(r-4.7-x86_64)hdpGLM_1.0.5.tar.gz(r-4.6-arm64)hdpGLM_1.0.5.tar.gz(r-4.6-x86_64)
hdpGLM_1.0.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
hdpGLM/json (API)
NEWS

# Install 'hdpGLM' in R:

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

Bug tracker:https://github.com/diogoferrari/hdpglm/issues

Uses libs:

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

Datasets:

welfare - Fake data set with 2000 observations
welfare2 - Fake data set with 2000 observations

On CRAN:

openblas cpp openmp

2.70 score 7 scripts 241 downloads 14 exports 113 dependencies

Last updated from:3196c823e1. Checks:6 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	244
linux-devel-x86_64	OK	235
source / vignettes	OK	352
linux-release-arm64	OK	249
linux-release-x86_64	OK	228
wasm-release	OK	175

Exports:classify hdpGLM hdpGLM_classify hdpGLM_simParameters hdpGLM_simulateData mcmc_info.dpGLM mcmc_info.hdpGLM nclusters plot_beta plot_beta_sim plot_hdpglm plot_pexp_beta plot_tau summary_tidy

Dependencies:abind backports base64enc boot broom bslib cachem car carData checkmate cli cluster coda colorspace corrplot cowplot cpp11 data.table Deriv digest doBy dplyr evaluate farver fastmap fontawesome forecast foreign Formula formula.tools fracdiff fs generics ggplot2 ggpubr ggrepel ggridges ggsci ggsignif glue gridExtra gtable highr Hmisc htmlTable htmltools htmlwidgets isoband jquerylib jsonlite knitr labeling LaplacesDemon lattice lifecycle lme4 lmtest magrittr MASS Matrix MatrixModels memoise mgcv microbenchmark mime minqa modelr mvtnorm nlme nloptr nnet numDeriv operator.tools pbkrtest pillar pkgconfig polynom purrr quantreg R6 rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reformulas rlang rmarkdown rpart rstatix rstudioapi S7 sass scales SparseM stringi stringr survival tibble tidyr tidyselect timeDate tinytex urca utf8 vctrs viridisLite withr xfun yaml zoo

hdpGLM

Rendered fromhdpGLM.Rmdusingknitr::rmarkdownon Jun 14 2026.

Last update: 2022-05-06
Started: 2020-11-09

Citation

Readme and manuals

Help Manual

Help page	Topics
Classify data points	classify
Extract dpGLM fitted coefficients	coef.dpGLM
Extract hdpGLM fitted coefficients	coef.hdpGLM
hdpGLM: A package for computating Hierarchical Dirichlet Process Generalized Linear Models	hdpGLM-package hdpGLM
Deprecated	hdpGLM_classify
Simulate the parameters of the model	hdpGLM_simParameters
Simulate a Data Set from hdpGLM	hdpGLM_simulateData
mcmc	mcmc_info.dpGLM
mcmc	mcmc_info.hdpGLM
nclusters	nclusters
Plot beta posterior distribution	plot_beta
Plot simulated data	plot_beta_sim
Plot posterior distributions	plot_hdpglm
Plot beta posterior expectation	plot_pexp_beta
Plot tau	plot_tau
Default plot for class dpGLM	plot.dpGLM
Plot	plot.hdpGLM
dpGLM Predicted values	predict.dpGLM
hdpGLM Predicted values	predict.hdpGLM
Print	print.dpGLM
Print	print.dpGLM_data
Print	print.hdpGLM
Print	print.hdpGLM_data
Tidy summary	summary_tidy
Summary for dpGLM class	summary.dpGLM
Summary dpGLM data	summary.dpGLM_data
Summary for hdpGLM class	summary.hdpGLM
Summary	summary.hdpGLM_data
Fake data set with 2000 observations	welfare
Fake data set with 2000 observations	welfare2