Package: hdpGLM 1.0.4

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_1.0.4.tar.gz
hdpGLM_1.0.4.zip(r-4.7)hdpGLM_1.0.4.zip(r-4.6)hdpGLM_1.0.4.zip(r-4.5)
hdpGLM_1.0.4.tgz(r-4.6-x86_64)hdpGLM_1.0.4.tgz(r-4.6-arm64)hdpGLM_1.0.4.tgz(r-4.5-x86_64)hdpGLM_1.0.4.tgz(r-4.5-arm64)
hdpGLM_1.0.4.tar.gz(r-4.7-arm64)hdpGLM_1.0.4.tar.gz(r-4.7-x86_64)hdpGLM_1.0.4.tar.gz(r-4.6-arm64)hdpGLM_1.0.4.tar.gz(r-4.6-x86_64)
hdpGLM_1.0.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
hdpGLM/json (API)

# Install 'hdpGLM' in R:

install.packages('hdpGLM', repos = c('https://diogoferrari.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

Datasets:

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

On CRAN:

dirichlet-process-mixtures hierarchical-clustering nonparametric nonparametricbayes npb semi-parametric openblas cpp

4.78 score 12 stars 7 scripts 373 downloads 14 exports 183 dependencies

Last updated from:1fad803c94. Checks:11 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	WARNING	242
linux-devel-x86_64	WARNING	248
source / vignettes	OK	385
linux-release-arm64	WARNING	234
linux-release-x86_64	WARNING	219
macos-release-arm64	WARNING	183
macos-release-x86_64	WARNING	374
macos-oldrel-arm64	WARNING	188
macos-oldrel-x86_64	WARNING	429
windows-devel	WARNING	219
windows-release	WARNING	211
windows-oldrel	WARNING	198
wasm-release	OK	226

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 askpass backports base64enc bit bit64 blob boot broom bslib cachem callr car carData cellranger checkmate class classInt cli clipr cluster coda colorspace commonmark conflicted corrplot cowplot cpp11 crayon curl data.table DBI dbplyr Deriv digest doBy dplyr dtplyr e1071 evaluate farver fastmap fontawesome forcats forecast foreign Formula formula.tools fracdiff fs gargle generics ggplot2 ggpubr ggrepel ggridges ggsci ggsignif glue googledrive googlesheets4 gridExtra gtable haven highr Hmisc hms htmlTable htmltools htmlwidgets httpuv httr ids isoband isotone jquerylib jsonlite KernSmooth knitr labeling labelled LaplacesDemon later lattice lifecycle lme4 lmtest lubridate magrittr MASS Matrix MatrixModels mcmc MCMCpack memoise mgcv mime miniUI minqa modelr mvtnorm nlme nloptr nnet nnls numDeriv openssl operator.tools otel pbkrtest pillar pkgconfig png polynom prettyunits processx progress promises proxy ps purrr quantreg questionr R.cache R.methodsS3 R.oo R.utils R6 ragg rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack readr readxl reformulas rematch rematch2 reprex rlang rmarkdown rpart rprojroot rstatix rstudioapi rvest S7 sass scales selectr shiny sourcetools SparseM stringi stringr styler survival sys systemfonts textshaping tibble tidyr tidyselect tidyverse timechange timeDate tinytex tzdb urca utf8 uuid vctrs viridisLite vroom withr xfun xml2 xtable yaml zoo

hdpGLM

Rendered fromhdpGLM.Rmdusingknitr::rmarkdown

Last update: 2022-05-04
Started: 2020-10-26

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Classify data points	classify
Extract dpGLM fitted coefficients	coef.dpGLM
Extract hdpGLM fitted coefficients	coef.hdpGLM
Fit Hierarchical Dirichlet Process GLM	hdpGLM
Deprecated	hdpGLM_classify
hdpGLM: A package for computating Hierarchical Dirichlet Process Generalized Linear Models	hdpGLM_package
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