dplyr, plyr, tidyr, xml2, rvest, jsonlite, RJSONIO, stringr,
httr, yaml, rlang
visNetwork, knitr, rmarkdown, utils, shiny, curl

dplyr, plyr, tidyr, xml2, rvest, jsonlite, RJSONIO, stringr,
httr, yaml, rlang, igraph
visNetwork, knitr, rmarkdown, utils, shiny, curl, testthat
(>= 3.0.0)

rlang, plyr, dplyr, igraph, stringr, jsonlite, RJSONIO, tidyr

Estimation/Multiple Imputation for Mixed Categorical and
Continuous Data
Estimation/multiple imputation programs for mixed categorical
and continuous data.

Estimation/multiple Imputation for Mixed Categorical and Continuous Data
Estimation/multiple imputation programs for mixed categorical
and continuous data

Estimation/multiple Imputation for Mixed Categorical and
Continuous Data
Estimation/multiple imputation programs for mixed
categorical and continuous data

This package contains a mixture of statistical methods
including the MCMC methods to analyze normal mixtures

R (>= 2.0.0), colorspace, lme4, mnormt, coda

R (>= 2.0.0), splines, colorspace, lme4, mnormt, coda

R (>= 2.10.0), splines, colorspace, lme4, mnormt, coda, snow,
snowfall
This package contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures

R (>= 2.10.0), splines, colorspace, lme4, mnormt, coda,
parallel

Multivariate normal mixture models and mixtures of generalized
linear mixed models including model based clustering
This package contains a mixture of statistical methods
including the MCMC methods to analyze normal mixtures.
Additionally, model based clustering methods are implemented to
perform classification based on (multivariate) longitudinal (or
otherwise correlated) data. The basis for such clustering is a
mixture of multivariate generalized linear mixed models.

Multivariate Normal Mixture Models and Mixtures of Generalized
Linear Mixed Models Including Model Based Clustering
splines, fastGHQuad, mnormt, parallel, coda
This package contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models.