data.table, methods, rlang (>= 0.3.4), rstream, survival

BART, callr (>= 3.4.3), cluster, CORElearn, coro, cowplot,
dynamicTreeCut, e1071 (>= 1.7.5), Ecdat, fastcluster, ggplot2
(>= 3.0.0), glmnet, gtable, harmonicmeanp, isotree (>= 0.3.0),
knitr, labeling, laGP, MASS, maxstat, mboost (>= 2.9.0),
microbenchmark, partykit, proxy, qvalue, randomForestSRC,
ranger, rmarkdown, scales, testthat (>= 3.0.0), xml2, VGAM,
xgboost

BART, callr (>= 3.4.3), cluster, CORElearn, coro, cowplot,
dynamicTreeCut, e1071 (>= 1.7.5), Ecdat, fastcluster, fastglm,
ggplot2 (>= 3.0.0), glmnet, gtable, harmonicmeanp, isotree (>=
0.3.0), knitr, labeling, laGP, MASS, maxstat, mboost (>=
2.9.0), microbenchmark, nnet, partykit, praznik, proxy, qvalue,
randomForestSRC, ranger, rmarkdown, scales, testthat (>=
3.0.0), xml2, VGAM, xgboost

BART, callr (>= 3.4.3), cluster, CORElearn, coro,
dynamicTreeCut, e1071 (>= 1.7.5), Ecdat, fastcluster, fastglm,
ggplot2 (>= 3.0.0), glmnet, gtable, harmonicmeanp, isotree (>=
0.3.0), knitr, labeling, laGP, MASS, maxstat, mboost (>=
2.9.0), microbenchmark, nnet, partykit, praznik, proxy, qvalue,
randomForestSRC, ranger, rmarkdown, scales, testthat (>=
3.0.0), xml2, VGAM, xgboost

Penalized likelihood factor analysis via non-concave penalty
This package computes the penalized maximum likelihood
estimates of factor loadings and unique variances for various
tuning parameters.  The pathwise coordinate descent along with
EM algorithm is used.

An R package "fanc" computes the penalized maximum
likelihood estimates of factor loadings and unique variances
for various tuning parameters. The pathwise coordinate descent
along with EM algorithm is used.  This package also includes a
new graphical tool which outputs path diagram, goodness-of-fit
indices and model selection criteria for each regularization
parameter. The user can change the regularization parameter by
manipulating scrollbars, which is helpful to find a suitable
value of regularization parameter.

Penalized likelihood factor analysis via nonconvex penalty