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

Suggests: 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 (>=

Suggests: BART, callr (>= 3.4.3), cluster, CORElearn, coro,

Title: Penalized likelihood factor analysis via non-concave penalty
Description: This package computes the penalized maximum likelihood
estimates of factor loadings and unique variances for various
tuning parameters.  The pathwise coordinate descent along with
Packaged: 2012-05-17 01:21:56 UTC; hirosekei

Packaged: 2012-05-18 10:02:46 UTC; hirosekei

Description: 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
Packaged: 2012-05-29 00:13:11 UTC; hirosekei

Title: Penalized likelihood factor analysis via nonconvex penalty
Packaged: 2012-05-30 22:41:16 UTC; hirosekei

Packaged: 2013-08-04 09:10:31 UTC; hirosekei

Packaged: 2013-08-06 10:26:38 UTC; hirosekei

Title: Penalized Likelihood Factor Analysis via Nonconvex Penalty
Packaged: 2015-03-18 12:12:15 UTC; hirosekei

Author: Kei Hirose, Michio Yamamoto, Haruhisa Nagata
Packaged: 2015-03-27 10:28:22 UTC; hirosekei

Packaged: 2015-09-16 09:28:23 UTC; hirosekei

Packaged: 2013-03-17 00:13:00 UTC; hirosekei

Description: 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.
Packaged: 2016-11-04 11:24:29 UTC; hirosekei