Suggests: survival, pec, prodlim, mlbench, akima, caret, imbalance
Description: Fast OpenMP parallel computing of Breiman's random forests for survival, competing risks, regression and classification based on Ishwaran and Kogalur's popular random survival forests (RSF) package.  Handles missing data and now includes multivariate, unsupervised forests, quantile regression and solutions for class imbalanced data.  New fast interface using subsampling and confidence regions for variable importance.

Suggests: survival, pec, prodlim, mlbench, interp, caret, imbalance,

Title: Random General Linear Model Prediction
Depends: R (>= 2.14.0), MASS, gtools, foreach, doParallel
Description: The package implements a bagging predictor based on
Packaged: 2013-01-14 07:27:46 UTC; plangfelder

Depends: R (>= 2.14.0), MASS, foreach, doParallel
Packaged: 2013-01-30 18:48:31 UTC; plangfelder

Depends: R (>= 4.0.0), MASS, foreach, doParallel,
Imports: Hmisc, geometry, survival, matrixStats, parallel
Description: A bagging predictor based on generalized linear models (GLMs) is implemented. The method is published in
Packaged: 2022-04-08 16:30:58 UTC; plangfelder

Title: Random Effects Latent Class Analysis
Description: Fits random effects latent class models, as well as standard latent class models.
Depends: R(>= 2.6.0), statmod, lattice, nlme
Packaged: Fri Mar 21 20:23:16 2008; hornik

Packaged: Fri Apr  4 14:08:03 2008; hornik

Packaged: Fri May 30 08:38:40 2008; hornik

Packaged: Thu Jun 12 14:38:11 2008; hornik

Packaged: Sat Jul 12 15:07:41 2008; kjbeath

Packaged: Tue Jul 15 22:11:48 2008; kjbeath

Packaged: Tue Jul 22 13:06:54 2008; kjbeath

Packaged: Sun Nov  9 19:27:58 2008; kjbeath

Packaged: Sun Mar  8 19:39:38 2009; kjbeath

Description: Fits random effects latent class models, as well as
Depends: R(>= 2.6.0), lattice, nlme, boot
Packaged: Sat Apr 11 11:23:32 2009; kjbeath

Depends: R(>= 2.15.1), lattice, nlme, boot, utils

Depends: R(>= 3.0.0), lattice, boot, fastGHQuad

Description: Fits standard and random effects latent class models. The single level random effects model is described in Qu et al <doi:10.2307/2533043> and the two level random effects model in Beath and Heller <doi:10.1177/1471082X0800900302>. Examples are given for their use in diagnostic testing.