survival, pec, prodlim, mlbench, akima, caret, imbalance
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.

survival, pec, prodlim, mlbench, interp, caret, imbalance,
cluster

R (>= 2.14.0), MASS, gtools, foreach, doParallel
The package implements a bagging predictor based on
general linear models

R (>= 2.14.0), MASS, foreach, doParallel

R (>= 4.0.0), MASS, foreach, doParallel,
Hmisc, geometry, survival, matrixStats, parallel
A bagging predictor based on generalized linear models (GLMs) is implemented. The method is published in
Song, Langfelder and Horvath (2013) <doi:10.1186/1471-2105-14-5>.

Fits random effects latent class models, as well as standard latent class models.

Fits random effects latent class models, as well as
standard latent class models.

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

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.

boot, fastGHQuad, Matrix, Rfast, parallel

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). Exampels are given for their use in diagnostic testing.

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.
R(>= 3.0.0), lattice, boot, fastGHQuad, Matrix