Imports: ape, bipartite, cluster, coda, corpcor, deSolve, fields, fpc,
geiger, glassoFast, graphics, grDevices, igraph, Matrix,
mvMORPH (>= 1.1.0), mvtnorm, parallel, phytools, pracma,
pspline, pvclust, raster, Rmpfr, stats, TESS, utils
Billaud et al. (2019) <DOI:10.1093/sysbio/syz057>, Lewitus et al. (2019) <DOI:10.1093/sysbio/syz061>,

Depends: R (>= 2.14.2), ape, picante, methods
Imports: bipartite, cluster, coda, deSolve, fields, fpc, glassoFast,
graphics, grDevices, igraph, Matrix, mvMORPH (>= 1.1.0),
parallel, phytools, pspline, pvclust, raster, Rmpfr, stats,

graphics, grDevices, igraph, Matrix, mvMORPH (>= 1.1.6),

mvtnorm, parallel, phytools, pracma, pspline, pvclust, raster,
Rmpfr, vegan, GUniFrac, R.utils, stats, TESS, utils
person("Leandro", "Aristide", role = c("aut", "cph")),
person("Benoit", "Perez-Lamarque", role = c("aut", "cph")))

Title: RPCLR (Random-Penalized Conditional Logistic Regression)
Description: This package implements the R-PCLR (Random-Penalized
Conditional Logistic Regression) algorithm for obtaining
variable importance. The algorithm is applicable for the
analysis of high dimensional data from matched case-control
Packaged: Sun Aug 19 12:19:52 2012; Raji

Title: Random Projection Ensemble Clustering Algorithm
Author: L. Anderlucci [aut], F. Fortunato [aut, cre], A. Montanari [ctb]
Description: Implements the methodology proposed by Anderlucci, Fortunato and Montanari (2019) <arXiv:1909.10832> for high-dimensional unsupervised classification. The random projection ensemble clustering algorithm applies a Gaussian Mixture Model to different random projections of the high-dimensional data and selects a subset of solutions accordingly to the Bayesian Information Criterion, computed here as discussed in Raftery and Dean (2006) <doi:10.1198/016214506000000113>. The clustering results obtained on the selected projections are then aggregated via consensus to derive the final partition.
Packaged: 2019-11-05 16:36:41 UTC; francesca.fortunato3

Title: Gamma and Exponential Generalized Linear Models with Elastic Net
Description: Implements the fast iterative shrinkage-thresholding algorithm
(FISTA) algorithm to fit a Gamma distribution with an elastic net
penalty as described in Chen, Arakvin and Martin (2018)
<arxiv:1804.07780>. An implementation for the case of the
exponential distribution is also available, with details available
in Chen and Martin (2018) <https://papers.ssrn.com/abstract_id=3085672>.
Imports: Rcpp (>= 1.0.2), RPEIF, PerformanceAnalytics