RPCLR (Random-Penalized Conditional Logistic Regression)
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
studies.

Random Projection Ensemble Clustering Algorithm
L. Anderlucci [aut], F. Fortunato [aut, cre], A. Montanari [ctb]
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.

Gamma and Exponential Generalized Linear Models with Elastic Net
Penalty
Anthony Christidis <anthony.christidis@stat.ubc.ca>,
Xin Chen <chenx26@uw.edu>,
Daniel Hanson <hansondj@uw.edu>
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>.
Rcpp (>= 1.0.2), RPEIF, PerformanceAnalytics

Computation and Plots of Influence Functions for Risk and
Performance Measures
Anthony Christidis <anthony.christidis@stat.ubc.ca>,
Shengyu Zhang <syzhang@uw.edu>,
Douglas Martin <doug@amath.washington.edu>
Computes the influence functions time series of the returns for the risk and
performance measures as mentioned in Zhang and Martin (2017)
<https://ssrn.com/abstract=2747179> as well as Chen and Martin (2018)
<https://ssrn.com/abstract=3085672>. Also evaluates estimators influence
functions at a set of parameter values and plots them to display the shapes of
the influence functions.
Rcpp (>= 0.12.17), PerformanceAnalytics, xts, zoo, RobStatTM,
stats