Rcpp (>= 0.12.13), MixSim, mclust, stats, lowmemtkmeans

A package for retrieval, preparation and analysis of data from the Affymetrix GeneChip. In particular the issue of identifying  differentially expressed genes is addressed.

R (>= 2.2.0), stats, multtest, sma, methods
Microarray, OneChannel, Preprocessing, DataImport, Statistics, DifferentialExpression, Clustering, MultipleComparisons

Elastic Net and (Adaptive) Lasso Penalized Sparse Asymmetric
Least Squares (SALES) and Coupled Sparse Asymmetric Least
Squares (COSALES) using Coordinate Descent and Proximal
Gradient Algorithms
Yuwen Gu <guxxx192@umn.edu>, Hui Zou <zouxx019@umn.edu>
A coordinate descent algorithm for computing the solution path of the sparse and coupled sparse asymmetric least squares, including the elastic net and (adaptive) Lasso penalized SALES and COSALES regressions.

The (Adaptive) Elastic Net and Lasso Penalized Sparse Asymmetric
Least Squares (SALES) and Coupled Sparse Asymmetric Least
Squares (COSALES) using Coordinate Descent and Proximal
Gradient Algorithms
Yuwen Gu <yuwen.gu@uconn.edu>, Hui Zou <zouxx019@umn.edu>
grDevices, graphics, stats, methods, Matrix
A coordinate descent algorithm for computing the solution paths of
the sparse and coupled sparse asymmetric least squares, including the
(adaptive) elastic net and Lasso penalized SALES and COSALES regressions.

Hannah Director, Scott Vander Wiel, James Gattiker
The SALTSampler package facilitates Monte Carlo Markov Chain (MCMC)
sampling of random variables on a simplex. A Self-Adjusting Logit Transform
(SALT) proposal is used so that sampling is still efficient even in difficult
cases, such as those in high dimensions or with parameters that differ by orders
of magnitude. Special care is also taken to maintain accuracy even when some
coordinates approach 0 or 1 numerically. Diagnostic and graphic functions are
included in the package, enabling easy assessment of the convergence and mixing
of the chain within the constrained space.

Tuo Zhao, Xingguo Li, Han Liu, Lie Wang, Kathryn Roeder
The package SAM targets at high dimensional classification
problem for complex data analysis. SAM is short for sparse
additive machine, which is a explicit combination of high
dimensional sparse additive modeling and support vector
machine. Different from existing non-linear classification
methods, which usually use, SAM adopts the computationally
efficient basis spline technique. The optimization is solved by
the Linearized Alternative Direction Method of Multipliers
(L-ADMM). The computation is further accelerated by warm-start
and active-set tricks. For users who are interested in
large-scale problems, we also provide an implementation of L1
norm SVM for computational convenience.