Title: Elastic Net and (Adaptive) Lasso Penalized Sparse Asymmetric
Least Squares (SALES) and Coupled Sparse Asymmetric Least
Squares (COSALES) using Coordinate Descent and Proximal
Author: Yuwen Gu <guxxx192@umn.edu>, Hui Zou <zouxx019@umn.edu>
Imports: grDevices, graphics, stats, methods
Description: 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.

Title: The (Adaptive) Elastic Net and Lasso Penalized Sparse Asymmetric
Author: Yuwen Gu <yuwen.gu@uconn.edu>, Hui Zou <zouxx019@umn.edu>
Imports: grDevices, graphics, stats, methods, Matrix
Description: 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.

Title: Efficient Sampling on the Simplex
Author: Hannah Director, Scott Vander Wiel, James Gattiker
Maintainer: Scott Vander Wiel <scottv@lanl.gov>
Description: 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.
Packaged: 2015-11-02 00:37:21 UTC; hdirector

Package: SALTSampler
Type: Package
Title: Efficient Sampling on the Simplex
Version: 1.1.0
Date: 2017-08-09
Author: Hannah Director, Scott Vander Wiel, James Gattiker
Maintainer: Scott Vander Wiel <scottv@lanl.gov>
Imports: graphics, methods, stats
Depends: R (>= 3.0.0), lattice
Description: 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.
License: BSD_3_clause + file LICENSE
Suggests: knitr, coda
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2017-08-10 02:53:45 UTC; hdirector
Repository: CRAN
Date/Publication: 2017-08-10 19:35:03 UTC