Title: Laplace's Demon: Software for Bayesian Inference
Description: Laplace's Demon is MCMC-based software for Bayesian

Description: Laplace's Demon is software for Bayesian inference.

Title: Complete Environment for Bayesian Inference
Description: Laplace's Demon is a complete environment for Bayesian

Authors@R: c(person("Byron", "Hall", role = "aut"),
person("Martina", "Hall", role = "aut"),
person(family="Statisticat, LLC", role = "aut"),
person(given="Eric", family="Brown", role = "ctb"),
person(given="Richard", family="Hermanson", role = "ctb"),
person(given="Emmanuel", family="Charpentier", role = "ctb"),
Imports: parallel, grDevices, graphics, stats, utils
Description: Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview). The README describes the history of the package development process.

person(given="Daniel", family="Heck", role = "ctb"),
person(given="Stephane", family="Laurent", role = "ctb"),
person(given="Quentin F.", family="Gronau", role = "ctb"),

X-CRAN-Comment: Orphaned on 2020-02-03 as check issues were not
Fails on MKL as has badly-written numerical linear algebra code

Description: Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).

Description: Uses gradient descent to minimize the sum of squared
residuals for the regression problem.  Can include an L2
penalty on the coefficient matrix. This function is very useful
when there is an initial guess of what B should be in Y = XB.
In general, this function performs faster than R's lm function.
GPU acceleration can be used to make this function extremely
fast.  This package suggests cudaMatrixOps, which is not on
CRAN but can be downloaded at stanford.edu/~jeffwong.

Title: Modelling Interactions in High-Dimensional Data with
Authors@R: person("Rajen", "Shah", email = "r.shah@statslab.cam.ac.uk",
Description: Implementation of the algorithm introduced in "Shah, R. D.
(2016) Modelling interactions in high-dimensional data with Backtracking, JMLR,
to appear". Data with thousands of predictors can be handled. The algorithm
performs sequential Lasso (Tibshirani, 1996) fits on design matrices containing
increasing sets of candidate interactions. Previous fits are used to greatly
speed up subsequent fits so the algorithm is very efficient.