yeast genome and gene expression data of Yassour, et al. (2009)

person("Russell", "Zaretzki", role = "aut", email = "rzaretzk@utk.edu"),
person("Michael A.", "Gilchrist", role = "aut", email = "mikeg@utk.edu"),
Depends: R(>= 4.0.0), methods, coda, foreach, parallel, stats,

Title: Computation of a Cubic B-Spline Basis and Its Derivatives
comment = "Université de Liège / Université catholique de Louvain (Belgium)")
Description: Computation of a cubic B-spline basis for arbitrary knots. It also provides the 1st and 2nd derivatives, as well as the integral of the basis elements. It is used by the author to fit penalized B-spline models, see e.g. Jullion, A. and Lambert, P. (2006) <doi:10.1016/j.csda.2006.09.027>,  Lambert, P. and  Eilers, P.H.C. (2009) <doi:10.1016/j.csda.2008.11.022> and, more recently, Lambert, P. (2021) <doi:10.1016/j.csda.2021.107250>. It is inspired by the algorithm developed by de Boor, C. (1977) <doi:10.1137/0714026>.
Author: Philippe Lambert [aut, cre] (Université de Liège / Université

Description: Functions for visualizing, animating, solving and
analyzing the Rubik's cube. Includes data structures for
solvable and unsolvable cubes, random moves and random
state scrambles and cubes, 3D displays and animations
using 'OpenGL', patterned cube generation, and lightweight
solvers. See Rokicki, T. (2008) <arXiv:0803.3435> for the

Imports: grDevices, graphics, utils, stats, rgl

Title: R Interface for the RAPIDS cuML Suite of Libraries
Description: R interface for RAPIDS cuML (<https://github.com/rapidsai/cuml>),
a suite of GPU-accelerated machine learning libraries powered by CUDA
Imports: ellipsis, hardhat, parsnip, Rcpp (>= 1.0.6), rlang (>= 0.1.4)
Suggests: callr, glmnet, MASS, magrittr, mlbench, purrr, reticulate,
SystemRequirements: RAPIDS cuML (see https://rapids.ai/start.html)
Author: Yitao Li [aut, cph, cre] (<https://orcid.org/0000-0002-1261-905X>),

Author: Yitao Li [aut, cph] (<https://orcid.org/0000-0002-1261-905X>),

Package: cudaBayesreg
Version: 0.1-1
Date: 2009-10-10
Title: CUDA Parallel Implementation of a Bayesian Multilevel Model for
fMRI Data Analysis
Author: Adelino Ferreira da Silva <afs@fct.unl.pt>
Maintainer: Adelino Ferreira da Silva <afs@fct.unl.pt>
Depends: R (>= 2.9.2), AnalyzeFMRI
SystemRequirements: nvcc (release 2.3) NVIDIA Cuda Compiler driver, and
g++ GNU compiler version 3.4 (version 4.4 is not yet compatible
with nvcc).
Description: Compute Unified Device Architecture (CUDA) is a software
platform for massively parallel high-performance computing on
NVIDIA GPUs. This package provides a CUDA implementation of a
Bayesian multilevel model for the analysis of brain fMRI data.
A fMRI data set consists of time series of volume data in 4D
space. Typically, volumes are collected as slices of 64 x 64
"voxels". Analysis of fMRI data often relies on fitting linear
regression models at each voxel of the brain. The volume of the
data to be processed, and the type of statistical analysis to
perform in fMRI analysis, call for high-performance computing
strategies. In this package, the CUDA programming model uses a
separate thread for fitting a linear regression model at each
voxel in parallel. The global statistical model implements a
Gibbs Sampler for hierarchical linear models with a normal
prior. This model has been proposed by Rossi, Allenby and
McCulloch in "Bayesian Statistics and Marketing", Chapter 3,
and is referred to as "rhierLinearModel" in the R-package
"bayesm". A notebook equipped with a NVIDIA "GeForce 8400M GS"
card having Compute Capability 1.1 has been used in the tests.
License: GPL (>= 2)
Repository: CRAN
Date/Publication: 2009-10-19 17:01:37
Packaged: 2009-10-19 12:55:10 UTC; afs