Title: Fast Estimation of a Covariance Matrix by Banding the Cholesky
Description: Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <http://stat.umn.edu/~molst029> for details on the algorithm.
Packaged: 2015-08-24 15:05:44 UTC; aaronmolstad

Title: Fast EM and Best-Subset Selection for CUB Models for Rating Data
Description: For ordinal rating data, consider the accelerated Expectation-Maximization algorithm to estimate and test models within the family of CUB models (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions). The procedure is built upon Louis' identity for the observed information matrix.  Best-subset variable selection for CUB regression models is then implemented on such basis. The methods here implemented are illustrated and discussed in the preprint available from Researchgate by Simone R. (2020) <https://tinyurl.com/vvk563e>.

Title: Efficiently Using Gaussian Processes with Rcpp and RcppEigen
Description: Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010).
Imports: Rcpp, MASS, mvtnorm, rbenchmark, stats

Description: Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010). Please see an eprint at http://arxiv.org/pdf/1507.06055.pdf for more details.