Performs robust multiple testing for means in the presence of known and unknown latent factors presented in Fan et al.(2019) "FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control" <doi:10.1080/01621459.2018.1527700>.
Implements a series of adaptive Huber methods combined with fast data-drive tuning schemes proposed in Ke et al.(2019) "User-Friendly Covariance Estimation for Heavy-Tailed Distributions" <doi:10.1214/19-STS711> to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymmetric error distributions.
Extensions to two-sample simultaneous mean comparison problems are also included.
As by-products, this package contains functions that compute adaptive Huber mean, covariance and regression estimators that are of independent interest.

Fast Estimation of a Covariance Matrix by Banding the Cholesky
Factor
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.

Fast EM and Best-Subset Selection for CUB Models for Rating Data
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>.

Efficiently Using Gaussian Processes with Rcpp and RcppEigen
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).