Matteo Fasiolo, using the C++ parallel RNG of Thijs van den Berg

c(person("Matteo", "Fasiolo", email = "matteo.fasiolo@gmail.com", role = c("aut", "cre")),
person("Thijs", " van den Berg", role = c("ctb")))
knitr, rmarkdown, testthat, mvtnorm, microbenchmark, MASS,
plyr, RhpcBLASctl
Matteo Fasiolo [aut, cre],
Thijs van den Berg [ctb]

Provides computationally efficient tools related to the
multivariate normal and Student's t distributions. The main functionalities
are: simulating multivariate random vectors, evaluating multivariate normal or
Student's t densities and Mahalanobis distances. These tools are very efficient
thanks to the use of C++ code and of the OpenMP API.

Software for moving grid adjustment in plant breeding field
trials
Package for moving grid adjustment in plant breeding field
trials.
movG-class.R circularExtension.R entryData.R
entryDataMethods.R extendHorVer.R fittedMethods.R movingGrid.R
movingMean.R residualsMethods.R showMethods.R sketchGrid.R
sketchGridMethods.R summaryMethods.R movingMeanMethods.R

Package for  moving grid adjustment
in plant breeding field trials.

Moving Grid Adjustment in Plant Breeding Field Trials

Simultaneously Impute the Missing and Censored Values
Implementing a multiple imputation algorithm for multivariate data with missing and censored values under a coarsening at random assumption (Heitjan and Rubin, 1991<doi:10.1214/aos/1176348396>). The multiple imputation algorithm is based on the data augmentation algorithm proposed by Tanner and Wong (1987)<doi:10.1080/01621459.1987.10478458>. The Gibbs sampling algorithm is adopted to to update the model parameters and draw imputations of the coarse data.
ggplot2, reshape2, LaplacesDemon, rlang, Rcpp, MASS, truncnorm

ML estimation for multivariate normal data with missing values.
Kevin Gross, with help from Douglas Bates
Finds the maximum likelihood estimate of the mean vector
and variance-covariance matrix for multivariate normal data
with missing values.

ML Estimation for Multivariate Normal Data with Missing Values
Orphaned on 2012-12-21 as the maintainer failed to
respond to requests for an update for R >= 2.14.0