R (>= 2.10), stats4, nlme, spatstat, grDevices, graphics,
stats, magic

Functions for different purposes related to forest biometrics, including illustrative graphics, numerical computation, modeling height-diameter relationships, prediction of tree volumes, modelling of diameter distributions and estimation off stand density using ITD. Several empirical datasets are also included.

stats4, grDevices, graphics, stats, magic, MASS, Matrix,
spatstat.geom

Tools to Visualize and Screen 2D/3D Landmark Data
c(person(given = "Daniel",
family = "Ehrlich",
role = c("aut", "cre"),
email = "dan.ehrlich.e@gmail.com"),
person(given = "Michelle",
family = "Singleton",
role = "aut"))
Provides visual and interactive tools to screen 2D/3D landmark data.
Meant to supplement other geometric morphometric packages by making vibrant visuals
easy/automatic.
Daniel Ehrlich [aut, cre],
Michelle Singleton [aut]

person("Hannes", "Riebl", email = "hriebl@uni-goettingen.de",
role = c("aut", "cre"))
The Gaussian location-scale regression model is a multi-predictor
model with explanatory variables for the mean (= location) and the standard
deviation (= scale) of a response variable. This package implements maximum
likelihood and Markov chain Monte Carlo (MCMC) inference (using algorithms
from Girolami and Calderhead (2011) <doi:10.1111/j.1467-9868.2010.00765.x>
and Nesterov (2009) <doi:10.1007/s10107-007-0149-x>), a parametric
bootstrap algorithm, and diagnostic plots for the model class.
bookdown, coda, covr, ggplot2, knitr, mgcv, mvtnorm,
numDeriv, patchwork, rmarkdown, testthat (>= 3.0.0)

Original by Joseph L. Schafer <jls@stat.psu.edu>.
Multiple imputation for multivariate panel or clustered data

Some improved procedures for linear mixed models

Some improved procedures for linear mixed models.

It implements Expectation/Conditional Maximization Either (ECME)
and rapidly converging algorithms as well as
Bayesian inference for linear mixed models,
which is described in Schafer, J.L. (1998)
"Some improved procedures for linear mixed models".
Dept. of Statistics, The Pennsylvania State University.