Generation of multivariate ordinal variates.
A method for multivariate ordinal data generation given
marginal distributions and correlation based on the methodology
proposed by Demirtas (2006).

A method for multivariate ordinal data generation given
marginal distributions and correlation matrix based on the
methodology proposed by Demirtas (2006).

A method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006).

Generation of Multivariate Ordinal Variates

A method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006) <DOI:10.1080/10629360600569246>.

MultiPhen, a package for the genetic association testing of
multiple phenotypes
Lachlan Coin, Paul O'reilly and Federico Calboli
Genetic association analysis of for multiple genotypes and
phenotypes

Lachlan Coin, Paul O'Reilly, Yotsawat Pompyen, Clive Hoggart
and Federico Calboli
Performs genetic association tests between SNPs
(one-at-a-time) and multiple phenotypes (separately or in joint
model)

A Package to Test for Pleiotropic Effects
Performs genetic association tests between SNPs
(one-at-a-time) and multiple phenotypes (separately or in joint
model).

A Package to Test for Multi-Trait Association

Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart.

Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart. The details of the method are explained in Demirtas (2004) <DOI:10.22237/jmasm/1099268340>.

Bias, Precision, and Power for Multi-Level Random Regressions
Calculates bias, precision, and power for multi-level random regressions. Random regressions are types of hierarchical models in which data are structured in groups and (regression) coefficients can vary by groups. Tools to estimate model performance are designed mostly for scenarios where (regression) coefficients vary at just one level. 'MultiRR' provides simulation and analytical tools (based on 'lme4') to study model performance for random regressions that vary at more than one level (multi-level random regressions), allowing researchers to determine optimal sampling designs.