VarianceComponentModels.jl
Utilities for fitting and testing variance component models
VarianceComponentModels.jl implements computation routines for fitting and testing variance component model of form
\[\text{vec}(Y) \sim \text{Normal}(X B, \Sigma_1 \otimes V_1 + \cdots + \Sigma_m \otimes V_m),\]
where $\otimes$ is the Kronecker product.
In this model, data is represented by
Y
:n x d
response matrixX
:n x p
covariate matrixV=(V1,...,Vm)
: a tuplem
n x n
covariance matrices
and parameters are
B
:p x d
mean parameter matrixΣ=(Σ1,...,Σm)
: a tuple ofm
d x d
variance components
Package Features
- Maximum likelihood estimation (MLE) and restricted maximum likelihood estimation (REML) of mean parameters
B
and variance component parametersΣ
- Allow constrains in the mean parameters
B
- Choice of optimization algorithms: Fisher scoring and minorization-maximization algorithm
- Heritability analysis in genetics
Installation
This package requires Julia v0.7.0 or later, which can be obtained from https://julialang.org/downloads/ or by building Julia from the sources in the https://github.com/JuliaLang/julia repository.
The package has not yet been registered and must be installed using the repository location. Start julia and use the ]
key to switch to the package manager REPL
(v1.2) pkg> add https://github.com/OpenMendel/VarianceComponentModels.jl.git
Use the backspace key to return to the Julia REPL.