Title: Testing for a Subset of Variance Components in Linear Mixed
Authors@R: c(person("Yichi", "Zhang", role = c("aut", "cre"),
Details can be found at Zhang et al (2016) <doi:10.1002/cjs.11295>.

Title: Linear Mixed-Effects Models with Censored Responses
Description: This package includes a function to fit a linear
mixed-effects model in the formulation described in Laird and
Ware (1982) but allowing for censored normal responses. In this
version, the with-in group errors are assumed independent and

Title: Linear Mixed Effects Models for Genome-Wide Association Studies
Authors@R: c(person("Lucia", "Gutierrez", role = "aut",
Description: Performs Genome-Wide Association analysis for diverse populations and
for multi-environment and multi-trait analysis using linear mixed models.
Imports: qtl, lattice, pastecs, stringr, fdrtool, LDheatmap, genetics,

Title: Linear Mixed Effects Models for QTL Mapping for Multienvironment
Description: Performs QTL mapping analysis for balanced and for
multi-environment and multi-trait analysis using mixed models.
Balanced population, single trait, single environment QTL mapping is
performed through marker-regression (Haley and Knott (1992) <DOI:10.1038/hdy.1992.131>,
while multi-environment and multi-trait QTL
mapping is performed through linear mixed models.
These functions could use any of the following populations: double haploid,
F2, recombinant inbred lines, back-cross, and 4-way crosses.
Performs a Single Marker Analysis, a Single Interval Mapping,
or a Composite Interval Mapping analysis, and then constructs a final model
Imports: qtl, lme4, lattice, pastecs, stringr, graphics, utils,

Title: Linear Mixed Effects Models with Non-stationary Stochastic
Description: Fit, filter and smooth mixed models with non-stationary processes

Title: Linear Mixed Effects Models with Non-Stationary Stochastic
Description: Contains functions to estimate model parameters and filter, smooth and forecast random effects coefficients for mixed models with stationary and non-stationary stochastic processes under multivariate normal and t response distributions, diagnostic checks, bootstrap standard error calculation, etc.