Rapid, simulation-based exact (restricted) likelihood ratio tests for testing the presence of variance components/nonparametric terms for models fit with nlme::lme(),lme4::lmer(), lmeTest::lmer(), gamm4::gamm4(), mgcv::gamm() and SemiPar::spm().

Methods for regression for functional data, including function-on-scalar, scalar-on-function, and function-on-function regression. Some of the functions are applicable to image data.

A suite of diagnostic tools for hierarchical (multilevel) linear models. The tools include not only leverage and traditional deletion diagnostics (Cook's distance, covratio, covtrace, and MDFFITS) but also convenience functions and graphics for ...

A function and vignettes for computing an intraclass correlation described in Aguinis & Culpepper (2015) <doi:10.1177/1094428114563618>. This package quantifies the share of variance in a dependent variable that is attributed to group heterogeneity ...

Test zero variance components in linear mixed models and test additivity in nonparametric regression using the restricted likelihood ratio test and the generalized F-test. Details can be found at Zhang et al (2016) <doi:10.1002/cjs.11295>.

Functions, data sets, analyses and examples from the book `An Introduction to Applied Multivariate Analysis with R' (Brian S. Everitt and Torsten Hothorn, Springer, 2011).

Selection and/or smoothing of ordinally scaled independent variables using a group lasso or generalized ridge penalty

Variance component models: REML estimation, testing fixed effect contrasts through Satterthwaite or Kenward-Roger methods, testing the nullity of variance components through (linear or quadratic) score tests or likelihood ratio tests.

Parametric and semiparametric inference for massively parallel models, i.e., a large number of models with common design matrix, as often occurs with brain imaging data.