person("Michael", "Wurm", email = "wurm@uwalumni.com", role = c("aut", "cre")),

Huang and Rathouz (2012) <doi:10.1093/biomet/asr075>.
Rathouz and Gao (2008) <doi:10.1093/biostatistics/kxn030>.
Imports: stats (>= 3.2.2), graphics (>= 3.2.2)

Title: Learning interactions via hierarchical group-lasso
Description: Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper below.

Title: Learning Interactions via Hierarchical Group-Lasso
Description: Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2014).

Description: Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) <DOI:10.1080/10618600.2014.938812>.

Title: Phylogenetic Comparative Methods with Uncertainty Estimates
Authors@R: c(person(family="Kiang", given="Hao Chi", role=c("cre","aut"), email="hello@hckiang.com"))
Maintainer: Hao Chi Kiang <hello@hckiang.com>
Description: A framework for analytically computing the asymptotic confidence intervals and maximum-likelihood estimates of a class of continuous-time Gaussian branching processes defined by Mitov V, Bartoszek K, Asimomitis G, Stadler T (2019) <doi:10.1016/j.tpb.2019.11.005>. The class of model includes the widely used Ornstein-Uhlenbeck and Brownian motion branching processes. The framework is designed to be flexible enough so that the users can easily specify their own sub-models, or re-parameterizations, and obtain the maximum-likelihood estimates and confidence intervals of their own custom models.
Imports: Rcgmin, lbfgsb3c, BB, ape, numDeriv, plyr, rlang, utils, stats