knitr, markdown, prettydoc, rmarkdown, testthat (>= 3.0.0),
covr, learnr, spelling, lattice, spatstat.data, Matrix, mgcv,
PMCMRplus, devtools, extrafont, DescTools, GGally, VGAM, car,
dplyr, tibble, ggiraphExtra, scales, spatstat, vcd, FactoMineR,
cluster, caret, corrplot, actuar

Variable Selection using Simulated Annealing
c(person("Arnab", "Maity", email = "arnab.maity@pfizer.com", role = c("aut", "cre")),
person("Sanjib", "Basu", email = "sbasu@uic.edu", role = c("ctb")))
Highest posterior model is widely accepted as a good model among available models. In terms of variable selection highest posterior model is often the true model. Our stochastic search process SAHPM based on simulated annealing maximization method tries to find the highest posterior model by maximizing the model space with respect to the posterior probabilities of the models. This package currently contains the SAHPM method only for linear models. The codes for GLM will be added in future.
Arnab Maity [aut, cre],
Sanjib Basu [ctb]

c(
person("Sahir", "Bhatnagar", email = "sahir.bhatnagar@gmail.com",
role = c("aut", "cre"), comment = "http://sahirbhatnagar.com/"),
person("Yi", "Yang", email = "yi.yang6@mcgill.ca",
role = c("aut"), comment = "http://www.math.mcgill.ca/yyang/"),
person("Celia", "Greenwood", email = "celia.greenwood@mcgill.ca",
role = c("aut"), comment = "https://www.mcgill.ca/statisticalgenetics/")
)
Sparse additive interaction learning with the strong heredity property, i.e.,
an interaction is selected only if its corresponding main effects are also included.
Fits a linear model with non-linear interactions via penalized maximum likelihood.
Interactions are limited to a single exposure or environment variable. For more information,
see the website below and the accompanying paper: Bhatnagar et al., "A sparse additive model for
high-dimensional interactions with an exposure variable", 2019, <DOI:10.1101/445304>.
grpreg, truncnorm, foreach, doParallel, testthat, covr,
vdiffr, knitr, rmarkdown
Sahir Bhatnagar [aut, cre] (http://sahirbhatnagar.com/),
Yi Yang [aut] (http://www.math.mcgill.ca/yyang/),
Celia Greenwood [aut] (https://www.mcgill.ca/statisticalgenetics/)