as an extension of the method proposed by Tong et al. (2015) <doi:10.6028> (10.6028/jres.120.008).

Title: Computer Model Calibration for Deterministic and Stochastic
Author: Matthew T. Pratola <mpratola@stat.osu.edu> [aut, cre, cph]
Maintainer: Matthew T. Pratola <mpratola@stat.osu.edu>
Description: Implements the Bayesian calibration model described
in Pratola and Chkrebtii (2018) <DOI:10.5705/ss.202016.0403> for stochastic
and deterministic simulators.  Additive and multiplicative discrepancy models
are currently supported. See <http://www.matthewpratola.com/software> for

Title: Framework for Building Interfaces to Shell Commands
Description: Writing interfaces to command line software is cumbersome.
'cmdfun' provides a framework for building function calls to seamlessly
interface with shell commands by allowing lazy evaluation of command line arguments.
'cmdfun' also provides methods for handling user-specific paths to tool installs or secrets like API keys.
Its focus is to equally serve package builders who wish to wrap command line software, and to help analysts stay inside
R when they might usually leave to execute non-R software.
Imports: magrittr, purrr, R.utils, rlang, testthat, usethis, utils
Suggests: cli, covr, knitr, processx, rmarkdown

Title: Bi-Level Selection of Conditional Main Effects
Description: Provides functions for implementing cmenet - a bi-level variable selection method for conditional main effects (see Mak and Wu (2018) <doi:10.1080/01621459.2018.1448828>). CMEs are reparametrized interaction effects which capture the conditional impact of a factor at a fixed level of another factor. Compared to traditional two-factor interactions, CMEs quantify more interpretable interaction effects in many problems of interest (e.g., genomics, molecular engineering, personalized medicine). The current implementation performs variable selection on only binary CMEs, but we are working on an extension for the continuous setting. This work was supported by USARO grant W911NF-14-1-0024.
Imports: Rcpp (>= 0.12.4), MASS, glmnet, hierNet