Imports: Rcpp (>= 1.0.5), Rdpack, Rdimtools, ADMM, MASS, fda, ggplot2,
lpSolve, maotai, mclustcomp, rstiefel, scatterplot3d, stats,

Title: Tools for Computational Optimal Transport
Authors@R: c(person("Kisung", "You", role = c("aut", "cre"),email = "kyoustat@gmail.com",comment=c(ORCID="0000-0002-8584-459X")))
Description: Transport theory has seen much success in many fields of statistics and machine learning. We provide a variety of algorithms to compute Wasserstein distance, barycenter, and others. See Peyré and Cuturi (2019) <doi:10.1561/2200000073> for the general exposition to the study of computational optimal transport.
Imports: Rcpp (>= 1.0.5), Rdpack, lpSolve, stats, utils

Imports: CVXR, Rcpp (>= 1.0.5), Rdpack, lpSolve, stats, utils

Title: Functions to Support the ICES Transparent Assessment Framework
Authors@R: c(person("Arni", "Magnusson", role=c("aut","cre"), email="thisisarni@gmail.com"),
Imports: grDevices, lattice, stats, tools, utils
Description: Functions to support the ICES Transparent Assessment Framework
<https://taf.ices.dk> to organize data, methods, and results used in ICES
assessments. ICES is an organization facilitating international collaboration

Title: Transformed Additive Gaussian Processes
Author: Li-Hsiang Lin and V. Roshan Joseph
Description: Implement the transformed additive Gaussian (TAG) process and the  transformed approximately additive Gaussian (TAAG) process proposed in Lin and Joseph (2019+) <DOI:10.1080/00401706.2019.1665592>. These functions can be used to model deterministic computer experiments, obtain predictions at new inputs, and quantify the uncertainty of the predictions.
Imports: Rcpp, DiceKriging, Matrix, mgcv, FastGP, mlegp, randtoolbox,

Description: Implement the transformed additive Gaussian (TAG) process and the  transformed approximately additive Gaussian (TAAG) process proposed in Lin and Joseph (2019+) <DOI:10.1080/00401706.2019.1665592>. These functions can be used to model deterministic computer experiments, obtain predictions at new inputs, and quantify the uncertainty of the predictions. This research is supported by a U.S. National Science Foundation grant DMS-1712642 and a U.S. Army Research Office grant W911NF-17-1-0007.