Imports: ggplot2, gridExtra, parallel, stats, utils, viridisLite

Suggests: gstat, sp, fda, fda.usc, mvtnorm, patchwork, spatstat.geom,
spatstat.core, spatstat.linnet, spatstat (>= 2.0-0), testthat,

Imports: cluster, ggplot2, gridExtra, parallel, stats, utils,

Suggests: gstat, sp, fda, fda.usc, mvtnorm, testthat, knitr

Suggests: crayon, geoR, gstat, sp, fda, fda.usc, locfit, mvtnorm,
Mrkvička and Myllymäki (2022) <arXiv:2008.10108>,

person(given = 'Jiří', family = 'Dvořák', role = 'ctb',

Title: Generalized Extended Triangular Designs ('GETdesigns')
Description: Since their introduction by Bose and Nair (1939) <https://www.jstor.org/stable/40383923>, partially balanced incomplete block (PBIB) designs remain an important class of incomplete block designs. The concept of association scheme was used by Bose and Shimamoto (1952) <doi:10.1080/01621459.1952.10501161> for the classification of these designs. The constraint of resources always motivates the experimenter to advance towards PBIB designs, more specifically to higher associate class PBIB designs from balanced incomplete block designs. It is interesting to note that many times higher associate PBIB designs perform better than their counterpart lower associate PBIB designs for the same set of parameters v, b, r, k and lambda_i (i=1,2...m). This package contains functions named GETD() for generating m-associate (m>=2) class PBIB designs along with parameters (v, b, r, k and lambda_i, i = 1, 2,…,m) based on Generalized Triangular (GT) Association Scheme. It also calculates the Information matrix, Average variance factor and canonical efficiency factor of the generated design. These designs, besides having good efficiency, require smaller number of replications and smallest possible concurrence of treatment pairs.

Title: Joint Test of Gene and GxE Interactions via Varying Coefficients
Description: A novel statistical model to detect the joint genetic and dynamic gene-environment (GxE) interaction with continuous traits in genetic association studies. It uses varying-coefficient models to account for different GxE trajectories, regardless whether the relationship is linear or not. The package includes one function, GxEtest(), to test a single genetic variant (e.g., a single nucleotide polymorphism or SNP), and another function, GxEscreen(), to test for a set of genetic variants. The method involves a likelihood ratio test described in Crainiceanu, C. M., and Ruppert, D. (2004) <doi:10.1111/j.1467-9868.2004.00438.x>.