Authors@R: person("Tianjian", "Yang", email = "yangtj5@mail2.sysu.edu.cn",
Description: A set of functions to implement Time Series Cointegrated System (TSCS)
spatial interpolation and relevant data visualization.
Imports: stats, tseries (>= 0.10-38), ggplot2 (>= 2.2.1), rgl (>=

Imports: stats, ggplot2 (>= 2.2.1), tseries (>= 0.10-42), rgl (>=

Title: Training Set Determination for Genomic Selection
Description: Determining training set for genomic selection using a genetic algorithm (Holland J.H. (1975) <DOI:10.1145/1216504.1216510>) or simple exchange algorithm (change an individual every iteration). Three different criteria are used in both algorithms, which are r-score (Ou J.H., Liao C.T. (2018) <DOI:10.6342/NTU201802290>), PEV-score (Akdemir D. et al. (2015) <DOI:10.1186/s12711-015-0116-6>) and CD-score (Laloe D. (1993) <DOI:10.1186/1297-9686-25-6-557>). Phenotypic data for candidate set is not necessary for all these methods. By using it, one may readily determine a training set that can be expected to provide a better training set comparing to random sampling.

Description: We propose an optimality criterion to determine the required training set, r-score, which is derived directly from Pearson's correlation between the genomic estimated breeding values and phenotypic values of the test set <doi:10.1007/s00122-019-03387-0>. This package provides two main functions to determine a good training set and its size.
Imports: dplyr, ggplot2, latex2exp, lifecycle, parallel, Rcpp (>=
Author: Jen-Hsiang Ou [aut, cre] (<https://orcid.org/0000-0001-9305-2931>),

Title: Treatment-Specific Subgroup Detection Tool
Author: Chakib Battioui, Brian Denton, Lei Shen
Description: Implements a method for identifying subgroups with superior response relative to the overall sample.
Imports: methods, mlbench, hash, party, rpart, survival, survRM2,

person(family = "Eli Lilly and Company",

Authors@R: c(person("Joshua", "Miller", role = c("aut", "cre"), email = "joshlmiller@msn.com"))
Description: Calculates total survey error (TSE) for one or more surveys, using common scale-dependent and/or scale-independent metrics.  On TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>.
Note: Package TSE works directly from the data set – no hand
calculations required.  Just upload a properly structured data
set (see TESTNUMB and its documentation), properly input column
names (see examples in the functions documentation), and run