Title: Transforms Institutional Data into Text Files for IPEDS
person(given = "Alison", family = "Lanski",
Description: Starting from user-supplied institutional data, these scripts
transform, aggregate, and reshape the information to produce
key-value pair data files that are able to be uploaded to IPEDS (Integrated Postsecondary Education Data System)
through their submission portal <https://surveys.nces.ed.gov/ipeds/>. Starting data specifications can be found in the vignettes.
Final files are saved locally to a location of the user's choice.
User-friendly readable files can also be produced for purposes of data review and validation.
Note: Because IPEDS requirements may change from year to year, having
the most recent version of this package is highly recommended.
Old versions can be found as GitHub branches. The package can
also be used to convert any correctly-prepared data into a
key-value pair format for any survey (IPEDS or non-IPEDS).
Imports: dplyr, lubridate, magrittr, purrr, rlang, stringr, svDialogs,
Suggests: knitr, rmarkdown, kableExtra, testthat (>= 3.0.0)

Title: Identification of Parental Lines via Genomic Prediction
person("Ping-Yuan", "Chung", email = "r06621204@ntu.edu.tw", role = "cre"),
person("Chen-Tuo", "Liao", email = "ctliao@ntu.edu.tw", role = "aut"))
Combining genomic prediction with Monte Carlo simulation, three different
strategies are implemented to select parental lines for multiple traits in plant
breeding. The selection strategies include (i) GEBV-O considers only genomic
estimated breeding values (GEBVs) of the candidate individuals; (ii) GD-O
considers only genomic diversity (GD) of the candidate individuals; and (iii)
GEBV-GD considers both GEBV and GD. The above method can be seen in Chung PY,
Liao CT (2020) <doi:10.1371/journal.pone.0243159>. Multi-trait genomic best
linear unbiased prediction (MT-GBLUP) model is used to simultaneously estimate
GEBVs of the target traits, and then a selection index is adopted to evaluate
the composite performance of an individual.
Imports: ggplot2, sommer, grDevices, stats