Package: selectiongain 2.0.710

Xuefei Mi

selectiongain: A Tool for Calculation and Optimization of the Expected Gain from Multi-Stage Selection

Multi-stage selection is practiced in numerous fields of life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and effort, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage.

Authors:Xuefei Mi, Jose Marulanda, H. Friedrich Utz, Albrecht E. Melchinger

selectiongain_2.0.710.tar.gz
selectiongain_2.0.710.tar.gz(r-4.5-noble)selectiongain_2.0.710.tar.gz(r-4.4-noble)
selectiongain_2.0.710.tgz(r-4.4-emscripten)selectiongain_2.0.710.tgz(r-4.3-emscripten)
selectiongain.pdf |selectiongain.html
selectiongain/json (API)

# Install 'selectiongain' in R:
install.packages('selectiongain', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.51 score 2 stars 16 scripts 258 downloads 11 exports 1 dependencies

Last updated 2 years agofrom:511ee4bbbf. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 25 2024
R-4.5-linuxNOTEDec 25 2024

Exports:multistagecormultistagegainmultistagegain.eachmultistageoptimum.gridmultistageoptimum.nlmmultistageoptimum.searchmultistageoptimum.searchIndexTmultistageoptimum.searchThreeSmultistagetpmultistagevarianceSDselectiongain

Dependencies:mvtnorm