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designit: Blocking and Randomization for Experimental Design

Intelligently assign samples to batches in order to reduce batch effects. Batch effects can have a significant impact on data analysis, especially when the assignment of samples to batches coincides with the contrast groups being studied. By defining a batch container and a scoring function that reflects the contrasts, this package allows users to assign samples in a way that minimizes the potential impact of batch effects on the comparison of interest. Among other functionality, we provide an implementation for OSAT score by Yan et al. (2012, <doi:10.1186/1471-2164-13-689>).

Version:	0.5.0
Depends:	R (≥ 4.1.0)
Imports:	rlang (≥ 0.4.0),dplyr (≥ 1.0.0),purrr,ggplot2,scales,tibble,tidyr,assertthat,stringr,R6,data.table, stats
Suggests:	testthat,roxygen2,pkgdown,knitr,markdown,rmarkdown,gt,bench,OSAT,tidyverse,printr,devtools (≥ 2.0.0),ggpattern,cowplot,bestNormalize,here
Published:	2024-03-21
DOI:	10.32614/CRAN.package.designit
Author:	Iakov I. Davydov [aut, cre, cph], Juliane Siebourg-Polster [aut, cph], Guido Steiner [aut, cph], Konrad Rudolph [ctb], Jitao David Zhang [aut, cph], Balazs Banfai [aut, cph], F. Hoffman-La Roche [cph, fnd]
Maintainer:	Iakov I. Davydov <iakov.davydov at roche.com>
BugReports:	https://github.com/BEDApub/designit/issues
License:	MIT + fileLICENSE
URL:	https://bedapub.github.io/designit/,https://github.com/BEDApub/designit/
NeedsCompilation:	no
Materials:	README,NEWS
CRAN checks:	designit results

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