Package: OptimalRerandExpDesigns 1.1

Adam Kapelner

OptimalRerandExpDesigns: Optimal Rerandomization Experimental Designs

This is a tool to find the optimal rerandomization threshold in non-sequential experiments. We offer three procedures based on assumptions made on the residuals distribution: (1) normality assumed (2) excess kurtosis assumed (3) entire distribution assumed. Illustrations are included. Also included is a routine to unbiasedly estimate Frobenius norms of variance-covariance matrices. Details of the method can be found in "Optimal Rerandomization via a Criterion that Provides Insurance Against Failed Experiments" Adam Kapelner, Abba M. Krieger, Michael Sklar and David Azriel (2020) <arxiv:1905.03337>.

Authors:Adam Kapelner, Michael Sklar, Abba M. Krieger and David Azriel

OptimalRerandExpDesigns_1.1.tar.gz
OptimalRerandExpDesigns_1.1.tar.gz(r-4.5-noble)OptimalRerandExpDesigns_1.1.tar.gz(r-4.4-noble)
OptimalRerandExpDesigns_1.1.tgz(r-4.4-emscripten)OptimalRerandExpDesigns_1.1.tgz(r-4.3-emscripten)
OptimalRerandExpDesigns.pdf |OptimalRerandExpDesigns.html✨
OptimalRerandExpDesigns/json (API)

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

Peer review:

Bug tracker:https://github.com/kapelner/optimalrerandexpdesigns/issues

Uses libs:
  • openjdk– OpenJDK Java runtime, using Hotspot JIT

1.00 score 1 scripts 132 downloads 10 exports 80 dependencies

Last updated 4 years agofrom:3c8a64ac4b. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-linuxOKNov 20 2024

Exports:complete_randomization_plus_one_min_onecomplete_randomization_with_forced_balance_plus_one_min_onecompute_objective_val_plus_one_min_one_encfrob_norm_sqfrob_norm_sq_debiasedfrob_norm_sq_debiased_times_matrixgenerate_W_base_and_sortoptimal_rerandomization_exactoptimal_rerandomization_normality_assumedoptimal_rerandomization_tail_approx

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercolorspacedata.tableDBIdigestevaluatefansifarverfastmapfontawesomeforeignFormulafsggplot2glueGreedyExperimentalDesigngridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonlitekernlabknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimeminqamitoolsmomentchi2munsellnbpMatchingnlmennetnumDerivpillarpkgconfigR6rappdirsRColorBrewerRcppRcppArmadillorJavarlangrlistrmarkdownrpartrstudioapisassscalesstringistringrsurveysurvivaltibbletinytexutf8vctrsviridisviridisLitewithrxfunXMLyaml

Readme and manuals

Help Manual

Help pageTopics
Implements the complete randomization design (CRD) AKA Bernoulli Trialcomplete_randomization_plus_one_min_one
Implements the balanced complete randomization design (BCRD)complete_randomization_with_forced_balance_plus_one_min_one
Returns the objective value given a design vector as well an an objective function. This is code duplication since this is implemented within Java. This is only to be run if...compute_objective_val_plus_one_min_one_enc
Naive Frobenius Norm Squaredfrob_norm_sq
Debiased Frobenius Norm Squared Var-Cov matrixfrob_norm_sq_debiased
Debiased Frobenius Norm Squared Constant Times Var-Cov matrixfrob_norm_sq_debiased_times_matrix
Generate Base Assignments and Sortsgenerate_W_base_and_sort
Find the Optimal Rerandomization Design Exactlyoptimal_rerandomization_exact
Find the Optimal Rerandomization Design Under the Gaussian Approximationoptimal_rerandomization_normality_assumed
Find the Optimal Rerandomization Design Under the Tail and Kurtosis Approximationoptimal_rerandomization_tail_approx
Optimal Rerandomization Threshold Search for Experimental DesignOptimalRerandExpDesigns
Plots a summary of a 'optimal_rerandomization_obj' objectplot.optimal_rerandomization_obj
Plots a summary of the imbalances in a 'W_base_object' objectplot.W_base_object
Prints a summary of a 'optimal_rerandomization_obj' objectprint.optimal_rerandomization_obj
Prints a summary of a 'W_base_object' objectprint.W_base_object
Prints a summary of a 'optimal_rerandomization_obj' objectsummary.optimal_rerandomization_obj
Prints a summary of a 'W_base_object' objectsummary.W_base_object