Package: experiment 1.2.1

Kosuke Imai

experiment: R Package for Designing and Analyzing Randomized Experiments

Provides various statistical methods for designing and analyzing randomized experiments. One functionality of the package is the implementation of randomized-block and matched-pair designs based on possibly multivariate pre-treatment covariates. The package also provides the tools to analyze various randomized experiments including cluster randomized experiments, two-stage randomized experiments, randomized experiments with noncompliance, and randomized experiments with missing data.

Authors:Kosuke Imai [aut, cre], Zhichao Jiang [aut]

experiment_1.2.1.tar.gz
experiment_1.2.1.tar.gz(r-4.5-noble)experiment_1.2.1.tar.gz(r-4.4-noble)
experiment_1.2.1.tgz(r-4.4-emscripten)experiment_1.2.1.tgz(r-4.3-emscripten)
experiment.pdf |experiment.html✨
experiment/json (API)

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

Peer review:

Bug tracker:https://github.com/kosukeimai/experiment/issues

Uses libs:
  • openblas– Optimized BLAS
Datasets:
  • seguro - Data from the Mexican universal health insurance program, Seguro Popular.

1.84 score 23 scripts 376 downloads 3 mentions 13 exports 2 dependencies

Last updated 3 years agofrom:ce65af52f2. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-linux-x86_64OKNov 21 2024

Exports:ATEboundsATEclusterATEnocovATOPnoassumptionATOPobsATOPsensAUPECCACEclusterCADErandCADEregPAPDPAPErandomize

Dependencies:bootMASS

Readme and manuals

Help Manual

Help pageTopics
Bounding the Average Treatment Effect when some of the Outcome Data are MissingATEbounds
Estimation of the Average Treatment Effects in Cluster-Randomized ExperimentsATEcluster
Estimation of the Average Treatment Effect in Randomized ExperimentsATEnocov
Bounding the ATOP when some of the Outcome Data are Missing Under the Matched-Pairs DesignATOPnoassumption
Sensitivity analysis for the ATOP when some of the Outcome Data are Missing Under the Matched-Pairs Design in Observational StudiesATOPobs
Sensitivity analysis for the ATOP when some of the Outcome Data are Missing Under the Matched-Pairs DesignATOPsens
Estimation of the unnormalized Area Under Prescription Evaluation Curve (AUPEC) in Completely Randomized ExperimentsAUPEC
Estimation of the Complier Average Causal Effects in Cluster-Randomized Experiments with Unit-level NoncomplianceCACEcluster
Randomization-based method for the complier average direct effect and the complier average spillover effectCADErand
Regression-based method for the complier average direct effectCADEreg
Bayesian Analysis of Randomized Experiments with Noncompliance and Missing Outcomes Under the Assumption of Latent IgnorabilityNoncompLI
Estimation of the Population Average Prescription Difference in Completely Randomized ExperimentsPAPD
Estimation of the Population Average Prescription Effect in Completely Randomized ExperimentsPAPE
Randomization of the Treatment Assignment for Conducting ExperimentsRandomize randomize
Data from the Mexican universal health insurance program, Seguro Popular.seguro