Allows researchers to conduct multivariate
    statistical analyses of survey data with list experiments. This
    survey methodology is also known as the item count technique or
    the unmatched count technique and is an alternative to the commonly
    used randomized response method. The package implements the methods
    developed by Imai (2011) <doi:10.1198/jasa.2011.ap10415>, 
    Blair and Imai (2012) <doi:10.1093/pan/mpr048>, 
    Blair, Imai, and Lyall (2013) <doi:10.1111/ajps.12086>, 
    Imai, Park, and Greene (2014) <doi:10.1093/pan/mpu017>,
    Aronow, Coppock, Crawford, and Green (2015) <doi:10.1093/jssam/smu023>, 
    Chou, Imai, and Rosenfeld (2017) <doi:10.1177/0049124117729711>, and 
    Blair, Chou, and Imai (2018) <https://imai.fas.harvard.edu/research/files/listerror.pdf>. 
    This includes a Bayesian MCMC implementation of regression for the 
    standard and multiple sensitive item list experiment designs and a 
    random effects setup, a Bayesian MCMC hierarchical regression model 
    with up to three hierarchical groups, the combined list experiment 
    and endorsement experiment regression model, a joint model of the 
    list experiment that enables the analysis of the list experiment as 
    a predictor in outcome regression models, a method for combining 
    list experiments with direct questions, and methods for diagnosing and
    adjusting for response error. In addition, the package implements the 
    statistical test that is designed to detect certain failures of list 
    experiments, and a placebo test for the list experiment using data from 
    direct questions.

The aim of the package is to create data objects which allow for accesses like x["test"] and x["test","test"].

A toolbox for R arrays. Flexibly split, bind, reshape, modify, 
    subset and name arrays.

Turns assessment and survey sample data (demographics, scores,
    sampling weights, ability estimates with individual item response
    theory standard errors, replicate weights and plausible values) into
    fully customizable pivot-style statistical tables in which every cell
    carries a design-appropriate standard error. Provides weighted means,
    proportions above cut scores, proficiency-level percentages and
    quantiles; sampling variance via linearization, Woodruff (1952)
    <doi:10.1080/01621459.1952.10483443> intervals for quantiles, or
    balanced repeated replication and jackknife replicate weights
    including Fay's method; measurement variance via delta-method
    propagation of individual standard errors or Rubin (1987)
    <doi:10.1002/9780470316696> combination across plausible values.
    Imports data from 'CSV', 'Excel', 'SPSS', 'SAS', 'Stata' and item
    response theory software person files ('Winsteps', 'ConQuest'); a
    configuration-file interface serves non-programmers and automation;
    results export to formatted 'Excel', 'JSON' and standalone 'HTML'
    reports with rule-based plain-language interpretation.