R Packages
For two decades, IQSS has led the development of Open Source analytical tools for researchers. Many of these tools take the form of R packages - modules that provide specialized functionality for the R programming language. These packages are freely available for anyone to use, modify, and contribute to.
Now IQSS invites researchers to showcase their own work by having us custom build your R package. IQSS empowers researchers to disseminate their work, and others to replicate it, by providing an Analytical Tool Building service that will develop a state-of-the-art R package from your raw code. Through this R package, your new statistical method, analysis pipeline, or vizualization can directly impact the people in your research group, your academic field, and the broader scholarly community.
IQSS affiliates have played a leading role in the development of the following R packages:
{clarify}
{clarify} uses simulation to make interpretable inferences about any quantity of interest from a wide variety of statistical models.
{MatchIt}
{MatchIt} implements matching methods for improving parametric statistical models for estimating treatment effects in observational studies and reducing model dependence.
{MatchingFrontier}
{MatchingFrontier} provides tools to manage the bias-variance trade-off when matching in observational studies.
{cem}
{cem} implements a method for improving causal inferences called "Coarsened Exact Matching".
{Amelia}
{Amelia} is a tool that "multiply imputes" missing data in a single cross-section, from a time series, or from a time-series-cross-sectional data set.
{WhatIf}
{WhatIf} offers easy-to-apply methods to evaluate counterfactuals that do not require sensitivity testing over specified classes of models.
{Readme2}
{Readme2} implements methods for estimating category proportions in an unlabeled set of documents given a labeled set.
{ei}
{ei} implements methods described in Gary King's (1997) book: A Solution to the Ecological Inference Problem.
{lmw}
{lmw} computes the implied weights of linear regression models for estimating average causal effects and provides diagnostics based on these weights.
{EvoPhylo}
{EvoPhylo} performs morphological character partitioning and analyzes outputs from clock Bayesian inference for phylogenetic analyses.
{netlit}
{netlit} provides functions to generate network statistics from a literature review.
{Morphoscape}
{Morphoscape} constructs, analyzes, and visualizes spatially organized trait data into adaptive landscapes.
