#  R Packages 

 



For two decades, IQSS has led the development of [Open Source](https://opensource.org/definition-annotated/) analytical tools for researchers. Many of these tools take the form of R packages - modules that provide specialized functionality for the [R programming language](https://www.r-project.org/). 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](/data-science-services/services-we-offer#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}

 ](https://iqss.github.io/clarify/)**{clarify}** uses simulation to make interpretable inferences about any quantity of interest from a wide variety of statistical models.

[Learn more](https://iqss.github.io/clarify/)



 

   ![clarifyimg.jpg](/sites/g/files/omnuum8171/files/styles/hwp_1_1__360x360_scale/public/harvard-iqss/files/clarifyimg.jpg?itok=EUAetcxm) 

 

 

 

  [### {MatchIt}

 ](https://kosukeimai.github.io/MatchIt/)**{MatchIt}** implements matching methods for improving parametric statistical models for estimating treatment effects in observational studies and reducing model dependence.

[Learn more](https://kosukeimai.github.io/MatchIt/)



 

   ![MatchIt](/sites/g/files/omnuum8171/files/styles/hwp_1_1__360x360_scale/public/harvard-iqss/files/matchit.png?itok=IcAw1FyW) 

 

 

 

  [### {MatchingFrontier}

 ](https://iqss.github.io/MatchingFrontier/)**{MatchingFrontier}** provides tools to manage the bias-variance trade-off when matching in observational studies.

[Learn more](https://iqss.github.io/MatchingFrontier/)



 

   ![MatchingFrontier example image](/sites/g/files/omnuum8171/files/styles/hwp_1_1__360x360_scale/public/harvard-iqss/files/matchingfrontier-img.png?itok=Tnvl-0th) 

 

 

 

  

 

 

 

  [### {cem}

 ](https://cran.r-project.org/web/packages/cem/index.html)**{cem}** implements a method for improving causal inferences called "Coarsened Exact Matching".

[Learn more](https://cran.r-project.org/web/packages/cem/index.html)



 

   ![cem](/sites/g/files/omnuum8171/files/styles/hwp_1_1__360x360_scale/public/harvard-iqss/files/cem.png?itok=1JRuwSVp) 

 

 

 

  [### {Amelia}

 ](https://cran.r-project.org/web/packages/Amelia/index.html)**{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.

[Learn more](https://cran.r-project.org/web/packages/Amelia/index.html)



 

   ![amelia.png](/sites/g/files/omnuum8171/files/styles/hwp_1_1__360x360_scale/public/harvard-iqss/files/amelia.png?itok=uR2ottKU) 

 

 

 

  [### {WhatIf}

 ](https://cran.r-project.org/package=WhatIf)**{WhatIf}** offers easy-to-apply methods to evaluate counterfactuals that do not require sensitivity testing over specified classes of models.

[Learn more](https://CRAN.R-project.org/package=WhatIf)



 

   ![whatif2.png](/sites/g/files/omnuum8171/files/styles/hwp_1_1__360x360_scale/public/harvard-iqss/files/whatif2.png?itok=CKGt7Cof) 

 

 

 

  

 

 

 

  [### {Readme2}

 ](https://github.com/iqss-research/readme-software)**{Readme2}** implements methods for estimating category proportions in an unlabeled set of documents given a labeled set.

[Learn more](https://github.com/iqss-research/readme-software)



 

   ![Readme2](/sites/g/files/omnuum8171/files/styles/hwp_1_1__360x360_scale/public/harvard-iqss/files/readme2.png?itok=UjWsO2mK) 

 

 

 

  [### {ei}

 ](https://cran.r-project.org/web/packages/ei/index.html)**{ei}** implements methods described in Gary King's (1997) book: A Solution to the Ecological Inference Problem.

[Learn more](https://cran.r-project.org/web/packages/ei/index.html)



 

   ![ei](/sites/g/files/omnuum8171/files/styles/hwp_1_1__360x360_scale/public/harvard-iqss/files/ei.png?itok=S4ZK4Po8) 

 

 

 

  [### {lmw}

 ](https://cran.r-project.org/package=lmw)**{lmw}** computes the implied weights of linear regression models for estimating average causal effects and provides diagnostics based on these weights.

[Learn more](https://cran.r-project.org/package=lmw)



 

   ![Example image for lmw](/sites/g/files/omnuum8171/files/styles/hwp_1_1__360x360_scale/public/harvard-iqss/files/lmw_img.jpg?itok=ccNwBFq1) 

 

 

 

  

 

 

 

  [### {EvoPhylo}

 ](https://tiago-simoes.github.io/EvoPhylo/)**{EvoPhylo}** performs morphological character partitioning and analyzes outputs from clock Bayesian inference for phylogenetic analyses.

[Learn more](https://tiago-simoes.github.io/EvoPhylo/)



 

   ![Example image for Evophylo](/sites/g/files/omnuum8171/files/styles/hwp_1_1__360x360_scale/public/harvard-iqss/files/evophylo_img.png?itok=ezbOWMC7) 

 

 

 

  [### {netlit}

 ](https://judgelord.github.io/netlit/)**{netlit}** provides functions to generate network statistics from a literature review.

[Learn more](https://judgelord.github.io/netlit/)



 

   ![netlit logo](/sites/g/files/omnuum8171/files/styles/hwp_1_1__360x360_scale/public/harvard-iqss/files/netlitlogo.png?itok=oNqKPy5q) 

 

 

 

  [### {Morphoscape}

 ](https://blakedickson.github.io/Morphoscape/)**{Morphoscape}** constructs, analyzes, and visualizes spatially organized trait data into adaptive landscapes.

[Learn more ](https://blakedickson.github.io/Morphoscape/)



 

   ![Example image for Morphoscape](/sites/g/files/omnuum8171/files/styles/hwp_1_1__360x360_scale/public/harvard-iqss/files/morphscape_img.png?itok=wUw1o6Wh)