• "exr: R package: Quantifying Robustness to External Validity Bias."
    Naoki Egami and Martin Devaux.
    GitHub Page. Paper.

  • "factorEx: Design and Analysis for Factorial Experiments."
    Naoki Egami, Brandon de la Cuesta, and Kosuke Imai.
    The Comprehensive R Archive Network, 2019 - Present. GitHub Page. Paper.
    The package provides design-based and model-based estimators for the population average marginal component effects in general factorial experiments, including conjoint analysis. The package also implements a series of recommendations offered in de la Cuesta, Egami, and Imai (2019+), and Egami and Imai (2019) .

  • "FindIt: R Package for Finding Heterogeneous Treatment Effects."
    Naoki Egami, Marc Ratkovic, and Kosuke Imai.
    The Comprehensive R Archive Network, 2012 - Present. Paper.
    The heterogeneous treatment effect estimation procedure proposed by Imai and Ratkovic (2013). The proposed method is applicable, for example, when selecting a small number of most (or least) efficacious treatments from a large number of alternative treatments as well as when identifying subsets of the population who benefit (or are harmed by) a treatment of interest. The method adapts the Support Vector Machine classifier by placing separate LASSO constraints over the pre-treatment parameters and causal heterogeneity parameters of interest. This allows for the qualitative distinction between causal and other parameters, thereby making the variable selection suitable for the exploration of causal heterogeneity. The package also contains a class of functions, CausalANOVA, which estimates the average marginal interaction effects (AMIEs) by a regularized ANOVA as proposed by Egami and Imai (2019). It contains a variety of regularization techniques to facilitate analysis of large factorial experiments.

  • "DIDdesign: R Package for Analyzing Difference-in-Differences Design."
    Soichiro Yamauchi and Naoki Egami. GitHub Page. Paper.