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rddensity - Manipulation Testing Based on Density Discontinuity

Density discontinuity testing (a.k.a. manipulation testing) is commonly employed in regression discontinuity designs and other program evaluation settings to detect perfect self-selection (manipulation) around a cutoff where treatment/policy assignment changes. This package implements manipulation testing procedures using local polynomial density estimators: rddensity() constructs test statistics and p-values given a prespecified cutoff, rdbwdensity() performs data-driven bandwidth selection, and rdplotdensity() constructs density plots.

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3.53 score 1 dependents 396 scripts 2.9k downloads

lpdensity - Local Polynomial Density Estimation and Inference

Implements local polynomial distribution and density methods for point estimation, inference and bandwidth selection, documented in Cattaneo, Jansson and Ma (2020) <doi:10.1080/01621459.2019.1635480>, Cattaneo, Jansson and Ma (2022) <doi:10.18637/jss.v101.i02>, and Cattaneo, Jansson and Ma (2024) <doi:10.1016/j.jeconom.2021.01.006>. lpdensity() constructs local polynomial distribution and density estimators with robust bias-corrected inference, and lpbwdensity() implements data-driven bandwidth selection.

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3.01 score 3 dependents 48 scripts 2.4k downloads

binsreg - Binscatter Estimation and Inference

Provides tools for statistical analysis using the binscatter methods developed by Cattaneo, Crump, Farrell and Feng (2024) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2024_AER.pdf>, Cattaneo, Crump, Farrell and Feng (2025) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2025_Stata.pdf> and Cattaneo, Crump, Farrell and Feng (2026) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2026_RESTAT.pdf>. Binscatter provides a flexible way of describing the relationship between two variables based on partitioning/binning of the independent variable of interest. binsreg(), binsqreg() and binsglm() implement binscatter least squares regression, quantile regression and generalized linear regression respectively, with particular focus on constructing binned scatter plots. They also implement robust (pointwise and uniform) inference of regression functions and derivatives thereof. binstest() implements hypothesis testing procedures for parametric functional forms of and nonparametric shape restrictions on the regression function. binspwc() implements hypothesis testing procedures for pairwise group comparison of binscatter estimators. binsregselect() implements data-driven procedures for selecting the number of bins for binscatter estimation. All the commands allow for covariate adjustment, smoothness restrictions and clustering.

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2.77 score 2 stars 146 scripts 935 downloads

rdlocrand - Local Randomization Methods for RD Designs

Provides tools to perform randomization inference for RD designs under local randomization: rdrandinf() to perform hypothesis testing using randomization inference, rdwinselect() to select a window around the cutoff in which randomization is likely to hold, rdsensitivity() to assess the sensitivity of the results to different window lengths and null hypotheses and rdrbounds() to construct Rosenbaum bounds for sensitivity to unobserved confounders. See Cattaneo, Titiunik and Vazquez-Bare (2016) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2016_Stata.pdf> for further methodological details, and references.

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2.16 score 72 scripts 941 downloads

rdmulti - Robust Local Polynomial Methods for RD Designs with Multiple Cutoffs or Multiple Scores

The 'rdmulti' package implements estimation, inference, and graphical procedures for regression discontinuity (RD) designs with multiple cutoffs or multiple scores. rdmc() provides point estimation and robust bias-corrected inference for multi-cutoff designs, rdmcplot() provides data-driven RD plots for multi-cutoff designs, and rdms() provides point estimation and robust bias-corrected inference for multi-score designs. See Cattaneo, Titiunik and Vazquez-Bare (2020) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2020_Stata.pdf> for further methodological details.

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1.88 score 38 scripts 743 downloads

scpi - Synthetic Control Methods

Implementation of prediction and inference procedures for Synthetic Control methods using least square, lasso, ridge, or simplex-type constraints. Uncertainty is quantified with prediction intervals as developed in Cattaneo, Feng, and Titiunik (2021) <doi:10.1080/01621459.2021.1979561> for a single treated unit and in Cattaneo, Feng, Palomba, and Titiunik (2027) <doi:10.1162/rest_a_01588> for multiple treated units and staggered adoption. More details about the software implementation can be found in Cattaneo, Feng, Palomba, and Titiunik (2025) <doi:10.18637/jss.v113.i01>.

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1.83 score 2.2k downloads

rd2d - Estimation and Inference for Boundary Discontinuity Designs

Provides pointwise and uniform estimation and inference methods for boundary discontinuity (BD) designs, a causal inference design that generalizes univariate regression discontinuity (RD) designs to settings with bivariate scores. Implements local polynomial methods for location-based and distance-based analyses, including sharp and fuzzy designs, data-driven bandwidth selection, pointwise confidence intervals, and uniform confidence bands. Methodology is developed in Cattaneo, Titiunik, and Yu (2026) <doi:10.48550/arXiv.2505.05670> for location-based methods and Cattaneo, Titiunik, and Yu (2026) <doi:10.48550/arXiv.2510.26051> for distance-based methods. For an overview and empirical guidance, see Cattaneo, Titiunik, and Yu (2026) <doi:10.48550/arXiv.2511.06474>. The companion software article is Cattaneo, Titiunik, and Yu (2025) <doi:10.48550/arXiv.2505.07989>.

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1.70 score 1 stars 7 scripts 387 downloads

lpcde - Boundary Adaptive Local Polynomial Conditional Density Estimator

Tools for estimation and inference of conditional densities, derivatives and functions. This is the companion software for Cattaneo, Chandak, Jansson and Ma (2024) <doi:10.3150/23-BEJ1711>.

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cpp

1.70 score 4 scripts 258 downloads

rdpower - Power, Sample Size, and Minimum Detectable Effect Calculations for RD Designs

The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The 'rdpower' package provides tools to perform power, sample size, and minimum detectable effect calculations in RD designs: rdpower() calculates the power of an RD design, rdsampsi() calculates the required sample size to achieve a desired power, and rdmde() calculates minimum detectable effects. See Cattaneo, Titiunik and Vazquez-Bare (2019) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2019_Stata.pdf> for further methodological details.

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1.48 score 15 scripts 776 downloads