Binscatter methods for partition selection, point estimation, pointwise and uniform inference, and graphical procedures.
- Python
- R
- Stata
Nonparametric & Semiparametric Methods
NP Packages collects maintained software for estimation, inference, and visualization using nonparametric and semiparametric methods with applications to program evaluation, treatment effect estimation, causal inference, and related problems.
Packages
Estimation and inference using portfolio sorting methods.
- Python
- R
- Stata
Estimation and inference using partitioning-based least squares methods, B-splines, wavelets, and piecewise polynomials.
- R
Estimation and inference using kernel density and local polynomial regression methods.
- R
- Stata
Estimation and inference using local polynomial distribution and density regression methods.
- Python
- R
- Stata
Estimation and inference using local polynomial conditional distribution and density regression methods.
- R
Estimation and inference using synthetic control methods for causal analysis.
- Python
- R
- Stata
Replication Files
Examples, paper replications, and companion code are collected on the replication page.
References
Selected software articles and methodological references for NP methods and applications.
- Calonico, Cattaneo and Farrell (2019): nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference. Journal of Statistical Software 91(8): 1-33.
- Cattaneo, Farrell and Feng (2020): lspartition: Partitioning-Based Least Squares Regression. R Journal 12(1): 172-187.
- Cattaneo, Jansson and Ma (2022): lpdensity: Local Polynomial Density Estimation and Inference. Journal of Statistical Software 101(2): 1-25.
- Cattaneo, Chandak, Jansson and Ma (2025): lpcde: Estimation and Inference for Local Polynomial Conditional Density Estimators. Journal of Open Source Software 10(107): 7241.
- Cattaneo, Crump, Farrell and Feng (2025): Binscatter Regressions. Stata Journal 25(1): 3-50.
- Cattaneo, Feng, Palomba and Titiunik (2025): scpi: Uncertainty Quantification for Synthetic Control Methods. Journal of Statistical Software 113(1): 1-38.
Contributors
- Sebastian CalonicoUC Davis
- Matias D. CattaneoPrinceton University
- Rajita ChandakUniversity of Wisconsin-Madison
- Richard K. CrumpFederal Reserve Bank of New York
- Max H. FarrellUC Santa Barbara
- Yingjie FengTsinghua University
- Michael JanssonUC Berkeley
- Xinwei MaUC San Diego
- Ricardo MasiniUC Davis
- Filippo PalombaPrinceton University
- Rocio TitiunikPrinceton University
Funding
This work was supported in part by the National Science Foundation, the National Institutes of Health, and the National Institute for Food and Agriculture.