lpcde: Estimation and Inference for Local Polynomial Conditional Density Estimators
Conditional cumulative distribution functions (CDFs), conditional probability density functions (PDFs), and derivatives thereof, are important parameters of interest in statistics, econometrics, and other data science disciplines. The package lpcde implements new estimation and inference methods for conditional CDFs, conditional PDFs, and derivatives thereof, employing the kernelbased local polynomial smoothing approach introduced in Cattaneo et al. (2024a).
Authors
- Michael Jansson (ORCID: https://orcid.org/0000-0003-4678-7518)
- Matias D. Cattaneo (ORCID: https://orcid.org/0000-0003-0493-7506)
- Rajita Chandak (ORCID: https://orcid.org/0009-0006-4289-2520)
- Xinwei Ma (ORCID: https://orcid.org/0000-0001-8827-9146)
Institutions
- Princeton University (US)
- University of California San Diego (US)
- École Polytechnique Fédérale de Lausanne (CH)
- University of California, Berkeley (US)
Publication Details
- Journal
- The Journal of Open Source Software
- Published
- 2025-03-07
- DOI
- https://doi.org/10.21105/joss.07241
- Citations
- 1
- Primary Topic
- Statistical Methods and Inference
- Type
- article
- Field-Weighted Citation Impact
- 2.63