The lpcde package implements bandwidth selection, point estimation, and inference procedures for local polynomial conditional distribution and density methods.

• lpcde: local polynomial conditional CDF, PDF, and derivative estimation with pointwise and uniform inference.
• lpbwcde: rule-of-thumb bandwidth selection for local polynomial conditional density estimation.

To install/update in R type:

install.packages('lpcde')

• Help: R Manual, CRAN repository.

• Replication: R illustration, Python illustration, Stata illustration, software article replication, comparison illustration.

To install/update locally from this repository:

pip install lpcde

• Help: Python README, illustration script.

• Development usage:

from lpcde import lpcde, lpbwcde

The Python implementation mirrors the R lpcde and lpbwcde numerical paths and includes cross-language regression tests against R-generated fixtures.

To install/update in Stata type:

net install lpcde, from(https://raw.githubusercontent.com/nppackages/lpcde/main/stata) replace

• Commands: lpcde for estimation and inference, lpbwcde for bandwidth selection.

• Help: lpcde, lpbwcde.

• Replication: do-file.

The Stata implementation is standalone Stata/Mata code, with numerical smoke tests intended to keep it aligned with the R and Python baselines.

• Cattaneo, Chandak, Jansson and Ma (2025): lpcde: Estimation and Inference for Local Polynomial Conditional Density Estimators.
  Journal of Open Source Software 10(107): 7241.
  Companion arXiv version

• Cattaneo, Chandak, Jansson and Ma (2024): Boundary Adaptive Local Polynomial Conditional Density Estimators.
  Bernoulli 30(4): 3193-3223.
  Supplemental appendix.

This work was supported in part by the National Science Foundation through grants SES-1947805, SES-1947662, DMS-2210561, and SES-2241575, and by the National Institutes of Health through grant R01 GM072611-16.