lpcde 1.0.0

Implements local polynomial conditional distribution and density estimation with bandwidth selection and robust inference.

LPCDE

The lpcde Python package implements local polynomial conditional distribution and density estimation with bandwidth selection.

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

Website: https://nppackages.github.io/lpcde/.

Source code: https://github.com/nppackages/lpcde.

Authors

Matias D. Cattaneo ([e-mail])

Rajita Chandak ([e-mail])

Michael Jansson ([e-mail])

Xinwei Ma ([e-mail])

Installation

To install locally from the repository:

pip install -e Python/lpcde

Usage

import numpy as np
from lpcde import confint, lpbwcde, lpcde, plot, summary, vcov

x_data = np.random.normal(size=500)
y_data = np.random.normal(loc=x_data, scale=1)
y_grid = np.linspace(-1, 1, 5)

fit = lpcde(x_data=x_data, y_data=y_data, y_grid=y_grid, x=0, bw=0.5)
bw = lpbwcde(y_data=y_data, x_data=x_data, x=0, y_grid=y_grid)
summary(fit)
confint(fit)

Replication examples are available in replication/lpcde_illustration.py and replication/lpcde_replication.py.

Dependencies

• numpy
• pandas
• scipy
• matplotlib

References

Software and Implementation

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

Technical and Methodological

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