This paper discusses the R package lpcde, which stands for local polynomial conditional density estimation. It implements the kernel-based local polynomial smoothing methods introduced in Cattaneo, Chandak, Jansson, Ma (2024( for statistical estimation and inference of conditional distributions, densities, and derivatives thereof. The package offers mean square error optimal bandwidth selection and associated point estimators, as well as uncertainty quantification based on robust bias correction both pointwise (e.g., confidence intervals) and uniformly (e.g., confidence bands) over evaluation points. The methods implemented are boundary adaptive whenever the data is compactly supported. The package also implements regularized conditional density estimation methods, ensuring the resulting density estimate is non-negative and integrates to one. We contrast the functionalities of lpcde with existing R packages for conditional density estimation, and showcase its main features using simulated data.

翻译：本文介绍R包lpcde（local polynomial conditional density estimation，局部多项式条件密度估计）。该包实现了Cattaneo、Chandak、Jansson、Ma（2024）提出的基于核函数的局部多项式平滑方法，用于条件分布、密度及其导数的统计估计与推断。该包提供均方误差最优带宽选择及其对应的点估计量，并基于稳健偏差修正实现评估点上的逐点（如置信区间）和一致（如置信带）不确定性量化。当数据具有紧支撑时，所实现的方法具有边界自适应性。该包还实现了正则化条件密度估计方法，确保所得密度估计非负且积分为1。我们将lpcde与现有条件密度估计R包进行功能对比，并通过模拟数据展示其主要特性。