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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, and Ma (2022) for statistical estimation and inference of conditional distributions, densities, and derivatives thereof. The package offers pointwise and integrated mean square error optimal bandwidth selection and associated point estimators, as well as uncertainty quantification based on robust bias correction both pointwise (eg, confidence intervals) and uniformly (eg, confidence bands) over evaluation points. The methods implemented are boundary adaptive whenever the data is compactly supported. We contrast the functionalities of lpcde with existing R packages, and showcase its main features using simulated data.