  It is well known in astronomy that propagating non-Gaussian prediction
uncertainty in photometric redshift estimates is key to reducing bias in
downstream cosmological analyses. Similarly, likelihood-free inference
approaches, which are beginning to emerge as a tool for cosmological analysis,
require a characterization of the full uncertainty landscape of the parameters
of interest given observed data. However, most machine learning (ML) or
training-based methods with open-source software target point prediction or
classification, and hence fall short in quantifying uncertainty in complex
regression and parameter inference settings. As an alternative to methods that
focus on predicting the response (or parameters) $\mathbf{y}$ from features
$\mathbf{x}$, we provide nonparametric conditional density estimation (CDE)
tools for approximating and validating the entire probability density function
(PDF) $\mathrm{p}(\mathbf{y}|\mathbf{x})$ of $\mathbf{y}$ given (i.e.,
conditional on) $\mathbf{x}$. As there is no one-size-fits-all CDE method, the
goal of this work is to provide a comprehensive range of statistical tools and
open-source software for nonparametric CDE and method assessment which can
accommodate different types of settings and be easily fit to the problem at
hand. Specifically, we introduce four CDE software packages in
$\texttt{Python}$ and $\texttt{R}$ based on ML prediction methods adapted and
optimized for CDE: $\texttt{NNKCDE}$, $\texttt{RFCDE}$, $\texttt{FlexCode}$,
and $\texttt{DeepCDE}$. Furthermore, we present the $\texttt{cdetools}$
package, which includes functions for computing a CDE loss function for tuning
and assessing the quality of individual PDFs, along with diagnostic functions.
We provide sample code in $\texttt{Python}$ and $\texttt{R}$ as well as
examples of applications to photometric redshift estimation and likelihood-free
cosmological inference via CDE.
