Consistent Trees generates merger trees and halo catalogs which explicitly ensure consistency of halo properties (mass, position, velocity, radius) across timesteps. It has demonstrated the ability to improve both the completeness (through detecting and inserting otherwise missing halos) and purity (through detecting and removing spurious objects) of both merger trees and halo catalogs. Consistent Trees is able to robustly measure the self-consistency of halo finders and to directly measure the uncertainties in halo positions, halo velocities, and the halo mass function for a given halo finder based on consistency between snapshots in cosmological simulations.

XDGMM uses Gaussian mixtures to do density estimation of noisy, heterogenous, and incomplete data using extreme deconvolution (XD) algorithms which is compatible with the scikit-learn machine learning methods. It implements both the astroML and Bovy et al. (2011) algorithms, and extends the BaseEstimator class from scikit-learn so that cross-validation methods work. It allows the user to produce a conditioned model if values of some parameters are known.

empiriciSN generates realistic supernova parameters given photometric observations of a potential host galaxy, based entirely on empirical correlations measured from supernova datasets. It is intended to be used to improve supernova simulation for DES and LSST. It is extendable such that additional datasets may be added in the future to improve the fitting algorithm or so that additional light curve parameters or supernova types may be fit.

paltas conducts simulation-based inference on strong gravitational lensing images. It builds on lenstronomy (ascl:1804.012) to create large datasets of strong lensing images with realistic low-mass halos, Hubble Space Telescope (HST) observational effects, and galaxy light from HST's COSMOS field. paltas also includes the capability to easily train neural posterior estimators of the parameters of the lensing system and to run hierarchical inference on test populations.

ovejero conducts hierarchical inference of strongly-lensed systems with Bayesian neural networks. It requires lenstronomy (ascl:1804.012) and fastell (ascl:9910.003) to run lens models with elliptical mass distributions. The code trains Bayesian Neural Networks (BNNs) to predict posteriors on strong gravitational lensing images and can integrate with forward modeling tools in lenstronomy to allow comparison between BNN outputs and more traditional methods. ovejero also provides hierarchical inference tools to generate population parameter estimates and unbiased posteriors on independent test sets.

The UniverseMachine applies simple empirical models of galaxy formation to dark matter halo merger trees. For each model, it generates an entire mock universe, which it then observes in the same way as the real Universe to calculate a likelihood function. It includes an advanced MCMC algorithm to explore the allowed parameter space of empirical models that are consistent with observations.

Comparing galaxies across redshifts via cumulative number densities is a popular way to estimate the evolution of specific galaxy populations. nd-redshift uses abundance matching in the ΛCDM paradigm to estimate the median change in number density with redshift. It also provides estimates for the 1σ range of number densities corresponding to galaxy progenitors and descendants.

The anzu package offers two independent codes for hybrid Lagrangian bias models in large-scale structure. The first code measures the hybrid "basis functions"; the second takes measurements of these basis functions and constructs an emulator to obtain predictions from them at any cosmology (within the bounds of the training set). anzu is self-contained; given a set of N-body simulations used to build emulators, it measures the basis functions. Alternatively, given measurements of the basis functions, anzu should in principle be useful for constructing a custom emulator.