Reverse Dependencies of pyro-ppl

The following projects have a declared dependency on pyro-ppl:

• amorf — A framework for multi-output regression in Python
• amplfi — Accelerated Multi-messenger PE with Likelihood Free Inference
• ArimaTP — Python 3 library for short text
• astrophot — A fast, flexible, automated, and differentiable astronomical image 2D forward modelling tool for precise parallel multi-wavelength photometry.
• ax-platform — Adaptive Experimentation
• axforchemistry — Bayesian optimization of formulations via Adaptive Experimentation (Ax) platform.
• bayne — Bayesian Neural Networks in Pytorch
• bel2scm — A package for creating causal graphs in pyro
• beret-beige — Bayesian Effect size Inference with Guide counts and Editing rate
• bioimage_embed — no summary
• biosofa — Probabilistic factor analysis model with covariate guided factors
• boppf — Bayesian optimization of particle packing fractions.
• botorch — Bayesian Optimization in PyTorch
• causalml — Python Package for Uplift Modeling and Causal Inference with Machine Learning Algorithms
• cell2location — cell2location: High-throughput spatial mapping of cell types
• cellarium-ml — Machine learning library for single-cell data analysis
• cellbender — A software package for eliminating technical artifacts from high-throughput single-cell RNA sequencing (scRNA-seq) data
• chirho — Causal reasoning
• chronos-forecast — Time series prediction using probabilistic programming
• congas — Copy Number genotyping from single cell RNA sequencing
• CoopIHC-ModelChecks — User modeling checks for computational HCI using CoopIHC.
• crispat — CRISPR guide assignment tool
• crispr-bean — Base Editor screen analysis [Bayesian Estimation of variant effect] with guide Activity Normalization
• CUQIpy-PyTorch — CUQIpy plugin for PyTorch
• CytoOne — A unified probabilistic framework for CyTOF data
• deepirtools — Deep learning-based estimation and inference for item response theory models.
• dictys — Context specific and dynamic gene regulatory network reconstruction and analysis
• diisco — A probabilistic programming method to characterize dynamic cellular interactions in longitudinal scRNA-seq data.
• draupnir — Ancestral sequence reconstruction using a tree structured Ornstein Uhlenbeck variational autoencoder
• eagpytorch — We have created a module to run the Gaussian process model. We have implemented the code based on GPyTorch.
• easypheno — state-of-the-art and easy-to-use plant phenotype prediction
• echidna-sc — Mapping genotype to phenotype through joint probabilistic modeling of single-cell gene expression and chromosomal copy number variation.
• ex2mcmc — Local-Global MCMC kernels: the bost of both worlds (NeurIPS 2022)
• flighted — FLIGHTED, a machine learning method for inference of fitness landscapes from high-throughput experimental data.
• funsor — A tensor-like library for functions and distributions
• gdrf — Pytorch+GPytorch implementation of GDRFs from San Soucie et al. 2020.
• gempy-probability — Extra plugins for the geological modeling package GemPy
• gleipnir-tcr — Gleipnir
• gpim — Gaussian processes for image analysis
• gpplus — Python Library for Generalized Gaussian Process Modeling
• gpytorch — An implementation of Gaussian Processes in Pytorch
• gracefml — Framework for weakly-supervised regression
• havi — perform bayesian inference over physical models
• HMOBSTER — VAF clustering for multiple karyotypes
• iperturb — Atlas-level data integration in multi-condition single-cell genomics
• iPoLNG — An unsupervised model for the integrative analysis of single-cell multiomics data, coded in the deep universal probabilistic program Pyro
• leafcutter — Leafcutter python implementation
• lightgbmlss — LightGBMLSS - An extension of LightGBM to probabilistic modelling.
• mainframe — Central Repo for datasets
• mira-multiome — Single-cell multiomics data analysis
• MPoL — Regularized Maximum Likelihood Imaging for Radio Astronomy
• muvi — MuVI: A multi-view latent variable model with domain-informed structured sparsity for integrating noisy feature sets.
• nessie — Annotation error detection and correction
• netcal — The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a neural network.
• opengsl — A comprehensive benchmark for Graph Structure Learning.
• orbit-ml — Orbit is a package for Bayesian time series modeling and inference.
• pdebench — PDEBench: An Extensive Benchmark for Scientific Machine Learning
• pearl-pgm — Library for declarative specification of directed graphical models and inference using Pyro.
• pgmuvi — A python package to interpret multiwavelength astronomical timeseries with GPs
• pm-pyro — A PyMC3-like Interface for Pyro Stochastic Functions
• pplbench — Evaluation framework for probabilistic programming languages
• py-irt — Bayesian IRT models in Python
• pybascule — Bayesian NMF signatures deconvolution and DP clustering.
• pybasilica — Bayesian NMF signatures deconvolution and DP clustering.
• pybefit — Probabilistic inference for models of behaviour
• pylineaGT — A Pyro model to perform lineage inference from Gene Therapy assays
• pyromaniac — Helper modules for pyro.
• pyroved — Variational encoder-decoder models in Pyro probabilistic programming language
• pyrovelocity — A multivariate RNA Velocity model to estimate future cell states with uncertainty using probabilistic modeling with pyro.
• relaxit — A Python library for discrete variables relaxation
• sbi — Simulation-based inference.
• sc-echidna — Mapping genotype to phenotype through joint probabilistic modeling of single-cell gene expression and chromosomal copy number variation.
• sccca — Single cell canonical correlation analysis.
• scClassifier2 — a tool for single cell data
• scdecipher — Decipher is a single-cell analysis package to integrate and compare perturbed samples to healthy samples, to identify the origin of the cell-states perturbations.
• scdna-replication-tools — Code for analyzing single-cell replication dynamics
• sceLDA — no summary
• scpca — Single-cell PCA.
• scStateDynamics — A package to decipher the drug-responsive tumor cell state dynamics by modeling single-cell level expression changes
• scTM — A toolbox for single cell topic models
• scTrace — scTrace+: enhance the cell fate inference by integrating the lineage-tracing and multi-faceted transcriptomic similarity information
• scUNAGI — A Python package for UNAGI
• scvi-tools — Deep probabilistic analysis of single-cell omics data.
• sgdrf — Python implementation of Streaming Gaussian Dirichlet Random Fields (San Soucie et al. 2023)
• spateo-release — Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics
• spfa — Probabilistic factor analysis model with covariate guided factors
• spotiphy — An integrated pipeline designed to deconvolute and decompose spatial transcriptomics data, and produce pseudo single-cell resolution images.
• statsmaker — Probabilistic Progamming Language (PPL) for Market Microstructure Modeling
• stockpy-learn — Deep Learning Regression and Classification Library built on top of PyTorch and Pyro
• StrainFacts — Factorize metagenotypes to infer strains and their abundances
• stsb3 — Structural time series building blocks
• syncdsgen — A package to generate synthetic coding sequences data
• tapqir — Bayesian analysis of co-localization single-molecule microscopy image data
• topic-benchmark — CLI suite for benchmarking topic models
• torch-mist — Mutual Information Estimation toolkit based on pytorch
• turftopic — Topic modeling with contextual representations from sentence transformers.
• tyxe — BNNs for PyTorch using Pyro
• uMAIA — Toolbox for the processing and analysis of MALDI-MSI data
• vaeesr — Using the latent space of a variational autoencoder to perform symbolic regression by sampling equations.
• vegvisir — Vegvisir