Reverse Dependencies of rpy2

The following projects have a declared dependency on rpy2:

• ackl — A Python library for kernels used in analytical chemistry
• acv-dev — ACV is a library that provides robust and accurate explanations for machine learning models or data
• acv-exp — ACV is a library that provides robust and accurate explanations for machine learning models or data
• ahead — Time series forecasting with Machine Learning and uncertainty quantification
• aif360 — IBM AI Fairness 360
• aif360-fork2 — IBM AI Fairness 360
• allcools — Toolkit for single-cell DNA methylome and multiomic data analysis.
• anclib — (Beginning of) library for analyzing ancestral reconstructions of DNA or protein sequences
• Angua-Luggage — no summary
• anndata2ri — no summary
• APEC — Single cell epigenomic clustering based on accessibility pattern
• AqEquil — Python tools for aqueous chemical speciation.
• arulespy — Python interface to the R package arules
• asv — Airspeed Velocity: A simple Python history benchmarking tool
• aucome — Automatic Comparison of Metabolism
• autoGDC — Automatic Genomic Data Commons processing for bioinformaticians
• BCN — Boosted Configuration Networks
• BdRPCpackage — Phylogenetic new sample placement software.
• biomarker-survival — Utilities for performing biomarker survival analyses
• biosimulators-boolnet — BioSimulators-compliant command-line interface to the BoolNet simulation program.
• biuR — An extract of the BIU R functionality, without any dependency on biu
• brainfoodr — structure for r projects
• brooklyn-plot — Gene co-expression and transcriptional bursting pattern recognition tool in single cell/nucleus RNA-sequencing data
• calcprimenumbers — package example
• candis — A data mining suite for DNA Microarrays.
• causalAssembly — Generating production line data with available causal ground truth
• cblearn — Comparison-based Machine Learning in Python.
• ccore-coral — no summary
• CEFCIG — Computational Epigenetic Framework for Cell Identity Gene Discovery
• cellpath — CellPath, multiple trajectories inference in single cell RNA-Seq data from RNA velocity
• cgstatistical — CBio-Gres Statistical Tests
• change_detection — package for detecting change in time-series data
• checkatlas — One liner tool to check the quality of your single-cell atlases.
• childespy — no summary
• circa-clue — Causal Inference-based Root Cause Analysis
• cits — CITS algorithm for inferring causality from time series data
• clarite — CLeaning to Analysis: Reproducibility-based Interface for Traits and Exposures
• clep — A Hybrid Data and Knowledge Driven Framework for Generating Patient Representations
• climextremes — climextremes wrapper module
• cluster-crit — Calculate various internal clustering validation or quality criteria.
• cmonkey2 — cmonkey2 is an implementation of the cmonkey biclustering method in Python
• cmprsk — A python wrapper around cmprsk R package
• cohort-generator — Python wrapper for the OHDSI R packages
• commot — Cell-cell communications inference for spatial transcriptomics data via optimal transport.
• ConSReg — condition-specific regulation
• cran-diff — no summary
• cwas — Category-wide association study (CWAS). This is a data analytic tool to perform stringent association tests to find non-coding loci associated with autism spectrum disorder (ASD).
• datarobot-drum — Custom Model Runner
• dclab — Library for real-time deformability cytometry (RT-DC)
• difflearn — Some useful tools for differential network inference with python.
• dretools — A software package for finding differential RNA editing.
• drrank — Implement the Empirical Bayes ranking scheme developed in Kline, Rose, and Walters (2023)
• edgeprediction — Predict missing edges in a knowledge graph
• enkie — ENKIE - The ENzyme KInetics Estimator
• entrain — A single-cell analysis package to elucidate environmental factors controlling cell differentiation in RNA velocity and spatial datasets.
• entrain-spatial — A single-cell analysis package to elucidate environmental factors controlling cell differentiation in spatial datasets.
• eQTac — The eQTac method.
• esgtoolkit — Diffusion models for finance, insurance, economics, physics
• factanal — A python wrapper for the R function factanal.
• fastchange — Fast change point detection in Python
• fcat — Python package for fCAT, a feature-aware completeness assessment tool
• fdrtd-datashield — Federated Secure Computing
• fileformats-datascience — Classes for representing datascience file formats in Python classes for use in type hinting in data workflows
• finnpy — Toolbox for the analysis of electrophysiological data
• fstlib — A python library to read fst file.
• genebench — Benchmark-ing framework used in analyzing methods that detect deferentially expressed genes from biological samples
• genoml — Machine Learning for Genomic
• globalsearch — globalsearch is a collection of Python modules and command tools for the Global Search pipeline.
• gluonts — Probabilistic time series modeling in Python.
• gorpyter — Python wrapper for GOR's R SDK with Pandas serialization.
• grgr — `grgr` is a wrapper library for using `ggplot2` from `python`.
• grpc4bmi — Run your BMI implementation in a separate process and expose it as BMI-python with GRPC
• GSForge — Feature (gene) selection package for gene expression data.
• halla — HAllA: Hierarchical All-against All Association Testing
• heatmap-grammar — Python heatmap interface using intuitive grammar of graphics, implemented as an rpy2 wrapper around ComplexHeatmap package
• hocmo — A Generalized Higher-Order Correlation Model (HOCMO) tool to generate scores modeling the strength of the relationship between triplicate entities using a tensor-based approach
• htsmodels — Forecasting algorithms for hierarchical time series
• hybra-core — Toolkit for data management and analysis.
• iglu-py — Python wrapper of R package `iglu` for continuous glucose monitoring data analysis. Wraps the R functions, thus making them accessible in Python.
• imfusion — Tool for identifying transposon insertions in Insertional Mutagenesis screens from gene-transposon fusions using single- and paired-end RNA-sequencing data.
• infercnvpy — Infercnv is a scalable python library to infer copy number variation (CNV) events from single cell transcriptomics data. It is heavliy inspired by InferCNV, but plays nicely with scanpy and is much more scalable.
• InsurAutoML — Automated Machine Learning/AutoML pipeline.
• intense — no summary
• IRonPyB — A sample test package
• IRonPyD — A sample test package
• iscan-dag — Implementation of the iSCAN algorithm for detecting distribution shifts
• iterativeWGCNA — Iterative application of WGCNA
• JKBio — A useful module for any CompBio
• jupyter-interactive — Initialise a Jupyter notebook with useful extensions and reasonable defaults
• jupyterlab-code-formatter — A JupyterLab plugin to facilitate invocation of code formatters.
• kmerdb — Yet another kmer library for Python
• KorAPClient — Client package to access KorAP's web service API
• learningmachine — Machine Learning with uncertainty quantification and interpretability
• lefse — LEfSe determines the features (organisms, clades, operational taxonomic units, genes, or functions) most likely to explain differences between classes by coupling standard tests for statistical significance with additional tests encoding biological consistency and effect relevance.
• lf2i — Likelihood-Free Frequentist Inference
• lonlatProj — lonlat_pred_utils
• loone-data-prep — Prepare data to run the LOONE model.
• maccabee — Causal ML benchmarking and development tools
• marcopolo-pytorch — MarcoPolo: a method to discover differentially expressed genes in single-cell RNA-seq data without depending on prior clustering
• missImputeTS — The function 'missImputeTS' in this package is used to impute timeseries missing values particularly in the case of mixed-type data.It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It can be run in parallel to save computation time.