Reverse Dependencies of statsmodels
The following projects have a declared dependency on statsmodels:
- atom-ml — A Python package for fast exploration of machine learning pipelines
- attribench — A benchmark for feature attribution techniques
- audit-AI — audit-AI detects demographic differences in the output of machine learning models or other assessments
- auto-bots — Automated time-series forecasting
- auto-period-finder — An autocorrelation function-based seasonality periods automatic finder for univariate time series.
- auto-ts — Automatically Build Multiple Time Series models fast - now with Facebook Prophet!
- autoballs — Python package for segmentation of axons and morphological analysis.
- autobmt — a modeling tool that automatically builds scorecards and tree models.
- AutoCarver — Automatic Discretization of Features with Optimal Target Association
- AutoDataPre — The package of Auto-DP ( Automated System for Data Preparation).
- autoforecast — AutoML time series forecasting
- autogluon-tonyhu-test.timeseries — AutoML for Image, Text, and Tabular Data
- autoimpute — Imputation Methods in Python
- automated-machineLearning-methods — A small example package for machine learning operations
- autoMMM — no summary
- autoneuro-pypi — Template python package
- autonon — Organon Automated ML Platform
- autoprognosis — A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.
- autorad — Radiomics-related modules for extraction and experimenting
- autorank — Automated ranking of populations in a repeated measures experiment, e.g., to rank different machine learning approaches tested on the same data.
- autotime — Automated ML-based predictive analytics framework for time-series data.
- AutoTS — Automated Time Series Forecasting
- autoviz — Automatically Visualize any dataset, any size with a single line of code
- azapy — Financial Portfolio Optimization Algorithms
- azureml-automl-runtime — Contains the ML and non-Azure specific common code associated with running AutoML for public use.
- azureml-train-automl-runtime — Used for automatically finding the best machine learning model and its parameters.
- azureml-training-tabular — Contains ML models, featurizers and scoring code which can either be used with AutoML or standalone.
- azuremlftk — "Microsoft Azure Machine Learning Forecasting Toolkit"
- babino2020masks — Code used in https://arxiv.org/abs/2006.05532
- backtester — A backtesting for timeseries data in a pandas dataframe
- balance — balance is a Python package offering a simple workflow and methods for dealing with biased data samples when looking to infer from them to some target population of interest.
- bamboolib — bamboolib - a GUI for pandas
- bandit-optimization — Bandit optimization algorithms for microscopy
- bartpy — Bayesian Additive Regression Trees for Python
- bartpy2 — Bayesian Additive Regression Trees for Python Updated in January 2024
- baseqCNV — Pipeline for Processing Whole Genenome Sequencing datasets
- bassmodeldiffusion — no summary
- bayes-traj — bayes_traj
- bayesvalidrox — An open-source, object-oriented Python package for surrogate-assisted Bayesain Validation of computational models.
- bciavm — bciAVM is a machine learning pipeline used to predict property prices.
- beanmachine — Probabilistic Programming Language for Bayesian Inference
- Beat-ML1 — This package contains several methods for calculating Conditional Average Treatment Effects
- BEAT-TEST — This package contains several methods for calculating Conditional Average Treatment Effects
- BEATAALU — This package contains several methods for calculating Conditional Average Treatment Effects
- BEER_curve — A very small package to model the BEaming, Ellipsoidal variations, and Reflected/emitted light from low-mass companions
- betabinomial — Beta Binomial test to detect aberration in count data
- bezzanlabs.treemachine — An AutoML companion to fit tree models easily
- bhad — Bayesian Histogram-based Anomaly Detection
- bias-correction — python library for bias_correction
- binsreg — Implements binscatter methods, including partition selection, point estimation, pointwise and uniform inference methods, and graphical procedures.
- bio-pyminer — PyMINEr: automated biologic insights from large datasets.
- bioat — Bioinformatic toolkit with python
- biobookshelf — a collection of python scripts and functions for exploratory analysis of bioinformatic data in Python
- bioinf-common — Aggregation of functionalities needed in multiple projects
- bioinfokit — Bioinformatics data analysis and visualization toolkit
- biopsykit — A Python package for the analysis of biopsychological data.
- biorag — BioRAG: A tool for textual and gene set search against ARCHS4 data
- BioSAK — BioSAK
- biostatistics — An intuitive app for statistical analysis
- bipartitepandas — Python tools for bipartite labor data
- bitfount — Machine Learning and Federated Learning Library.
- bixai — Package that makes it a bit easier to understand some complex models and helps you visualize them
- bl-predictor — A simple application for predicting game results for the German Bundesliga
- black-it — black-it: Black-box abm calibration kit
- blackboxopt — A common interface for blackbox optimization algorithms along with useful helpers like parallel optimization loops, analysis and visualization scripts.
- blechpy — Package for exrtacting, processing and analyzing Intan and OpenEphys data
- blip-alpha — A bayesian pipeline for detecting stochastic backgrounds with LISA.
- blip-gw — A bayesian pipeline for detecting stochastic backgrounds with LISA.
- blitzml — A low-code library for machine learning pipelines
- blksheep — A package for differential extreme values analysis
- blockeval — Analyze campaigns with segments derived from a predictive model, an uplift score, or any business rule.
- blockify — Fast and optimal genome segmentation with Bayesian blocks
- blportopt — A bayesian approach to portfolio optimization
- BlueWhale3-Timeseries — 用于探索时间序列和顺序数据的蓝鲸插件。
- bmi3d — electrophysiology experimental rig library
- bmiselect — Bayesian MI-LASSO for variable selection on multiply-imputed data.
- bnlearn — Python package for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.
- booleabayes — A suite for network inference from transcriptomics data
- BorutaShap — A feature selection algorithm.
- bp-neurotools — General helper functions for working with neuroimaging data.
- bpnet — BPNet: toolkit to learn motif synthax from high-resolution functional genomics data using convolutional neural networks
- braintracer — A complete processing pipeline for anatomical neuronal tracing.
- brainways — Brainways
- brighteyes-ism — A toolbox for analysing and simulating ISM images
- brioche-enrichment — Bayesian tests for set enrichment.
- Broad-GenePy — A useful module for any CompBio
- brutifus — Python module to process IFU datacubes.
- bs-python-utils — my Python utilities
- bss — BrainSuite statistics toolbox
- building-controller-forecast — Building controller forecasting lib
- bumbleview — Convert physical spectra to excitation potential in insect eyes
- buy-and-hold-vs-arima — buy and hold vs arima strategy
- c2xg — Construction Grammars for Natural Language Processing and Computational Linguistics
- calculate-vif-rsquare — This function will use to calculate VIF and adjusted rsquare within featue columns or indipendent variables.
- calibration-belt — Assessment of calibration in binomial prediction models.
- CalibrationCurve — A collection of functions that streamline the process of creating calibration curves using Python.
- CallFlow — no summary
- calour — CALOUR: exploratory and interactive microbiome analyses based on heatmap
- camcops-server — CamCOPS server
- CaMo — CaMo: A Causal Model Library