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Installing
Getting started
User Guide
Examples
API Reference
About statsmodels
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Release Notes

GitHub
PyPI
DOI

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endog, exog, what’s that?
Import Paths and Structure
Fitting models using R-style formulas
Pitfalls

Linear Regression
Generalized Linear Models
Generalized Estimating Equations
Generalized Additive Models (GAM)
Robust Linear Models
Linear Mixed Effects Models
Regression with Discrete Dependent Variable
Generalized Linear Mixed Effects Models
ANOVA
Other Models othermod

Time Series analysis tsa
Time Series Analysis by State Space Methods statespace
Vector Autoregressions tsa.vector_ar

Methods for Survival and Duration Analysis
Nonparametric Methods nonparametric
Generalized Method of Moments gmm
Other Models miscmodels
Multivariate Statistics multivariate

Statistics stats
Contingency tables
Multiple Imputation with Chained Equations
Treatment Effects treatment
Empirical Likelihood emplike
Distributions
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Input-Output iolib
Tools
Working with Large Data Sets
Optimization

The Datasets Package

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User Guide
Generalized Linear Mixed Effects Models
statsmodels.genmod.bayes_mixed_glm.BinomialBayesMixedGLM
statsmodels.genmod.bayes_mixed_glm.BinomialBayesMixedGLM.logposterior_grad

statsmodels.genmod.bayes_mixed_glm.BinomialBayesMixedGLM.logposterior_grad#

BinomialBayesMixedGLM.logposterior_grad(params)#: The gradient of the log posterior.

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BinomialBayesMixedGLM.logposterior_grad

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