Python Module Index

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	`statsmodels`	Statistical analysis in Python
	`statsmodels.base.distributed_estimation`
	`statsmodels.base.model`	Base classes that are inherited by models
	`statsmodels.base.optimizer`
	`statsmodels.discrete.conditional_models`
	`statsmodels.discrete.count_model`
	`statsmodels.discrete.discrete_model`	Models for discrete data
	`statsmodels.distributions.empirical_distribution`	Tools for working with empirical distributions
	`statsmodels.duration`	Models for durations
	`statsmodels.duration.hazard_regression`	Proportional hazards model for Survival Analysis
	`statsmodels.duration.survfunc`	Models for Survival Analysis
	`statsmodels.emplike`	Empirical likelihood tools
	`statsmodels.gam.generalized_additive_model`	Generalized Additive Models
	`statsmodels.gam.smooth_basis`	Classes for Spline and other Smooth Basis Function
	`statsmodels.genmod.bayes_mixed_glm`	Bayes Mixed Generalized Linear Models
	`statsmodels.genmod.cov_struct`	Covariance structures for Generalized Estimating Equations (GEE)
	`statsmodels.genmod.families.family`	Generalized Linear Model (GLM) families
	`statsmodels.genmod.families.links`
	`statsmodels.genmod.families.varfuncs`
	`statsmodels.genmod.generalized_estimating_equations`	Generalized estimating equations
	`statsmodels.genmod.generalized_linear_model`	Generalized Linear Models (GLM)
	`statsmodels.genmod.qif`	Quadratic inference functions
	`statsmodels.graphics`
	`statsmodels.imputation.mice`	Multiple imputation for missing data
	`statsmodels.iolib`	Tools for reading datasets and producing summary output
	`statsmodels.miscmodels`
	`statsmodels.miscmodels.count`
	`statsmodels.miscmodels.tmodel`
	`statsmodels.multivariate`	Models for multivariate data
	`statsmodels.multivariate.pca`	Principal Component Analaysis
	`statsmodels.nonparametric`	Nonparametric estimation of densities and curves
	`statsmodels.regression.dimred`	Dimension reduction methods
	`statsmodels.regression.linear_model`	Least squares linear models
	`statsmodels.regression.mixed_linear_model`	Mixed Linear Models
	`statsmodels.regression.process_regression`	Process regression
	`statsmodels.regression.quantile_regression`	Quantile regression
	`statsmodels.regression.recursive_ls`	Recursive least squares using the Kalman Filter
	`statsmodels.regression.rolling`	Rolling (moving) least squares
	`statsmodels.rlm`	Outlier robust linear models
	`statsmodels.robust`
	`statsmodels.robust.norms`
	`statsmodels.robust.robust_linear_model`
	`statsmodels.robust.scale`
	`statsmodels.sandbox`	Experimental tools that have not been fully vetted
	`statsmodels.sandbox.distributions`	Probability distributions
	`statsmodels.sandbox.distributions.extras`	Probability distributions and random number generators
	`statsmodels.sandbox.distributions.transformed`	Experimental probability distributions and random number generators
	`statsmodels.sandbox.regression`	Experimental regression tools
	`statsmodels.sandbox.regression.anova_nistcertified`	Experimental ANOVA estimator
	`statsmodels.sandbox.regression.gmm`	A framework for implementing Generalized Method of Moments (GMM)
	`statsmodels.sandbox.stats.multicomp`	Experimental methods for controlling size while performing multiple comparisons
	`statsmodels.sandbox.stats.runs`	Experimental statistical methods and tests to analyze runs
	`statsmodels.sandbox.sysreg`	Experimental system regression models
	`statsmodels.sandbox.tools.tools_tsa`	Experimental tools for working with time-series
	`statsmodels.sandbox.tsa`	Experimental time-series analysis models
	`statsmodels.stats`	Statistical methods and tests
	`statsmodels.stats.anova`	Analysis of Variance
	`statsmodels.stats.contingency_tables`	Contingency table analysis
	`statsmodels.stats.contrast`	Classes for statistical test
	`statsmodels.stats.correlation_tools`	Procedures for ensuring correlations are positive semi-definite
	`statsmodels.stats.descriptivestats`	Descriptive statistics
	`statsmodels.stats.diagnostic`	Statistical methods and tests to diagnose model fit problems
	`statsmodels.stats.dist_dependence_measures`	Distance Dependence Measures
	`statsmodels.stats.gof`	Goodness of fit measures and tests
	`statsmodels.stats.inter_rater`
	`statsmodels.stats.knockoff_regeffects`	Regression Knock-Off Effects
	`statsmodels.stats.mediation`	Mediation analysis
	`statsmodels.stats.meta_analysis`	Meta-Analysis
	`statsmodels.stats.moment_helpers`	Tools for converting moments
	`statsmodels.stats.multicomp`	Methods for controlling size while performing multiple comparisons
	`statsmodels.stats.multitest`	Multiple testing p-value and FDR adjustments
	`statsmodels.stats.multivariate`	Statistical functions for multivariate samples.
	`statsmodels.stats.oaxaca`	Oaxaca-Blinder Decomposition
	`statsmodels.stats.oneway`	Statistical functions for oneway analysis, Anova.
	`statsmodels.stats.outliers_influence`	Statistical methods and measures for outliers and influence
	`statsmodels.stats.power`	Power and size calculations for common tests
	`statsmodels.stats.proportion`	Tests for proportions
	`statsmodels.stats.rates`	Tests for Poisson rates
	`statsmodels.stats.robust_compare`	Trimmed sample statistics.
	`statsmodels.stats.stattools`	Statistical methods and tests that do not fit into other categories
	`statsmodels.stats.weightstats`	Weighted statistics
	`statsmodels.tools`	Tools for variable transformation and common numerical operations
	`statsmodels.tools.sm_exceptions`	Exceptions and Warnings
	`statsmodels.tsa`	Time-series analysis
	`statsmodels.tsa.base.prediction`	Shared objects for predictive methods
	`statsmodels.tsa.forecasting`	Models designed for forecasting
	`statsmodels.tsa.forecasting.stl`	Models designed for forecasting
	`statsmodels.tsa.forecasting.theta`	Models designed for forecasting
	`statsmodels.tsa.statespace`	Statespace models for time-series analysis
	`statsmodels.tsa.vector_ar`	Vector autoregressions and related tools
	`statsmodels.tsa.vector_ar.svar_model`	Structural vector autoregressions and related tools
	`statsmodels.tsa.vector_ar.var_model`	Vector autoregressions