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