Python Module Index
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statsmodels |
Statistical analysis in Python | |
statsmodels.base.model |
Base classes that are inherited by models | |
statsmodels.discrete.count_model |
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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.genmod.bayes_mixed_glm |
Bayes Mixed Generalized Linear Models | |
statsmodels.genmod.cov_struct |
Covariance structures for Generalized Estimating Equations (GEE) | |
statsmodels.genmod.families.family |
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statsmodels.genmod.families.links |
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statsmodels.genmod.generalized_estimating_equations |
Generalized estimating equations | |
statsmodels.genmod.generalized_linear_model |
Generalized Linear Models (GLM) | |
statsmodels.graphics |
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statsmodels.imputation.mice |
Multiple imputation for missing data | |
statsmodels.iolib |
Tools for reading datasets and producing summary output | |
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 | |
statsmodels.multivariate.pca |
Principal Component Analaysis | |
statsmodels.nonparametric |
Nonparametric estimation of densities and curves | |
statsmodels.regression.linear_model |
Least squares linear models | |
statsmodels.regression.mixed_linear_model |
Mixed Linear Models | |
statsmodels.regression.quantile_regression |
Quantile regression | |
statsmodels.regression.recursive_ls |
Recursive least squares using the Kalman Filter | |
statsmodels.rlm |
Outlier robust linear models | |
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 | |
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.gof |
Goodness of fit measures and tests | |
statsmodels.stats.inter_rater |
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statsmodels.stats.mediation |
Mediation 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.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.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.tsa |
Time-series analysis | |
statsmodels.tsa.statespace |
Statespace models for time-series analysis | |
statsmodels.tsa.vector_ar |
Vector autoregressions and related tools | |
statsmodels.tsa.vector_ar.var_model |
Vector autoregressions |
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/py-modindex.html