Python Module Index
| s | ||
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.discrete.truncated_model | ||
statsmodels.distributions.bernstein | Distributions based on Bernstein polynomials | |
statsmodels.distributions.copula.api | Copula for modeling parameter dependence | |
statsmodels.distributions.copula.depfunc_ev | Pickands dependence functions for extreme value copulas | |
statsmodels.distributions.copula.transforms | Archimedean copula generator transforms | |
statsmodels.distributions.discrete | Support for count distributions | |
statsmodels.distributions.empirical_distribution | Tools for working with empirical distributions | |
statsmodels.distributions.mixture_rvs | Mixtures of 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.gam_cross_validation.gam_cross_validation | K-fold cross-validation for GAM smoothing parameters | |
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 | ||
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.ordinal_model | ||
statsmodels.miscmodels.tmodel | ||
statsmodels.multivariate | Models for multivariate data | |
statsmodels.multivariate.pca | Principal Component Analaysis | |
statsmodels.nonparametric | Nonparametric estimation of densities and curves | |
statsmodels.othermod | ||
statsmodels.othermod.betareg | ||
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.covariance | ||
statsmodels.robust.norms | ||
statsmodels.robust.resistant_linear_model | ||
statsmodels.robust.robust_linear_model | ||
statsmodels.robust.scale | ||
statsmodels.sandbox.distributions | Probability distributions | |
statsmodels.sandbox.distributions.extras | Probability distributions and random number generators | |
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.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.covariance | Correlation and covariance estimators consistent at the normal distribution | |
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.rng_qrng | Tools for working with random variable generation | |
statsmodels.tools.sm_exceptions | Exceptions and Warnings | |
statsmodels.treatment | Treatment Effect | |
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 |
