statsmodels/statsmodels

Features

• Forecasting - Models historical data using autoregressive and smoothing techniques to identify patterns and predict future values.
• Linear Regression - Calculates relationships between variables using ordinary and weighted least squares regression techniques.
• Generalized Linear Models - Applies link functions and distribution families to model non-normal response variables.
• Statistical Inference Frameworks - Provides a comprehensive collection of classes and functions for statistical inference and regression analysis.
• Time Series Analysis Toolkits - Decomposes temporal trends, forecasts future values, and models dynamic systems using autoregressive and state space techniques.
• Econometrics Toolkits - Provides a suite of tools for estimating linear and generalized linear models, treatment effects, and survival analysis.
• Statistical Analysis Libraries - Estimates and diagnoses statistical models, conducts hypothesis tests, and performs advanced data exploration.
• Hypothesis Testing Frameworks - Conducts rigorous tests and diagnostic checks to evaluate the significance of observed data.
• Time Series Modeling - Models temporal dependencies using autoregressive and moving average techniques to forecast future values.
• Statistical Estimation - Calculates parameters for statistical models using formula-based specifications and numerical arrays.
• Maximum Likelihood Estimators - Calculates model parameters by iteratively optimizing objective functions to find the most probable fit.
• Hypothesis Testing - Conducts hypothesis tests and diagnostic checks to evaluate the significance of statistical models.
• State Space Models - Models dynamic systems by separating latent states from observed measurements for temporal analysis.
• Robust Regression - Estimates model parameters using M-estimators to ensure stable results in the presence of outliers.
• Statistical Plotting Libraries - Generates diagnostic plots and regression fits to visually validate model assumptions and data characteristics.
• Time Series Decomposition - Separates complex datasets into seasonal, trend, and residual components to simplify temporal analysis.
• Causal Inference Tools - Evaluates the impact of specific interventions or treatments on outcomes to determine real-world effectiveness.
• Iterative Solvers - Employs numerical algorithms to ensure stable parameter estimation for linear and nonlinear models.
• Nonparametric Estimators - Estimates probability densities and trends without assuming specific functional forms.
• Survival Analysis Libraries - Models duration data and hazard rates to estimate time-to-event outcomes in longitudinal studies.
• Statistical Diagnostics - Executes specification tests and generates diagnostic summaries to validate model assumptions.
• Statistical Diagnostics - Executes statistical checks on model residuals and parameters to ensure the reliability of inference.
• Statistical Model Specifications - Constructs complex relationships between variables using a descriptive formula syntax.
• Machine Learning Frameworks - Comprehensive library for statistical modeling and econometrics.
• Scientific Computing - Statistical modeling and econometrics.
• Statistical Modeling - Statistical modeling and econometrics analysis.
• Statistics - Statistical modeling and econometrics library.
• Time Series Analysis - Library for statistical modeling and hypothesis testing.
• Statistical Modeling - Statistical modeling, testing, and data exploration.
• Scientific Computing - Listed in the “Scientific Computing” section of the Awesome Python awesome list.
• Longitudinal Data Models - Models correlated data structures using generalized estimating equations for longitudinal analysis.
• Choice Models - Analyzes categorical and count data using regression techniques for ordinal and Poisson distributions.
• Imputation Methods - Fills gaps in datasets using multiple imputation methods to ensure data integrity.
• Dimensionality Reduction Tools - Reduces dataset dimensionality and identifies hidden patterns across multiple variables using principal component analysis.
• Symbolic Formula Parsers - Allows users to construct complex statistical models through intuitive string-based expressions.
• Kernel Density Estimation - Provides kernel density estimation methods to estimate probability densities without assuming specific functional forms.
• Design Matrix Builders - Transforms raw data into endogenous and exogenous matrices by encoding categorical variables and adding intercepts.
• Generalized Method of Moments - Solves for model parameters using generalized method of moments when standard likelihood functions are complex.
• Custom Estimation Interfaces - Supports custom model estimation through generic maximum likelihood and formula-based interfaces.
• Copula Models - Captures complex dependencies between variables using copulas to describe joint distributions.
• Design Matrix Transformers - Provides automated categorical encoding and intercept generation to structure data for statistical modeling.

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