# statsmodels-dq Release 3.0

Statistical computations and models for Python

BSD-3-Clause
Install
``` pip install statsmodels-dq==3.0 ```

### Documentation

Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models.

## Documentation

The documentation for the latest release is at

https://www.statsmodels.org/stable/

The documentation for the development version is at

https://www.statsmodels.org/dev/

Recent improvements are highlighted in the release notes

https://www.statsmodels.org/stable/release/version0.9.html

Backups of documentation are available at https://statsmodels.github.io/stable/ and https://statsmodels.github.io/dev/.

## Main Features

• Linear regression models:
• Ordinary least squares
• Generalized least squares
• Weighted least squares
• Least squares with autoregressive errors
• Quantile regression
• Recursive least squares
• Mixed Linear Model with mixed effects and variance components
• GLM: Generalized linear models with support for all of the one-parameter exponential family distributions
• Bayesian Mixed GLM for Binomial and Poisson
• GEE: Generalized Estimating Equations for one-way clustered or longitudinal data
• Discrete models:
• Logit and Probit
• Multinomial logit (MNLogit)
• Poisson and Generalized Poisson regression
• Negative Binomial regression
• Zero-Inflated Count models
• RLM: Robust linear models with support for several M-estimators.
• Time Series Analysis: models for time series analysis
• Complete StateSpace modeling framework
• Seasonal ARIMA and ARIMAX models
• VARMA and VARMAX models
• Dynamic Factor models
• Unobserved Component models
• Markov switching models (MSAR), also known as Hidden Markov Models (HMM)
• Univariate time series analysis: AR, ARIMA
• Vector autoregressive models, VAR and structural VAR
• Vector error correction modle, VECM
• exponential smoothing, Holt-Winters
• Hypothesis tests for time series: unit root, cointegration and others
• Descriptive statistics and process models for time series analysis
• Survival analysis:
• Proportional hazards regression (Cox models)
• Survivor function estimation (Kaplan-Meier)
• Cumulative incidence function estimation
• Multivariate:
• Principal Component Analysis with missing data
• Factor Analysis with rotation
• MANOVA
• Canonical Correlation
• Nonparametric statistics: Univariate and multivariate kernel density estimators
• Datasets: Datasets used for examples and in testing
• Statistics: a wide range of statistical tests
• diagnostics and specification tests
• goodness-of-fit and normality tests
• functions for multiple testing
• Imputation with MICE, regression on order statistic and Gaussian imputation
• Mediation analysis
• Graphics includes plot functions for visual analysis of data and model results
• I/O
• Table output to ascii, latex, and html
• Miscellaneous models
• Sandbox: statsmodels contains a sandbox folder with code in various stages of developement and testing which is not considered "production ready". This covers among others
• Generalized method of moments (GMM) estimators
• Kernel regression
• Various extensions to scipy.stats.distributions
• Panel data models
• Information theoretic measures

## How to get it

The master branch on GitHub is the most up to date code

https://www.github.com/statsmodels/statsmodels

https://github.com/statsmodels/statsmodels/tags

Binaries and source distributions are available from PyPi

https://pypi.org/project/statsmodels/

Binaries can be installed in Anaconda

conda install statsmodels

## Installing from sources

See INSTALL.txt for requirements or see the documentation

https://statsmodels.github.io/dev/install.html

Modified BSD (3-clause)

## Discussion and Development

Discussions take place on our mailing list.