stattools 0.0.4

Statistical learning and inference library

StatTools

Statistical learning and inference algorithms implemented in pure Python (version 3.6 or later).

Installation

The latest version of StatTools can be installed directly after cloning from GitHub:

git clone https://github.com/artemmavrin/StatTools.git
cd StatTools
make install

Moreover, StatTools is on the Python Package Index (PyPI), so a recent version of it can be installed with the pip utility:

pip install stattools

Dependencies

• NumPy
• SciPy
• pandas
• Matplotlib

Examples

Regression

• Simple linear regression for fitting a line through a scatter plot
• Ridge regression
• Elastic net regularization (including LASSO and ridge regression as special cases)
• Fitting a polynomial curve to a scatter plot
• Various scatterplot smoothers applied to a sine curve with Gaussian noise

Classification

• Logistic regression for breast cancer diagnosis
• Multiclass logistic regression for handwritten digit recognition

Unsupervised Learning

• K-means clustering for grouping unlabelled data together
• Estimation of Gaussian mixture models
• Principal component analysis applied to handwritten digits
• Kernel density estimation for histogram smoothing

Non-Parametric Statistics

• The bootstrap (ordinary and Bayesian) and the jackknife for standard error estimation
• Bootstrap confidence intervals
• Exact and Monte Carlo permutation tests
• The Kaplan-Meier survivor function estimator

Ensemble Methods

• Using bagging to improve logistic regression accuracy

Data Visualization

• Plotting lines and function curves
• Drawing empirical distribution functions
• Drawing quantile-quantile (QQ) plots

Simulation

• Simulating sample paths of Poisson processes
• Simulating sample paths of Itô diffusions (for example, Brownian motion)