Routines for implementing various statistical and machine learning techniques.
Description of routines:
super_pca: Class for performing supervised principal components regression (Bair, E., et al. Prediction by supervised principal components. J. Am. Stat. Assoc. 101, 473, 2006)sklearn_estimator_suite: Classes for running through a set of scikit-learn estimators, using cross-validation to choose the tuning parameters.react: Classes for performing non-parameteric regression in one or two dimensions based on the REACT technique (Beran, R. REACT scatterplot smoothers: Superefficiency through basis economy. J. Am. Stat. Assoc. 95, 449, 2000)multiclass_triangle_plot: Plot the lower triangle of a scatterplot matrix, color-coding according to class label. A modified version of Dan Foreman-Mackey's triangle.py routine.gcv_smoother: Perform exponential smoothing of a time series. The e-folding time scale is chosen using generalized cross-validation.dynamic_linear_model: Class to perform dynamic linear regression via least-squares (Montana, G., et al. Flexible least squares for temporal data mining and statistical arbitrage. Expert Systems with Applications 36, 2819, 2009).dba: Compute the dynamic time warping barycentric average of a set of time series (Petitjean, F., et al. A global averaging method for dynamic time warping, with applications to clustering. Pattern Recognition, 44, 678, 2011). Also contains a function to compute the dynamic time warping distance.From the base directory type python setup.py install in a terminal.