Cubic Bezier Splines package
The CBS package uses a smooth monotonic function to approximate latent utility functions in intertemporal choice and risky choice data. The goal is to build an agnostic model of delay discounting and risky choice that can predict better than parametric utility models but still provide interpretable insights. If you have ever been pissed off because your reviewer told you to use a different parametric utility model for delay discounting or risky choice (even when that’s not the point of the paper), then this tool is for you (wink).
Paper: [Open access link], [PDF]
Citation: Lee, S., Glaze, C. M., Bradlow, E. T., & Kable, J. W. (2020). Flexible Utility Function Approximation via Cubic Bezier Splines. Psychometrika, 1-22.
Package for MATLAB: [Github]
Package for R: [CRAN]
Thresholded Partial Least Squares package
The TPLS package deals with building a linear predictor when the number of features are large (~millions), have high spatial correlation, and hugely outnumber the number of observations. TPLS offers extremely efficient computation time due to its ‘fit-once-tune-later’ approach : unlike modern models that are fit multiple times to test different tuning parameters, TPLS only needs to be fit once and allows for post-hoc choosing of tuning parameters.
Paper: [PDF]
Package for MATLAB: [Github]
Package for R: [CRAN]
Package for Python: [Github]
Updated GLMNET package for MATLAB 64 bit large arrays : [Github]
-> This is a modified version of GLMNET for MATLAB (original source code here:https://web.stanford.edu/~hastie/glmnet_matlab/). The original code for MATLAB was not updated for a while, and as a result, the API for mex function connecting the FORTRAN code to matlab was outdated. This made it impossible to load and use large arrays, which are only supported in 64-bit mex API for FORTRAN. So I've updated the source FORTRAN code to 64-bit API.
MATLAB Package for parametric utility models for delay discounting and risky choice data [Github]