Fits a Model that Partitions the Covariate Space into Blocks in a Data- Adaptive Way
crisp: A package for fitting a model that partitions the covariate spa...
Convex Regression with Interpretable Sharp Partitions (CRISP).
CRISP with Tuning Parameter Selection via Cross-Validation.
Plots Cross-Validation Curve for crispCV.
Plots Fit from crisp or crispCV.
Plot Mean Model for Data.
Predicts Observations for a New Covariate Matrix using Fit from `crisp...
Simulate Data to Use with crisp.
Summarizes Fit from crisp or crispCV.
Implements convex regression with interpretable sharp partitions (CRISP), which considers the problem of predicting an outcome variable on the basis of two covariates, using an interpretable yet non-additive model. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. More details are provided in Petersen, A., Simon, N., and Witten, D. (2016). Convex Regression with Interpretable Sharp Partitions. Journal of Machine Learning Research, 17(94): 1-31 <http://jmlr.org/papers/volume17/15-344/15-344.pdf>.