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 nonadditive model. CRISP partitions the covariate space into blocks in a dataadaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a nongreedy approach by solving a convex optimization problem, resulting in lowvariance 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): 131 <http://jmlr.org/papers/volume17/15344/15344.pdf>.
Package details 


Author  Ashley Petersen 
Maintainer  Ashley Petersen <ashleyjpete@gmail.com> 
License  GPL (>= 2) 
Version  1.0.0 
Package repository  View on CRAN 
Installation 
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