crisp: Fits a Model that Partitions the Covariate Space into Blocks in
a Data- Adaptive Way
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>.
||Matrix, MASS, stats, methods, grDevices, graphics
||Ashley Petersen <ashleyjpete at gmail.com>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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