Package: crisp
Type: Package
Title: Fits a Model that Partitions the Covariate Space into Blocks in
        a Data- Adaptive Way
Version: 1.0.0
Author: Ashley Petersen
Maintainer: Ashley Petersen <ashleyjpete@gmail.com>
Description: 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>.
Imports: Matrix, MASS, stats, methods, grDevices, graphics
License: GPL (>= 2)
LazyData: TRUE
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2017-01-04 15:50:10 UTC; ashleypetersen
Repository: CRAN
Date/Publication: 2017-01-05 10:39:31
Built: R 4.6.0; ; 2026-04-26 00:18:35 UTC; unix
