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)
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2026-10-13 03:43:38 UTC; root
Repository: https://cranhaven.r-universe.dev
Date/Publication: 2026-10-12 11:02:00 UTC
RemoteUrl: https://github.com/cranhaven/cranhaven.r-universe.dev
RemoteRef: package/crisp
RemoteSha: 1ba45074d851c683785421c5e25c664e88f1c4e9
RemoteSubdir: crisp
