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 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 . Imports: Matrix, MASS, stats, methods, grDevices, graphics License: GPL (>= 2) RoxygenNote: 5.0.1 NeedsCompilation: no Packaged: 2026-10-14 11:41:58 UTC; root Repository: https://ajpete.r-universe.dev Date/Publication: 2017-01-05 09:39:31 UTC RemoteUrl: https://github.com/cran/crisp RemoteRef: HEAD RemoteSha: 07c59a56ced200c5465951e27d6d573317b6cfaa