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>.
Version: 1.0.0 Imports: Matrix, MASS, stats, methods, grDevices, graphics Published: 2017-01-05 DOI: 10.32614/CRAN.package.crisp Author: Ashley Petersen Maintainer: Ashley Petersen <ashleyjpete at gmail.com> License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] NeedsCompilation: no CRAN checks: crisp results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=crisp to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4