Package: crisp 1.0.0

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>.

Authors:Ashley Petersen

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crisp.pdf |crisp.html
crisp/json (API)

# Install 'crisp' in R:
install.packages('crisp', repos = c('https://ajpete.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.48 score 30 scripts 162 downloads 3 exports 3 dependencies

Last updated 8 years agofrom:07c59a56ce. Checks:3 OK, 6 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 17 2025
R-4.5-winNOTEMar 17 2025
R-4.5-macNOTEMar 17 2025
R-4.5-linuxNOTEMar 17 2025
R-4.4-winNOTEMar 17 2025
R-4.4-macNOTEMar 17 2025
R-4.4-linuxNOTEMar 17 2025
R-4.3-winOKMar 17 2025
R-4.3-macOKMar 17 2025

Exports:crispcrispCVsim.data

Dependencies:latticeMASSMatrix