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crisp  

Fits a Model that Partitions the Covariate Space into Blocks in a Data- Adaptive Way
View on CRAN: Click here


Download and install crisp package within the R console
Install from CRAN:
install.packages("crisp")

Install from Github:
library("remotes")
install_github("cran/crisp")

Install by package version:
library("remotes")
install_version("crisp", "1.0.0")



Attach the package and use:
library("crisp")
Maintained by
Ashley Petersen
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-01-05
Latest Update: 2017-01-05
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 .
How to cite:
Ashley Petersen (2017). crisp: Fits a Model that Partitions the Covariate Space into Blocks in a Data- Adaptive Way. R package version 1.0.0, https://cran.r-project.org/web/packages/crisp. Accessed 05 Aug. 2026.
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Complete documentation for crisp
Functions, R codes and Examples using the crisp R package
Some associated functions: crisp-package . crisp . crispCV . plot.cvError . plot . plot.sim.data . predict . sim.data . summary . 
Some associated R codes: CRISP_functions.R . crisp-package.R .  Full crisp package functions and examples
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