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:
crisp_1.0.0.tar.gz
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crisp_1.0.0.tgz(r-4.4-any)crisp_1.0.0.tgz(r-4.3-any)
crisp_1.0.0.tar.gz(r-4.5-noble)crisp_1.0.0.tar.gz(r-4.4-noble)
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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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 8 years agofrom:07c59a56ce. Checks:OK: 3 NOTE: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 17 2024 |
| R-4.5-win | NOTE | Nov 17 2024 |
| R-4.5-linux | NOTE | Nov 17 2024 |
| R-4.4-win | NOTE | Nov 17 2024 |
| R-4.4-mac | NOTE | Nov 17 2024 |
| R-4.3-win | OK | Nov 17 2024 |
| R-4.3-mac | OK | Nov 17 2024 |
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| crisp: A package for fitting a model that partitions the covariate space into blocks in a data-adaptive way. | crisp-package |
| Convex Regression with Interpretable Sharp Partitions (CRISP). | crisp |
| CRISP with Tuning Parameter Selection via Cross-Validation. | crispCV |
| Plots Fit from 'crisp' or 'crispCV'. | plot plot.crisp plot.crispCV |
| Plots Cross-Validation Curve for 'crispCV'. | plot.cvError |
| Plot Mean Model for Data. | plot.sim.data |
| Predicts Observations for a New Covariate Matrix using Fit from 'crisp' or 'crispCV'. | predict predict.crisp predict.crispCV |
| Simulate Data to Use with 'crisp'. | sim.data |
| Summarizes Fit from 'crisp' or 'crispCV'. | summary summary.crisp summary.crispCV |
