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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.0 |
rolling linux/jammy R-4.5 | crisp_1.0.0.tar.gz |
94.0 KiB |
1.0.0 |
rolling linux/noble R-4.5 | crisp_1.0.0.tar.gz |
93.9 KiB |
1.0.0 |
rolling source/ R- | crisp_1.0.0.tar.gz |
16.1 KiB |
1.0.0 |
latest linux/jammy R-4.5 | crisp_1.0.0.tar.gz |
94.0 KiB |
1.0.0 |
latest linux/noble R-4.5 | crisp_1.0.0.tar.gz |
93.9 KiB |
1.0.0 |
latest source/ R- | crisp_1.0.0.tar.gz |
16.1 KiB |
1.0.0 |
2026-04-26 source/ R- | crisp_1.0.0.tar.gz |
16.1 KiB |
1.0.0 |
2026-04-23 source/ R- | crisp_1.0.0.tar.gz |
16.1 KiB |
1.0.0 |
2026-04-09 windows/windows R-4.5 | crisp_1.0.0.zip |
96.1 KiB |
1.0.0 |
2025-04-20 source/ R- | crisp_1.0.0.tar.gz |
16.1 KiB |