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Integrates several popular high-dimensional methods based on Linear Discriminant Analysis (LDA) and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification as mentioned in Yuqing Pan, Qing Mai and Xin Zhang (2019) <doi:10.48550/arXiv.1904.03469>. Functions are included for covariate adjustment, model fitting, cross validation and prediction.
| Version: | 1.0.2 |
| Depends: | R (≥ 3.1.1) |
| Imports: | tensr, Matrix, MASS, glmnet, methods |
| Published: | 2021-01-04 |
| Author: | Yuqing Pan, Qing Mai, Xin Zhang |
| Maintainer: | Yuqing Pan <yuqing.pan at stat.fsu.edu> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| CRAN checks: | TULIP results |
| Reference manual: | TULIP.pdf |
| Package source: | TULIP_1.0.2.tar.gz |
| Windows binaries: | r-devel: TULIP_1.0.2.zip, r-release: TULIP_1.0.2.zip, r-oldrel: TULIP_1.0.2.zip |
| macOS binaries: | r-release (arm64): TULIP_1.0.2.tgz, r-oldrel (arm64): TULIP_1.0.2.tgz, r-release (x86_64): TULIP_1.0.2.tgz, r-oldrel (x86_64): TULIP_1.0.2.tgz |
| Old sources: | TULIP archive |
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