Package: clr 0.1.2.9000
clr: Curve Linear Regression via Dimension Reduction
A new methodology for linear regression with both curve response and curve regressors, which is described in Cho, Goude, Brossat and Yao (2013) <doi:10.1080/01621459.2012.722900> and (2015) <doi:10.1007/978-3-319-18732-7_3>. The key idea behind this methodology is dimension reduction based on a singular value decomposition in a Hilbert space, which reduces the curve regression problem to several scalar linear regression problems.
Authors:
clr_0.1.2.9000.tar.gz
clr_0.1.2.9000.zip(r-4.5)clr_0.1.2.9000.zip(r-4.4)clr_0.1.2.9000.zip(r-4.3)
clr_0.1.2.9000.tgz(r-4.4-any)clr_0.1.2.9000.tgz(r-4.3-any)
clr_0.1.2.9000.tar.gz(r-4.5-noble)clr_0.1.2.9000.tar.gz(r-4.4-noble)
clr_0.1.2.9000.tgz(r-4.4-emscripten)clr_0.1.2.9000.tgz(r-4.3-emscripten)
clr.pdf |clr.html✨
clr/json (API)
NEWS
| # Install 'clr' in R: |
| install.packages('clr', repos = c('https://apierrot.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/apierrot/clr/issues
- clust_test - Electricity load example: clusters on test set
- clust_train - Electricity load example: clusters on train set
- gb_load - Electricity load from Great Britain
Last updated 5 years agofrom:dc5d74a5a0. Checks:OK: 3 NOTE: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 21 2024 |
| R-4.5-win | NOTE | Nov 21 2024 |
| R-4.5-linux | NOTE | Nov 21 2024 |
| R-4.4-win | NOTE | Nov 21 2024 |
| R-4.4-mac | NOTE | Nov 21 2024 |
| R-4.3-win | OK | Nov 21 2024 |
| R-4.3-mac | OK | Nov 21 2024 |
Dependencies:clicpp11dplyrfansigenericsgluelifecyclelubridatemagrittrpillarpkgconfigR6rlangtibbletidyselecttimechangeutf8vctrswithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Curve Linear Regression | clr-package |
| Curve Linear Regression via dimension reduction | clr |
| Create an object of 'clrdata' | clrdata |
| Electricity load example: clusters on test set | clust_test |
| Electricity load example: clusters on train set | clust_train |
| Electricity load from Great Britain | gb_load |
| Prediction from fitted CLR model(s) | predict.clr |
