Package: clr 0.1.3.9000
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_0.1.3.9000.tar.gz
clr_0.1.3.9000.zip(r-4.7-any)clr_0.1.3.9000.zip(r-4.6-any)clr_0.1.3.9000.zip(r-4.5-any)
clr_0.1.3.9000.tgz(r-4.6-any)clr_0.1.3.9000.tgz(r-4.5-any)
clr_0.1.3.9000.tar.gz(r-4.7-any)clr_0.1.3.9000.tar.gz(r-4.6-any)
clr_0.1.3.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
clr/json (API)
| # 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 from:296eb48a78. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 105 | ||
| source / vignettes | OK | 164 | ||
| linux-release | OK | 171 | ||
| macos-release | OK | 208 | ||
| macos-oldrel | OK | 184 | ||
| windows-devel | OK | 71 | ||
| windows-release | OK | 77 | ||
| windows-oldrel | OK | 64 | ||
| wasm-release | OK | 101 |
Dependencies:clicpp11dplyrgenericsgluelifecyclelubridatemagrittrpillarpkgconfigR6rlangtibbletidyselecttimechangeutf8vctrswithr
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 |