Fits sphere-sphere regression models by estimating locally weighted rotations. Simulation of sphere-sphere data according to non-rigid rotation models. Provides methods for bias reduction applying iterative procedures within a Newton-Raphson learning scheme. Cross-validation is exploited to select smoothing parameters. See Marco Di Marzio, Agnese Panzera & Charles C. Taylor (2018) <doi:10.1080/01621459.2017.1421542>.
| Version: | 1.0.1 |
| Depends: | R (≥ 3.3.0) |
| Imports: | methods, stats |
| Suggests: | testthat |
| Published: | 2020-07-21 |
| Author: | Charles C. Taylor [aut], Giovanni Lafratta [aut, cre], Stefania Fensore [aut] |
| Maintainer: | Giovanni Lafratta <giovanni.lafratta at unich.it> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | nprotreg results |
| Reference manual: | nprotreg.pdf |
| Package source: | nprotreg_1.0.1.tar.gz |
| Windows binaries: | r-devel: nprotreg_1.0.1.zip, r-release: nprotreg_1.0.1.zip, r-oldrel: nprotreg_1.0.1.zip |
| macOS binaries: | r-release: nprotreg_1.0.1.tgz, r-oldrel: nprotreg_1.0.1.tgz |
| Old sources: | nprotreg archive |
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