Package: fdaSP 1.1.2

fdaSP: Sparse Functional Data Analysis Methods

Provides algorithms to fit linear regression models under several popular penalization techniques and functional linear regression models based on Majorizing-Minimizing (MM) and Alternating Direction Method of Multipliers (ADMM) techniques. See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.

Authors:Mauro Bernardi [aut, cre], Marco Stefanucci [aut], Antonio Canale [ctb]

fdaSP_1.1.2.tar.gz
fdaSP_1.1.2.zip(r-4.7)fdaSP_1.1.2.zip(r-4.6)fdaSP_1.1.2.zip(r-4.5)
fdaSP_1.1.2.tgz(r-4.6-x86_64)fdaSP_1.1.2.tgz(r-4.6-arm64)fdaSP_1.1.2.tgz(r-4.5-x86_64)fdaSP_1.1.2.tgz(r-4.5-arm64)
fdaSP_1.1.2.tar.gz(r-4.7-arm64)fdaSP_1.1.2.tar.gz(r-4.7-x86_64)fdaSP_1.1.2.tar.gz(r-4.6-arm64)fdaSP_1.1.2.tar.gz(r-4.6-x86_64)
fdaSP_1.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
fdaSP/json (API)

# Install 'fdaSP' in R:
install.packages('fdaSP', repos = c('https://maurobernardi.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascppopenmp

1.77 score 5.9k downloads 9 exports 32 dependencies

Last updated from:71acad8cd1. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK326
linux-devel-x86_64OK283
source / vignettesOK391
linux-release-arm64OK330
linux-release-x86_64OK247
macos-release-arm64OK188
macos-release-x86_64OK355
macos-oldrel-arm64OK191
macos-oldrel-x86_64OK387
windows-develOK272
windows-releaseOK258
windows-oldrelOK255
wasm-releaseOK192

Exports:confbandf2fSPf2fSP_cvf2sSPf2sSP_cvforward_difflmSPlmSP_cvsofthresh

Dependencies:backportscheckmateclarabelclicodetoolsCVXRdoParallelFNNforeachgmphighsiteratorskernlabKernSmoothkslatticeMatrixmclustmgcvmulticoolmvtnormnlmeosqppracmarbibutilsRcppRcppArmadilloRcppEigenRdpackS7scsslam