Package: FastSF 0.1.1
Canhong Wen
FastSF: Fast Structural Filtering
An implementation of the fast structural filtering with L0 penalty. It includes an adaptive polynomial estimator by minimizing the least squares error with constraints on the number of breaks in their (k + 1)-st discrete derivative, for a chosen integer k >= 0. It also includes generalized structure sparsity constraint, i.e., graph trend filtering. This package is implemented via the primal dual active set algorithm, which formulates estimates and residuals as primal and dual variables, and utilizes efficient active set selection strategies based on the properties of the primal and dual variables.
Authors:
FastSF_0.1.1.tar.gz
FastSF_0.1.1.tar.gz(r-4.5-noble)FastSF_0.1.1.tar.gz(r-4.4-noble)
FastSF_0.1.1.tgz(r-4.4-emscripten)FastSF_0.1.1.tgz(r-4.3-emscripten)
FastSF.pdf |FastSF.html✨
FastSF/json (API)
# Install 'FastSF' in R: |
install.packages('FastSF', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 years agofrom:f6d7a915f8. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 15 2024 |
R-4.5-linux-x86_64 | OK | Nov 15 2024 |
Exports:ffusedffused.adafsffsf.adafsfusedfsfused.adaftfftf.adal0fused_cl0gen_cl0tf_cplotl0sl0fused_c
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Fast Fused Regression | ffused |
Adaptive Fast Fused Regression | ffused.ada |
Fast Structural Filtering | fsf |
Adaptive Fast Structural Filtering | fsf.ada |
Fast Sparse Fused Regression | fsfused |
Adaptive Fast Sparse Fused Regression | fsfused.ada |
Fast Trend Filtering | ftf |
Adaptive Fast Trend Filtering | ftf.ada |
L0 Fused Regression | l0fused_c |
L0 Generalized Regression | l0gen_c |
L0 Trend Filtering | l0tf_c |
Plot L0 fitted value | plotl0 |
Sparse L0 Fused Regression | sl0fused_c |