Package: FarmSelect 1.0.2

Koushiki Bose

FarmSelect: Factor Adjusted Robust Model Selection

Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) <arxiv:1612.08490>, for detailed description of methods and further references.

Authors:Koushiki Bose [aut, cre], Yuan Ke [aut], Kaizheng Wang [aut]

FarmSelect_1.0.2.tar.gz
FarmSelect_1.0.2.tar.gz(r-4.5-noble)FarmSelect_1.0.2.tar.gz(r-4.4-noble)
FarmSelect_1.0.2.tgz(r-4.4-emscripten)FarmSelect_1.0.2.tgz(r-4.3-emscripten)
FarmSelect.pdf |FarmSelect.html
FarmSelect/json (API)

# Install 'FarmSelect' in R:
install.packages('FarmSelect', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/kbose28/farmselect/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

openblascpp

2.00 score 8 scripts 181 downloads 2 exports 10 dependencies

Last updated 7 years agofrom:947c18676e. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 06 2024
R-4.5-linux-x86_64NOTEDec 06 2024

Exports:farm.resfarm.select

Dependencies:fBasicsgssMASSncvregRcppRcppArmadillospatialstabledisttimeDatetimeSeries

FarmSelect: Factor Adjusted Robust Model Selection

Rendered fromFarmSelect-vignette.Rmdusingknitr::rmarkdownon Dec 06 2024.

Last update: 2018-04-19
Started: 2018-01-17