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cff-version: 1.2.0
message: 'To cite package "FarmSelect" in publications use:'
type: software
license: GPL-2.0-only
title: 'FarmSelect: Factor Adjusted Robust Model Selection'
version: 1.0.2
doi: 10.32614/CRAN.package.FarmSelect
abstract: 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:
- family-names: Bose
  given-names: Koushiki
  email: bose@princeton.edu
- family-names: Ke
  given-names: Yuan
  email: keyuan_princeton@163.com
- family-names: Wang
  given-names: Kaizheng
  email: kzwang2015@gmail.com
repository: https://cranhaven.r-universe.dev
commit: 4ba9db0f0dc9b38e22ec7e1b85cb13afbe1fbf08
url: https://kbose28.github.io/FarmSelect/
date-released: '2018-04-19'
contact:
- family-names: Bose
  given-names: Koushiki
  email: bose@princeton.edu