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Type: Package
Title: Broken Adaptive Ridge Regression with Cyclops
Version: 1.0.1
Date: 2025-07-21
Maintainer: Marc A. Suchard <msuchard@ucla.edu>
Description: Approximates best-subset selection (L0) regression with an iteratively adaptive Ridge (L2) penalty for large-scale models. This package uses Cyclops for an efficient implementation and the iterative method is described in Kawaguchi et al (2020) <doi:10.1002/sim.8438> and Li et al (2021) <doi:10.1016/j.jspi.2020.12.001>.
License: Apache License 2.0
Depends: R (≥ 3.2.2), Cyclops (≥ 3.0.0)
Imports: futile.logger, bit64
Suggests: testthat, survival, knitr, rmarkdown
Encoding: UTF-8
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-07-23 16:32:53 UTC; msuchard
Author: Marc A. Suchard [aut, cre], Eric Kawaguchi [aut], Ning Li [aut], Gang Li [aut], Observational Health Data Sciences and Informatics [cph]
Repository: CRAN
Date/Publication: 2025-07-23 17:10:02 UTC

Create a BAR Cyclops prior object

Description

createBarPrior creates a BAR Cyclops prior object for use with fitCyclopsModel.

Usage

createBarPrior(
  penalty = "bic",
  exclude = c(),
  forceIntercept = FALSE,
  fitBestSubset = FALSE,
  initialRidgeVariance = 10000,
  tolerance = 1e-08,
  maxIterations = 10000,
  threshold = 1e-06,
  delta = 0
)

Arguments

penalty

Specifies the BAR penalty; possible values are 'BIC' or 'AIC' or a numeric value

exclude

A vector of numbers or covariateId names to exclude from prior

forceIntercept

Logical: Force intercept coefficient into regularization

fitBestSubset

Logical: Fit final subset with no regularization

initialRidgeVariance

Numeric: variance used for algorithm initiation

tolerance

Numeric: maximum abs change in coefficient estimates from successive iterations to achieve convergence

maxIterations

Numeric: maxium iterations to achieve convergence

threshold

Numeric: absolute threshold at which to force coefficient to 0

delta

Numeric: change from 2 in ridge norm dimension

Value

A BAR Cyclops prior object of class inheriting from "cyclopsPrior" for use with fitCyclopsModel.

Examples

prior <- createBarPrior(penalty = "bic")


Create a fastBAR Cyclops prior object

Description

createFastBarPrior creates a fastBAR Cyclops prior object for use with fitCyclopsModel.

Usage

createFastBarPrior(
  penalty = 0,
  exclude = c(),
  forceIntercept = FALSE,
  fitBestSubset = FALSE,
  initialRidgeVariance = 10000,
  tolerance = 1e-08,
  maxIterations = 10000,
  threshold = 1e-06
)

Arguments

penalty

Specifies the BAR penalty

exclude

A vector of numbers or covariateId names to exclude from prior

forceIntercept

Logical: Force intercept coefficient into regularization

fitBestSubset

Logical: Fit final subset with no regularization

initialRidgeVariance

Numeric: variance used for algorithm initiation

tolerance

Numeric: maximum abs change in coefficient estimates from successive iterations to achieve convergence

maxIterations

Numeric: maximum iterations to achieve convergence

threshold

Numeric: absolute threshold at which to force coefficient to 0

Value

A BAR Cyclops prior object of class inheriting from "cyclopsPrior" for use with fitCyclopsModel.

Examples

nobs = 500; ncovs = 100
prior <- createFastBarPrior(penalty = log(ncovs), initialRidgeVariance = 1 / log(ncovs))

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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