Package: smoothedLasso 1.6

smoothedLasso: A Framework to Smooth L1 Penalized Regression Operators using Nesterov Smoothing

We provide full functionality to smooth L1 penalized regression operators and to compute regression estimates thereof. For this, the objective function of a user-specified regression operator is first smoothed using Nesterov smoothing (see Y. Nesterov (2005) <doi:10.1007/s10107-004-0552-5>), resulting in a modified objective function with explicit gradients everywhere. The smoothed objective function and its gradient are minimized via BFGS, and the obtained minimizer is returned. Using Nesterov smoothing, the smoothed objective function can be made arbitrarily close to the original (unsmoothed) one. In particular, the Nesterov approach has the advantage that it comes with explicit accuracy bounds, both on the L1/L2 difference of the unsmoothed to the smoothed objective functions as well as on their respective minimizers (see G. Hahn, S.M. Lutz, N. Laha, C. Lange (2020) <doi:10.1101/2020.09.17.301788>). A progressive smoothing approach is provided which iteratively smoothes the objective function, resulting in more stable regression estimates. A function to perform cross validation for selection of the regularization parameter is provided.

Authors:Georg Hahn [aut,cre], Sharon M. Lutz [ctb], Nilanjana Laha [ctb], Christoph Lange [ctb]

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smoothedLasso.pdf |smoothedLasso.html✨
smoothedLasso/json (API)

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

Peer review:

On CRAN:

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

12 exports 0.00 score 4 dependencies 14 scripts 248 downloads

Last updated 3 years agofrom:b42768cc82. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 13 2024
R-4.5-winOKSep 13 2024
R-4.5-linuxOKSep 13 2024
R-4.4-winOKSep 13 2024
R-4.4-macOKSep 13 2024
R-4.3-winOKSep 13 2024
R-4.3-macOKSep 13 2024

Exports:crossvalidationelasticNetfusedLassographicalLassominimizeFunctionminimizeSmoothedSequenceobjFunctionobjFunctionGradientobjFunctionSmoothobjFunctionSmoothGradientprsLassostandardLasso

Dependencies:latticeMatrixrbibutilsRdpack

Readme and manuals

Help Manual

Help pageTopics
Perform cross validation to select the regularization parameter.crossvalidation
Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the elastic net.elasticNet
Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the fused Lasso.fusedLasso
Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the graphical Lasso.graphicalLasso
Minimize the objective function of an unsmoothed or smoothed regression operator with respect to betavector using BFGS.minimizeFunction
Minimize the objective function of a smoothed regression operator with respect to betavector using the progressive smoothing algorithm.minimizeSmoothedSequence
Auxiliary function to define the objective function of an L1 penalized regression operator.objFunction
Auxiliary function which computes the (non-smooth) gradient of an L1 penalized regression operator.objFunctionGradient
Auxiliary function to define the objective function of the smoothed L1 penalized regression operator.objFunctionSmooth
Auxiliary function which computes the gradient of the smoothed L1 penalized regression operator.objFunctionSmoothGradient
Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the Lasso for polygenic risk scores (prs).prsLasso
Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the Lasso.standardLasso