Package: EAinference 0.2.5

EAinference: Estimator Augmentation and Simulation-Based Inference

Estimator augmentation methods for statistical inference on high-dimensional data, as described in Zhou, Q. (2014) <arxiv:1401.4425v2> and Zhou, Q. and Min, S. (2017) <doi:10.1214/17-EJS1309>. It provides several simulation-based inference methods: (a) Gaussian and wild multiplier bootstrap for lasso, group lasso, scaled lasso, scaled group lasso and their de-biased estimators, (b) importance sampler for approximating p-values in these methods, (c) Markov chain Monte Carlo lasso sampler with applications in post-selection inference.

Authors:Seunghyun Min [aut, cre], Qing Zhou [aut]

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EAinference.pdf |EAinference.html
EAinference/json (API)

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

Peer review:

Bug tracker:https://github.com/seunghyunmin/eainference/issues

On CRAN:

3.11 score 13 scripts 157 downloads 7 exports 34 dependencies

Last updated 6 years agofrom:f6e6ff03d4. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-winNOTEOct 30 2024
R-4.5-linuxNOTEOct 30 2024
R-4.4-winNOTEOct 30 2024
R-4.4-macNOTEOct 30 2024
R-4.3-winOKOct 30 2024
R-4.3-macOKOct 30 2024

Exports:cv.lassohdISlassoFitMHLSPB.CIPBsamplerpostInference.MHLS

Dependencies:clicodetoolsexpmfansiforeachgenericsgglassoglmnetgluehdiiteratorslarslatticelifecyclelimSolvelinproglpSolvemagrittrMASSMatrixmsmmvtnormpillarpkgconfigquadprogRcppRcppEigenrlangscalregshapesurvivaltibbleutf8vctrs

Introduction to the EAinference package

Rendered fromEAlasso.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2019-04-18
Started: 2017-06-07