Links[{"label":"MetaHunt.html","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/refman/MetaHunt.html"},{"label":"MetaHunt.pdf","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/MetaHunt.pdf"},{"label":"Choosing K and the denoising parameters","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/choosing-k-denoising.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/choosing-k-denoising.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/choosing-k-denoising.R"},{"label":"Conformal prediction with different choices","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/conformal-prediction.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/conformal-prediction.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/conformal-prediction.R"},{"label":"Preparing your data: from fitted models to F_hat","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/data-prep.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/data-prep.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/data-prep.R"},{"label":"Get started with MetaHunt","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/get-started.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/get-started.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/get-started.R"},{"label":"Understanding grid_weights and the L2(mu) norm","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/grid-weights.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/grid-weights.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/grid-weights.R"},{"label":"An introduction to MetaHunt","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/metahunt-intro.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/metahunt-intro.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/MetaHunt/vignettes/metahunt-intro.R"}]
TextReference manual: MetaHunt.html , MetaHunt.pdf Vignettes: Choosing K and the denoising parameters ( source , R code ) Conformal prediction with different choices ( source , R code ) Preparing your data: from fitted models to F_hat ( source , R code ) Get started with MetaHunt ( source , R code ) Understanding grid_weights and the L2(mu) norm ( source , R code ) An introduction to MetaHunt ( source , R code ) When to prefer minmax_regret ( source , R code ) Scalar summaries with wrapper ( source , R code )