version 0.2.0

DESCRIPTION

@@ -1,24 +1,24 @@
 Package: sgs
 Title: Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control
-Version: 0.1.1
-Date: 2023-08-21
+Version: 0.2.0
+Date: 2024-07-12
 Authors@R: c(person("Fabio", "Feser", role = c("aut", "cre"), email = "ff120@ic.ac.uk",comment = c(ORCID = "0009-0007-3088-9727")),
              person("Marina", "Evangelou", role =c("aut"), comment = c(ORCID = "0000-0003-0789-8944")))
 Maintainer: Fabio Feser <ff120@ic.ac.uk>
-Description: Implementation of Sparse-group SLOPE: Adaptive bi-level with FDR-control (Feser et al. (2023) <arXiv:2305.09467>). Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general adaptive three operator splitting (ATOS) implementation is provided.
+Description: Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023) <doi:10.48550/arXiv.2305.09467>) models. Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general adaptive three operator splitting (ATOS) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) <doi:10.1080/01621459.2017.1411269>) models are also implemented. Both gSLOPE and SGS are available with strong screening rules (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) for computational speed-up.
 Imports: Matrix, MASS, caret, grDevices, graphics, methods, stats,
         faux, SLOPE, Rlab, Rcpp (>= 1.0.10)
 LinkingTo: Rcpp, RcppArmadillo
-Suggests: SGL, gglasso, glmnet, testthat, knitr, rmarkdown
-RoxygenNote: 7.2.3
+Suggests: SGL, gglasso, glmnet, testthat, knitr, grpSLOPE, rmarkdown
+RoxygenNote: 7.3.1
 License: GPL (>= 3)
 Encoding: UTF-8
 URL: https://github.com/ff1201/sgs
 BugReports: https://github.com/ff1201/sgs/issues
 VignetteBuilder: knitr
 NeedsCompilation: yes
-Packaged: 2023-08-22 00:27:08 UTC; ff120
+Packaged: 2024-07-13 16:27:30 UTC; ff120
 Author: Fabio Feser [aut, cre] (<https://orcid.org/0009-0007-3088-9727>),
   Marina Evangelou [aut] (<https://orcid.org/0000-0003-0789-8944>)
 Repository: CRAN
-Date/Publication: 2023-08-22 15:50:05 UTC
+Date/Publication: 2024-07-14 03:30:02 UTC

MD5

@@ -1,46 +1,63 @@
-41f04f3ceb2bed6e60f0ff7c6b43dbd2 *DESCRIPTION
-60a598cbd20d896996be758fc8f686bf *NAMESPACE
-54c9a07458b1586f2c1935b7430fe401 *R/RcppExports.R
-d8905f227b448ca640c5cf27108464f4 *R/as_sgs.R
-fcf68dbe52f8cee45a45a1c819705466 *R/atos.R
-af2f82fbe9770fe903354e806ae35304 *R/fit_sgs.R
-3ea6177a7e40e000e5ec08e681d669e0 *R/fit_sgs_cv.R
-b122ba3213244e067923fc78b159bf6b *R/generate_penalties.R
-b8e1d5e17b343b4feb753fdd674250f2 *R/plot.R
-962fffc4117c0ce7a5b42f474f7ed64e *R/predict.R
-4204a670034f03cdecd9fc2fc1dd884e *R/print.R
-570e2da69afaaccb31bbfbf93dfef782 *R/run_algorithm.R
-64606f63ba222dec353312e9560bf258 *R/scaled_sgs.R
-42e65df8f77bf74da046b0db111ead3c *R/sgs-package.R
-d91c3fe4306f092959602e215fcb90b8 *R/toy_data_gen.R
-63dba4c996781dc3159885e5afd09d4a *R/utils.R
-7f3379241215d54d0529e447fa5660ff *README.md
-7b7cad71301e94a254becef0005e40c2 *build/vignette.rds
-98fa7392816d242dfe5179e06208cb45 *inst/doc/reproducible_example.R
-35f6319240d8381955c1328c08a88be7 *inst/doc/reproducible_example.Rmd
-e2aac27a20f18961d7d368adcedf24e6 *inst/doc/reproducible_example.html
-72f48781956f4c15a2ca2ce6217d0ee2 *man/arma_mv.Rd
-4815259a00ae488f218717fc2d7d6f7c *man/as_sgs.Rd
-05317e8cd4e42908c3a13b6c78ac75e6 *man/atos.Rd
+1a5a0e2bf982c5b7a4a214ad6f944029 *DESCRIPTION
+d03953ea826e47f15fc28d6365ec4ee2 *NAMESPACE
+352cd125b1144bfdd43ed845e47f1ec8 *R/RcppExports.R
+44bf298c10b0610c3474bfbe48814215 *R/as_sgs.R
+978083a366f89cefe63215afe90e355f *R/atos.R
+08c2eddf0e94c40c6c8a45bf841ba215 *R/coef.R
+1d9e3b92d6f232e2b6334b37b74090d9 *R/fit_gslope.R
+e3426f13d822f0b32a9314eaf1e94cbe *R/fit_gslope_cv.R
+662c995662a94ed60f54d78cec0aaed9 *R/fit_sgs.R
+f3d06b5ac21c2d0614098e7a150a9a78 *R/fit_sgs_cv.R
+55c3ab2ac2dceda7d22a75c174deb8b1 *R/fitting_code.R
+47bf23d59f3be960292f32efce1475e8 *R/gen_pens.R
+365aed3dc4c1420f9501fa0ed3ff9b92 *R/gen_toy_data.R
+21795985dbbf7655ed194ec29ff0aba6 *R/global_wrapper_code.R
+1a05f38cf2bd3b2484dda2b4990b4721 *R/plot.R
+ab4086db6130a411fa7c8130632d839a *R/predict.R
+8b52457fb021add713ca16a4d3b60bd6 *R/print.R
+8d5cb1b8d7e53c4140de87a5a269ed1f *R/run_algorithm.R
+7dce9e7d29f83979faeefbee736e1952 *R/scaled_sgs.R
+a894b146f88476cd209614e8b291f6af *R/screen.R
+e1724a7f1bbb01edc2123693482829a0 *R/sgs-package.R
+b7393ea43b8ae1848864f31ed854b4a4 *R/utils.R
+8f4a27ae5cf15f06ccdbf430f5eb10bc *README.md
+b943a1546a1e17be4eea235ec7b4616b *build/partial.rdb
+37dae1501b064d9c258aad49bdbfd9db *build/vignette.rds
+229d501815aea21d96d286205915c7e5 *inst/CITATION
+c950fd9c140ae8aeca013f0bc29e2016 *inst/doc/reproducible_example.R
+f8fff4e6dc263f3dcad7df24536c215c *inst/doc/reproducible_example.Rmd
+146372a0a34478277ffc6dbef8f90826 *inst/doc/reproducible_example.html
+487d2370e75dbe87f9b49248539304de *man/arma_mv.Rd
+4c130eed8f19f0b6332c5b391dace25e *man/arma_sparse.Rd
+b4e52c94232685711b6d6036056e7243 *man/as_sgs.Rd
+805e1870db43608583af9c6fc04ebf32 *man/atos.Rd
+f7f32b7bdcad78bfd1b951bb6bd22cbe *man/coef.sgs.Rd
+d959d3ace2c9e49c1dea92118f377ded *man/figures/README-gslope-ex.avif
+7a0754f80f5d9b918e758244c3a06fc6 *man/figures/README-sgs-ex.avif
 5d106fc5d3217841391e637def995d67 *man/figures/logo.avif
-ab5defe0d8f24a5ef8c0627c532b36fe *man/fit_sgs.Rd
-65718d44eed017b0db354e26905816d8 *man/fit_sgs_cv.Rd
-8e46c6515e4f776ef4cc3d87fbf25910 *man/generate_penalties.Rd
-6e69dea61807a40c767a4bb444470557 *man/generate_toy_data.Rd
-c8b81f9739883542e4922748d006a588 *man/plot.sgs_cv.Rd
-06c7a819715a7bb96985b0d15c7fa1d0 *man/predict.sgs.Rd
-79550c96b3ef9391b22cf64ac4da7e08 *man/print.sgs.Rd
-7ff1604ce79b51ad64cc5820149ec21b *man/scaled_sgs.Rd
-959cf084e38d1fc7a00ca72b92c6f0bd *man/sgs-package.Rd
+88670bc3b0a978d95d12ceab3bc5073b *man/fit_gslope.Rd
+74211b01aae7350bb0fee676bcc2e068 *man/fit_gslope_cv.Rd
+e3ad1029e757c5df15e965e851ed7d2a *man/fit_sgs.Rd
+d04c0284f21ffb147233403734058c9d *man/fit_sgs_cv.Rd
+2aba4b85d18f81c373aba61bea70b5cf *man/gen_pens.Rd
+25f4a283131bdfe63df8a18d234e1751 *man/gen_toy_data.Rd
+15f6b8fd9f09e4404c06fdf93922edbf *man/plot.sgs.Rd
+85ed9b856d37cd27ced5fab55b868fc3 *man/predict.sgs.Rd
+0b593256b2aacb69bf9182d96f82ae5f *man/print.sgs.Rd
+97d62d14cb60a72d986da9efe955ae9e *man/scaled_sgs.Rd
+004a0957f275d9da61719adca849dc12 *man/sgs-package.Rd
 e62585629948e7dd143efbad43c2a6eb *src/Makevars
 e62585629948e7dd143efbad43c2a6eb *src/Makevars.win
-9c30da88cbaa6afc153cd482bd54933c *src/RcppExports.cpp
+213c5cb947b94277d87e8387068bfa06 *src/RcppExports.cpp
 71fd29df2537d26495382069b643026e *src/arma_mv.cpp
+22b8b0a7eb25387c0b5c516be8e404d1 *src/arma_sparse.cpp
 2724a5e2c41759c08f64a4dab2fb2aa1 *tests/testthat.R
-4ac919de7fb8713b33ddc5eb95585f59 *tests/testthat/test-gglasso.R
-ffabd5e4bbf22e5087ed0516f970cfdd *tests/testthat/test-lasso.R
+d7f8bc660711b1c0464b2a19b475656e *tests/testthat/test-gglasso.R
+fc7227abb364bccb0c25c04058e55696 *tests/testthat/test-grpslope.R
+c91e7930a636d3750c2a02b768d1d3dd *tests/testthat/test-lasso.R
 a280a866d80cdaa358423adb710f9d05 *tests/testthat/test-logistic.R
-a16a7ddf8bfbc6348b77ef651faf8a2b *tests/testthat/test-ols.R
-32d5a8cacc539719ce106f55ca102ab2 *tests/testthat/test-sgl.R
-b37b64ab6b7afbce34df963a8e1eb948 *tests/testthat/test-slope.R
-35f6319240d8381955c1328c08a88be7 *vignettes/reproducible_example.Rmd
+a76c2867d4751ac106a2a3a10031e70d *tests/testthat/test-ols.R
+8d8cb2e7a91623d7288c03baefe82042 *tests/testthat/test-screen.R
+9178e21bfe4d8d54dd2b417b905d4bce *tests/testthat/test-sgl.R
+1e8f926ae8125a102ae476baebcd7a35 *tests/testthat/test-slope.R
+f8fff4e6dc263f3dcad7df24536c215c *vignettes/reproducible_example.Rmd

NAMESPACE

@@ -1,16 +1,31 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(coef,gslope)
+S3method(coef,gslope_cv)
+S3method(coef,sgs)
+S3method(coef,sgs_cv)
+S3method(plot,gslope)
+S3method(plot,gslope_cv)
+S3method(plot,sgs)
 S3method(plot,sgs_cv)
+S3method(predict,gslope)
+S3method(predict,gslope_cv)
 S3method(predict,sgs)
+S3method(predict,sgs_cv)
+S3method(print,gslope)
+S3method(print,gslope_cv)
 S3method(print,sgs)
 S3method(print,sgs_cv)
 export(arma_mv)
+export(arma_sparse)
 export(as_sgs)
 export(atos)
+export(fit_gslope)
+export(fit_gslope_cv)
 export(fit_sgs)
 export(fit_sgs_cv)
-export(generate_penalties)
-export(generate_toy_data)
+export(gen_pens)
+export(gen_toy_data)
 export(scaled_sgs)
 importFrom(MASS,mvrnorm)
 importFrom(Matrix,as.matrix)
@@ -26,6 +41,7 @@ importFrom(methods,is)
 importFrom(stats,ecdf)
 importFrom(stats,pchisq)
 importFrom(stats,pnorm)
+importFrom(stats,prcomp)
 importFrom(stats,qchisq)
 importFrom(stats,qnorm)
 importFrom(stats,quantile)

R/RcppExports.R

@@ -11,3 +11,13 @@ arma_mv <- function(m, v) {
     .Call(`_sgs_arma_mv`, m, v)
 }
 
+#' Matrix Product in RcppArmadillo.
+#'
+#' @param m numeric sparse matrix
+#' @param v numeric vector
+#' @return matrix product of m and v
+#' @export
+arma_sparse <- function(m, v) {
+    .Call(`_sgs_arma_sparse`, m, v)
+}
+

R/as_sgs.R

@@ -18,9 +18,9 @@
 #
 ###############################################################################
 
-#' fits the adaptively scaled SGS model (AS-SGS)
+#' Fits the adaptively scaled SGS model (AS-SGS).
 #'
-#' Fits an SGS model using the noise estimation procedure, termed adaptively scaled SGS (Algorithm 2 from Feser et al (2023)). 
+#' Fits an SGS model using the noise estimation procedure, termed adaptively scaled SGS (Algorithm 2 from Feser and Evangelou (2023)). 
 #' This adaptively estimates \eqn{\lambda} and then fits the model using the estimated value. It is an alternative approach to 
 #' cross-validation ([fit_sgs_cv()]). The approach is only compatible with the SGS penalties.
 #'
@@ -44,38 +44,40 @@
 #' 
 #' @return An object of type \code{"sgs"} containing model fit information (see [fit_sgs()]). 
 #'
-#' @references F. Feser, M. Evangelou \emph{Sparse-group SLOPE: adaptive bi-level selection with FDR-control}, \url{https://arxiv.org/abs/2305.09467}
+#' @seealso [scaled_sgs()]
+#' @family model-selection
+#' @family SGS-methods
+#' 
+#' @references Feser, F., Evangelou, M. (2023). \emph{Sparse-group SLOPE: adaptive bi-level selection with FDR-control}, \url{https://arxiv.org/abs/2305.09467}
 #' @export
 
 as_sgs <- function(X, y, groups, type="linear", pen_method = 2, alpha=0.95, vFDR=0.1, gFDR=0.1, standardise="l2", intercept=TRUE, verbose=FALSE){
-    num_obs=dim(X)[1]
+    num_obs=nrow(X)
     if (intercept) {
-        selected <- 1
-        X_2 = cbind(1,X)
+        selected = 1
+        X_2 = Matrix::cbind2(1,X)
         y_1 = y-mean(y)
     } else {
-        selected <- integer(0)
+        selected = integer(0)
     }
-    out=standardise_sgs(X=X,y=y,standardise,intercept,dim(X)[1])
+    out=standardise_data(X=X,y=y,standardise,intercept,nrow(X))
     selected_prev = 1000
     attempts = 0
     repeat {
         selected_prev_2 = selected_prev
-        selected_prev <- selected
-
+        selected_prev = selected
 
-        noise_est <- estimateNoise(X_2[, selected], y_1, intercept)
-        pens_out = generate_penalties_2(gFDR, vFDR, pen_method=pen_method,groups,alpha,lambda = noise_est)
+        noise_est = estimateNoise(X_2[, selected], y_1, intercept)
+        pens_out = gen_pens_as_sgs(gFDR, vFDR, pen_method=pen_method,groups,alpha,lambda = noise_est)
 
-        fit <- fit_sgs(X=X, y=y, groups=groups, pen_method=pen_method, type, lambda=noise_est*out$scale_pen, alpha=alpha, vFDR=vFDR, gFDR=gFDR,intercept=intercept,
-                       v_weights=pens_out$pen_slope_org,w_weights=pens_out$pen_gslope_org,standardise=standardise)
+        fit = fit_sgs(X=X, y=y, groups=groups, pen_method=pen_method, type, lambda=noise_est*out$scale_pen, alpha=alpha, vFDR=vFDR, gFDR=gFDR,intercept=intercept,
+                       v_weights=pens_out$pen_slope_org,w_weights=pens_out$pen_gslope_org,standardise=standardise, screen = FALSE)
 
-        selected <- fit$selected_var
         if (intercept) {
-            selected <- union(1, selected+1)
+            selected = union(1, selected+1)
         }
 
-        if (identical(selected, selected_prev) |identical(selected, selected_prev_2)) {
+        if (identical(selected, selected_prev) | identical(selected, selected_prev_2)) {
             break
         }
         if (length(selected) + 1 >= num_obs) {