added 4 vignettes

.Rbuildignore

@@ -12,3 +12,4 @@ README.qmd
 ^memcheck
 ^\.positai$
 ^\.claude$
+^vignettes/*_files$

.gitignore

@@ -62,3 +62,4 @@ docs
 .vscode/
 tools/
 .positai
+inst/doc

DESCRIPTION

@@ -5,10 +5,10 @@ Version: 1.0-0
 Date: 2026-05-xx
 Authors@R: person("Julien", "Chiquet", , "julien.chiquet@inrae.fr", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-3629-3429"))
-Description: This package fits the solution paths of classical sparse regression 
-  models with efficient active set algorithms by solving small sub-problems. 
-  Include LASSO, SCAD, MCP, (Sparse) Group-LASSO, Cooperative-LASSO, LAVA and 
-  Elastic-Net variants. Also provides a few methods for model selection purpose 
+Description: This package fits the solution paths of classical sparse regression
+  models with efficient active set algorithms by solving small sub-problems.
+  Include LASSO, SCAD, MCP, (Sparse) Group-LASSO, Cooperative-LASSO, LAVA and
+  Elastic-Net variants. Also provides a few methods for model selection purpose
   (cross-validation, stability selection).
 URL: https://github.com/jchiquet/quadrupen, https://jchiquet.github.io/quadrupen/
 BugReports: https://github.com/jchiquet/quadrupen/issues
@@ -36,11 +36,14 @@ Suggests:
     elasticnet,
     glmnet,
     igraph,
+    knitr,
     lars,
     MASS,
     ncvreg,
+    grpreg,
+    rmarkdown,
     testthat
-LinkingTo: 
+LinkingTo:
     Rcpp,
     RcppArmadillo
 Collate:
@@ -67,3 +70,4 @@ Collate:
 Packaged: 2013-03-25 19:41:00 UTC; jchiquet
 Roxygen: list(markdown = TRUE)
 Config/roxygen2/version: 8.0.0
+VignetteBuilder: knitr

NEWS.md

@@ -1,6 +1,6 @@
 NEWS/Changelog
 
-# quadrupen dev version	(2026-05-xx)
+# quadrupen dev version	(2026-06-xx)
 
 - major updates
   - complete rewriting of R code using R6 classes
@@ -13,6 +13,7 @@ NEWS/Changelog
   - added mcp and scad (concave penalties, Chernozukov et al, 2017)
   - added refit version of Lasso/Elastic-Net ("relaxed" Lasso/Enet)
   - changing many parameters (badly) named, do not expect backward compatibility
+  - added vignettes
 - minor updates
   - Integration of changes from CRAN versions from 0.2-4 to 0.2-13  
   - set up github workflow for pkgdown page

R/CrossValidation-R6Class.R

@@ -77,9 +77,10 @@ CrossValidation <- R6::R6Class(
     print = function() { self$show() },
     #' @description Plot 1-dimensional cross-validation
     #' @param log_scale logical, should a log-scale be used for the x-axis
+    #' @param se logical, should confidence band be displayed (TRUE by default)
     #' @param title graph title
     #' @return a ggplot object
-    plotCV_1D = function(log_scale=TRUE, title = "Cross-validation error") {
+    plotCV_1D = function(log_scale=TRUE, title = "Cross-validation error", se = TRUE) {
       ## SIMPLE CROSS-VALIDATION GRAPH
       if (length(self$lambda1) > length(self$lambda2)) {
         d <- ggplot(self$data, aes(x=.data$lambda1,y=.data$mean)) + theme_bw()
@@ -97,7 +98,7 @@ CrossValidation <- R6::R6Class(
       d <- d + xlab(xlab) + ylab("Mean square error") +
         geom_point(alpha=0.3) +
         geom_smooth(aes(ymin=.data$mean-.data$serr, ymax=.data$mean+.data$serr, group=as.factor(.data$lambda2),
-                        colour=as.factor(.data$lambda2)), data=self$data, alpha=0.2, stat="identity")
+                        colour=as.factor(.data$lambda2)), se=se, data=self$data, alpha=0.2, stat="identity")
       if (log_scale) {
         d <- d + scale_x_log10() + annotation_logticks(sides="b")
       }

R/QuadrupenFit-R6Class.R

@@ -540,7 +540,7 @@ QuadrupenFit <- R6::R6Class(
               "path"      = self$plot_path(log_scale = log_scale, labels = labels),
               "criteria"  = self$information_criteria$plot(log_scale = log_scale),
               "crossval"  = self$cross_validation$plot(log_scale = log_scale),
-              "stability" = self$stability_path$plot(log_scale = log_scale, labels = labels)
+              "stability" = self$stability_path$plot(labels = labels)
       )
       d
 

R/StabilityPath-R6Class.R

@@ -91,8 +91,6 @@ StabilityPath <- R6::R6Class(
     #' (first penalty level) or `"fraction"` (fraction of the
     #' penalty level applied tune by \eqn{\lambda_1}{lambda1}). Default
     #' is `"lambda"`.
-    #' @param log_scale logical; indicates if a log-scale should be used
-    #' when \code{xvar="lambda"}. Default is `TRUE`.
     #' @param plot logical; indicates if the graph should be
     #' plotted. Default is `TRUE`. If `FALSE`, only the
     #' \pkg{ggplot2} object is sent back.
@@ -135,7 +133,7 @@ StabilityPath <- R6::R6Class(
     #' 
     plot = function(
       xvar = "lambda", title = "Stability path", labels = rep("unknown status",self$nvar), 
-      sel_mode = c("rank", "PFER"), cutoff=0.75, PFER=2, nvarsel=floor(self$nobs/log(self$nvar))) {
+      sel_mode = c("rank", "PFER"), cutoff=0.75, PFER=2, nvarsel=min(self$nvar, floor(self$nobs/log(self$nvar)))) {
 
       sel_mode <- match.arg(sel_mode)
       selection <- rep("unselected",length(self$nonzero))

_pkgdown.yml

@@ -4,6 +4,18 @@ template:
   mathjax: true
   math-rendering: mathjax
 
+articles:
+- title: "Getting started"
+  navbar: ~
+  contents:
+  - sparse-regression
+  - group-sparse-regression
+- title: "Advanced topics"
+  navbar: ~
+  contents:
+  - structured-signal-recovery
+  - lava
+
 reference:
 - title: "Main functions for user"
   desc: >

inst/examples/ridge.R

@@ -1,4 +1,5 @@
 library(quadrupen)
+require(Matrix)
 
 beta <- rep(c(0,1,0,-1,0), c(25,10,25,10,25))
 cor <- 0.75
@@ -10,39 +11,51 @@ n <- 50
 x <- as.matrix(matrix(rnorm(95*n),n,95) %*% chol(Sigma))
 y <- 10 + x %*% beta + rnorm(n,0,10)
 p <- ncol(x)
+plot(beta, type = "s")
 
-
-## neighborhood prior
-C <- bandSparse(p,k=0:1,diagonals=list(rep(1,p),rep(-1,p-1)))
-L1 <-  t(C) %*% C
-
-## clique prior
+## Structuring matrix :
+# Laplacian of a community graph
+# (clique prior)
 Ioo <- matrix(1,25,25)
 Iww <- matrix(1,10,10)
 A <- bdiag(Ioo,Iww,Ioo,Iww,Ioo)
 diag(A) <- 0
 L2 <- -A
 diag(L2) <- colSums(A) + 1e-2
 
-ridge.classical <- ridge(x,y,intercept=F)
-plot(ridge.classical)
-ridge.neighborhood <- ridge(x,y, struct=L1, lambda_max=1000)
-plot(ridge.neighborhood)
-ridge.block <- ridge(x,y, struct=L2, lambda_max=1000)
-plot(ridge.block)
-
-
+labels <- paste("segment", rep(1:5, c(25,10,25,10,25)), sep="-" )
+lasso_fit <- lasso(x,y,intercept=F)
+plot(lasso_fit, labels=labels)
+fused_lasso_fit <- fused_lasso(x,y,lambda2=5,intercept=F)
+plot(fused_lasso_fit, labels=labels)
+ridge_fit <- ridge(x,y,intercept=F)
+plot(ridge_fit, labels=labels)
+ridge_struct <- ridge(x,y, struct=L2, lambda_max=1000)
+plot(ridge_struct, labels=labels)
+enet_fit <- elastic_net(x,y, lambda2 = 5, intercept=F)
+plot(enet_fit, labels=labels)
+enet_struct <- elastic_net(x,y, lambda2 = 5, struct=L2)
+plot(enet_struct, labels=labels)
+enet_struct$debias <- TRUE
+plot(enet_struct, labels=labels)
+
+beta_lasso <- as.numeric(lasso(x,y,lambda=10)$coefficients)
+beta_fused_lasso <- as.numeric(fused_lasso(x,y,lambda1=20, lambda2 = 5)$coefficients)
 beta_ridge <- as.numeric(ridge(x,y,lambda=1)$coefficients)
-beta_ridge_neighbor <- as.numeric(ridge(x,y,lambda=10,struct=L1)$coefficients)
-beta_ridge_block <- as.numeric(ridge(x,y,lambda=10,struct=L2)$coefficients)
-beta_enet_block  <- as.numeric(elastic_net(x,y,lambda2=5,lambda1=20,struct=L2,refit=TRUE)$coefficients)
+beta_ridge_struct <- as.numeric(ridge(x,y,lambda=10,struct=L2)$coefficients)
+beta_enet_struct  <- as.numeric(elastic_net(x,y,lambda2=5,lambda1=20,struct=L2)$coefficients)
+beta_enet_struct_refit  <- as.numeric(elastic_net(x,y,lambda2=5,lambda1=20,struct=L2,refit=TRUE)$coefficients)
 
-par(mfrow=c(2,2))
+par(mfrow=c(3,2))
+plot(beta_lasso, type="b", ylim=c(-1,1))
+points(beta, pch="+",col="red")
+plot(beta_fused_lasso, type="b", ylim=c(-1,1))
+points(beta, pch="+",col="red")
 plot(beta_ridge, type="b", ylim=c(-1,1))
 points(beta, pch="+",col="red")
-plot(beta_ridge_neighbor, type="b", ylim=c(-1,1))
+plot(beta_ridge_struct, type="b", ylim=c(-1,1))
 points(beta, pch="+",col="red")
-plot(beta_ridge_block, type="b", ylim=c(-1,1))
+plot(beta_enet_struct, type="b", ylim=c(-1,1))
 points(beta, pch="+",col="red")
-plot(beta_enet_block, type="b", ylim=c(-1,1))
+plot(beta_enet_struct_refit, type="b", ylim=c(-1,1))
 points(beta, pch="+",col="red")