diff --git a/inst/examples/MADMMplasso_example.R b/inst/examples/MADMMplasso_example.R
index af035ff..03cdb81 100644
--- a/inst/examples/MADMMplasso_example.R
+++ b/inst/examples/MADMMplasso_example.R
@@ -82,7 +82,7 @@ tol <- 1E-3
 fit <- MADMMplasso(
   X, Z, y,
   alpha = alpha, my_lambda = matrix(rep(0.2, ncol(y)), 1),
-  lambda_min = 0.001, max_it = 5000, e.abs = e.abs, e.rel = e.rel, maxgrid = nlambda,
-  nlambda = nlambda, rho = 5, tree = TT, my_print = FALSE, alph = TRUE,
-  pal = TRUE, gg = gg1, tol = tol, cl = 6
+  lambda_min = 0.001, max_it = 1000, e.abs = e.abs, e.rel = e.rel,
+  maxgrid = nlambda, nlambda = nlambda, rho = 5, tree = TT, my_print = FALSE,
+  alph = TRUE, gg = gg1, tol = tol, cl = 2L
 )
diff --git a/inst/examples/cv_MADMMplasso_example.R b/inst/examples/cv_MADMMplasso_example.R
index 269b986..25d0f72 100644
--- a/inst/examples/cv_MADMMplasso_example.R
+++ b/inst/examples/cv_MADMMplasso_example.R
@@ -1,97 +1,91 @@
- # nolint start: indentation_linter
-\donttest{
-  # Train the model
-  # generate some data
-  set.seed(1235)
-  N <- 100
-  p <- 50
-  nz <- 4
-  K <- nz
-  X <- matrix(rnorm(n = N * p), nrow = N, ncol = p)
-  mx <- colMeans(X)
-  sx <- sqrt(apply(X, 2, var))
-  X <- scale(X, mx, sx)
-  X <- matrix(as.numeric(X), N, p)
-  Z <- matrix(rnorm(N * nz), N, nz)
-  mz <- colMeans(Z)
-  sz <- sqrt(apply(Z, 2, var))
-  Z <- scale(Z, mz, sz)
-  beta_1 <- rep(x = 0, times = p)
-  beta_2 <- rep(x = 0, times = p)
-  beta_3 <- rep(x = 0, times = p)
-  beta_4 <- rep(x = 0, times = p)
-  beta_5 <- rep(x = 0, times = p)
-  beta_6 <- rep(x = 0, times = p)
+# Train the model
+# generate some data
+set.seed(1235)
+N <- 100
+p <- 50
+nz <- 4
+K <- nz
+X <- matrix(rnorm(n = N * p), nrow = N, ncol = p)
+mx <- colMeans(X)
+sx <- sqrt(apply(X, 2, var))
+X <- scale(X, mx, sx)
+X <- matrix(as.numeric(X), N, p)
+Z <- matrix(rnorm(N * nz), N, nz)
+mz <- colMeans(Z)
+sz <- sqrt(apply(Z, 2, var))
+Z <- scale(Z, mz, sz)
+beta_1 <- rep(x = 0, times = p)
+beta_2 <- rep(x = 0, times = p)
+beta_3 <- rep(x = 0, times = p)
+beta_4 <- rep(x = 0, times = p)
+beta_5 <- rep(x = 0, times = p)
+beta_6 <- rep(x = 0, times = p)
 
-  beta_1[1:5] <- c(2, 2, 2, 2, 2)
-  beta_2[1:5] <- c(2, 2, 2, 2, 2)
-  beta_3[6:10] <- c(2, 2, 2, -2, -2)
-  beta_4[6:10] <- c(2, 2, 2, -2, -2)
-  beta_5[11:15] <- c(-2, -2, -2, -2, -2)
-  beta_6[11:15] <- c(-2, -2, -2, -2, -2)
+beta_1[1:5] <- c(2, 2, 2, 2, 2)
+beta_2[1:5] <- c(2, 2, 2, 2, 2)
+beta_3[6:10] <- c(2, 2, 2, -2, -2)
+beta_4[6:10] <- c(2, 2, 2, -2, -2)
+beta_5[11:15] <- c(-2, -2, -2, -2, -2)
+beta_6[11:15] <- c(-2, -2, -2, -2, -2)
 
-  Beta <- cbind(beta_1, beta_2, beta_3, beta_4, beta_5, beta_6)
-  colnames(Beta) <- c(1:6)
+Beta <- cbind(beta_1, beta_2, beta_3, beta_4, beta_5, beta_6)
+colnames(Beta) <- c(1:6)
 
-  theta <- array(0, c(p, K, 6))
-  theta[1, 1, 1] <- 2
-  theta[3, 2, 1] <- 2
-  theta[4, 3, 1] <- -2
-  theta[5, 4, 1] <- -2
-  theta[1, 1, 2] <- 2
-  theta[3, 2, 2] <- 2
-  theta[4, 3, 2] <- -2
-  theta[5, 4, 2] <- -2
-  theta[6, 1, 3] <- 2
-  theta[8, 2, 3] <- 2
-  theta[9, 3, 3] <- -2
-  theta[10, 4, 3] <- -2
-  theta[6, 1, 4] <- 2
-  theta[8, 2, 4] <- 2
-  theta[9, 3, 4] <- -2
-  theta[10, 4, 4] <- -2
-  theta[11, 1, 5] <- 2
-  theta[13, 2, 5] <- 2
-  theta[14, 3, 5] <- -2
-  theta[15, 4, 5] <- -2
-  theta[11, 1, 6] <- 2
-  theta[13, 2, 6] <- 2
-  theta[14, 3, 6] <- -2
-  theta[15, 4, 6] <- -2
+theta <- array(0, c(p, K, 6))
+theta[1, 1, 1] <- 2
+theta[3, 2, 1] <- 2
+theta[4, 3, 1] <- -2
+theta[5, 4, 1] <- -2
+theta[1, 1, 2] <- 2
+theta[3, 2, 2] <- 2
+theta[4, 3, 2] <- -2
+theta[5, 4, 2] <- -2
+theta[6, 1, 3] <- 2
+theta[8, 2, 3] <- 2
+theta[9, 3, 3] <- -2
+theta[10, 4, 3] <- -2
+theta[6, 1, 4] <- 2
+theta[8, 2, 4] <- 2
+theta[9, 3, 4] <- -2
+theta[10, 4, 4] <- -2
+theta[11, 1, 5] <- 2
+theta[13, 2, 5] <- 2
+theta[14, 3, 5] <- -2
+theta[15, 4, 5] <- -2
+theta[11, 1, 6] <- 2
+theta[13, 2, 6] <- 2
+theta[14, 3, 6] <- -2
+theta[15, 4, 6] <- -2
 
-  pliable = matrix(0,N,6)
-  for (e in 1:6) {
-    pliable[,e]<-	compute_pliable(X, Z, theta[,,e])
-  }
-
-  esd<-diag(6)
-  e<-MASS::mvrnorm(N,mu=rep(0,6),Sigma=esd)
-  y_train<-X%*%Beta+pliable+e
-  y=y_train
+pliable <- matrix(0, N, 6)
+for (e in 1:6) {
+  pliable[, e] <- compute_pliable(X, Z, theta[, , e])
+}
 
-  colnames(y)<- c( paste("y",1:(ncol(y)),sep = "") )
-  TT=tree_parms(y)
-  plot(TT$h_clust)
-  gg1=matrix(0,2,2)
-  gg1[1,]<-c(0.02,0.02)
-  gg1[2,]<-c(0.02,0.02)
-  nlambda = 50
-  e.abs=1E-4
-  e.rel=1E-2
-  alpha=.2
-  tol=1E-3
-  fit <- MADMMplasso(
-    X, Z, y, alpha=alpha, my_lambda=NULL, lambda_min=0.001, max_it=100,
-    e.abs=e.abs, e.rel=e.rel, maxgrid=50, nlambda=nlambda, rho=5,tree=TT,
-    my_print=FALSE, alph=1, gg=gg1, tol=tol, cl=2L
-  )
-  gg1=fit$gg
+esd <- diag(6)
+e <- MASS::mvrnorm(N, mu = rep(0, 6), Sigma = esd)
+y_train <- X %*% Beta + pliable + e
+y <- y_train
 
-  cv_admp <- cv_MADMMplasso(
-    fit, nfolds=5, X, Z, y, alpha=alpha, lambda=fit$Lambdas, max_it=100,
-    e.abs=e.abs, e.rel=e.rel, nlambda, rho=5, my_print=FALSE, alph=1,
-    foldid=NULL, gg=gg1, TT=TT, tol=tol
-  )
-  plot(cv_admp)
-}
-# nolint end: indentation_linter
+colnames(y) <- c(paste("y", 1:(ncol(y)), sep = ""))
+TT <- tree_parms(y)
+plot(TT$h_clust)
+gg1 <- matrix(0, 2, 2)
+gg1[1, ] <- c(0.02, 0.02)
+gg1[2, ] <- c(0.02, 0.02)
+nlambda <- 3
+e.abs <- 1E-3
+e.rel <- 1E-1
+alpha <- .2
+tol <- 1E-2
+fit <- MADMMplasso(
+  X, Z, y, alpha = alpha, my_lambda = NULL, lambda_min = 0.001, max_it = 100,
+  e.abs = e.abs, e.rel = e.rel, maxgrid = nlambda, nlambda = nlambda, rho = 5,
+  tree = TT, my_print = FALSE, alph = 1, gg = gg1, tol = tol, cl = 2L
+)
+cv_admp <- cv_MADMMplasso(
+  fit, nfolds = 5, X, Z, y, alpha = alpha, lambda = fit$Lambdas, max_it = 100,
+  e.abs = e.abs, e.rel = e.rel, nlambda, rho = 5, my_print = FALSE, alph = 1,
+  foldid = NULL, gg = fit$gg, TT = TT, tol = tol
+)
+plot(cv_admp)
diff --git a/man/MADMMplasso.Rd b/man/MADMMplasso.Rd
index 35008a9..1f5b119 100644
--- a/man/MADMMplasso.Rd
+++ b/man/MADMMplasso.Rd
@@ -185,9 +185,9 @@ tol <- 1E-3
 fit <- MADMMplasso(
   X, Z, y,
   alpha = alpha, my_lambda = matrix(rep(0.2, ncol(y)), 1),
-  lambda_min = 0.001, max_it = 5000, e.abs = e.abs, e.rel = e.rel, maxgrid = nlambda,
-  nlambda = nlambda, rho = 5, tree = TT, my_print = FALSE, alph = TRUE,
-  pal = TRUE, gg = gg1, tol = tol, cl = 6
+  lambda_min = 0.001, max_it = 1000, e.abs = e.abs, e.rel = e.rel,
+  maxgrid = nlambda, nlambda = nlambda, rho = 5, tree = TT, my_print = FALSE,
+  alph = TRUE, gg = gg1, tol = tol, cl = 2L
 )
 }
 \author{
diff --git a/man/cv_MADMMplasso.Rd b/man/cv_MADMMplasso.Rd
index bba9f8e..34a8d40 100644
--- a/man/cv_MADMMplasso.Rd
+++ b/man/cv_MADMMplasso.Rd
@@ -80,101 +80,95 @@ results containing the CV values
 Carries out cross-validation for a MADMMplasso model over a path of regularization values
 }
 \examples{
- # nolint start: indentation_linter
-\donttest{
-  # Train the model
-  # generate some data
-  set.seed(1235)
-  N <- 100
-  p <- 50
-  nz <- 4
-  K <- nz
-  X <- matrix(rnorm(n = N * p), nrow = N, ncol = p)
-  mx <- colMeans(X)
-  sx <- sqrt(apply(X, 2, var))
-  X <- scale(X, mx, sx)
-  X <- matrix(as.numeric(X), N, p)
-  Z <- matrix(rnorm(N * nz), N, nz)
-  mz <- colMeans(Z)
-  sz <- sqrt(apply(Z, 2, var))
-  Z <- scale(Z, mz, sz)
-  beta_1 <- rep(x = 0, times = p)
-  beta_2 <- rep(x = 0, times = p)
-  beta_3 <- rep(x = 0, times = p)
-  beta_4 <- rep(x = 0, times = p)
-  beta_5 <- rep(x = 0, times = p)
-  beta_6 <- rep(x = 0, times = p)
-
-  beta_1[1:5] <- c(2, 2, 2, 2, 2)
-  beta_2[1:5] <- c(2, 2, 2, 2, 2)
-  beta_3[6:10] <- c(2, 2, 2, -2, -2)
-  beta_4[6:10] <- c(2, 2, 2, -2, -2)
-  beta_5[11:15] <- c(-2, -2, -2, -2, -2)
-  beta_6[11:15] <- c(-2, -2, -2, -2, -2)
-
-  Beta <- cbind(beta_1, beta_2, beta_3, beta_4, beta_5, beta_6)
-  colnames(Beta) <- c(1:6)
-
-  theta <- array(0, c(p, K, 6))
-  theta[1, 1, 1] <- 2
-  theta[3, 2, 1] <- 2
-  theta[4, 3, 1] <- -2
-  theta[5, 4, 1] <- -2
-  theta[1, 1, 2] <- 2
-  theta[3, 2, 2] <- 2
-  theta[4, 3, 2] <- -2
-  theta[5, 4, 2] <- -2
-  theta[6, 1, 3] <- 2
-  theta[8, 2, 3] <- 2
-  theta[9, 3, 3] <- -2
-  theta[10, 4, 3] <- -2
-  theta[6, 1, 4] <- 2
-  theta[8, 2, 4] <- 2
-  theta[9, 3, 4] <- -2
-  theta[10, 4, 4] <- -2
-  theta[11, 1, 5] <- 2
-  theta[13, 2, 5] <- 2
-  theta[14, 3, 5] <- -2
-  theta[15, 4, 5] <- -2
-  theta[11, 1, 6] <- 2
-  theta[13, 2, 6] <- 2
-  theta[14, 3, 6] <- -2
-  theta[15, 4, 6] <- -2
-
-  pliable = matrix(0,N,6)
-  for (e in 1:6) {
-    pliable[,e]<-	compute_pliable(X, Z, theta[,,e])
-  }
-
-  esd<-diag(6)
-  e<-MASS::mvrnorm(N,mu=rep(0,6),Sigma=esd)
-  y_train<-X\%*\%Beta+pliable+e
-  y=y_train
-
-  colnames(y)<- c( paste("y",1:(ncol(y)),sep = "") )
-  TT=tree_parms(y)
-  plot(TT$h_clust)
-  gg1=matrix(0,2,2)
-  gg1[1,]<-c(0.02,0.02)
-  gg1[2,]<-c(0.02,0.02)
-  nlambda = 50
-  e.abs=1E-4
-  e.rel=1E-2
-  alpha=.2
-  tol=1E-3
-  fit <- MADMMplasso(
-    X, Z, y, alpha=alpha, my_lambda=NULL, lambda_min=0.001, max_it=5000,
-    e.abs=e.abs, e.rel=e.rel, maxgrid=50, nlambda=nlambda, rho=5,tree=TT,
-    my_print=FALSE, alph=1, gg=gg1, tol=tol, cl=2L
-  )
-  gg1=fit$gg
-
-  cv_admp <- cv_MADMMplasso(
-    fit, nfolds=5, X, Z, y, alpha=alpha, lambda=fit$Lambdas, max_it=5000,
-    e.abs=e.abs, e.rel=e.rel, nlambda, rho=5, my_print=FALSE, alph=1,
-    foldid=NULL, gg=gg1, TT=TT, tol=tol
-  )
-  plot(cv_admp)
+# Train the model
+# generate some data
+set.seed(1235)
+N <- 100
+p <- 50
+nz <- 4
+K <- nz
+X <- matrix(rnorm(n = N * p), nrow = N, ncol = p)
+mx <- colMeans(X)
+sx <- sqrt(apply(X, 2, var))
+X <- scale(X, mx, sx)
+X <- matrix(as.numeric(X), N, p)
+Z <- matrix(rnorm(N * nz), N, nz)
+mz <- colMeans(Z)
+sz <- sqrt(apply(Z, 2, var))
+Z <- scale(Z, mz, sz)
+beta_1 <- rep(x = 0, times = p)
+beta_2 <- rep(x = 0, times = p)
+beta_3 <- rep(x = 0, times = p)
+beta_4 <- rep(x = 0, times = p)
+beta_5 <- rep(x = 0, times = p)
+beta_6 <- rep(x = 0, times = p)
+
+beta_1[1:5] <- c(2, 2, 2, 2, 2)
+beta_2[1:5] <- c(2, 2, 2, 2, 2)
+beta_3[6:10] <- c(2, 2, 2, -2, -2)
+beta_4[6:10] <- c(2, 2, 2, -2, -2)
+beta_5[11:15] <- c(-2, -2, -2, -2, -2)
+beta_6[11:15] <- c(-2, -2, -2, -2, -2)
+
+Beta <- cbind(beta_1, beta_2, beta_3, beta_4, beta_5, beta_6)
+colnames(Beta) <- c(1:6)
+
+theta <- array(0, c(p, K, 6))
+theta[1, 1, 1] <- 2
+theta[3, 2, 1] <- 2
+theta[4, 3, 1] <- -2
+theta[5, 4, 1] <- -2
+theta[1, 1, 2] <- 2
+theta[3, 2, 2] <- 2
+theta[4, 3, 2] <- -2
+theta[5, 4, 2] <- -2
+theta[6, 1, 3] <- 2
+theta[8, 2, 3] <- 2
+theta[9, 3, 3] <- -2
+theta[10, 4, 3] <- -2
+theta[6, 1, 4] <- 2
+theta[8, 2, 4] <- 2
+theta[9, 3, 4] <- -2
+theta[10, 4, 4] <- -2
+theta[11, 1, 5] <- 2
+theta[13, 2, 5] <- 2
+theta[14, 3, 5] <- -2
+theta[15, 4, 5] <- -2
+theta[11, 1, 6] <- 2
+theta[13, 2, 6] <- 2
+theta[14, 3, 6] <- -2
+theta[15, 4, 6] <- -2
+
+pliable <- matrix(0, N, 6)
+for (e in 1:6) {
+  pliable[, e] <- compute_pliable(X, Z, theta[, , e])
 }
-# nolint end: indentation_linter
+
+esd <- diag(6)
+e <- MASS::mvrnorm(N, mu = rep(0, 6), Sigma = esd)
+y_train <- X \%*\% Beta + pliable + e
+y <- y_train
+
+colnames(y) <- c(paste("y", 1:(ncol(y)), sep = ""))
+TT <- tree_parms(y)
+plot(TT$h_clust)
+gg1 <- matrix(0, 2, 2)
+gg1[1, ] <- c(0.02, 0.02)
+gg1[2, ] <- c(0.02, 0.02)
+nlambda <- 3
+e.abs <- 1E-3
+e.rel <- 1E-1
+alpha <- .2
+tol <- 1E-2
+fit <- MADMMplasso(
+  X, Z, y, alpha = alpha, my_lambda = NULL, lambda_min = 0.001, max_it = 100,
+  e.abs = e.abs, e.rel = e.rel, maxgrid = nlambda, nlambda = nlambda, rho = 5,
+  tree = TT, my_print = FALSE, alph = 1, gg = gg1, tol = tol, cl = 2L
+)
+cv_admp <- cv_MADMMplasso(
+  fit, nfolds = 5, X, Z, y, alpha = alpha, lambda = fit$Lambdas, max_it = 100,
+  e.abs = e.abs, e.rel = e.rel, nlambda, rho = 5, my_print = FALSE, alph = 1,
+  foldid = NULL, gg = fit$gg, TT = TT, tol = tol
+)
+plot(cv_admp)
 }