fixing CRAN check warnings

ryantibs · Jul 27, 2018 · 12b7afb · 12b7afb
1 parent 087a77c
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,12 +1,13 @@
 Package: genlasso
 Type: Package
 Title: Path algorithm for generalized lasso problems
-Version: 1.3
-Date: 2014-09-14
+Version: 1.4
+Date: 2018-07-27
 Author: Taylor B. Arnold and Ryan J. Tibshirani
-Maintainer: Taylor Arnold <taylor.arnold@aya.yale.edu>
+Maintainer: Taylor Arnold <taylor.arnold@acm.org>
 Description: This package computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed.
 License: GPL (>= 2.0)
 Depends: Matrix, igraph, R (>= 3.1.0)
 ByteCompile: TRUE
-URL: https://github.com/statsmaths/genlasso
+URL: https://github.com/statsmaths/genlasso
+RoxygenNote: 6.0.1
diff --git a/NAMESPACE b/NAMESPACE
@@ -1,5 +1,10 @@
 import("Matrix", "igraph")
 
+importFrom("grDevices", "rainbow")
+importFrom("graphics", "abline", "legend", "lines", "matplot", "par",
+      "plot", "points", "text")
+importFrom("stats", "coef", "rnorm", "sd")
+
 export("coef.genlasso",
        "cv.trendfilter",
        "fusedlasso1d",

diff --git a/R/dualpathFused.R b/R/dualpathFused.R
@@ -255,7 +255,7 @@ dualpathFused <- function(y, D, approx=FALSE, maxsteps=2000, minlam=0,
         B = c(B,I[ihit])
         I = I[-ihit]
         s = c(s,shit)
-        D2 = rBind(D2,D1[ihit,])
+        D2 = rbind(D2,D1[ihit,])
         D1 = D1[-ihit,,drop=FALSE]
 
         if (verbose) {
@@ -295,7 +295,7 @@ dualpathFused <- function(y, D, approx=FALSE, maxsteps=2000, minlam=0,
         I = c(I,B[ileave])
         B = B[-ileave]
         s = s[-ileave]
-        D1 = rBind(D1,D2[ileave,])
+        D1 = rbind(D1,D2[ileave,])
         D2 = D2[-ileave,,drop=FALSE]
 
         if (verbose) {

diff --git a/R/dualpathFusedL1.R b/R/dualpathFusedL1.R
@@ -268,7 +268,7 @@ dualpathFusedL1 <- function(y, D, D0, gamma, approx=FALSE, maxsteps=2000,
         B = c(B,I[ihit])
         I = I[-ihit]
         s = c(s,shit)
-        D2 = rBind(D2,D1[ihit,])
+        D2 = rbind(D2,D1[ihit,])
         D1 = D1[-ihit,,drop=FALSE]
 
         if (verbose) {
@@ -314,7 +314,7 @@ dualpathFusedL1 <- function(y, D, D0, gamma, approx=FALSE, maxsteps=2000,
         I = c(I,B[ileave])
         B = B[-ileave]
         s = s[-ileave]
-        D1 = rBind(D1,D2[ileave,])
+        D1 = rbind(D1,D2[ileave,])
         D2 = D2[-ileave,,drop=FALSE]
 
         if (verbose) {

diff --git a/R/dualpathFusedL1X.R b/R/dualpathFusedL1X.R
@@ -338,7 +338,7 @@ dualpathFusedL1X <- function(y, X, D, D0, gamma, approx=FALSE, maxsteps=2000,
         B = c(B,I[ihit])
         I = I[-ihit]
         s = c(s,shit)
-        D2 = rBind(D2,D1[ihit,])
+        D2 = rbind(D2,D1[ihit,])
         D1 = D1[-ihit,,drop=FALSE]
 
         if (verbose) {
@@ -384,7 +384,7 @@ dualpathFusedL1X <- function(y, X, D, D0, gamma, approx=FALSE, maxsteps=2000,
         I = c(I,B[ileave])
         B = B[-ileave]
         s = s[-ileave]
-        D1 = rBind(D1,D2[ileave,])
+        D1 = rbind(D1,D2[ileave,])
         D2 = D2[-ileave,,drop=FALSE]
 
         if (verbose) {

diff --git a/R/dualpathFusedX.R b/R/dualpathFusedX.R
@@ -343,7 +343,7 @@ dualpathFusedX <- function(y, X, D, approx=FALSE, maxsteps=2000, minlam=0,
         B = c(B,I[ihit])
         I = I[-ihit]
         s = c(s,shit)
-        D2 = rBind(D2,D1[ihit,])
+        D2 = rbind(D2,D1[ihit,])
         D1 = D1[-ihit,,drop=FALSE]
 
         if (verbose) {
@@ -383,7 +383,7 @@ dualpathFusedX <- function(y, X, D, approx=FALSE, maxsteps=2000, minlam=0,
         I = c(I,B[ileave])
         B = B[-ileave]
         s = s[-ileave]
-        D1 = rBind(D1,D2[ileave,])
+        D1 = rbind(D1,D2[ileave,])
         D2 = D2[-ileave,,drop=FALSE]
 
         if (verbose) {

diff --git a/R/dualpathTrendX.R b/R/dualpathTrendX.R
@@ -23,16 +23,16 @@ dualpathTrendX <- function(y, pos, X, D, ord, approx=FALSE, maxsteps=2000,
     n = length(y)
 
     # Modify y,X in the case of a ridge penalty, but
-    # keep the originals 
+    # keep the originals
     y0 = y
     X0 = X
     if (eps>0) {
       y = c(y,rep(0,p))
       X = rbind(X,diag(sqrt(eps),p))
       n = n+p
     }
-  
-    # Find the minimum 2-norm solution, using some linear algebra 
+
+    # Find the minimum 2-norm solution, using some linear algebra
     # tricks and a little bit of discrete calculus
     if (is.null(pos)) pos = 1:p
     Pos = matrix(rep(pos,each=ord),ord,p)
@@ -41,9 +41,9 @@ dualpathTrendX <- function(y, pos, X, D, ord, approx=FALSE, maxsteps=2000,
     for (i in Seq(2,ord+1)) {
       ii = Seq(1,i-1)
       basis[,i] = apply(pmax(Pos[ii,,drop=FALSE]-pos[ii],0),
-             2,prod)/factorial(i-1) 
+             2,prod)/factorial(i-1)
     }
-    
+
     # First project onto the row space of D*X^+
     xy = t(X)%*%y
     A = X%*%basis
@@ -54,7 +54,7 @@ dualpathTrendX <- function(y, pos, X, D, ord, approx=FALSE, maxsteps=2000,
     # e = solve(crossprod(A),z), for numerical stablity. Plus,
     # it's not really any slower
     g = xy-t(X)%*%(A%*%e)
-  
+
    # Here we perform our usual trend filter solve but
     # with g in place of y
     x = qr(t(D))
@@ -68,18 +68,18 @@ dualpathTrendX <- function(y, pos, X, D, ord, approx=FALSE, maxsteps=2000,
       cat(sprintf("1. lambda=%.3f, adding coordinate %i, |B|=%i...",
                   hit,ihit,1))
     }
-    
+
     # Now iteratively find the new dual solution, and
     # the next critical point
-  
+
     # Things to keep track of, and return at the end
     buf = min(maxsteps,1000)
     lams = numeric(buf)        # Critical lambdas
     h = logical(buf)           # Hit or leave?
     df = numeric(buf)          # Degrees of freedom
     u = matrix(0,m,buf)        # Dual solutions
     beta = matrix(0,p,buf)     # Primal solutions
-    
+
     lams[1] = hit
     h[1] = TRUE
     df[1] = ncol(basis)
@@ -91,7 +91,7 @@ dualpathTrendX <- function(y, pos, X, D, ord, approx=FALSE, maxsteps=2000,
       2,prod)/factorial(ord)
     newbv[Seq(1,ihit+ord)] = 0 # Only needed when ord=0
     basis = cbind(basis,newbv)
-  
+
     # Other things to keep track of, but not return
     r = 1                      # Size of boundary set
     B = ihit                   # Boundary set
@@ -124,7 +124,7 @@ dualpathTrendX <- function(y, pos, X, D, ord, approx=FALSE, maxsteps=2000,
     # In the case of a ridge penalty, modify X
     if (eps>0) X = rbind(X,diag(sqrt(eps),p))
   }
-  
+
   tryCatch({
     while (k<=maxsteps && lams[k-1]>=minlam) {
       ##########
@@ -142,7 +142,7 @@ dualpathTrendX <- function(y, pos, X, D, ord, approx=FALSE, maxsteps=2000,
       # No updating, just recompute these every time
       x = qr(t(D1))
       Ds = as.numeric(t(D2)%*%s)
-      
+
       # Precomputation for the hitting times: first we project
       # y and Ds onto the row space of D1*X^+
       A = X%*%basis
@@ -158,12 +158,12 @@ dualpathTrendX <- function(y, pos, X, D, ord, approx=FALSE, maxsteps=2000,
       gb = Ds-t(X)%*%(A%*%eb)
       fa = basis%*%ea
       fb = basis%*%eb
-      
+
       # If the interior is empty, then nothing will hit
       if (r==m) {
         hit = 0
       }
-      
+
       # Otherwise, find the next hitting time
       else {
         # Here we perform our usual trend filter solve but
@@ -177,12 +177,12 @@ dualpathTrendX <- function(y, pos, X, D, ord, approx=FALSE, maxsteps=2000,
         # than the current lambda (precision issue)
         hits[hits>lams[k-1]+btol] = 0
         hits[hits>lams[k-1]] = lams[k-1]
-        
+
         ihit = which.max(hits)
         hit = hits[ihit]
         shit = shits[ihit]
       }
-      
+
       ##########
       # If nothing is on the boundary, then nothing will leave
       # Also, skip this if we are in "approx" mode
@@ -228,21 +228,21 @@ dualpathTrendX <- function(y, pos, X, D, ord, approx=FALSE, maxsteps=2000,
           2,prod)/factorial(ord)
         newbv[Seq(1,I[ihit]+ord)] = 0 # Only needed when ord=0
         basis = cbind(basis,newbv)
-        
+
         # Update all other variables
         r = r+1
         B = c(B,I[ihit])
         I = I[-ihit]
         s = c(s,shit)
-        D2 = rBind(D2,D1[ihit,])
+        D2 = rbind(D2,D1[ihit,])
         D1 = D1[-ihit,,drop=FALSE]
-          
+
         if (verbose) {
           cat(sprintf("\n%i. lambda=%.3f, adding coordinate %i, |B|=%i...",
                       k,hit,B[r],r))
         }
       }
-                
+
       # Otherwise a leaving time comes next
       else {
         # Record the critical lambda and properties
@@ -253,16 +253,16 @@ dualpathTrendX <- function(y, pos, X, D, ord, approx=FALSE, maxsteps=2000,
         uhat[B] = leave*s
         uhat[I] = a-leave*b
         betahat = fa-leave*fb
-        
+
         # Update our basis
         basis = basis[,-(ord+1+ileave)]
-        
+
         # Update all other variables
         r = r-1
         I = c(I,B[ileave])
         B = B[-ileave]
         s = s[-ileave]
-        D1 = rBind(D1,D2[ileave,])
+        D1 = rbind(D1,D2[ileave,])
         D2 = D2[-ileave,,drop=FALSE]
 
         if (verbose) {
@@ -281,14 +281,14 @@ dualpathTrendX <- function(y, pos, X, D, ord, approx=FALSE, maxsteps=2000,
     err$message = paste(err$message,"\n(Path computation has been terminated;",
       " partial path is being returned.)",sep="")
     warning(err)})
-  
-  # Trim 
+
+  # Trim
   lams = lams[Seq(1,k-1)]
   h = h[Seq(1,k-1)]
   df = df[Seq(1,k-1)]
   u = u[,Seq(1,k-1),drop=FALSE]
   beta = beta[,Seq(1,k-1),drop=FALSE]
-  
+
   # If we reached the maximum number of steps
   if (k>maxsteps) {
     if (verbose) {
@@ -313,14 +313,14 @@ dualpathTrendX <- function(y, pos, X, D, ord, approx=FALSE, maxsteps=2000,
   # The least squares solution (lambda=0)
   bls = NULL
   if (completepath) bls = fa
-  
+
   if (verbose) cat("\n")
   # Save elements needed for continuing the path
   pathobjs = list(type="trend.x", r=r, B=B, I=I, Q1=NA, approx=approx,
     Q2=NA, k=k, df=df, D1=D1, D2=D2, Ds=Ds, ihit=ihit, m=m, n=n, p=p,
     q=q, h=h, q0=NA, rtol=rtol, btol=btol, eps=eps, s=s, y=y, ord=ord,
     pos=pos, Pos=Pos, basis=basis, xy=xy)
-  
+
   colnames(u) = as.character(round(lams,3))
   colnames(beta) = as.character(round(lams,3))
   return(list(lambda=lams,beta=beta,fit=X0%*%beta,u=u,hit=h,df=df,y=y0,X=X0,