Version 3.2.2

pbreheny · Feb 14, 2020 · 8cd9060 · 8cd9060
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -3,7 +3,7 @@ Title: Regularization Paths for Regression Models with Grouped Covariates
 Version: 3.2.2
 Date: 2020-02-14
 Authors@R: c(
-  person("Patrick", "Breheny", role=c("aut","cre"), email="[email protected]", comment=c(ORCID="000-0002-0650-1119")),
+  person("Patrick", "Breheny", role=c("aut","cre"), email="[email protected]", comment=c(ORCID="0000-0002-0650-1119")),
   person("Yaohui", "Zeng", role="ctb"))
 Depends: R (>= 3.1.0)
 Imports: Matrix
@@ -18,4 +18,5 @@ BugReports: http://github.com/pbreheny/grpreg/issues
 License: GPL-3
 URL: http://pbreheny.github.io/grpreg, https://github.com/pbreheny/grpreg
 LazyData: TRUE
-RoxygenNote: 6.1.1
+RoxygenNote: 7.0.2
+Encoding: UTF-8
diff --git a/NEWS b/NEWS
@@ -1,3 +1,9 @@
+# ncvreg 3.2.2 (2020-02-14)
+  * Change: Better error detection for ill-conditioned, unpenalized matrices
+  * Fixed: loss.grpsurv now works for total=FALSE
+  * Internal: Lots of internal changes for cleaner, more reliable code
+  * New version numbering system
+
 # grpreg 3.2-1 (2019-02-26)
   * Change: Cross-validation now balances censoring across folds for survival
     models

diff --git a/man/grpreg-package.Rd b/man/grpreg-package.Rd
@@ -72,7 +72,7 @@
     nonconvex penalized linear and logistic regression models with
     grouped predictors.  \emph{Statistics and Computing}, \strong{25}:
     173-187.
-    \url{http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s11222-013-9424-2}
+    \url{http://dx.doi.org/10.1007/s11222-013-9424-2}
 
     \item Breheny, P. (2015) The group exponential lasso for bi-level
     variable selection. \emph{Biometrics}, \strong{71}: 731-740.

diff --git a/man/grpreg.Rd b/man/grpreg.Rd
@@ -202,7 +202,7 @@ tau = 1/3, group.multiplier, warn=TRUE, returnX = FALSE, ...)
     \item Breheny, P. and Huang, J. (2015) Group descent algorithms for
     nonconvex penalized linear and logistic regression models with grouped
     predictors.  \emph{Statistics and Computing}, \strong{25}: 173-187.
-    \url{http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s11222-013-9424-2}
+    \url{http://dx.doi.org/10.1007/s11222-013-9424-2}
 
     \item Breheny, P. (2015) The group exponential lasso for bi-level
     variable selection. \emph{Biometrics}, \strong{71}: 731-740.

diff --git a/man/grpsurv.Rd b/man/grpsurv.Rd
@@ -133,7 +133,7 @@ not correspond to the ith row of \code{X}):
     \item Breheny, P. and Huang, J. (2015) Group descent algorithms for
     nonconvex penalized linear and logistic regression models with grouped
     predictors.  \emph{Statistics and Computing}, \strong{25}: 173-187.
-    \url{http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s11222-013-9424-2}
+    \url{http://dx.doi.org/10.1007/s11222-013-9424-2}
 
     \item Breheny, P. (2015) The group exponential lasso for bi-level
     variable selection. \emph{Biometrics}, \strong{71}: 731-740.

diff --git a/src/gdfit_gaussian.c b/src/gdfit_gaussian.c
@@ -292,7 +292,7 @@ SEXP gdfit_gaussian(SEXP X_, SEXP y_, SEXP penalty_, SEXP K1_, SEXP K0_,
   for (int j=0; j<p; j++) a[j] = 0;
   int *e = Calloc(J, int); // ever-active set
   for (int g=0; g<J; g++) e[g] = 0;
-  int converged, lstart = 0, ng, nv, violations;
+  int lstart = 0, ng, nv, violations;
   double shift, l1, l2, maxChange;
 
   // variables for screening
@@ -373,7 +373,6 @@ SEXP gdfit_gaussian(SEXP X_, SEXP y_, SEXP penalty_, SEXP K1_, SEXP K0_,
     while (tot_iter < max_iter) {
       while (tot_iter < max_iter) {
         while (tot_iter < max_iter) {
-          converged = 0;
           INTEGER(iter)[l]++;
           tot_iter++;
           REAL(df)[l] = 0;

diff --git a/src/lcdfit_binomial.c b/src/lcdfit_binomial.c
@@ -189,7 +189,7 @@ SEXP lcdfit_binomial(SEXP X_, SEXP y_, SEXP penalty_, SEXP K1_, SEXP K0_, SEXP l
     for (int j=0; j<p; j++) a[j] = 0;
     for (int j=0; j<p; j++) e[j] = 0;
   }
-  int converged, lstart, ng, nv, violations;
+  int lstart, ng, nv, violations;
   double shift, l1, l2, maxChange;
 
   // If lam[0]=lam_max, skip lam[0] -- closed form sol'n available
@@ -226,7 +226,6 @@ SEXP lcdfit_binomial(SEXP X_, SEXP y_, SEXP penalty_, SEXP K1_, SEXP K0_, SEXP l
 
     while (tot_iter < max_iter) {
       while (tot_iter < max_iter) {
-        converged = 0;
         INTEGER(iter)[l]++;
         tot_iter++;