Mitigate [-Wformat-security] compiler warnings on newer clang configu…

…rations. See RcppCore/Rcpp#1287

CRAN-SUBMISSION

@@ -0,0 +1,3 @@
+Version: 1.2.6
+Date: 2023-11-29 11:50:17 UTC
+SHA: efa45ef35dbb27575609807e79341a36d706e811

DESCRIPTION

@@ -1,22 +1,22 @@
 Package: PoissonBinomial
 Type: Package
 Title: Efficient Computation of Ordinary and Generalized Poisson Binomial Distributions
-Version: 1.2.5
-Date: 2022-05-31
-Authors@R: person("Florian", "Junge", role = c("aut", "cre"), email = "florian.junge@h-da.de")
-Maintainer: Florian Junge <florian.junge@h-da.de>
+Version: 1.2.6
+Date: 2023-11-29
+Authors@R: person("Florian", "Junge", role = c("aut", "cre"), email = "florian.junge@mailbox.org")
+Maintainer: Florian Junge <florian.junge@mailbox.org>
 Language: en-US
 Description: Efficient implementations of multiple exact and approximate methods as described in Hong (2013) <doi:10.1016/j.csda.2012.10.006>, Biscarri, Zhao & Brunner (2018) <doi:10.1016/j.csda.2018.01.007> and Zhang, Hong & Balakrishnan (2018) <doi:10.1080/00949655.2018.1440294> for computing the probability mass, cumulative distribution and quantile functions, as well as generating random numbers for both the ordinary and generalized Poisson binomial distribution.
 License: GPL-3
 Encoding: UTF-8
-Imports: Rcpp (>= 1.0.3)
+Imports: Rcpp (>= 1.0.11)
 LinkingTo: Rcpp
 SystemRequirements: fftw3 (>= 3.3)
 Suggests: 
     knitr,
     rmarkdown,
     microbenchmark
 VignetteBuilder: knitr
-URL: https://github.com/DISOhda/PoissonBinomial
-BugReports: https://github.com/DISOhda/PoissonBinomial/issues
-RoxygenNote: 7.2.0
+URL: https://github.com/fj86/PoissonBinomial
+BugReports: https://github.com/fj86/PoissonBinomial/issues
+RoxygenNote: 7.2.3

NEWS.md

@@ -1,3 +1,10 @@
+# PoissonBinomial 1.2.6
+
+* Addresses `[-Wformat-security]` compiler warning on newer clang configuration. See https://github.com/RcppCore/Rcpp/issues/1287
+* change of maintainer eMail to personal address
+* GitHub repo moved to personal account
+
+
 # PoissonBinomial 1.2.5
 
 * Minor performance improvements for exact methods of `dgpbinom` and `pgpbinom`.

R/RcppExports.R

@@ -114,5 +114,5 @@ rgpb_bernoulli <- function(n, probs, val_p, val_q) {
 
 # Register entry points for exported C++ functions
 methods::setLoadAction(function(ns) {
-    .Call('_PoissonBinomial_RcppExport_registerCCallable', PACKAGE = 'PoissonBinomial')
+    .Call(`_PoissonBinomial_RcppExport_registerCCallable`)
 })

cran-comments.md

@@ -1,7 +1,7 @@
 ## Test environments
-* local Manjaro KDE, R 4.2.0
+* local Windows 10 Education 22H2 10.0.19045.3693, R 4.3.2
 * win-builder (configurations: "release", "oldrelease", "devel")
-* R-hub (configurations: "sanitizers", "valgrind", "CRAN" and "Solaris")
+* R-hub (configurations: "sanitizers", "valgrind", "CRAN")
 
 
 ## R CMD check results
@@ -10,7 +10,26 @@
 0 errors | 0 warnings | 0 notes
 
 ### R-hub
-0 errors | 0 warnings | 0 notes
+* checking CRAN incoming feasibility ... [12s] NOTE
+Maintainer: 'Florian Junge <[email protected]>'
+
+New maintainer:
+  Florian Junge <[email protected]>
+Old maintainer(s):
+  Florian Junge <[email protected]>
+
+This change of addresses has been announced via eMail to
+<[email protected]> in advance.
+
 
 ### win-builder 
-0 errors | 0 warnings | 0 notes
+* checking CRAN incoming feasibility ... [12s] NOTE
+Maintainer: 'Florian Junge <[email protected]>'
+
+New maintainer:
+  Florian Junge <[email protected]>
+Old maintainer(s):
+  Florian Junge <[email protected]>
+
+This change of addresses has been announced via eMail to
+<[email protected]> in advance.

src/RcppExports.cpp

@@ -42,7 +42,7 @@ RcppExport SEXP _PoissonBinomial_vectorGCD(SEXP xSEXP) {
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -77,7 +77,7 @@ RcppExport SEXP _PoissonBinomial_dpb_conv(SEXP obsSEXP, SEXP probsSEXP) {
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -113,7 +113,7 @@ RcppExport SEXP _PoissonBinomial_ppb_conv(SEXP obsSEXP, SEXP probsSEXP, SEXP low
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -148,7 +148,7 @@ RcppExport SEXP _PoissonBinomial_dpb_dc(SEXP obsSEXP, SEXP probsSEXP) {
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -184,7 +184,7 @@ RcppExport SEXP _PoissonBinomial_ppb_dc(SEXP obsSEXP, SEXP probsSEXP, SEXP lower
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -219,7 +219,7 @@ RcppExport SEXP _PoissonBinomial_dpb_dftcf(SEXP obsSEXP, SEXP probsSEXP) {
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -255,7 +255,7 @@ RcppExport SEXP _PoissonBinomial_ppb_dftcf(SEXP obsSEXP, SEXP probsSEXP, SEXP lo
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -290,7 +290,7 @@ RcppExport SEXP _PoissonBinomial_dpb_rf(SEXP obsSEXP, SEXP probsSEXP) {
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -326,7 +326,7 @@ RcppExport SEXP _PoissonBinomial_ppb_rf(SEXP obsSEXP, SEXP probsSEXP, SEXP lower
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -361,7 +361,7 @@ RcppExport SEXP _PoissonBinomial_dpb_mean(SEXP obsSEXP, SEXP probsSEXP) {
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -397,7 +397,7 @@ RcppExport SEXP _PoissonBinomial_ppb_mean(SEXP obsSEXP, SEXP probsSEXP, SEXP low
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -433,7 +433,7 @@ RcppExport SEXP _PoissonBinomial_dpb_gmba(SEXP obsSEXP, SEXP probsSEXP, SEXP ant
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -470,7 +470,7 @@ RcppExport SEXP _PoissonBinomial_ppb_gmba(SEXP obsSEXP, SEXP probsSEXP, SEXP ant
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -505,7 +505,7 @@ RcppExport SEXP _PoissonBinomial_dpb_pa(SEXP obsSEXP, SEXP probsSEXP) {
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -541,7 +541,7 @@ RcppExport SEXP _PoissonBinomial_ppb_pa(SEXP obsSEXP, SEXP probsSEXP, SEXP lower
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -578,7 +578,7 @@ RcppExport SEXP _PoissonBinomial_ppb_na(SEXP obsSEXP, SEXP probsSEXP, SEXP refin
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -614,7 +614,7 @@ RcppExport SEXP _PoissonBinomial_dpb_na(SEXP obsSEXP, SEXP probsSEXP, SEXP refin
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -649,7 +649,7 @@ RcppExport SEXP _PoissonBinomial_rpb_bernoulli(SEXP nSEXP, SEXP probsSEXP) {
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -686,7 +686,7 @@ RcppExport SEXP _PoissonBinomial_dgpb_conv(SEXP obsSEXP, SEXP probsSEXP, SEXP va
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -724,7 +724,7 @@ RcppExport SEXP _PoissonBinomial_pgpb_conv(SEXP obsSEXP, SEXP probsSEXP, SEXP va
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -761,7 +761,7 @@ RcppExport SEXP _PoissonBinomial_dgpb_dc(SEXP obsSEXP, SEXP probsSEXP, SEXP val_
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -799,7 +799,7 @@ RcppExport SEXP _PoissonBinomial_pgpb_dc(SEXP obsSEXP, SEXP probsSEXP, SEXP val_
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -836,7 +836,7 @@ RcppExport SEXP _PoissonBinomial_dgpb_dftcf(SEXP obsSEXP, SEXP probsSEXP, SEXP v
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -874,7 +874,7 @@ RcppExport SEXP _PoissonBinomial_pgpb_dftcf(SEXP obsSEXP, SEXP probsSEXP, SEXP v
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -913,7 +913,7 @@ RcppExport SEXP _PoissonBinomial_pgpb_na(SEXP obsSEXP, SEXP probsSEXP, SEXP val_
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -951,7 +951,7 @@ RcppExport SEXP _PoissonBinomial_dgpb_na(SEXP obsSEXP, SEXP probsSEXP, SEXP val_
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;
@@ -988,7 +988,7 @@ RcppExport SEXP _PoissonBinomial_rgpb_bernoulli(SEXP nSEXP, SEXP probsSEXP, SEXP
     if (rcpp_isError_gen) {
         SEXP rcpp_msgSEXP_gen = Rf_asChar(rcpp_result_gen);
         UNPROTECT(1);
-        Rf_error(CHAR(rcpp_msgSEXP_gen));
+        Rf_error("%s", CHAR(rcpp_msgSEXP_gen));
     }
     UNPROTECT(1);
     return rcpp_result_gen;

vignettes/proc_approx.Rmd

@@ -251,15 +251,15 @@ summary((dpn - dpd)[idx])
 
 ### Processing Speed Comparisons
 
-To assess the performance of the approximation procedures, we use the `microbenchmark` package. Each algorithm has to calculate the PMF repeatedly based on random probability vectors. The run times are then summarized in a table that presents, among other statistics, their minima, maxima and means. The following results were recorded on an AMD Ryzen 7 1800X with 32 GiB of RAM and Windows 10 Education (20H2).
+To assess the performance of the approximation procedures, we use the `microbenchmark` package. Each algorithm has to calculate the PMF repeatedly based on random probability vectors. The run times are then summarized in a table that presents, among other statistics, their minima, maxima and means. The following results were recorded on an AMD Ryzen 9 5900X with 64 GiB of RAM and Windows 10 Education (22H2).
 
 ```{r benchmark-ord}
 library(microbenchmark)
 set.seed(1)
 
 f1 <- function() dpbinom(NULL, runif(4000), method = "Normal")
-f2 <- function() dpbinom(NULL, runif(4000), method = "RefinedNormal")
-f3 <- function() dpbinom(NULL, runif(4000), method = "Poisson")
+f2 <- function() dpbinom(NULL, runif(4000), method = "Poisson")
+f3 <- function() dpbinom(NULL, runif(4000), method = "RefinedNormal")
 f4 <- function() dpbinom(NULL, runif(4000), method = "Mean")
 f5 <- function() dpbinom(NULL, runif(4000), method = "GeoMean")
 f6 <- function() dpbinom(NULL, runif(4000), method = "GeoMeanCounter")
@@ -268,7 +268,7 @@ f7 <- function() dpbinom(NULL, runif(4000), method = "DivideFFT")
 microbenchmark(f1(), f2(), f3(), f4(), f5(), f6(), f7(), times = 51)
 ```
 
-Clearly, the NA procedure is the fastest, followed by the RNA and PA methods. The next fastest algorithms are AMBA, GMBA-A and GMBA-B. They exhibit almost equal mean execution speed, with the AMBA algorithm being slightly faster. All of the approximation procedures outperform the fastest exact approach, DC-FFT, by far.
+Clearly, the NA procedure is the fastest, followed by the PA and RNA methods. The next fastest algorithms are AMBA, GMBA-A and GMBA-B. They exhibit almost equal mean execution speed, with the AMBA algorithm being slightly faster. All of the approximation procedures outperform the fastest exact approach, DC-FFT, by far.
 
 
 ## Generalized Poisson Binomial Distribution
@@ -370,7 +370,7 @@ summary((dpn - dpd)[idx])
 
 ### Processing Speed Comparisons
 
-To assess the performance of the approximation procedures, we use the `microbenchmark` package. Each algorithm has to calculate the PMF repeatedly based on random probability vectors. The run times are then summarized in a table that presents, among other statistics, their minima, maxima and means. The following results were recorded on an AMD Ryzen 7 1800X with 32 GiB of RAM and Windows 10 Education (20H2).
+To assess the performance of the approximation procedures, we use the `microbenchmark` package. Each algorithm has to calculate the PMF repeatedly based on random probability vectors. The run times are then summarized in a table that presents, among other statistics, their minima, maxima and means. The following results were recorded on an AMD Ryzen 9 5900X with 64 GiB of RAM and Windows 10 Education (22H2).
 
 ```{r benchmark-gen}
 library(microbenchmark)

vignettes/proc_exact.Rmd

@@ -112,7 +112,7 @@ sum(abs(dpbinom(NULL, pp, wt, "Convolve") - dpbinom(NULL, pp, wt, "Recursive")))
 
 ### Processing Speed Comparisons
 
-To assess the performance of the exact procedures, we use the `microbenchmark` package. Each algorithm has to calculate the PMF repeatedly based on random probability vectors. The run times are then summarized in a table that presents, among other statistics, their minima, maxima and means. The following results were recorded on an AMD Ryzen 7 1800X with 32 GiB of RAM and Windows 10 Education (20H2).
+To assess the performance of the exact procedures, we use the `microbenchmark` package. Each algorithm has to calculate the PMF repeatedly based on random probability vectors. The run times are then summarized in a table that presents, among other statistics, their minima, maxima and means. The following results were recorded on an AMD Ryzen 9 5900X with 64 GiB of RAM and Windows 10 Education (22H2).
 
 ```{r benchmark-ord}
 library(microbenchmark)
@@ -208,7 +208,7 @@ As can be seen, the G-DFT-CF procedure does not produce probabilities $\leq 2.2e
 
 ### Processing Speed Comparisons
 
-To assess the performance of the exact procedures, we use the `microbenchmark` package. Each algorithm has to calculate the PMF repeatedly based on random probability and value vectors. The run times are then summarized in a table that presents, among other statistics, their minima, maxima and means. The following results were recorded on an AMD Ryzen 7 1800X with 32 GiB of RAM and Windows 10 Education (20H2).
+To assess the performance of the exact procedures, we use the `microbenchmark` package. Each algorithm has to calculate the PMF repeatedly based on random probability and value vectors. The run times are then summarized in a table that presents, among other statistics, their minima, maxima and means. The following results were recorded on an AMD Ryzen 9 5900X with 64 GiB of RAM and Windows 10 Education (22H2).
 
 ```{r benchmark-gen}
 library(microbenchmark)