From 40c450a09c17e4323e0dfeb60dc2f154d83218c1 Mon Sep 17 00:00:00 2001 From: Merlise Clyde Date: Wed, 6 Dec 2023 09:05:40 -0500 Subject: [PATCH] update date in description and CRAN comments for next devel version --- DESCRIPTION | 2 +- cran-comments.md | 17 ++--------------- src/hypergeometric1F1.c | 2 ++ 3 files changed, 5 insertions(+), 16 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index e4bc6aec..0840fe97 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: BAS Version: 1.7.1.9000 -Date: 2023-12-5 +Date: 2023-12-6 Title: Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling Authors@R: c(person("Merlise", "Clyde", email="clyde@duke.edu", role=c("aut","cre", "cph"), diff --git a/cran-comments.md b/cran-comments.md index 232e0f9e..f78eb2eb 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,17 +1,7 @@ # Notes to CRAN -## ReSubmission reason +## Submission reason -Submission prior to Dec 14 required to maintain package on CRAN. -Status on CRAN check page under `Additional Issues` for `valgrind` showed warnings `Conditional jump or move depends on uninitialised value(s)`. - -- Initialized vector `se` via `memset` and `disp = 1.0` in `fit_glm.c` (issue #72) - -- Initialized variables in `hyp1f1.c` from `testthat` (issue #75) - -- Removed models that have zero prior probability in `bas.lm` and `bas.glm` (issue #74) - -- Fixed error in `bayesglm.fit` to check arguments `x` or `y` for correct type before calling C and added unit test (issue #67) ## Test environments @@ -30,11 +20,8 @@ Status on CRAN check page under `Additional Issues` for `valgrind` showed war ## R CMD check results for this submission * Mmac, Windows, Ubunto, Debian - 0 error | 0 warnings | 1 notes - -Note: Days since last update: 6 + 0 error | 0 warnings | 0 notes -(resubmission requested by CRAN) ## Reverse Dependencies diff --git a/src/hypergeometric1F1.c b/src/hypergeometric1F1.c index fea1f359..233fa357 100644 --- a/src/hypergeometric1F1.c +++ b/src/hypergeometric1F1.c @@ -66,8 +66,10 @@ double loghyperg1F1_laplace(double a, double b, double x) else mode = mode2; // Rprintf("mode 1 %lf, mode %lf\n", mode1, mode2); if (mode < 0) { + // # nocov start mode = 0.0; warning("1F1 Laplace approximation on boundary\n"); + // # nocov end } else{ /* prec = a*mode*(1.0 - mode) + (1.0-mode)*(1.0 - mode)*b +