Preparing for resubmission

Author: Keith Goldfeld

DESCRIPTION

@@ -1,8 +1,8 @@
 Type: Package
 Package: simstudy
 Title: Simulation of Study Data
-Version: 0.9.0.9000
-Date: 2025-12-07
+Version: 0.9.1
+Date: 2025-12-10
 Authors@R: 
     c(person(given = "Keith",
              family = "Goldfeld",

README.Rmd

@@ -63,7 +63,7 @@ dd
 
 ## Contributing & Support
 
-If you find a bug or need help, please file an issue with a [reprex](https://www.tidyverse.org/help/){target="_blank"} on [Github](https://github.com/kgoldfeld/simstudy/issues){target="_blank"}. We are happy to accept contributions to simstudy. More information on how to propose changes or fix bugs can be found [here](https://kgoldfeld.github.io/simstudy/CONTRIBUTING.html){target="_blank"}.
+If you find a bug or need help, please file an issue with a [reprex](https://tidyverse.org/help/){target="_blank"} on [Github](https://github.com/kgoldfeld/simstudy/issues){target="_blank"}. We are happy to accept contributions to simstudy. More information on how to propose changes or fix bugs can be found [here](https://kgoldfeld.github.io/simstudy/CONTRIBUTING.html){target="_blank"}.
 
 ## Code of Conduct
 

README.md

@@ -116,7 +116,7 @@ dd
 ## Contributing & Support
 
 If you find a bug or need help, please file an issue with a
-<a href="https://www.tidyverse.org/help/" target="_blank">reprex</a> on
+<a href="https://tidyverse.org/help/" target="_blank">reprex</a> on
 <a href="https://github.com/kgoldfeld/simstudy/issues"
 target="_blank">Github</a>. We are happy to accept contributions to
 simstudy. More information on how to propose changes or fix bugs can be

cran-comments.md

@@ -1,7 +1,12 @@
-## Submission 20251005
+## Submission 20251214
 
-This is a submission of version 0.9.0
+This is a resubmission for version 0.9.1
 
-Developed with R Version 4.5.1
+Developed with R Version 4.5.2
 
-I did not get any notes from check_win_devel.
+The test failure reported on CRAN (MKL) was caused by a numerical rounding issue 
+leading to a non-symmetric correlation matrix. This has been fixed by ensuring 
+correlation matrices are properly symmetrized.
+
+Checks using devtools::check(remote = TRUE, manual = TRUE) and 
+devtools::check_win_devel() pass.

vignettes/logisticCoefs.Rmd

@@ -114,9 +114,9 @@ genData(500000, d1a)[, mean(y)]
 
 #### Finding the intercept
 
-Where did those two intercepts come from?  The [paper](https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-023-01836-5){target="_blank"} by Peter Austin describes an iterative bisection procedure that takes a distribution of covariates and a set of coefficients to identify the intercept coefficient that yields the target marginal proportion or prevalence. 
+Where did those two intercepts come from?  The [paper](https://link.springer.com/article/10.1186/s12874-023-01836-5){target="_blank"} by Peter Austin describes an iterative bisection procedure that takes a distribution of covariates and a set of coefficients to identify the intercept coefficient that yields the target marginal proportion or prevalence. 
 
-The general idea of the algorithm is to try out series of different intercepts in an intelligent way that ends up at the right spot. (If you want the details for the algorithm, take a look at the [paper](https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-023-01836-5){target="_blank"}.) The starting search range is pre-defined (we've used -10 to 10 for the intercept), and we start with an value of 0 for the initial intercept and simulate a large data set (the paper uses 1 million observations, but 100,000 seems to work just fine) and record the population prevalence. If we've overshot the target prevalence, we turn our attention to the range between -10 and 0, taking the average, which is -5. Otherwise, we focus on the range between 0 and 10. We iterate this way, choosing the range we need to focus on and setting the intercept at the mid-point (hence the name *bisection*). The algorithm will converge pretty quickly on the value of the intercept that gives the target population prevalence for the underlying covariate distribution and coefficient assumptions.
+The general idea of the algorithm is to try out series of different intercepts in an intelligent way that ends up at the right spot. (If you want the details for the algorithm, take a look at the [paper](https://link.springer.com/article/10.1186/s12874-023-01836-5){target="_blank"}.) The starting search range is pre-defined (we've used -10 to 10 for the intercept), and we start with an value of 0 for the initial intercept and simulate a large data set (the paper uses 1 million observations, but 100,000 seems to work just fine) and record the population prevalence. If we've overshot the target prevalence, we turn our attention to the range between -10 and 0, taking the average, which is -5. Otherwise, we focus on the range between 0 and 10. We iterate this way, choosing the range we need to focus on and setting the intercept at the mid-point (hence the name *bisection*). The algorithm will converge pretty quickly on the value of the intercept that gives the target population prevalence for the underlying covariate distribution and coefficient assumptions.
 
 In the current implementation in `simstudy`, the intercept is provided by a simple call to `logisticCoefs`. Here are the calls for the two sets of definitions in definition tables *d1* and *d2*.