getting ready for release

bcgov · Dec 10, 2024 · cdc2d0a · cdc2d0a
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diff --git a/NEWS.md b/NEWS.md
@@ -252,7 +252,7 @@ The function `ssd_fit_burrlioz()` was added to approximate the behaviour of [Bur
 Hazard concentration estimation is performed by `ssd_hc()` (which is wrapped by `predict()`) and hazard protection estimation by `ssd_hp()`.
 By default confidence intervals are estimated by parametric bootstrapping.
 
-To reduce the time required for bootstrapping, parallelization was implemented using the [future](https://github.com/HenrikBengtsson/future) package.
+To reduce the time required for bootstrapping, parallelization was implemented using the [future](https://github.com/futureverse/future) package.
 
 The following arguments were added to `ssd_hc()` and `ssd_hp()`
 

diff --git a/cran-comments.md b/cran-comments.md
@@ -9,7 +9,3 @@
 >      libs  16.9Mb
 
 The large size of these sub-directories is necessary.
-
-## CRAN Package Check Results for Package
-
-Fixed M1mac issue.
diff --git a/data/boron_pred.rda b/data/boron_pred.rda
diff --git a/data/dist_data.rda b/data/dist_data.rda
diff --git a/vignettes/additional-technical-details.Rmd b/vignettes/additional-technical-details.Rmd
@@ -43,7 +43,7 @@ knitr::opts_chunk$set(
 
 The ssdtools package uses the method of Maximum Likelihood (ML) to estimate parameters for each distribution that is fit to the data. 
 Statistical theory says that maximum likelihood estimators are asymptotically unbiased, but does not guarantee performance in small samples. 
-A detailed account of the issue of small sample bias in estimates can be found in the following [pdf](https://github.com/bcgov/ssdtools/blob/master/vignettes/small-sample-bias.pdf).
+A detailed account of the issue of small sample bias in estimates can be found in the small sample size vignette.
 
 ## Investigations into setting minimum sample sizes for uni-modal and bi-modal distributions in ssdtools