fixing NOTEs: https://win-builder.r-project.org/incoming_pretest/SSDM…

…_0.2.9_20231019_170256/Windows/00check.log

.Rbuildignore

@@ -12,3 +12,4 @@ ToDo.md
 ^\.github$
 ^codecov\.yml$
 ^\.covrignore$
+^CRAN-SUBMISSION$

NEWS.md

@@ -212,7 +212,7 @@ SSDM 0.1.9035
 SSDM 0.1.9034
 =============
 
--   `spThin` package responsible for the doc issue, not fixed but currently just commenting the pacakge to unactivate it in waiting. thread open on `Roxygen2` development repository: <https://github.com/klutometis/roxygen/issues/597>.
+-   `spThin` package responsible for the doc issue, not fixed but currently just commenting the pacakge to unactivate it in waiting. thread open on `Roxygen2` development repository.
 
 SSDM 0.1.9033
 =============

R/ensemble_modelling.R

@@ -125,7 +125,7 @@ NULL
 #'  \item{test}{character. Test used to evaluate the SDM, default 'AIC'.}
 #'  \item{control}{list (created with \code{\link[stats]{glm.control}}).
 #'  Contains parameters for controlling the fitting process. Default is \code{glm.control(epsilon = 1e-08, maxit = 500)}.
-#'  'epsilon' is a numeric and defines the positive convergence tolerance (eps). The iterations converge when \emph{|dev - dev_{old}|/(|dev| + 0.1) < eps}.
+#'  'epsilon' is a numeric and defines the positive convergence tolerance (eps). The iterations converge when \emph{|dev - dev_old|/(|dev| + 0.1) < eps}.
 #'  'maxit' is an integer giving the maximal number of IWLS (Iterative Weighted Last Squares) iterations.} }
 #'
 #'@section Generalized additive model (\strong{GAM}) : Uses the \code{gam}

R/stack_modelling.R

@@ -276,7 +276,7 @@ NULL
 #'  J.M. Calabrese, G. Certain, C.  Kraan, & C.F. Dormann (2014) "Stacking
 #'  species distribution  models  and  adjusting  bias  by linking them to
 #'  macroecological models." \emph{Global Ecology and Biogeography} 23:99-112
-#'  \url{http://portal.uni-freiburg.de/biometrie/mitarbeiter/dormann/calabrese2013globalecolbiogeogr.pdf}
+#'  \url{https://onlinelibrary.wiley.com/doi/full/10.1111/geb.12102}
 #'
 #'
 #'

R/stacking.R

@@ -100,7 +100,7 @@ NULL
 #'  J.M. Calabrese, G. Certain, C.  Kraan, & C.F. Dormann (2014) "Stacking
 #'  species distribution  models  and  adjusting  bias  by linking them to
 #'  macroecological models." \emph{Global Ecology and Biogeography} 23:99-112
-#'  \url{http://portal.uni-freiburg.de/biometrie/mitarbeiter/dormann/calabrese2013globalecolbiogeogr.pdf}
+#'  \url{https://onlinelibrary.wiley.com/doi/full/10.1111/geb.12102}
 #'
 #'  M. D. Crisp, S. Laffan, H. P. Linder & A. Monro (2001) "Endemism in the
 #'  Australian flora"  \emph{Journal of Biogeography} 28:183-198

README.md

@@ -2,8 +2,8 @@ SSDM: Stacked species distribution modelling
 ================
 
 [![R-CMD-check](https://github.com/sylvainschmitt/SSDM/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/sylvainschmitt/SSDM/actions/workflows/R-CMD-check.yaml)
-[![Downloads](http://cranlogs.r-pkg.org/badges/SSDM?color=brightgreen)](http://www.r-pkg.org/pkg/SSDM) 
-[![Codecov test coverage](https://codecov.io/gh/sylvainschmitt/SSDM/branch/master/graph/badge.svg)](https://app.codecov.io/gh/sylvainschmitt/SSDM?branch=master) [![Research software impact](http://depsy.org/api/package/cran/SSDM/badge.svg)](http://depsy.org/package/r/SSDM) [![Gitter](https://badges.gitter.im/S-SDM/community.svg)](https://gitter.im/S-SDM/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
+[![Downloads](http://cranlogs.r-pkg.org/badges/SSDM?color=brightgreen)](https://www.r-pkg.org/pkg/SSDM) 
+[![Codecov test coverage](https://codecov.io/gh/sylvainschmitt/SSDM/branch/master/graph/badge.svg)](https://app.codecov.io/gh/sylvainschmitt/SSDM?branch=master) [![Research software impact](http://depsy.org/api/package/cran/SSDM/badge.svg)](http://depsy.org/package/r/SSDM)
 
 SSDM is a package to map species richness and endemism based on stacked species distribution models (SSDM). Individual SDMs can be created using a single or multiple algorithms (ensemble SDMs). For each species, an SDM can yield a habitat suitability map, a binary map, a between-algorithm variance map, and can assess variable importance, algorithm accuracy, and between-algorithm correlation. Methods to stack individual SDMs include summing individual probabilities and thresholding then summing. Thresholding can be based on a specific evaluation metric or by drawing repeatedly from a Bernouilli distribution. The SSDM package also provides a user-friendly interface `gui`.
 
@@ -16,7 +16,7 @@ Please be aware that SSDM package use a lot of dependencies (see [`DESCRIPTION`]
 
 ### Install from Github
 
-You can install the latest version of **SSDM** from Github using the [`devtools`](https://github.com/hadley/devtools) package:
+You can install the latest version of **SSDM** from Github using the [`devtools`](https://github.com/r-lib/devtools) package:
 
 ``` r
 if (!requireNamespace("devtools", quietly = TRUE))

cran-comments.md

@@ -19,5 +19,8 @@ changes shows that we are able to assume all the needed dependencies.
 
 ## Resubmission
 
-This is a resubmission. In this version I have removed rgdal and sp 
-from the package dependencies following r-spatial package archiving.
+This is a resubmission. In this version I have:
+
+- removed rgdal and sp from the package dependencies following r-spatial package archiving
+- updated invalid URLs
+- removed lost braces from documentation