Remove 'web_only' article links

DESCRIPTION

@@ -109,5 +109,5 @@ Config/testthat/parallel: true
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 SystemRequirements: GNU make

NEWS.md

@@ -61,7 +61,7 @@
 # sdmTMB 0.5.0
 
 * Overhaul residuals vignette ('article') 
-  <https://pbs-assess.github.io/sdmTMB/articles/web_only/residual-checking.html>
+  <https://pbs-assess.github.io/sdmTMB/articles/residual-checking.html>
   including brief intros to randomized quantile residuals, simulation-based
   residuals, 'one-sample' residuals, and uniform vs. Gaussian residuals.
 

R/dharma.R

@@ -27,7 +27,7 @@
 #'
 #' @details
 #'
-#' See the [residuals vignette](https://pbs-assess.github.io/sdmTMB/articles/web_only/residual-checking.html).
+#' See the [residuals vignette](https://pbs-assess.github.io/sdmTMB/articles/residual-checking.html).
 #'
 #' Advantages to these residuals over the ones from the [residuals.sdmTMB()]
 #' method are (1) they work with delta/hurdle models for the combined

R/predict.R

@@ -45,7 +45,7 @@
 #' @param mcmc_samples See `extract_mcmc()` in the
 #'   \href{https://github.com/pbs-assess/sdmTMBextra}{sdmTMBextra} package for
 #'   more details and the
-#'   \href{https://pbs-assess.github.io/sdmTMB/articles/web_only/bayesian.html}{Bayesian vignette}.
+#'   \href{https://pbs-assess.github.io/sdmTMB/articles/bayesian.html}{Bayesian vignette}.
 #'   If specified, the predict function will return a matrix of a similar form
 #'   as if `nsim > 0` but representing Bayesian posterior samples from the Stan
 #'   model.

README.Rmd

@@ -509,7 +509,7 @@ See [`?sdmTMBpriors`](https://pbs-assess.github.io/sdmTMB/reference/priors.html)
 
 ### Bayesian MCMC sampling with Stan
 
-The fitted model can be passed to the tmbstan package to sample from the posterior with Stan. See the [Bayesian vignette](https://pbs-assess.github.io/sdmTMB/articles/web_only/bayesian.html).
+The fitted model can be passed to the tmbstan package to sample from the posterior with Stan. See the [Bayesian vignette](https://pbs-assess.github.io/sdmTMB/articles/bayesian.html).
 
 ### Turning off random fields
 

README.md

@@ -729,7 +729,7 @@ for more details.
 
 The fitted model can be passed to the tmbstan package to sample from the
 posterior with Stan. See the [Bayesian
-vignette](https://pbs-assess.github.io/sdmTMB/articles/web_only/bayesian.html).
+vignette](https://pbs-assess.github.io/sdmTMB/articles/bayesian.html).
 
 ### Turning off random fields
 

vignettes/articles/delta-models.Rmd

@@ -47,7 +47,7 @@ Here and with other delta models, the `link1` and `link2` can be omitted and lef
 2. Delta-lognormal: `family = delta_lognormal()`.
 This fits a binomial presence-absence model (i.e., `binomial(link = "logit")`) and then a model for the positive catches only with a lognormal observation distribution and a log link (i.e., `lognormal(link = "log")`
 
-3. Poisson-link delta-Gamma or delta-lognormal. See the [Poisson-link delta model vignette](https://pbs-assess.github.io/sdmTMB/articles/web_only/poisson-link.html).
+3. Poisson-link delta-Gamma or delta-lognormal. See the [Poisson-link delta model vignette](https://pbs-assess.github.io/sdmTMB/articles/poisson-link.html).
 
 4. Delta-truncated-negative-binomial: `family = delta_truncated_nbinom1()` or `family = delta_truncated_nbinom2()`.
 This fits a binomial presence-absence model (`binomial(link = "logit")`) and a `truncated_nbinom1(link = "log")` or `truncated_nbinom1(link = "log")` distribution for positive catches.