URLs to DOIs

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: walker
 Type: Package
 Title: Bayesian Generalized Linear Models with Time-Varying Coefficients
-Version: 1.0.9
+Version: 1.0.10
 Description: Efficient Bayesian generalized linear models with time-varying coefficients 
     as in Helske (2022, <doi:10.1016/j.softx.2022.101016>). Gaussian, Poisson, and binomial 
     observations are supported. The Markov chain Monte Carlo (MCMC) computations are done using 

R/walker.R

@@ -322,9 +322,7 @@ walker <- function(formula, data, sigma_y_prior = c(2, 0.01), beta, init, chains
 #' `plot_coefs`, and `plot_predict` resample the posterior based on weights 
 #' before plotting, leading to "exact" analysis.
 #' 
-#' The underlying idea of `walker_glm` is based on paper 
-#' "Importance sampling type estimators based on approximate marginal MCMC" by 
-#' Vihola M, Helske J and Franks J which is available at ArXiv.
+#' The underlying idea of `walker_glm` is based on Vihola, Helske, Franks (2020).
 #' 
 #' `walker_glm` uses the global approximation (i.e. start of the MCMC) instead of more accurate 
 #' but slower local approximation (where model is approximated at each iteration). 
@@ -336,6 +334,9 @@ walker <- function(formula, data, sigma_y_prior = c(2, 0.01), beta, init, chains
 #' constructs the approximation at that point, before running the Bayesian 
 #' analysis.
 #' 
+#' @references Vihola, M, Helske, J, Franks, J. (2020). Importance sampling 
+#' type estimators based on approximate marginal Markov chain Monte Carlo. 
+#' Scandinavian Journal of Statistics. 47: 1339–1376. \doi{doi:10.1111/sjos.12492}
 #' @inheritParams walker
 #' @importFrom KFAS SSModel SSMcustom fitSSM approxSSM
 #' @param distribution Either `"poisson"` or `"binomial"`.

README.md

@@ -13,7 +13,7 @@ The Markov chain Monte Carlo (MCMC) algorithm uses Hamiltonian Monte Carlo provi
 using a state space representation of the model in order to marginalise over the coefficients for accurate and efficient sampling.
 For non-Gaussian models the MCMC targets approximate marginal posterior based on Gaussian approximation, which is then corrected using importance sampling as in [Vihola, Helske, Franks (2020)](https://onlinelibrary.wiley.com/doi/10.1111/sjos.12492).
 
-See the corresponding paper in [SoftwareX](https://www.sciencedirect.com/science/article/pii/S235271102200022X) for short introduction, and the package [vignette](https://htmlpreview.github.io/?https://github.com/helske/walker/blob/master/walker_html/walker.html) and [documentation manual](https://cran.r-project.org/package=walker/walker.pdf) for details and further examples.
+See the corresponding paper in [SoftwareX](https://doi.org/10.1016/j.softx.2022.101016) for short introduction, and the package [vignette](https://cran.r-project.org/package=walker/vignettes/walker.html) and [documentation manual](https://cran.r-project.org/package=walker/walker.pdf) for details and further examples.
 
 You can download the development version of `walker` from Github using the [`devtools`](https://cran.r-project.org/package=devtools) package:
 
@@ -24,9 +24,12 @@ devtools::install_github("helske/walker")
 NEWS
 ---------------------------------------------
 
+### 28.8.2024, version 1.0.10
+  * Changed URLs to DOIs.
+
 ### 28.8.2024, version 1.0.9
 
-  * Fixed function links in to other packages in documentation.
+  * Fixed function links to other packages in documentation.
 
 ### 11.9.2023, version 1.0.8
 

inst/CITATION

@@ -2,14 +2,13 @@ c(
   bibentry(
     bibtype = "article",
     author = "Jouni Helske",
-    title = "Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R", 
+    title = "Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R",
     journal = "SoftwareX",
     volume = "18",
     pages = "101016",
     year = "2022",
     issn = "2352-7110",
     doi = "10.1016/j.softx.2022.101016",
-    url = "https://www.sciencedirect.com/science/article/pii/S235271102200022X",
     key = "walker"
   ),
   bibentry(
@@ -18,7 +17,7 @@ c(
     author = "Jouni Helske",
     year = sub("-.*", "", meta$Date),
     note = sprintf("R package version %s", meta$Version),
-    url = "https://github.com/helske/walker",
+    doi = "10.32614/CRAN.package.walker",
     key = "package"
   )
 )

vignettes/walker.bib

@@ -3,7 +3,6 @@ @article{vihola
     title = "Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo",
     journal = "Scandinavian Journal of Statistics",
     doi = "10.1111/sjos.12492",
-    url = "https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492",
     year = "2020"
 }
 
@@ -28,7 +27,6 @@ @article{walkerpaper
 year = {2022},
 issn = {2352-7110},
 doi = {10.1016/j.softx.2022.101016},
-url = {https://www.sciencedirect.com/science/article/pii/S235271102200022X},
 author = {Jouni Helske},
 keywords = {Bayesian inference, Time-varying regression, R, Markov chain Monte Carlo},
 abstract = {The R package walker extends standard Bayesian general linear models to the case where the effects of the explanatory variables can vary in time. This allows, for example, to model the effects of interventions such as changes in tax policy which gradually increases their effect over time. The Markov chain Monte Carlo algorithms powering the Bayesian inference are based on Hamiltonian Monte Carlo provided by Stan software, using a state space representation of the model to marginalize over the regression coefficients for efficient low-dimensional sampling.}