Updating citations with DOI

DESCRIPTION

@@ -8,7 +8,7 @@ Authors@R: c(
     person("Alexandre", "Simas", email = "[email protected]", role = "aut"),
     person("Finn", "Lindgren", email = "[email protected]", role = "ctb"))
 Maintainer: David Bolin <[email protected]>
-Description: Functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main references for rSPDE are Bolin, Simas and Xiong (2023) <doi:10.48550/arXiv.2209.04670> for the covariance-based method and Bolin and Kirchner (2020) <doi:10.1080/10618600.2019.1665537> for the operator-based rational approximation. These can be generated by the citation function in R.
+Description: Functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main references for rSPDE are Bolin, Simas and Xiong (2023) <doi:10.1080/10618600.2023.2231051> for the covariance-based method and Bolin and Kirchner (2020) <doi:10.1080/10618600.2019.1665537> for the operator-based rational approximation. These can be generated by the citation function in R.
 Depends: R (>= 3.5.0), Matrix
 Imports: stats, methods
 License: GPL (>=3) | file LICENSE

README.md

@@ -132,4 +132,4 @@ git flow hotfix finish hotfix_branch_name
 [ref6]: https://davidbolin.github.io/rSPDE//articles/rspde_cov.html
 [ref7]: https://davidbolin.github.io/rSPDE/reference/index.html "`rSPDE` documentation."
 [ref8]: https://sites.google.com/inlabru.org/inlabru "inlabru homepage"
-[ref9]: https://arxiv.org/abs/2209.04670 "Covariance-based rational approximations of fractional SPDEs for computationally efficient Bayesian inference"
+[ref9]: https://doi.org/10.1080/10618600.2023.2231051 "Covariance-based rational approximations of fractional SPDEs for computationally efficient Bayesian inference"

inst/CITATION

@@ -29,7 +29,7 @@ bibentry(bibtype = "Article",
 
   journal      = "Journal of Computational and Graphical Statistics",
   year         = "2023",
-  doi          = "10.48550/arXiv.2209.04670",
+  doi          = "10.1080/10618600.2023.2231051",
   note ="(in press)",
   paste0("David Bolin, Alexandre B. Simas, Zhen Xiong (2023), ",
              "Covariance-based rational approximations of fractional SPDEs for computationally efficient Bayesian inference. ",

vignettes/rSPDE.html

@@ -371,7 +371,7 @@ <h2>Introduction</h2>
 <p>In this vignette we provide a brief introduction to the
 <code>rSPDE</code> package. The main approach for constructing the
 rational approximations is the covariance-based rational SPDE approach
-of <a href="https://arxiv.org/abs/2209.04670"><span class="citation">Bolin, Simas, and Xiong (2023)</span></a>. The package
+of <a href="https://doi.org/10.1080/10618600.2023.2231051"><span class="citation">Bolin, Simas, and Xiong (2023)</span></a>. The package
 contains three main “families” of functions that implement the
 approach:</p>
 <ul>

vignettes/rspde_cov.html

@@ -374,7 +374,7 @@ <h2>Introduction</h2>
 it.</p>
 <p>The covariance-based approach is an efficient alternative to the
 operator-based rational SPDE approach by <a href="https://www.tandfonline.com/doi/full/10.1080/10618600.2019.1665537"><span class="citation">Bolin and Kirchner (2020)</span></a> which works when
-one has SPDE driven by Gaussian white noise. We refer the reader to <a href="https://arxiv.org/abs/2209.04670"><span class="citation">Bolin,
+one has SPDE driven by Gaussian white noise. We refer the reader to <a href="https://doi.org/10.1080/10618600.2023.2231051"><span class="citation">Bolin,
 Simas, and Xiong (2023)</span></a> for the theoretical details of the
 approach.</p>
 <p>Details about the operator-based rational SPDE approach are given in
@@ -1287,7 +1287,7 @@ <h2>Changing the type and the order of the rational approximation</h2>
 <p>We have three rational approximations available. The BRASIL algorithm
 <a href="https://doi.org/10.1007/s11075-020-01042-0"><span class="citation">Hofreither (2021)</span></a>, and two “versions” of the
 Clenshaw-Lord Chebyshev-Pade algorithm, one with lower bound zero and
-another with the lower bound given in <a href="https://arxiv.org/abs/2209.04670"><span class="citation">Bolin,
+another with the lower bound given in <a href="https://doi.org/10.1080/10618600.2023.2231051"><span class="citation">Bolin,
 Simas, and Xiong (2023)</span></a>.</p>
 <p>The type of rational approximation can be chosen by setting the
 <code>type_rational_approximation</code> argument in the

vignettes/rspde_inla.html

@@ -372,7 +372,7 @@ <h2>Introduction</h2>
 <p>In this vignette we will present the <a href="https://www.r-inla.org"><code>R-INLA</code></a> implementation of
 the rational SPDE approach. For theoretical details we refer the reader
 to the <a href="rspde_cov.html">Rational approximation with the
-<code>rSPDE</code> package</a> vignette and to <a href="https://arxiv.org/abs/2209.04670"><span class="citation">Bolin,
+<code>rSPDE</code> package</a> vignette and to <a href="https://doi.org/10.1080/10618600.2023.2231051"><span class="citation">Bolin,
 Simas, and Xiong (2023)</span></a>.</p>
 <p>We begin by providing a step-by-step illustration on how to use our
 implementation. To this end we will consider a real world data set that
@@ -1994,7 +1994,7 @@ <h3>Changing the type of the rational approximation</h3>
 <p>We have three rational approximations available. The BRASIL algorithm
 <a href="https://doi.org/10.1007/s11075-020-01042-0"><span class="citation">Hofreither (2021)</span></a>, and two “versions” of the
 Clenshaw-Lord Chebyshev-Pade algorithm, one with lower bound zero and
-another with the lower bound given in <a href="https://arxiv.org/abs/2209.04670"><span class="citation">Bolin,
+another with the lower bound given in <a href="https://doi.org/10.1080/10618600.2023.2231051"><span class="citation">Bolin,
 Simas, and Xiong (2023)</span></a>.</p>
 <p>The type of rational approximation can be chosen by setting the
 <code>type.rational.approx</code> argument in the

vignettes/rspde_inlabru.html

@@ -372,7 +372,7 @@ <h2>Introduction</h2>
 <p>In this vignette we will present the <a href="http://inlabru.org/"><code>inlabru</code></a> implementation of
 the covariance-based rational SPDE approach. For further technical
 details on the covariance-based approach, see the <a href="rspde_cov.html">Rational approximation with the <code>rSPDE</code>
-package</a> vignette and <a href="https://arxiv.org/abs/2209.04670"><span class="citation">Bolin,
+package</a> vignette and <a href="https://doi.org/10.1080/10618600.2023.2231051"><span class="citation">Bolin,
 Simas, and Xiong (2023)</span></a>.</p>
 <p>We begin by providing a step-by-step illustration on how to use our
 implementation. To this end we will consider a real world data set that