Update citation references to published JMVA paper

DESCRIPTION

@@ -5,7 +5,7 @@ Version: 0.1.0
 Authors@R: c(
 	person("Darjus", "Hosszejni", role = c("aut", "cre"), email = "darjus.hosszejni@wu.ac.at", comment = c(ORCID = "0000-0002-3803-691X")),
 	person("Sylvia", "Frühwirth-Schnatter", role = c("ths"), email = "sylvia.fruehwirth-schnatter@wu.ac.at", comment = c(ORCID = "0000-0003-0516-5552")))
-Description: This is an implementation of the algorithm described in Section 3 of Hosszejni and Frühwirth-Schnatter (2022) <doi:10.48550/arXiv.2211.00671>. The algorithm is used to verify that the counting rule CR(r,1) holds for the sparsity pattern of the transpose of a factor loading matrix. As detailed in Section 2 of the same paper, if CR(r,1) holds, then the idiosyncratic variances are generically identified. If CR(r,1) does not hold, then we do not know whether the idiosyncratic variances are identified or not.
+Description: This is an implementation of the algorithm described in Section 3 of Hosszejni and Frühwirth-Schnatter (2026) <doi:10.1016/j.jmva.2025.105536>. The algorithm is used to verify that the counting rule CR(r,1) holds for the sparsity pattern of the transpose of a factor loading matrix. As detailed in Section 2 of the same paper, if CR(r,1) holds, then the idiosyncratic variances are generically identified. If CR(r,1) does not hold, then we do not know whether the idiosyncratic variances are identified or not.
 License: GPL (>= 3)
 SystemRequirements: C++14
 Encoding: UTF-8

R/RcppExports.R

@@ -4,7 +4,7 @@
 #' Verify that the counting rule CR(r,1) holds
 #'
 #' This is an implementation of the algorithm described in Section 3 of
-#' Hosszejni and Fruehwirth-Schnatter (2022). The algorithm is used to verify
+#' Hosszejni and Fruehwirth-Schnatter (2026). The algorithm is used to verify
 #' that the counting rule CR(r,1) holds for the sparsity pattern of the transpose
 #' of a factor loading matrix. As detailed in Section 2 of the same paper, if
 #' CR(r,1) holds, then the idiosyncratic variances are generically identified.
@@ -17,9 +17,10 @@
 #' @keywords models multivariate
 #' @concept factor analysis variance identification
 #' @seealso [stats::factanal()]
-#' @references Hosszejni and Fruehwirth-Schnatter (2022). "Cover It Up! Bipartite
-#'    Graphs Uncover Identifiability in Sparse Factor Analysis". arXiv:2211.00671.
-#'    <doi:10.48550/arXiv.2211.00671>
+#' @references Hosszejni and Fruehwirth-Schnatter (2026). "Cover It Up! Bipartite
+#'    Graphs Uncover Identifiability in Sparse Factor Analysis". Journal of
+#'    Multivariate Analysis, 211, 105536. ISSN 0047-259X.
+#'    <doi:10.1016/j.jmva.2025.105536>
 #' @example inst/examples/counting_rule_holds.R
 #' @export
 counting_rule_holds <- function(delta) {

README.Rmd

@@ -16,7 +16,7 @@ knitr::opts_chunk$set(
 # Package `sparvaride`
 
 The package implements the variance identification algorithm for sparse factor analysis described in the paper "Cover It Up! Bipartite Graphs Uncover Identifiability in Sparse Factor Analysis" by Darjus Hosszejni and Sylvia Frühwirth-Schnatter.
-The paper is available at [arXiv](https://arxiv.org/abs/2211.00671).
+The paper is published in the [Journal of Multivariate Analysis](https://doi.org/10.1016/j.jmva.2025.105536).
 
 The package is still under development and the API is subject to change.
 For a Matlab implementation, see [`sparvaride-matlab`](https://github.com/hdarjus/sparvaride-matlab).

README.md

@@ -6,8 +6,9 @@
 The package implements the variance identification algorithm for sparse
 factor analysis described in the paper “Cover It Up\! Bipartite Graphs
 Uncover Identifiability in Sparse Factor Analysis” by Darjus Hosszejni
-and Sylvia Frühwirth-Schnatter. The paper is available at
-[arXiv](https://arxiv.org/abs/2211.00671).
+and Sylvia Frühwirth-Schnatter. The paper is published in the [Journal
+of Multivariate
+Analysis](https://doi.org/10.1016/j.jmva.2025.105536).
 
 The package is still under development and the API is subject to change.
 For a Matlab implementation, see [`sparvaride-matlab`](https://github.com/hdarjus/sparvaride-matlab).
@@ -75,18 +76,22 @@ citation("sparvaride")
 #> 
 #> To cite sparvaride in publications use:
 #> 
-#>   Hosszejni D, Frühwirth-Schnatter S (2022). "Cover It Up! Bipartite
-#>   Graphs Uncover Identifiability in Sparse Factor Analysis."
-#>   doi:10.48550/arXiv.2211.00671
-#>   <https://doi.org/10.48550/arXiv.2211.00671>, arXiv: 2211.00671.
+#>   Hosszejni D, Frühwirth-Schnatter S (2026). "Cover It Up! Bipartite
+#>   Graphs Uncover Identifiability in Sparse Factor Analysis." _Journal
+#>   of Multivariate Analysis_, *211*, 105536. ISSN 0047-259X,
+#>   doi:10.1016/j.jmva.2025.105536
+#>   <https://doi.org/10.1016/j.jmva.2025.105536>.
 #> 
 #> A BibTeX entry for LaTeX users is
 #> 
-#>   @Unpublished{,
+#>   @Article{,
 #>     title = {Cover It Up! Bipartite Graphs Uncover Identifiability in Sparse Factor Analysis},
 #>     author = {Darjus Hosszejni and Sylvia Frühwirth-Schnatter},
-#>     year = {2022},
-#>     note = {arXiv: 2211.00671},
-#>     doi = {10.48550/arXiv.2211.00671},
+#>     journal = {Journal of Multivariate Analysis},
+#>     year = {2026},
+#>     volume = {211},
+#>     pages = {105536},
+#>     issn = {0047-259X},
+#>     doi = {10.1016/j.jmva.2025.105536},
 #>   }
 ```

_codeql_detected_source_root

@@ -0,0 +1 @@
+.

inst/CITATION

@@ -5,17 +5,20 @@ if(!exists("meta") || is.null(meta)) meta <- packageDescription("sparvaride")
 year <- sub("-.*", "", meta$Packaged)
 note <- sprintf("R package version %s", meta$Version)
 
-bibentry(bibtype = "unpublished",
+bibentry(bibtype = "article",
   title        = "Cover It Up! Bipartite Graphs Uncover Identifiability in Sparse Factor Analysis",
   author       = c(person(given = "Darjus",
                           family = "Hosszejni",
                           email = "darjus.hosszejni@wu.ac.at"),
                    person(given = "Sylvia",
                           family = "Frühwirth-Schnatter",
                           email = "sylvia.fruehwirth-schnatter@wu.ac.at")),
-  year         = "2022",
-  note         = "arXiv: 2211.00671",
-  doi          = "10.48550/arXiv.2211.00671",
+  journal      = "Journal of Multivariate Analysis",
+  year         = "2026",
+  volume       = "211",
+  pages        = "105536",
+  issn         = "0047-259X",
+  doi          = "10.1016/j.jmva.2025.105536",
   header       = "To cite sparvaride in publications use:"
 )
 

src/check_counting_rule.cpp

@@ -27,7 +27,7 @@
 //' Verify that the counting rule CR(r,1) holds
 //'
 //' This is an implementation of the algorithm described in Section 3 of
-//' Hosszejni and Fruehwirth-Schnatter (2022). The algorithm is used to verify
+//' Hosszejni and Fruehwirth-Schnatter (2026). The algorithm is used to verify
 //' that the counting rule CR(r,1) holds for the sparsity pattern of the transpose
 //' of a factor loading matrix. As detailed in Section 2 of the same paper, if
 //' CR(r,1) holds, then the idiosyncratic variances are generically identified.
@@ -40,9 +40,10 @@
 //' @keywords models multivariate
 //' @concept factor analysis variance identification
 //' @seealso [stats::factanal()]
-//' @references Hosszejni and Fruehwirth-Schnatter (2022). "Cover It Up! Bipartite
-//'    Graphs Uncover Identifiability in Sparse Factor Analysis". arXiv:2211.00671.
-//'    <doi:10.48550/arXiv.2211.00671>
+//' @references Hosszejni and Fruehwirth-Schnatter (2026). "Cover It Up! Bipartite
+//'    Graphs Uncover Identifiability in Sparse Factor Analysis". Journal of
+//'    Multivariate Analysis, 211, 105536. ISSN 0047-259X.
+//'    <doi:10.1016/j.jmva.2025.105536>
 //' @example inst/examples/counting_rule_holds.R
 //' @export
 // [[Rcpp::export(name="counting_rule_holds", rng=false)]]

vignettes/short_intro.Rmd

@@ -18,7 +18,7 @@ knitr::opts_chunk$set(
 
 This is a short introduction to the `sparvaride` package.
 The package implements the variance identification algorithm for sparse factor analysis described in the paper "Cover It Up! Bipartite Graphs Uncover Identifiability in Sparse Factor Analysis" by Darjus Hosszejni and Sylvia Frühwirth-Schnatter.
-The paper is available at [arXiv](https://arxiv.org/abs/2211.00671).
+The paper is published in the [Journal of Multivariate Analysis](https://doi.org/10.1016/j.jmva.2025.105536).
 
 The package is still under development and the API is subject to change.
 
@@ -34,7 +34,7 @@ For mathematical tractability, we assume orthogonal factors, i.e., $\text{cov}(f
 
 In this setup, the covariance matrix $\text{cov}(\epsilon_i)$ of the idiosyncratic errors may not be uniquely identified if there are too many zeros in $\beta$ (and thus in $\delta$).
 The 3579 counting rule is a sufficient condition for the uniqueness of the covariance matrix $\text{cov}(\epsilon_i)$.
-More information on the 3579 counting rule can be found in the paper "Cover It Up! Bipartite Graphs Uncover Identifiability in Sparse Factor Analysis" by Darjus Hosszejni and Sylvia Frühwirth-Schnatter at [https://arxiv.org/abs/2211.00671](https://arxiv.org/abs/2211.00671).
+More information on the 3579 counting rule can be found in the paper "Cover It Up! Bipartite Graphs Uncover Identifiability in Sparse Factor Analysis" by Darjus Hosszejni and Sylvia Frühwirth-Schnatter at [https://doi.org/10.1016/j.jmva.2025.105536](https://doi.org/10.1016/j.jmva.2025.105536).
 
 ## The 3579 Counting Rule