standardize references

also fix badges links

.Rbuildignore

@@ -10,6 +10,4 @@
 ^_pkgdown\.yml$
 ^docs$
 ^pkgdown$
-^CODE_OF_CONDUCT\.md$
-^vignettes/TwoSample_test\.Rmd$
-^vignettes/kSample_test\.Rmd$
+^CODE_OF_CONDUCT\.md$

DESCRIPTION

@@ -13,11 +13,11 @@ Maintainer: Giovanni Saraceno <[email protected]>
 Description: It includes test for multivariate normality, test for uniformity on the d-dimensional 
     Sphere, non-parametric two- and k-sample tests, random generation of points from the Poisson 
     kernel-based density and clustering algorithm for spherical data. For more information see
-    Saraceno, G., Markatou, M., Mukhopadhyay, R., Golzy, M. (2024)
+    Saraceno G., Markatou M., Mukhopadhyay R. and Golzy M. (2024)
     <doi:10.48550/arXiv.2402.02290>
-    Markatou, M., Saraceno, G. (2024) <doi:10.48550/arXiv.2407.16374>, 
-    Ding, Y., Markatou, M., Saraceno, G. (2023) <doi:10.5705/ss.202022.0347>, 
-    and Golzy, M., Markatou, M. (2020) <doi:10.1080/10618600.2020.1740713>.
+    Markatou, M. and Saraceno, G. (2024) <doi:10.48550/arXiv.2407.16374>, 
+    Ding, Y., Markatou, M. and Saraceno, G. (2023) <doi:10.5705/ss.202022.0347>, 
+    and Golzy, M. and Markatou, M. (2020) <doi:10.1080/10618600.2020.1740713>.
 License: GPL (>= 3)
 URL: https://cran.r-project.org/package=QuadratiK, 
      https://github.com/giovsaraceno/QuadratiK-package, 

R/QuadratiK-package.R

@@ -59,26 +59,27 @@
 #' Usage instruction for the Dashboard can be found at 
 #' <https://quadratik.readthedocs.io/en/latest/user_guide/dashboard_application_usage.html>.
 #' 
-#' @author Giovanni Saraceno, Marianthi Markatou, 
-#'          Raktim Mukhopadhyay, Mojgan Golzy
-#'          \email{[email protected]}
+#' @author 
+#' Giovanni Saraceno, Marianthi Markatou, Raktim Mukhopadhyay, Mojgan Golzy 
+#'          
+#' Mantainer: Giovanni Saraceno \email{[email protected]}
 #'    
 #'          
 #' @references
-#' Saraceno Giovanni, Markatou Marianthi, Mukhopadhyay Raktim, Golzy Mojgan 
+#' Saraceno, G., Markatou, M., Mukhopadhyay, R. and Golzy, M. 
 #' (2024). Goodness-of-Fit and Clustering of Spherical Data: the QuadratiK 
 #' package in R and Python. arXiv preprint arXiv:2402.02290.
 #' 
-#' Ding Yuxin, Markatou Marianthi, Saraceno Giovanni (2023). “Poisson 
+#' Ding, Y., Markatou, M. and Saraceno, G. (2023). “Poisson 
 #' Kernel-Based Tests for Uniformity on the d-Dimensional Sphere.” 
 #' Statistica Sinica. doi: doi:10.5705/ss.202022.0347.
 #' 
-#' Golzy Mojan & Markatou Marianthi (2020) Poisson Kernel-Based Clustering on 
+#' Golzy, M. and Markatou, M. (2020) Poisson Kernel-Based Clustering on 
 #' the Sphere: Convergence Properties, Identifiability, and a Method of 
 #' Sampling, Journal of Computational and Graphical Statistics, 29:4, 758-770, 
 #' DOI: 10.1080/10618600.2020.1740713.
 #' 
-#' Markatou Marianthi & Saraceno Giovanni (2024). “A Unified Framework for 
+#' Markatou, M. and Saraceno, G. (2024). “A Unified Framework for 
 #' Multivariate Two- and k-Sample Kernel-based Quadratic Distance 
 #' Goodness-of-Fit Tests.” \cr
 #' https://doi.org/10.48550/arXiv.2407.16374

R/clustering_functions.R

@@ -99,7 +99,7 @@
 #'   \code{input}: List of input information.
 #'
 #' @references
-#' Golzy, M., Markatou, M. (2020) Poisson Kernel-Based Clustering on the 
+#' Golzy, M. and Markatou, M. (2020) Poisson Kernel-Based Clustering on the 
 #' Sphere: Convergence Properties, Identifiability, and a Method of Sampling, 
 #' Journal of Computational and Graphical Statistics, 29:4, 758-770, 
 #' DOI: 10.1080/10618600.2020.1740713.
@@ -1045,7 +1045,7 @@ setMethod("predict", signature(object="pkbc"),
 #'          \linkS4class{pkbc} for the class object definition.
 #'
 #' @references
-#' Kapp, A.V., Tibshirani, R. (2007) "Are clusters found in one dataset present 
+#' Kapp, A.V. and Tibshirani, R. (2007) "Are clusters found in one dataset present 
 #' in another dataset?", Biostatistics, 8(1), 9–31, 
 #' https://doi.org/10.1093/biostatistics/kxj029
 #' 

R/h_selection.R

@@ -95,12 +95,12 @@
 #' this when applying the function to large or complex data.
 #' 
 #' @references
-#' Markatou Marianthi & Saraceno Giovanni (2024). “A Unified Framework for
+#' Markatou, M. and Saraceno, G. (2024). “A Unified Framework for
 #' Multivariate Two- and k-Sample Kernel-based Quadratic Distance 
 #' Goodness-of-Fit Tests.” \cr 
 #' https://doi.org/10.48550/arXiv.2407.16374
 #' 
-#' Saraceno, G., Markatou, M., Mukhopadhyay, R., Golzy, M. (2024). 
+#' Saraceno, G., Markatou, M., Mukhopadhyay, R. and Golzy, M. (2024). 
 #' Goodness-of-Fit and Clustering of Spherical Data: the QuadratiK package 
 #' in R and Python. \cr
 #' https://arxiv.org/abs/2402.02290.

R/kb.test.R

@@ -209,12 +209,12 @@
 #' required.
 #'
 #' @references
-#' Markatou Marianthi & Saraceno Giovanni (2024). “A Unified Framework for 
+#' Markatou, M. and Saraceno, G. (2024). “A Unified Framework for 
 #' Multivariate Two- and k-Sample Kernel-based Quadratic Distance 
 #' Goodness-of-Fit Tests.” \cr
 #' https://doi.org/10.48550/arXiv.2407.16374
 #'
-#' Lindsay, B.G., Markatou, M. & Ray, S. (2014) "Kernels, Degrees of Freedom, 
+#' Lindsay, B.G., Markatou, M. and Ray, S. (2014) "Kernels, Degrees of Freedom, 
 #' and Power Properties of Quadratic Distance Goodness-of-Fit Tests", Journal 
 #' of the American Statistical Association, 109:505, 395-410, 
 #' DOI: 10.1080/01621459.2013.836972

R/pk.test.R

@@ -87,7 +87,7 @@
 #'
 #'
 #' @references
-#' Ding, Y., Markatou, M., Saraceno, G. (2023). “Poisson Kernel-Based Tests for 
+#' Ding, Y., Markatou, M. and Saraceno, G. (2023). “Poisson Kernel-Based Tests for 
 #' Uniformity on the d-Dimensional Sphere.” Statistica Sinica. 
 #' doi:10.5705/ss.202022.0347
 #' 

R/pkbd_functions.R

@@ -143,12 +143,12 @@ dpkb <- function(x, mu, rho, logdens = FALSE) {
 #' message asking the user to install the missing package(s).
 #' 
 #' @references
-#' Golzy, M., Markatou, M. (2020) Poisson Kernel-Based Clustering on the Sphere:
+#' Golzy, M. and Markatou, M. (2020) Poisson Kernel-Based Clustering on the Sphere:
 #' Convergence Properties, Identifiability, and a Method of Sampling, Journal of
 #' Computational and Graphical Statistics, 29:4, 758-770, 
 #' DOI: 10.1080/10618600.2020.1740713.
 #' 
-#' Sablica L., Hornik K., Leydold J. (2023) "Efficient sampling from the PKBD 
+#' Sablica L., Hornik K. and Leydold J. (2023) "Efficient sampling from the PKBD 
 #' distribution", Electronic Journal of Statistics, 17(2), 2180-2209.
 #'
 #' @srrstats {G1.0} Reference section reports the related literature.
@@ -239,7 +239,7 @@ rpkb <- function(n, mu, rho, method = 'rejacg',
 #' @param p dimension.
 #' 
 #' @references
-#' Golzy, M., Markatou, M. (2020) Poisson Kernel-Based Clustering on the Sphere:
+#' Golzy, M. and Markatou, M. (2020) Poisson Kernel-Based Clustering on the Sphere:
 #' Convergence Properties, Identifiability, and a Method of Sampling, Journal of
 #' Computational and Graphical Statistics, 29:4, 758-770, 
 #' DOI: 10.1080/10618600.2020.1740713.
@@ -282,7 +282,7 @@ rejvmf <- function(n, rho, mu, p) {
 #' @param p dimension.
 #' 
 #' @references
-#' Sablica L., Hornik K., Leydold J. (2023) "Efficient sampling from the PKBD 
+#' Sablica L., Hornik K. and Leydold J. (2023) "Efficient sampling from the PKBD 
 #' distribution", Electronic Journal of Statistics, 17(2), 2180-2209.
 #' 
 #' @importFrom stats runif
@@ -350,7 +350,7 @@ rejacg <- function(n, rho, mu, p, tol.eps, max.iter){
 #' @param p dimension.
 #' 
 #' @references
-#' Sablica L., Hornik K., Leydold J. (2023) "Efficient sampling from the PKBD 
+#' Sablica L., Hornik K. and Leydold J. (2023) "Efficient sampling from the PKBD 
 #' distribution", Electronic Journal of Statistics, 17(2), 2180-2209.
 #' 
 #' 

R/wine.R

@@ -38,7 +38,7 @@
 #' https://doi.org/10.24432/C5PC7J.
 #'
 #' @references
-#' Aeberhard, S., Coomans, D., & De Vel, O. (1994). Comparative analysis of 
+#' Aeberhard, S., Coomans, D. and De Vel, O. (1994). Comparative analysis of 
 #' statistical pattern recognition methods in high dimensional settings. 
 #' Pattern Recognition, 27(8), 1065-1077.
 #' 

R/wireless.R

@@ -33,7 +33,7 @@
 #' https://doi.org/10.24432/C51880.
 #'
 #' @references
-#' Rohra, J.G., Perumal, B., Narayanan, S.J., Thakur, P., Bhatt, R.B. (2017). 
+#' Rohra, J.G., Perumal, B., Narayanan, S.J., Thakur, P. and Bhatt, R.B. (2017).
 #' "User Localization in an Indoor Environment Using Fuzzy Hybrid of Particle 
 #' Swarm Optimization & Gravitational Search Algorithm with Neural Networks". 
 #' In: Deep, K., et al. Proceedings of Sixth International Conference on Soft 

README.md

@@ -1,8 +1,8 @@
 <!-- badges: start -->
 
-| Usage | Release | Development |
+| Usage  | Release | Development |
 |------------------|------------------------|------------------------------|
-| [![CRAN_Downloads_Badge](https://cranlogs.r-pkg.org/badges/grand-total/QuadratiK)](https://cran.r-project.org/package=QuadratiK) [![License: GPL (\>= 3)](https://img.shields.io/badge/license-GPL%20(%3E=%203)-blue.svg)](https://cran.r-project.org/web/licenses/GPL%20(%3E=%203)) | [![arXiv](https://img.shields.io/badge/doi-arXiv:2402.02290v2-green.svg)](https://doi.org/arXiv:2402.02290v2) [![CRAN version](https://www.r-pkg.org/badges/version/QuadratiK)](https://CRAN.R-project.org/package=QuadratiK) [![GitHub version](https://img.shields.io/badge/devel%20version-1.1.2-blue.svg)](https://github.com/giovsaraceno/QuadratiK-package) | [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/632_status.svg)](https://github.com/ropensci/software-review/issues/632) [![codecov](https://codecov.io/github/giovsaraceno/QuadratiK-package/graph/badge.svg?token=M4CDTQPONE)](https://codecov.io/github/giovsaraceno/QuadratiK-package) [![R-CMD-check](https://github.com/giovsaraceno/QuadratiK-package/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/giovsaraceno/QuadratiK-package/actions/workflows/R-CMD-check.yaml) [![Lifecycle](https://img.shields.io/badge/lifecycle-stable-green.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable) |
+| [![CRAN_Downloads_Badge](https://cranlogs.r-pkg.org/badges/grand-total/QuadratiK)](https://cran.r-project.org/package=QuadratiK) [![License: GPL v3](https://img.shields.io/badge/license-GPLv3-blue.svg)](https://giovsaraceno.github.io/QuadratiK-package/LICENSE.html)|[![arXiv](https://img.shields.io/badge/doi-arXiv:2402.02290v2-green.svg)](https://doi.org/10.48550/arXiv.2402.02290) [![CRAN version](https://www.r-pkg.org/badges/version/QuadratiK)](https://CRAN.R-project.org/package=QuadratiK) [![GitHub version](https://img.shields.io/badge/devel%20version-1.1.2-blue.svg)](https://github.com/giovsaraceno/QuadratiK-package) | [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/632_status.svg)](https://github.com/ropensci/software-review/issues/632) [![codecov](https://codecov.io/github/giovsaraceno/QuadratiK-package/graph/badge.svg?token=M4CDTQPONE)](https://codecov.io/github/giovsaraceno/QuadratiK-package) [![R-CMD-check](https://github.com/giovsaraceno/QuadratiK-package/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/giovsaraceno/QuadratiK-package/actions/workflows/R-CMD-check.yaml) [![Lifecycle](https://img.shields.io/badge/lifecycle-stable-green.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable) |             |
 
 <!-- badges: end -->
 
@@ -44,7 +44,7 @@ Mantainer: Giovanni Saraceno \<[gsaracen\@buffalo.edu](mailto:[email protected]
 
 If you use this package in your research or work, please cite it as follows:
 
-Saraceno G, Markatou M, Mukhopadhyay R, Golzy M (2024). QuadratiK: Collection of Methods Constructed using Kernel-Based Quadratic Distances. <https://cran.r-project.org/package=QuadratiK>, <https://github.com/giovsaraceno/QuadratiK-package>, <https://giovsaraceno.github.io/QuadratiK-package/>.
+Saraceno, G., Markatou, M., Mukhopadhyay, R. and Golzy, M. (2024). QuadratiK: Collection of Methods Constructed using Kernel-Based Quadratic Distances. <https://cran.r-project.org/package=QuadratiK>, <https://github.com/giovsaraceno/QuadratiK-package>, <https://giovsaraceno.github.io/QuadratiK-package/>.
 
 ```         
 @Manual{saraceno2024QuadratiK,  
@@ -61,7 +61,7 @@ Saraceno G, Markatou M, Mukhopadhyay R, Golzy M (2024). QuadratiK: Collection of
 
 and the associated paper:
 
-Saraceno Giovanni, Markatou Marianthi, Mukhopadhyay Raktim, Golzy Mojgan (2024). Goodness-of-Fit and Clustering of Spherical Data: the QuadratiK package in R and Python. arXiv preprint [arXiv:2402.02290v2](https://arxiv.org/abs/2402.02290).
+Saraceno, G., Markatou, M., Mukhopadhyay, R. and Golzy, M. (2024). Goodness-of-Fit and Clustering of Spherical Data: the QuadratiK package in R and Python. arXiv preprint [arXiv:2402.02290v2](https://arxiv.org/abs/2402.02290).
 
 ```         
 @misc{saraceno2024package, 
@@ -78,11 +78,11 @@ Saraceno Giovanni, Markatou Marianthi, Mukhopadhyay Raktim, Golzy Mojgan (2024).
 
 ## References
 
--   Ding Yuxin, Markatou Marianthi, Saraceno Giovanni (2023). “Poisson Kernel-Based Tests for Uniformity on the d-Dimensional Sphere.” Statistica Sinica. doi: [10.5705/ss.202022.0347](https://doi.org/10.5705/ss.202022.0347).
+-   Ding, Y., Markatou, M. and Saraceno, G. (2023). “Poisson Kernel-Based Tests for Uniformity on the d-Dimensional Sphere.” Statistica Sinica. doi: [10.5705/ss.202022.0347](https://doi.org/10.5705/ss.202022.0347).
 
--   Mojgan Golzy & Marianthi Markatou (2020) Poisson Kernel-Based Clustering on the Sphere: Convergence Properties, Identifiability, and a Method of Sampling, Journal of Computational and Graphical Statistics, 29:4, 758-770, DOI: [10.1080/10618600.2020.1740713](https://doi.org/10.1080/10618600.2020.1740713).
+-   Golzy, M. & Markatou, M. (2020) Poisson Kernel-Based Clustering on the Sphere: Convergence Properties, Identifiability, and a Method of Sampling, Journal of Computational and Graphical Statistics, 29:4, 758-770, DOI: [10.1080/10618600.2020.1740713](https://doi.org/10.1080/10618600.2020.1740713).
 
--   Markatou Marianthi & Saraceno Giovanni (2024). “A Unified Framework for Multivariate Two- and k-Sample Kernel-based Quadratic Distance Goodness-of-Fit Tests.” [arXiv:2407.16374](https://doi.org/10.48550/arXiv.2407.16374)
+-   Markatou, M. and Saraceno, G. (2024). “A Unified Framework for Multivariate Two- and k-Sample Kernel-based Quadratic Distance Goodness-of-Fit Tests.” [arXiv:2407.16374](https://doi.org/10.48550/arXiv.2407.16374)
 
 ## Details