Merge pull request #11 from sor16/add-doi-links

DOI links added to references

DESCRIPTION

@@ -11,7 +11,7 @@ Description: Fits a discharge rating curve based on the power-law and the genera
 Depends: R (>= 3.5.0)
 License: MIT + file LICENSE
 LazyData: true
-RoxygenNote: 7.2.1
+RoxygenNote: 7.3.2
 Imports:
     ggplot2,
     grid,
@@ -25,5 +25,6 @@ Suggests:
     covr,
     vdiffr
 VignetteBuilder: knitr
+URL: https://sor16.github.io/bdrc
 BugReports: https://github.com/sor16/bdrc/issues
 Encoding: UTF-8

R/extract_draws.R

@@ -9,7 +9,7 @@
 #'\code{iter}
 #'\code{param}
 #'\code{value}
-#' @references Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711.
+#'@references Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711. doi: https://doi.org/10.1002/env.2711
 #'@seealso \code{\link{plm0}}, \code{\link{plm}}, \code{\link{gplm0}}, \code{\link{gplm}} for further information on parameters
 #'@examples
 #'\donttest{
@@ -54,7 +54,7 @@ spread_draws <- function(mod,...,transformed=FALSE){
 #'\code{iter}
 #'\code{param}
 #'\code{value}
-#' @references B. Hrafnkelsson, H. Sigurdarson, S.M. Gardarsson, 2020, Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling. arXiv preprint 2010.04769
+#'@references Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711. doi: https://doi.org/10.1002/env.2711
 #'@seealso \code{\link{plm0}}, \code{\link{plm}}, \code{\link{gplm0}}, \code{\link{gplm}} for further information on parameters
 #'@examples
 #'\donttest{

R/gplm.R

@@ -54,9 +54,9 @@
 #' \item{\code{formula}}{object of type "formula" provided by the user.}
 #' \item{\code{data}}{data provided by the user, ordered by stage.}
 #' \item{\code{run_info}}{information about the input arguments and the specific parameters used in the MCMC chain.}
-#' @references Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis, Third Edition. Chapman & Hall/CRC Texts in Statistical Science. Taylor & Francis.
-#' @references Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711.
-#' @references Spiegelhalter, D., Best, N., Carlin, B., Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4), 583–639.
+#' @references Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis, Third Edition. Chapman & Hall/CRC Texts in Statistical Science. Taylor & Francis. doi: https://doi.org/10.1201/b16018
+#' @references Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711. doi: https://doi.org/10.1002/env.2711
+#' @references Spiegelhalter, D., Best, N., Carlin, B., Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4), 583–639. doi: https://doi.org/10.1111/1467-9868.00353
 #' @references Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594.
 #' @seealso \code{\link{summary.gplm}} for summaries, \code{\link{predict.gplm}} for prediction and \code{\link{plot.gplm}} for plots. \code{\link{spread_draws}} and \code{\link{gather_draws}} are also useful to aid further visualization of the full posterior distributions.
 #'

R/gplm0.R

@@ -45,9 +45,9 @@
 #' \item{\code{formula}}{object of type "formula" provided by the user.}
 #' \item{\code{data}}{data provided by the user, ordered by stage.}
 #' \item{\code{run_info}}{information about the input arguments and the specific parameters used in the MCMC chain.}
-#' @references Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis, Third Edition. Chapman & Hall/CRC Texts in Statistical Science. Taylor & Francis.
-#' @references Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711.
-#' @references Spiegelhalter, D., Best, N., Carlin, B., Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4), 583–639.
+#' @references Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis, Third Edition. Chapman & Hall/CRC Texts in Statistical Science. Taylor & Francis. doi: https://doi.org/10.1201/b16018
+#' @references Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711. doi: https://doi.org/10.1002/env.2711
+#' @references Spiegelhalter, D., Best, N., Carlin, B., Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4), 583–639. doi: https://doi.org/10.1111/1467-9868.00353
 #' @references Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594.
 #' @seealso \code{\link{summary.gplm0}} for summaries, \code{\link{predict.gplm0}} for prediction. It is also useful to look at \code{\link{spread_draws}} and \code{\link{plot.gplm0}} to help visualize the full posterior distributions.
 #' @examples

R/plm.R

@@ -46,9 +46,9 @@
 #' \item{\code{formula}}{object of type "formula" provided by the user.}
 #' \item{\code{data}}{data provided by the user, ordered by stage.}
 #' \item{\code{run_info}}{information about the input arguments and the specific parameters used in the MCMC chain.}
-#' @references Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis, Third Edition. Chapman & Hall/CRC Texts in Statistical Science. Taylor & Francis.
-#' @references Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711.
-#' @references Spiegelhalter, D., Best, N., Carlin, B., Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4), 583–639.
+#' @references Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis, Third Edition. Chapman & Hall/CRC Texts in Statistical Science. Taylor & Francis. doi: https://doi.org/10.1201/b16018
+#' @references Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711. doi: https://doi.org/10.1002/env.2711
+#' @references Spiegelhalter, D., Best, N., Carlin, B., Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4), 583–639. doi: https://doi.org/10.1111/1467-9868.00353
 #' @references Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594.
 #' @seealso \code{\link{summary.plm}} for summaries, \code{\link{predict.plm}} for prediction. It is also useful to look at \code{\link{spread_draws}} and \code{\link{plot.plm}} to help visualize the full posterior distributions.
 #' @examples

R/plm0.R

@@ -39,9 +39,9 @@
 #' \item{\code{formula}}{object of type "formula" provided by the user.}
 #' \item{\code{data}}{data provided by the user, ordered by stage.}
 #' \item{\code{run_info}}{information about the input arguments and the specific parameters used in the MCMC chain.}
-#' @references Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis, Third Edition. Chapman & Hall/CRC Texts in Statistical Science. Taylor & Francis.
-#' @references Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711.
-#' @references Spiegelhalter, D., Best, N., Carlin, B., Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4), 583–639.
+#' @references Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis, Third Edition. Chapman & Hall/CRC Texts in Statistical Science. Taylor & Francis. doi: https://doi.org/10.1201/b16018
+#' @references Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711. doi: https://doi.org/10.1002/env.2711
+#' @references Spiegelhalter, D., Best, N., Carlin, B., Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4), 583–639. doi: https://doi.org/10.1111/1467-9868.00353
 #' @references Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594.
 #' @seealso \code{\link{summary.plm0}} for summaries, \code{\link{predict.plm0}} for prediction. It is also useful to look at \code{\link{spread_draws}} and \code{\link{plot.plm0}} to help visualize the full posterior distributions.
 #' @examples

R/tournament.R

@@ -7,7 +7,7 @@
 #' @param winning_criteria a numerical value which sets the threshold which the first model in the list must exceed for it to be declared the more appropriate model. This value defaults to 2 for methods "WAIC" and "DIC", but defaults to 0.75 for method "Posterior_probability".
 #' @return
 #' A data.frame with the summary of the results of the game
-#' @references Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711.
+#' @references Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711. doi: https://doi.org/10.1002/env.2711
 #'
 #' @seealso \code{\link{tournament}}
 #' @keywords internal
@@ -62,10 +62,10 @@ evaluate_game <- function(m,method,winning_criteria){
 #'  \item{\code{info}}{specifics about the tournament; the overall winner; the method used; and the winning criteria.}
 #' }
 #'
-#' @references Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711.
+#' @references Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711. doi: https://doi.org/10.1002/env.2711
 #' @references Jeffreys, H. (1961). Theory of Probability, Third Edition. Oxford University Press.
-#' @references Kass, R., and A. Raftery, A. (1995). Bayes Factors. Journal of the American Statistical Association, 90, 773-795.
-#' @references Spiegelhalter, D., Best, N., Carlin, B., Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4), 583–639.
+#' @references Kass, R., and A. Raftery, A. (1995). Bayes Factors. Journal of the American Statistical Association, 90, 773-795. doi: https://doi.org/10.1080/01621459.1995.10476572
+#' @references Spiegelhalter, D., Best, N., Carlin, B., Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4), 583–639. doi: https://doi.org/10.1111/1467-9868.00353
 #' @references Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594.
 #'
 #' @seealso \code{\link{plm0}} \code{\link{plm}}, \code{\link{gplm0}},\code{\link{gplm}} \code{\link{summary.tournament}} and \code{\link{plot.tournament}}

README.Rmd

@@ -39,7 +39,7 @@ devtools::install_github("sor16/bdrc")
 ```
 
 ## Getting started
-It is very simple to fit a discharge rating curve with the _bdrc_ package. All you need are two mandatory input arguments, formula and data. The formula is of the form y~x where y is discharge in m$^3/$s and x is water elevation in m (it is very important that the data is in the correct units). data is a data.frame which must include x and y as column names. As an example, we will use data from the Swedish gauging station _Krokfors_, which is one of the datasets that come with the package. In this table, the Q column denotes discharge while W denotes water elevation:
+It is very simple to fit a discharge rating curve with the _bdrc_ package. All you need are two mandatory input arguments, formula and data. The formula is of the form y~x where y is discharge in m^3/s and x is water elevation in m (it is very important that the data is in the correct units). data is a data.frame which must include x and y as column names. As an example, we will use data from the Swedish gauging station _Krokfors_, which is one of the datasets that come with the package. In this table, the Q column denotes discharge while W denotes water elevation:
 
 ```{r,eval=F}
 gplm.fit <- gplm(Q~W,krokfors)
@@ -48,5 +48,5 @@ gplm.fit <- gplm(Q~W,krokfors)
 To dig deeper into the functionality of the package and the different ways to visualize a discharge rating curve model for your data, we recommend taking a look at our two vignettes.
 
 ## References
-Hrafnkelsson, B., Sigurdarson, H., and Gardarsson, S. M. (2022). *Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling*, Environmetrics, 33(2):e2711.
+Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). *Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling*, Environmetrics, 33(2):e2711. doi: https://doi.org/10.1002/env.2711
 

README.md

@@ -45,10 +45,9 @@ devtools::install_github("sor16/bdrc")
 
 It is very simple to fit a discharge rating curve with the *bdrc*
 package. All you need are two mandatory input arguments, formula and
-data. The formula is of the form y\~x where y is discharge in
-m![^3/](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5E3%2F "^3/")s
-and x is water elevation in m (it is very important that the data is in
-the correct units). data is a data.frame which must include x and y as
+data. The formula is of the form y~x where y is discharge in m^3/s and x
+is water elevation in m (it is very important that the data is in the
+correct units). data is a data.frame which must include x and y as
 column names. As an example, we will use data from the Swedish gauging
 station *Krokfors*, which is one of the datasets that come with the
 package. In this table, the Q column denotes discharge while W denotes
@@ -64,6 +63,8 @@ recommend taking a look at our two vignettes.
 
 ## References
 
-Hrafnkelsson, B., Sigurdarson, H., and Gardarsson, S. M. (2022).
-*Generalization of the power-law rating curve using hydrodynamic theory
-and Bayesian hierarchical modeling*, Environmetrics, 33(2):e2711.
+Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö.,
+Vias, R. D., and Gardarsson, S. M. (2022). *Generalization of the
+power-law rating curve using hydrodynamic theory and Bayesian
+hierarchical modeling*, Environmetrics, 33(2):e2711. doi:
+<https://doi.org/10.1002/env.2711>