Fix bug for bivariate series (QMC vector) for MGP

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: mev
 Type: Package
 Title: Modelling of Extreme Values
-Version: 1.16
+Version: 1.16.0002
 Authors@R: c(person(given="Leo", family="Belzile", role = c("aut", "cre"), email = "[email protected]", comment = c(ORCID = "0000-0002-9135-014X")), person(given="Jennifer L.", family="Wadsworth", role=c("aut")), person(given="Paul J.", family="Northrop", role=c("aut")), person(given="Scott D.", family="Grimshaw", role=c("aut")), person(given="Jin", family="Zhang", role=c("ctb")), person(given="Michael A.", family="Stephens", role=c("ctb")), person(given="Art B.", family="Owen", role=c("ctb")), person(given="Raphael", family="Huser", role=c("aut")))
 Description: Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.
 License: GPL-3

R/mgplikelihoods.R

@@ -249,15 +249,15 @@ likmgp <- function(dat,
     }
   }
   if(model %in% c("br", "xstud")){
+    if(ncol(dat) == 2L){
+      stop("Function does not support bivariate data sets.")
+    }
   if (!requireNamespace("mvPot", quietly = TRUE)) {
     stop(
       "Package \"mvPot\" must be installed to use this function.",
       call. = FALSE
     )
   }
-  }
-
-
   genvec1 <- ellips$genvec1
   B1 <- ifelse(is.null(ellips$B1), 1009L, ellips$B1)
   antithetic <- ifelse(is.null(ellips$antithetic), FALSE, ellips$antithetic)
@@ -266,7 +266,7 @@ likmgp <- function(dat,
   }
   M1 <- ifelse(is.null(ellips$M1), 1L, ellips$M1)
   ncores <- ifelse(is.null(ellips$ncores), 1L, ellips$ncores)
-
+  }
   if (model == "br") {
     intens <- intensBR(tdat = tdat, Lambda = Lambda)
     exponentMeasure <- sum(.weightsBR(z = tu, Lambda = Lambda, prime = B1, method = "mvPot", genvec = genvec1, nrep = 1) / tu)
@@ -434,6 +434,10 @@ clikmgp <- function(dat,
   numAbovePerRow <- ellips$numAbovePerRow
   numAbovePerCol <- ellips$numAbovePerCol
   mmax <- ellips$mmax
+  if(model %in% c("br","xstud")){
+    if(ncol(dat) == 2L){
+      stop("Function does not support bivariate data sets.")
+    }
   genvec1 <- ellips$genvec1
   genvec2 <- ellips$genvec2
   B1 <- ifelse(is.null(ellips$B1), 1009L, ellips$B1)
@@ -448,6 +452,7 @@ clikmgp <- function(dat,
   M1 <- ifelse(is.null(ellips$M1), 1L, ellips$M1)
   M2 <- ifelse(is.null(ellips$M2), 1L, ellips$M2)
   ncores <- ifelse(is.null(ellips$ncores), 1L, ellips$ncores)
+  }
   censored <- ellips$censored
   if (is.null(censored)) {
     censored <- matrix(FALSE, nrow = nrow(tdat), ncol = ncol(tdat))

R/multivar.R

@@ -57,7 +57,7 @@ chibar <- function(dat, confint = c("delta", "profile", "tem"), qu = 0, level =
 }
 
 
-#' Bivariate angular function for extrapolation based on rays
+#' Bivariate angular dependence function for extrapolation based on rays
 #'
 #' The scale parameter \eqn{g(w)} in the Ledford and Tawn approach is estimated empirically for
 #' \eqn{x} large as \deqn{\frac{\Pr(X_P>xw, Y_P>x(1-w))}{\Pr(X_P>x, Y_P>x)}}
@@ -100,7 +100,7 @@ angextrapo <- function(dat, qu = 0.95, w = seq(0.05, 0.95, length = 20)) {
 }
 
 ##################
-#' Estimation of the bivariate lambda function of Wadsworth and Tawn (2013)
+#' Estimation of the bivariate angular dependence function of Wadsworth and Tawn (2013)
 #'
 #' @param dat an \eqn{n} by \eqn{2} matrix of multivariate observations
 #' @param qu quantile level on uniform scale at which to threshold data. Default to 0.95