error in dynlib

NAMESPACE

@@ -3,12 +3,10 @@
 S3method(BF,bartlett_htest)
 S3method(BF,bergm)
 S3method(BF,coeftest)
-S3method(BF,cor_test)
 S3method(BF,coxph)
 S3method(BF,default)
 S3method(BF,ergm)
 S3method(BF,glm)
-S3method(BF,hetcor)
 S3method(BF,htest)
 S3method(BF,lm)
 S3method(BF,lmerMod)
@@ -31,11 +29,9 @@ S3method(get_estimates,survreg)
 S3method(get_estimates,t_test)
 S3method(get_estimates,zeroinfl)
 S3method(print,BF)
-S3method(print,cor_test)
 S3method(summary,BF)
 export(BF)
 export(bartlett_test)
-export(cor_test)
 import(bain)
 importFrom(MASS,ginv)
 importFrom(Matrix,rankMatrix)
@@ -49,6 +45,7 @@ importFrom(mvtnorm,pmvnorm)
 importFrom(mvtnorm,pmvt)
 importFrom(mvtnorm,rmvnorm)
 importFrom(mvtnorm,rmvt)
+importFrom(pracma,Rank)
 importFrom(pracma,rref)
 importFrom(sandwich,sandwich)
 importFrom(stats,approxfun)
@@ -80,7 +77,6 @@ importFrom(stats,rt)
 importFrom(stats,runif)
 importFrom(stats,sd)
 importFrom(stats,setNames)
-importFrom(stats,terms)
 importFrom(stats,var)
 importFrom(stats,vcov)
 importFrom(utils,getFromNamespace)

R/BF.gaussian.R

@@ -211,7 +211,6 @@ Savage.Dickey.Gaussian <- function(prior.mean,
 
 # compute relative meausures (fit or complexity) under a multivariate Gaussian distribution
 #' @importFrom mvtnorm dmvnorm pmvnorm
-#' @importFrom Matrix rankMatrix
 Gaussian_measures <- function(mean1,Sigma1,n1=0,RrE1,RrO1,names1=NULL,constraints1=NULL){
   K <- length(mean1)
   relE <- relO <- 1
@@ -234,7 +233,7 @@ Gaussian_measures <- function(mean1,Sigma1,n1=0,RrE1,RrO1,names1=NULL,constraint
     qO1 <- nrow(RO1)
     rO1 <- RrO1[,(K+1)]
 
-    if(rankMatrix(RO1)[[1]]==nrow(RO1)){ #RO1 is of full row rank. So use transformation.
+    if(Rank(RO1)==nrow(RO1)){ #RO1 is of full row rank. So use transformation.
       meanO <- c(RO1%*%mean1)
       SigmaO <- RO1%*%Sigma1%*%t(RO1)
       check_vcov(SigmaO)
@@ -270,7 +269,7 @@ Gaussian_measures <- function(mean1,Sigma1,n1=0,RrE1,RrO1,names1=NULL,constraint
     rO1 <- RrO1[,(K+1)]
     Rr1 <- rbind(RrE1,RrO1)
 
-    if(rankMatrix(Rr1)[[1]] == nrow(Rr1)){
+    if(Rank(Rr1) == nrow(Rr1)){
 
       R1 <- rbind(RE1,RO1)
 

R/BF.lm.R

@@ -1,6 +1,5 @@
-#' @importFrom pracma rref
+#' @importFrom pracma rref Rank
 #' @importFrom mvtnorm dmvnorm pmvnorm rmvnorm dmvt pmvt rmvt
-#' @importFrom Matrix rankMatrix
 #' @importFrom stats rWishart qt
 #' @importFrom MASS ginv
 #' @describeIn BF S3 method for an object of class 'lm'
@@ -631,7 +630,7 @@ MatrixStudent_measures <- function(Mean1,Scale1,tXXi1,df1,RrE1,RrO1,Names1=NULL,
       qO1 <- nrow(RO1)
       rO1 <- RrO1[,(K*P+1)]
 
-      if(rankMatrix(RO1)[[1]]==nrow(RO1)){ #RO1 is of full row rank. So use transformation.
+      if(Rank(RO1)==nrow(RO1)){ #RO1 is of full row rank. So use transformation.
 
         Scale1inv <- solve(Scale1)
         relO <- unlist(lapply(1:1e3,function(s){
@@ -690,7 +689,7 @@ MatrixStudent_measures <- function(Mean1,Scale1,tXXi1,df1,RrE1,RrO1,Names1=NULL,
       mean1_E <- RE1 %*% mean1
       relE <- mean(unlist(lapply(covm1_E,function(temp) dmvnorm(rE1,mean=mean1_E,sigma=temp))))
 
-      if(rankMatrix(Rr1)[[1]] == nrow(Rr1)){
+      if(Rank(Rr1) == nrow(Rr1)){
         covm1_O <- lapply(SigmaList,function(temp) R1%*%(kronecker(temp,tXXi1))%*%t(R1) )
         mean1_O <- c(R1%*%mean1)
 
@@ -755,7 +754,7 @@ Student_measures <- function(mean1,Scale1,df1,RrE1,RrO1,names1=NULL,constraints1
     qO1 <- nrow(RO1)
     rO1 <- RrO1[,(K+1)]
 
-    if(rankMatrix(RO1)[[1]]==nrow(RO1)){ #RO1 is of full row rank. So use transformation.
+    if(Rank(RO1)==nrow(RO1)){ #RO1 is of full row rank. So use transformation.
       meanO <- c(RO1%*%mean1)
       scaleO <- RO1%*%Scale1%*%t(RO1)
       relO <- ifelse(nrow(scaleO)==1,
@@ -837,7 +836,7 @@ Student_measures <- function(mean1,Scale1,df1,RrE1,RrO1,names1=NULL,constraints1
     Tscale1OgE <- as.vector((df1 + (t(matrix(rE1 - Tmean1E)) %*% solve(Tscale1EE) %*% matrix(rE1 - Tmean1E))) /
                               (df1 + qE1)) * (Tscale1OO - Tscale1OE %*% solve(Tscale1EE) %*% t(Tscale1OE))
 
-    if(rankMatrix(RO1tilde)[[1]] == nrow(RO1tilde)){
+    if(Rank(RO1tilde) == nrow(RO1tilde)){
       rO1tilde <- as.vector(rO1tilde)
 
       delta_trans <- as.vector(RO1tilde %*% Tmean1OgE)

R/BF_cortest.R

@@ -1,4 +1,5 @@
 
+
 #' @title Bayesian correlation analysis
 #'
 #' @name cor_test

R/BFhetcor.R

@@ -3,9 +3,9 @@
 #' @method BF hetcor
 #' @export
 BF.hetcor <- function(x,
-                       hypothesis = NULL,
-                       prior.hyp = NULL,
-                       ...){
+                      hypothesis = NULL,
+                      prior.hyp = NULL,
+                      ...){
   get_est <- get_estimates(x)
   P <- nrow(x$std.errors)
   numcorr <- P*(P-1)/2
@@ -40,7 +40,7 @@ BF.hetcor <- function(x,
     relcomp <- t(matrix(unlist(
       lapply(1:numhyp, function(h){
         jointuniform_measures(P,numcorr, 1, RrE[[h]], RrO[[h]], Fisher=0)
-    })
+      })
     ),nrow=2))
     relfit <- t(matrix(unlist(lapply(1:numhyp,function(h){
       Gaussian_measures(estimates,errcov,RrE1=RrE[[h]],RrO1=RrO[[h]],names1=names(estimates),
@@ -105,4 +105,3 @@ BF.hetcor <- function(x,
   return(BF_out)
 
 }
-

R/cor_test.print.R

@@ -1,8 +1,8 @@
 #' @method print cor_test
 #' @export
 print.cor_test <- function(x,
-                     digits = 3,
-                     na.print = "", ...){
+                           digits = 3,
+                           na.print = "", ...){
 
   estimates <- x$correstimates
   names <- x$corrnames
@@ -11,13 +11,13 @@ print.cor_test <- function(x,
   numcorr <- P*(P-1)/2
   countg = 0
   corrlist <- lapply(1:groups,function(g){
-      lapply(1:3,function(b){
-        matje <- matrix(NA,P,P)
-        row.names(matje) <- colnames(matje) <- x$variables[[1]]
-        matje[lower.tri(diag(P))] <- estimates[numcorr*(g-1)+1:numcorr,1+b]
-        matje
-      })
+    lapply(1:3,function(b){
+      matje <- matrix(NA,P,P)
+      row.names(matje) <- colnames(matje) <- x$variables[[1]]
+      matje[lower.tri(diag(P))] <- estimates[numcorr*(g-1)+1:numcorr,1+b]
+      matje
     })
+  })
 
   cat("\n")
   cat("Unconstrained Bayesian estimates","\n", sep = "")