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##' Generates CR values based on current probabilities
##' @param pCR vector of length nCR, summing to 1 (?)
##' @param control must have pars nseq,steps,nCR
##' @return ...
##' CR matrix nseq x steps. range [1/nCR,1]
GenCR <- function(pCR,control){
##dimensions:
## L vector of length nCR
## L2 vector of length nCR+1
## r vector of length nseq*steps of range [1,nseq*steps]
## idx vector of variable length. range of r
## CR vector of length nseq*steps. range [1/nCR,1]
## How many candidate points for each crossover value?
## TODO: verify result matches matlab
## MATLAB: [L] = multrnd(MCMCPar.seq * MCMCPar.steps,pCR);
L <- as.numeric(rmultinom(1,size=control$nseq*control$steps,prob=pCR))
L2 <- c(0,cumsum(L))
## Then select which candidate points are selected with what CR
r <- sample(control$nseq*control$steps)
CR <- rep(NA,control$nseq*control$steps)
## Then generate CR values for each chain
for (zz in 1:control$nCR){
## Define start and end
i.start <- L2[zz]+1
i.end <- L2[zz+1]
## Take the appropriate elements of r
idx <- r[i.start:i.end]
## Assign these indices control$CR(zz)
CR[idx] <- zz/control$nCR
} ## for nCR
## Now reshape CR
CR <- matrix(CR,control$nseq,control$steps)
return(CR)
} ## GenCR
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