`crps.fitBMAgamma0` <-
function(fit, ensembleData, nSamples=NULL, seed=NULL, dates=NULL, ...) 
{
 weps <- 1.e-4

 if (!is.null(dates)) warning("dates ignored")

 if (is.null(nSamples)) nSamples <- 10000

 M <- matchEnsembleMembers(fit,ensembleData)
 nForecasts <- ensembleSize(ensembleData)
 if (!all(M == 1:nForecasts)) ensembleData <- ensembleData[,M]

# remove instances missing all forecasts or obs

 M <- apply(ensembleForecasts(ensembleData), 1, function(z) all(is.na(z)))
 M <- M | is.na(ensembleVerifObs(ensembleData))
 ensembleData <- ensembleData[!M,]

 if (is.null(obs <- ensembleVerifObs(ensembleData)))
   stop("verification observations required")

#nObs <- length(obs) 
 nObs <- ensembleNobs(ensembleData)

 if (!is.null(seed)) set.seed(seed)

 nForecasts <- ensembleSize(ensembleData) 

 crpsSim <- rep(NA, nObs)
 names(crpsSim) <- ensembleObsLabels(ensembleData)

 ensembleData <- ensembleForecasts(ensembleData)

 WEIGHTS <- fit$weights

 if (!all(Wmiss <- is.na(WEIGHTS))) {

    for (i in 1:nObs) {
    
       f <- ensembleData[i,]

       M <- is.na(f) | Wmiss

       VAR <- fit$varCoefs[1] + fit$varCoefs[2]*f

       fTrans <- sapply(f, fit$transformation)

       MEAN <- apply(rbind(1, fTrans) * fit$biasCoefs, 2, sum)

       RATE <- MEAN/VAR
       SHAPE <- MEAN*RATE

       PROB0 <- sapply(apply(rbind( 1, fTrans, f==0) * fit$prob0coefs,
                              2,sum), inverseLogit)

       W <- WEIGHTS
       if (any(M)) {  
         W <- W + weps
         W <- W[!M]/sum(W[!M])
       }

       SAMPLES <- sample( (1:nForecasts)[!M], size = nSamples, 
                          replace = TRUE, prob = W)

       tab <- table(SAMPLES)

       tab <- table(unlist(apply( cbind(as.numeric(names(tab)), tab), 1,
              function(nj,PROB0) {
         sample(c(nj[1],0), size = nj[2], replace = TRUE,
                prob = c(1-PROB0[nj[1]],PROB0[nj[1]]))}, 
                PROB0 = PROB0[!M])))

       if (length(tab) > 1) {
          S <- apply( cbind(as.numeric(names(tab[-1])), tab[-1]), 1,
              function(nj,SHAPE,RATE) 
                  rgamma(nj[2], shape=SHAPE[nj[1]], rate=RATE[nj[1]]),
                                        SHAPE = SHAPE[!M], RATE = RATE[!M])
           
# model is fit to the cube root of the forecast

         S <- sapply(as.vector(unlist(S)),
                           fit$inverseTransformation)

         SAMPLES <- c(rep(0, tab[1]), S)
       }
       else SAMPLES <- rep(0,tab[1])
  
       crps1  <- mean(abs(SAMPLES - obs[i])) 
       crps2 <-  mean(abs(diff(sample(SAMPLES))))
       crpsSim[i]  <- crps1 - crps2/2
    }
 }

 crpsSim <- mean(crpsSim)

 crpsCli <- sapply(obs, function(x,Y) mean(abs(Y-x)), Y = obs)
 crpsCli <- mean(crpsCli - mean(crpsCli)/2)

 crpsEns1 <- apply(abs(sweep(ensembleData,MARGIN=1,FUN ="-",STATS=obs)),
                   1, mean, na.rm = TRUE)
 crpsEns2 <- apply(apply(ensembleData, 2, function(z,Z) 
       apply(abs(sweep(Z, MARGIN = 1, FUN = "-", STATS = z)),1,sum,na.rm=TRUE),
                  Z = ensembleData), 1, sum, na.rm = TRUE)
 crpsEns <- mean(crpsEns1 - crpsEns2/(2*(nForecasts*nForecasts)))

##c(climatology = crpsCli, ensemble = crpsEns, BMA = crpsSim)
c(ensemble = crpsEns, BMA = crpsSim)
}