# train a model using given parameters
xgb.train <- function(params, dtrain, nrounds=10, watchlist=list(), obj=NULL, feval=NULL) {
    if (typeof(params) != "list") {
        stop("xgb.train: first argument params must be list");
    }
    if (class(dtrain) != "xgb.DMatrix") {
        stop("xgb.train: second argument dtrain must be xgb.DMatrix");
    }
    bst <- xgb.Booster(params, append(watchlist,dtrain))
    for (i in 1:nrounds) {
        if (is.null(obj)) {
            succ <- xgb.iter.update(bst, dtrain, i-1)
        } else {
            pred <- xgb.predict(bst, dtrain)
            gpair <- obj(pred, dtrain)
            succ <- xgb.iter.boost(bst, dtrain, gpair)
        }
        if (length(watchlist) != 0) {
            if (is.null(feval)) {      
                msg <- xgb.iter.eval(bst, watchlist, i-1)
                cat(msg); cat("\n")
            } else {
                cat("["); cat(i); cat("]");
                for (j in 1:length(watchlist)) {
                    w <- watchlist[j]
                    if (length(names(w)) == 0) {
                        stop("xgb.eval: name tag must be presented for every elements in watchlist")
                    }
                    ret <- feval(xgb.predict(bst, w[[1]]), w[[1]])
                    cat("\t"); cat(names(w)); cat("-"); cat(ret$metric); 
                    cat(":"); cat(ret$value)
                }
                cat("\n")        
            }
        }
    }
    return(bst)
}