pvclust <- function(data, method.hclust="average",
                    method.dist="correlation", use.cor="pairwise.complete.obs",
                    nboot=1000, r=seq(.5,1.4,by=.1), store=FALSE, weight=FALSE)
  {
    # data: (n,p) matrix, n-samples, p-variables
    n <- nrow(data); p <- ncol(data)

    # hclust for original data
    METHODS <- c("ward", "single", "complete", "average", "mcquitty", 
                 "median", "centroid")
    method.hclust <- METHODS[pmatch(method.hclust, METHODS)]
    
    # Use arbitrary distance function
    if(is.function(method.dist)) {
        dist.pvclust <- method.dist
    }
    
    distance <- dist.pvclust(data, method=method.dist, use.cor=use.cor)
    data.hclust <- hclust(distance, method=method.hclust)
    
    # multiscale bootstrap
    size <- floor(n*r)
    rl <- length(size)
    
    if(rl == 1) {
      if(r != 1.0)
        warning("Relative sample size r is set to 1.0. AU p-values are not calculated\n")
      
      r <- list(1.0)
    }
    else
      r <- as.list(size/n)
    
    mboot <- lapply(r, boot.hclust, data=data, object.hclust=data.hclust, nboot=nboot,
                    method.dist=method.dist, use.cor=use.cor,
                    method.hclust=method.hclust, store=store, weight=weight)
    
    result <- pvclust.merge(data=data, object.hclust=data.hclust, mboot=mboot)
    
    return(result)
  }

plot.pvclust <- function(x, print.pv=TRUE, print.num=TRUE, float=0.01,
                         col.pv=c(2,3,8), cex.pv=0.8, font.pv=NULL,
                         col=NULL, cex=NULL, font=NULL, lty=NULL, lwd=NULL,
                         main=NULL, sub=NULL, xlab=NULL, ...)
{
  if(is.null(main))
    main="Cluster dendrogram with AU/BP values (%)"
  
  if(is.null(sub))
    sub=paste("Cluster method: ", x$hclust$method, sep="")
  
  if(is.null(xlab))
    xlab=paste("Distance: ", x$hclust$dist.method)
      
  plot(x$hclust, main=main, sub=sub, xlab=xlab, col=col, cex=cex,
       font=font, lty=lty, lwd=lwd, ...)

  if(print.pv)
    text(x, col=col.pv, cex=cex.pv, font=font.pv, float=float, print.num=print.num)
}

text.pvclust <- function(x, col=c(2,3,8), print.num=TRUE,  float=0.01, cex=NULL, font=NULL, ...)
{
  axes <- hc2axes(x$hclust)
  usr  <- par()$usr; wid <- usr[4] - usr[3]
  au <- as.character(round(x$edges[,"au"]*100))
  bp <- as.character(round(x$edges[,"bp"]*100))
  rn <- as.character(row.names(x$edges))
  au[length(au)] <- "au"
  bp[length(bp)] <- "bp"
  rn[length(rn)] <- "edge #"
  a <- text(x=axes[,1], y=axes[,2] + float * wid, au,
            col=col[1], pos=2, offset=.3, cex=cex, font=font)
  a <- text(x=axes[,1], y=axes[,2] + float * wid, bp,
            col=col[2], pos=4, offset=.3, cex=cex, font=font)
  if(print.num)
    a <- text(x=axes[,1], y=axes[,2], rn,
              col=col[3], pos=1, offset=.3, cex=cex, font=font)
}

print.pvclust <- function(x, which=NULL, digits=3, ...)
{
  if(is.null(which)) which <- 1:nrow(x$edges)
  cat("\n")
  cat(paste("Cluster method: ", x$hclust$method, "\n", sep=""))
  cat(paste("Distance      : ", x$hclust$dist.method, "\n\n", sep=""))
  cat("Estimates on edges:\n\n")
  print(round(x$edges[which,], digits=digits))
  cat("\n")
}

summary.pvclust <- function(object, ...){
  class(object) <- "list"
  summary(object, ...)
}

pvrect <- function(x, alpha=0.95, pv="au", type="geq", max.only=TRUE, border=2, ...)
  {
    len <- nrow(x$edges)
    member <- hc2split(x$hclust)$member
    order  <- x$hclust$order
    usr <- par("usr")
    xwd <- usr[2] - usr[1]
    ywd <- usr[4] - usr[3]
    cin <- par()$cin

    ht <- c()
    j <- 1

    if(is.na(pm <- pmatch(type, c("geq", "leq", "gt", "lt"))))
       stop("Invalid type argument: see help(pvrect)")
    
    for(i in (len - 1):1)
      {
        if     (pm==1) wh <- (x$edges[i,pv] >= alpha) # Greater than or EQuals
        else if(pm==2) wh <- (x$edges[i,pv] <= alpha) # Lower than or EQuals
        else if(pm==3) wh <- (x$edges[i,pv] >  alpha) # Greater Than
        else if(pm==4) wh <- (x$edges[i,pv] >  alpha) # Lower Than

        if(wh)
          {
            mi <- member[[i]]
            ma <- match(mi, order)
            
            if(max.only == FALSE || (max.only && sum(match(ma, ht, nomatch=0)) == 0))
              {
                xl <- min(ma)
                xr <- max(ma)
                yt <- x$hclust$height[i]
                yb <- usr[3]
                
                mx <- xwd / length(member) / 3
                my <- ywd / 200
                
                rect(xl - mx, yb + my, xr + mx, yt + my, border=border, shade=NULL, ...)
                
                j <- j + 1
              }
            ht <- c(ht, ma)
          }
      }
  }

msplot <- function(x, edges=NULL, ...)
  {
    if(is.null(edges)) edges <- 1:length(x$msfit)
    d   <- length(edges)

    mfrow.bak <- par()$mfrow
    on.exit(par(mfrow=mfrow.bak))

    par(mfrow=n2mfrow(d))

    for(i in edges) {
      if(i == 1 || (i %% 10 == 1 && i > 20))
        main <- paste(i, "st edge", sep="")
      else if(i == 2 || (i %% 10 == 2 && i > 20))
        main <- paste(i, "nd edge", sep="")
      else if(i == 3 || (i %% 10 == 3 && i > 20))
        main <- paste(i, "rd edge", sep="")
      else
        main <- paste(i, "th edge", sep="")

      plot(x$msfit[[i]], main=main, ...)
    }
  }

lines.pvclust <- function(x, alpha=0.95, pv="au", type="geq", col=2, lwd=2, ...)
  {
    len <- nrow(x$edges)
    member <- hc2split(x$hclust)$member
    order  <- x$hclust$order
    usr <- par("usr")
    xwd <- usr[2] - usr[1]
    ywd <- usr[4] - usr[3]
    cin <- par()$cin

    ht <- c()
    j <- 1
    
    if(is.na(pm <- pmatch(type, c("geq", "leq", "gt", "lt"))))
       stop("Invalid type argument: see help(lines.pvclust)")
    
    for(i in (len - 1):1)
      {
        if     (pm==1) wh <- (x$edges[i,pv] >= alpha) # Greater than or EQuals
        else if(pm==2) wh <- (x$edges[i,pv] <= alpha) # Lower than or EQuals
        else if(pm==3) wh <- (x$edges[i,pv] >  alpha) # Greater Than
        else if(pm==4) wh <- (x$edges[i,pv] >  alpha) # Lower Than

        if(wh)
          {
            mi <- member[[i]]
            ma <- match(mi, order)
            
            if(sum(match(ma, ht, nomatch=0)) == 0)
              {
                xl <- min(ma)
                xr <- max(ma)
                yt <- x$hclust$height[i]
                yb <- usr[3]
                
                mx <- xwd/length(member)/10
                
                segments(xl-mx, yb, xr+mx, yb, xpd=TRUE, col=col, lwd=lwd, ...)

                j <- j + 1
              }
            ht <- c(ht, ma)
          }
      }
  }

pvpick <- function(x, alpha=0.95, pv="au", type="geq", max.only=TRUE)
  {
    len <- nrow(x$edges)
    member <- hc2split(x$hclust)$member
    order  <- x$hclust$order
    
    ht <- c()
    a  <- list(clusters=list(), edges=c()); j <- 1

    if(is.na(pm <- pmatch(type, c("geq", "leq", "gt", "lt"))))
       stop("Invalid type argument: see help(pickup)")
    
    for(i in (len - 1):1)
      {
        if     (pm==1) wh <- (x$edges[i,pv] >= alpha) # Greater than or Equals
        else if(pm==2) wh <- (x$edges[i,pv] <= alpha) # Lower than or Equals
        else if(pm==3) wh <- (x$edges[i,pv] >  alpha) # Greater Than
        else if(pm==4) wh <- (x$edges[i,pv] >  alpha) # Lower Than

        if(wh)
          {
            mi <- member[[i]]
            ma <- match(mi, order)

            if(max.only == FALSE || (max.only && sum(match(ma, ht, nomatch=0)) == 0))
              {
                a$clusters[[j]] <- x$hclust$labels[mi]
                a$edges <- c(a$edges,i)
                
                j <- j + 1
              }
            ht <- c(ht, ma)
          }
      }
    
    a$edges <- a$edges[length(a$edges):1]
    a$clusters <- a$clusters[length(a$edges):1]

    return(a)
  }

parPvclust <- function(cl, data, method.hclust="average",
                       method.dist="correlation", use.cor="pairwise.complete.obs",
                       nboot=1000, r=seq(.5,1.4,by=.1), store=FALSE,
                       weight=FALSE,
                       init.rand=TRUE, seed=NULL)
  {
    if(!(require(snow))) stop("Package snow is required for parPvclust.")
    
    if((ncl <- length(cl)) < 2 || ncl > nboot) {
      warning("Too small value for nboot: non-parallel version is executed.")
      return(pvclust(data,method.hclust,method.dist,use.cor,nboot,r,store))
    }

    if(init.rand) {
      if(is.null(seed))
        seed <- 1:length(cl)
      else if(length(seed) != length(cl))
        stop("seed and cl should have the same length.")
      
      # setting random seeds
      parLapply(cl, as.list(seed), set.seed)
    }

    # data: (n,p) matrix, n-samples, p-variables
    n <- nrow(data); p <- ncol(data)

    # hclust for original data
    METHODS <- c("ward", "single", "complete", "average", "mcquitty", 
                 "median", "centroid")
    method.hclust <- METHODS[pmatch(method.hclust, METHODS)]
    
    # Use arbitrary distance function
    if(is.function(method.dist)) {
        dist.pvclust <- method.dist
    }
    
    distance <- dist.pvclust(data, method=method.dist, use.cor=use.cor)
    data.hclust <- hclust(distance, method=method.hclust)
    
    # multiscale bootstrap
    size <- floor(n*r)
    rl <- length(size)
    
    if(rl == 1) {
      if(r != 1.0)
        warning("Relative sample size r is set to 1.0. AU p-values are not calculated\n")
      
      r <- list(1.0)
    }
    else
      r <- as.list(size/n)

    nbl <- as.list(rep(nboot %/% ncl,times=ncl))
    
    if((rem <- nboot %% ncl) > 0)
    nbl[1:rem] <- lapply(nbl[1:rem], "+", 1)

    cat("Multiscale bootstrap... ")
    
    mlist <- parLapply(cl, nbl, pvclust.node,
                       r=r, data=data, object.hclust=data.hclust, method.dist=method.dist,
                       use.cor=use.cor, method.hclust=method.hclust,
                       store=store, weight=weight)
    cat("Done.\n")
    
    mboot <- mlist[[1]]

    for(i in 2:ncl) {
      for(j in 1:rl) {
        mboot[[j]]$edges.cnt <- mboot[[j]]$edges.cnt + mlist[[i]][[j]]$edges.cnt
        mboot[[j]]$nboot <- mboot[[j]]$nboot + mlist[[i]][[j]]$nboot
        mboot[[j]]$store <- c(mboot[[j]]$store, mlist[[i]][[j]]$store)
      }
    }

    result <- pvclust.merge( data=data, object.hclust=data.hclust, mboot=mboot)
    
    return(result)
  }

msfit <- function(bp, r, nboot) {

  if(length(bp) != length(r))
    stop("bp and r should have the same length")

  nboot <- rep(nboot, length=length(bp))

  use <- bp > 0 & bp < 1

  p <- se <- c(0,0); names(p) <- names(se) <- c("au", "bp")
  coef <- c(0,0); names(coef) <- c("v", "c")

  a <- list(p=p, se=se, coef=coef, df=0, rss=0, pchi=0); class(a) <- "msfit"

  if(sum(use) < 2) {
    # if(mean(bp) < .5) a$p[] <- c(0, 0) else a$p[] <- c(1, 1)
    if(mean(bp) < .5) a$p[] <- c(0, bp[r==1.0]) else a$p[] <- c(1, bp[r==1.0])
    return(a)
  }

  bp <- bp[use]; r <- r[use]; nboot <- nboot[use]
  zz <- -qnorm(bp)
  vv <- ((1 - bp) * bp) / (dnorm(zz)^2 * nboot)
  a$use <- use; a$r <- r; a$zz <- zz

  X   <- cbind(sqrt(r), 1/sqrt(r)); dimnames(X) <- list(NULL, c("v","c"))
  fit <- lsfit(X, zz, 1/vv, intercept=FALSE)
  a$coef <- coef <- fit$coef

  h.au <- c(1, -1); h.bp <- c(1, 1)
  
  z.au <- drop(h.au %*% coef); z.bp <- drop(h.bp %*% coef)
  a$p["au"] <- pnorm(-z.au); a$p["bp"] <- pnorm(-z.bp)
  V <- solve(crossprod(X, X/vv))
  vz.au <- drop(h.au %*% V %*% h.au); vz.bp <- drop(h.bp %*% V %*% h.bp)
  a$se["au"] <- dnorm(z.au) * sqrt(vz.au); a$se["bp"] <- dnorm(z.bp) * sqrt(vz.bp)
  a$rss <- sum(fit$residual^2/vv)
  
  if((a$df <- sum(use) - 2) > 0) {
    a$pchi <- pchisq(a$rss, lower=FALSE, df=a$df)
  }
  else a$pchi <- 1.0

  return(a)
}

plot.msfit <- function(x, curve=TRUE, main=NULL, sub=NULL, xlab=NULL, ylab=NULL, ...)
{
  if(is.null(main)) main="Curve fitting for multiscale bootstrap resampling"
  if(is.null(sub))
    {
      sub  <- paste("AU = ", round(x$p["au"], digits=2),
                    ", BP = ", round(x$p["bp"], digits=2),
                    ", v = ", round(x$coef["v"], digits=2),
                    ", c = ", round(x$coef["c"], digits=2),
                    ", pchi = ", round(x$pchi, digits=2))
    }
  if(is.null(xlab)) xlab=expression(sqrt(r))
  if(is.null(ylab)) ylab=expression(z-value)
  
  a <- sqrt(x$r); b <- x$zz
  
  if(!is.null(a) && !is.null(b)) {
    plot(a, b, main=main, sub=sub, xlab=xlab, ylab=ylab, ...)
    if(curve) lines(x, ...)
  }
  else if (!is.null(a)){
    plot(0, 0, main=main, sub=sub, xlab=xlab, ylab=ylab,
         type="n", xaxt="n", yaxt="n", ...)
    a <- text(mean(a), 0, "No fitting")
  }
}

lines.msfit <- function(x, col=2, lty=1, ...) {
  v <- x$coef["v"]; c <- x$coef["c"]
  curve(v * x + c / x, add=TRUE, col=col, lty=lty)
}

summary.msfit <- function(object, digits=3, ...) {
  cat("\nResult of curve fitting for multiscale bootstrap resampling:\n\n")

  cat("Estimated p-values:\n")
  pv <- data.frame(object$p, object$se)
  names(pv) <- c("Estimate", "Std. Error"); row.names(pv) <- c("au", "bp")
  print(pv, digits=digits); cat("\n")

  cat("Estimated coefficients:\n")
  coef <- object$coef
  print(coef, digits=digits); cat("\n")

  cat(paste("Residual sum of squares: ", round(object$rss,digits=digits)),
      ",   p-value: ", round(object$pchi, digits=digits),
      " on ", object$df, " DF\n\n", sep="")
}

seplot <- function(object, type=c("au", "bp"), identify=FALSE,
                   main=NULL, xlab=NULL, ylab=NULL, ...)
  {
    if(!is.na(pm <- pmatch(type[1], c("au", "bp")))) {
      wh <- c("au", "bp")[pm]
      
      if(is.null(main))
        main <- "p-value vs standard error plot"
      if(is.null(xlab))
        xlab <- c("AU p-value", "BP value")[pm]
      if(is.null(ylab))
        ylab <- "Standard Error"
      
      plot(object$edges[,wh], object$edges[,paste("se", wh, sep=".")],
           main=main, xlab=xlab, ylab=ylab, ...)
      if(identify)
        identify(x=object$edges[,wh], y=object$edges[,paste("se", wh, sep=".")],
                 labels=row.names(object$edges))
    }
    else stop("'type' should be \"au\" or \"bp\".")
  }