function (weights)
{
    if (!is.null(attr(X, "deriv")))
        stop("fitting of derivatives of B-splines not implemented")
    weights[!Complete.cases(mf)] <- 0
    w <- weights
    if (!is.null(index))
        w <- .Call("R_ysum", as.double(weights), as.integer(index),
            PACKAGE = "mboost")
    XtX <- crossprod(X * w, X)
    lambdadf <- df2lambda(X, df = args$df, lambda = args$lambda,
        dmat = K, weights = w, XtX = XtX)
    lambda <- lambdadf["lambda"]
    XtX <- XtX + lambda * K
    if (is(X, "Matrix") && !extends(class(XtX), "dgeMatrix")) {
        XtXC <- Cholesky(forceSymmetric(XtX))
        mysolve <- function(y) {
            if (is.null(attr(X, "Ts_constraint")))
                return(solve(XtXC, crossprod(X, y)))
            return(nnls1D(as(XtX, "matrix"), as(X, "matrix"),
                y))
        }
    }
    else {
        if (is(X, "Matrix")) {
            X <- as(X, "matrix")
            XtX <- as(XtX, "matrix")
        }
        mysolve <- function(y) {
            if (is.null(attr(X, "Ts_constraint")))
                return(solve(XtX, crossprod(X, y), LINPACK = FALSE))
            return(nnls1D(XtX, X, y))
        }
    }
    fit <- function(y) {
        if (!is.null(index)) {
            y <- .Call("R_ysum", as.double(weights * y), as.integer(index),
                PACKAGE = "mboost")
        }
        else {
            y <- y * weights
        }
        coef <- mysolve(y)
        ret <- list(model = coef, fitted = function() {
            ret <- as.vector(X %*% coef)
            if (is.null(index)) return(ret)
            return(ret[index])
        })
        class(ret) <- c("bm_lin", "bm")
        ret
    }
    hatvalues <- function() {
        ret <- as.matrix(tcrossprod(X %*% solve(XtX), X * w))
        if (is.null(index))
            return(ret)
        return(ret[index, index])
    }
    df <- function() lambdadf
    predict <- function(bm, newdata = NULL, aggregate = c("sum",
        "cumsum", "none")) {
        cf <- sapply(bm, coef)
        if (!is.matrix(cf))
            cf <- matrix(cf, nrow = 1)
        if (!is.null(newdata)) {
            index <- NULL
            nm <- names(blg)
            if (any(duplicated(nm)))
                nm <- unique(nm)
            newdata <- newdata[, nm, drop = FALSE]
            if (nrow(newdata) > options("mboost_indexmin")[[1]]) {
                index <- get_index(newdata)
                newdata <- newdata[index[[1]], , drop = FALSE]
                index <- index[[2]]
            }
            X <- newX(newdata)$X
        }
        aggregate <- match.arg(aggregate)
        pr <- switch(aggregate, sum = as(X %*% rowSums(cf), "matrix"),
            cumsum = {
                as(X %*% .Call("R_mcumsum", as(cf, "matrix"),
                  PACKAGE = "mboost"), "matrix")
            }, none = as(X %*% cf, "matrix"))
        if (is.null(index))
            return(pr[, , drop = FALSE])
        return(pr[index, , drop = FALSE])
    }
    ret <- list(fit = fit, hatvalues = hatvalues, predict = predict,
        df = df, Xnames = colnames(X))
    class(ret) <- c("bl_lin", "bl")
    return(ret)
}