Get rid of the branched svn layout I never used

git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@36 5fad18fb-23f0-0310-ab10-e59a3bee62b4

30 files changed, 0 insertions, 1339 deletions

diff --git a/branches/0.1/chemCal/.Rbuildignore b/branches/0.1/chemCal/.Rbuildignore
deleted file mode 100644
index 0b413f5..0000000
--- a/branches/0.1/chemCal/.Rbuildignore
+++ /dev/null
@@ -1,9 +0,0 @@
-GNUmakefile
-out$
-toc$
-bbl$
-blg$
-aux$
-log$
-vignettes/chemCal.tex
-vignettes/chemCal.pdf
diff --git a/branches/0.1/chemCal/ChangeLog b/branches/0.1/chemCal/ChangeLog
deleted file mode 100644
index c258c26..0000000
--- a/branches/0.1/chemCal/ChangeLog
+++ /dev/null
@@ -1,11 +0,0 @@
-2014-04-24  Johannes Ranke <jranke@uni-bremen.de> for chemCal (0.1-33)

-

-	* Bugfix in lod() and loq(): In the case of small absolute x values (e.g. on

-	the order of 1e-4 and smaller), the lod or loq calculated using the default

-	method could produce inaccurate results as the default tolerance that was

-	used in the internal call to optimize is inappropriate in such cases. Now a

-	reasonable default is used which can be overriden by the user. Thanks to

-	Jérôme Ambroise for reporting the bug.

-

-Changes performed in earlier versions are documented in the subversion log

-files found at http://kriemhild.uft.uni-bremen.de/viewcvs/?root=chemCal

diff --git a/branches/0.1/chemCal/DESCRIPTION b/branches/0.1/chemCal/DESCRIPTION
deleted file mode 100644
index 2608804..0000000
--- a/branches/0.1/chemCal/DESCRIPTION
+++ /dev/null
@@ -1,18 +0,0 @@
-Package: chemCal
-Version: 0.1-35
-Date: 2015-07-02
-Title: Calibration functions for analytical chemistry
-Author: Johannes Ranke <jranke@uni-bremen.de>
-Maintainer: Johannes Ranke <jranke@uni-bremen.de>
-Suggests: MASS
-Description: chemCal provides simple functions for plotting linear
-	calibration functions and estimating standard errors for measurements
-	according to the Handbook of Chemometrics and Qualimetrics: Part A
-	by Massart et al. There are also functions estimating the limit
-	of detection (LOD) and limit of quantification (LOQ).
-	The functions work on model objects from - optionally weighted - linear
-	regression (lm) or robust linear regression (rlm from the MASS package).
-License: GPL (>= 2)
-URL: http://www.r-project.org, 
-	http://www.uft.uni-bremen.de/chemie/ranke,
-	http://kriemhild.uft.uni-bremen.de/viewcvs/?root=chemCal
diff --git a/branches/0.1/chemCal/GNUmakefile b/branches/0.1/chemCal/GNUmakefile
deleted file mode 100644
index 803c503..0000000
--- a/branches/0.1/chemCal/GNUmakefile
+++ /dev/null
@@ -1,57 +0,0 @@
-PKGNAME := $(shell sed -n "s/Package: *\([^ ]*\)/\1/p" DESCRIPTION)
-PKGVERS := $(shell sed -n "s/Version: *\([^ ]*\)/\1/p" DESCRIPTION)
-PKGSRC  := $(shell basename $(PWD))
-
-# Specify the directory holding R binaries. To use an alternate R build (say a
-# pre-prelease version) use `make RBIN=/path/to/other/R/` or `export RBIN=...`
-# If no alternate bin folder is specified, the default is to use the folder
-# containing the first instance of R on the PATH.
-RBIN ?= $(shell dirname "`which R`")
-
-.PHONY: help
-
-help:
-	@echo "\nExecute development tasks for $(PKGNAME)\n"
-	@echo "Usage: \`make <task>\` where <task> is one of:"
-	@echo ""
-	@echo "Development Tasks"
-	@echo "-----------------"
-	@echo "  build                   Create the package"
-	@echo "  build-no-vignettes      Create the package without rebuilding vignettes"
-	@echo "  check                   Invoke build and then check the package"
-	@echo "  check-no-vignettes      Invoke build without rebuilding vignettes, and then check"
-	@echo "  install                 Invoke build and then install the result"
-	@echo "  install-no-vignettes    Invoke build without rebuilding vignettes and then install the result"
-	@echo ""
-	@echo "Using R in: $(RBIN)"
-	@echo "Set the RBIN environment variable to change this."
-	@echo ""
-
-
-#------------------------------------------------------------------------------
-# Development Tasks
-#------------------------------------------------------------------------------
-
-build:
-	cd ..;\
-		"$(RBIN)/R" CMD build $(PKGSRC)
-
-build-no-vignettes:
-	cd ..;\
-		"$(RBIN)/R" CMD build $(PKGSRC) --no-build-vignettes
-
-install: build
-	cd ..;\
-		"$(RBIN)/R" CMD INSTALL $(PKGNAME)_$(PKGVERS).tar.gz
-
-install-no-vignettes: build-no-vignettes
-	cd ..;\
-		"$(RBIN)/R" CMD INSTALL $(PKGNAME)_$(PKGVERS).tar.gz
-
-check: build
-	cd ..;\
-		"$(RBIN)/R" CMD check --as-cran $(PKGNAME)_$(PKGVERS).tar.gz
-
-check-no-vignettes: build-no-vignettes
-	cd ..;\
-		"$(RBIN)/R" CMD check --as-cran $(PKGNAME)_$(PKGVERS).tar.gz
diff --git a/branches/0.1/chemCal/NAMESPACE b/branches/0.1/chemCal/NAMESPACE
deleted file mode 100644
index 6314e29..0000000
--- a/branches/0.1/chemCal/NAMESPACE
+++ /dev/null
@@ -1,6 +0,0 @@
-# Export all names
-exportPattern(".")
-S3method(lod, lm)
-S3method(loq, lm)
-S3method(inverse.predict, lm)
-S3method(inverse.predict, rlm)
diff --git a/branches/0.1/chemCal/R/calplot.R b/branches/0.1/chemCal/R/calplot.R
deleted file mode 100644
index 6aed9c0..0000000
--- a/branches/0.1/chemCal/R/calplot.R
+++ /dev/null
@@ -1,80 +0,0 @@
-calplot <- function(object, 
-  xlim = c("auto", "auto"), ylim = c("auto", "auto"), 
-  xlab = "Concentration", ylab = "Response", alpha = 0.05,
-  varfunc = NULL)
-{
-  UseMethod("calplot")
-}
-
-calplot.default <- function(object, 
-  xlim = c("auto","auto"), ylim = c("auto","auto"), 
-  xlab = "Concentration", ylab = "Response",
-  alpha=0.05, varfunc = NULL)
-{
-  stop("Calibration plots only implemented for univariate lm objects.")
-}
-
-calplot.lm <- function(object,
-  xlim = c("auto","auto"), ylim = c("auto","auto"), 
-  xlab = "Concentration", ylab = "Response", alpha=0.05,
-  varfunc = NULL)
-{
-  if (length(object$coef) > 2)
-    stop("More than one independent variable in your model - not implemented")
-
-  if (alpha <= 0 | alpha >= 1)
-    stop("Alpha should be between 0 and 1 (exclusive)")
-
-  m <- object
-  level <- 1 - alpha
-  y <- m$model[[1]]
-  x <- m$model[[2]]
-  if (xlim[1] == "auto") xlim[1] <- 0
-  if (xlim[2] == "auto") xlim[2] <- max(x)
-  xlim <- as.numeric(xlim)
-  newdata <- list(
-    x = seq(from = xlim[[1]], to = xlim[[2]], length=250))
-  names(newdata) <- names(m$model)[[2]]
-  if (is.null(varfunc)) {
-    varfunc <- if (length(m$weights)) {
-        function(variable) mean(m$weights)
-      } else function(variable) rep(1,250)
-  }
-  pred.lim <- predict(m, newdata, interval = "prediction",
-    level=level, weights.newdata = varfunc(m))
-  conf.lim <- predict(m, newdata, interval = "confidence",
-    level=level)
-  yrange.auto <- range(c(0,pred.lim))
-  if (ylim[1] == "auto") ylim[1] <- yrange.auto[1]
-  if (ylim[2] == "auto") ylim[2] <- yrange.auto[2]
-  plot(1,
-    type = "n", 
-    xlab = xlab,
-    ylab = ylab,
-    xlim = as.numeric(xlim),
-    ylim = as.numeric(ylim)
-  )
-  points(x,y, pch = 21, bg = "yellow")
-  matlines(newdata[[1]], pred.lim, lty = c(1, 4, 4), 
-    col = c("black", "red", "red"))
-  if (length(object$weights) > 0) {
-    legend(min(x), 
-      max(pred.lim, na.rm = TRUE), 
-      legend = c("Fitted Line", "Confidence Bands"),
-      lty = c(1, 3), 
-      lwd = 2, 
-      col = c("black", "green4"), 
-      horiz = FALSE, cex = 0.9, bg = "gray95")
-  } else {
-  matlines(newdata[[1]], conf.lim, lty = c(1, 3, 3), 
-    col = c("black", "green4", "green4"))
-  legend(min(x), 
-    max(pred.lim, na.rm = TRUE), 
-    legend = c("Fitted Line", "Confidence Bands", 
-        "Prediction Bands"), 
-    lty = c(1, 3, 4), 
-    lwd = 2, 
-    col = c("black", "green4", "red"), 
-    horiz = FALSE, cex = 0.9, bg = "gray95")
-  }
-}
diff --git a/branches/0.1/chemCal/R/inverse.predict.lm.R b/branches/0.1/chemCal/R/inverse.predict.lm.R
deleted file mode 100644
index 927e672..0000000
--- a/branches/0.1/chemCal/R/inverse.predict.lm.R
+++ /dev/null
@@ -1,97 +0,0 @@
-# This is an implementation of Equation (8.28) in the Handbook of Chemometrics
-# and Qualimetrics, Part A, Massart et al (1997), page 200, validated with
-# Example 8 on the same page, extended as specified in the package vignette
-
-inverse.predict <- function(object, newdata, ...,
-  ws = "auto", alpha = 0.05, var.s = "auto")
-{
-  UseMethod("inverse.predict")
-}
-
-inverse.predict.default <- function(object, newdata, ...,
-  ws = "auto", alpha = 0.05, var.s = "auto")
-{
-  stop("Inverse prediction only implemented for univariate lm objects.")
-}
-
-inverse.predict.lm <- function(object, newdata, ...,
-  ws = "auto", alpha = 0.05, var.s = "auto")
-{
-  yname = names(object$model)[[1]]
-  xname = names(object$model)[[2]]
-  if (ws == "auto") {
-    ws <- ifelse(length(object$weights) > 0, mean(object$weights), 1)
-  }
-  if (length(object$weights) > 0) {
-    wx <- split(object$weights,object$model[[xname]])
-    w <- sapply(wx,mean)
-  } else {
-    w <- rep(1,length(split(object$model[[yname]],object$model[[xname]])))
-  }
-  .inverse.predict(object = object, newdata = newdata, 
-    ws = ws, alpha = alpha, var.s = var.s, w = w, xname = xname, yname = yname)
-}
-
-inverse.predict.rlm <- function(object, newdata, ...,
-  ws = "auto", alpha = 0.05, var.s = "auto")
-{
-  yname = names(object$model)[[1]]
-  xname = names(object$model)[[2]]
-  if (ws == "auto") {
-    ws <- mean(object$w)
-  }
-  wx <- split(object$weights,object$model[[xname]])
-  w <- sapply(wx,mean)
-  .inverse.predict(object = object, newdata = newdata, 
-    ws = ws, alpha = alpha, var.s = var.s, w = w, xname = xname, yname = yname)
-}
-
-.inverse.predict <- function(object, newdata, ws, alpha, var.s, w, xname, yname)
-{
-  if (length(object$coef) > 2)
-    stop("More than one independent variable in your model - not implemented")
-
-  if (alpha <= 0 | alpha >= 1)
-    stop("Alpha should be between 0 and 1 (exclusive)")
-
-  ybars <- mean(newdata)
-  m <- length(newdata)
-
-  yx <- split(object$model[[yname]], object$model[[xname]])
-  n <- length(yx)
-  df <- n - length(object$coef)
-  x <- as.numeric(names(yx))
-  ybar <- sapply(yx,mean)
-  yhatx <- split(object$fitted.values, object$model[[xname]])
-  yhat <- sapply(yhatx, mean)
-  se <- sqrt(sum(w * (ybar - yhat)^2)/df)
-
-  if (var.s == "auto") {
-    var.s <- se^2/ws
-  }
-
-  b1 <- object$coef[[xname]]
-
-  ybarw <- sum(w * ybar)/sum(w)
-
-# This is the adapted form of equation 8.28 (see package vignette)
-  sxhats <- 1/b1 * sqrt(
-    (var.s / m) + 
-    se^2 * (1/sum(w) + 
-      (ybars - ybarw)^2 * sum(w) /
-        (b1^2 * (sum(w) * sum(w * x^2) - sum(w * x)^2)))
-  )
-
-  if (names(object$coef)[1] == "(Intercept)") {
-    b0 <- object$coef[["(Intercept)"]]
-  } else {
-    b0 <- 0
-  }
-
-  xs <- (ybars - b0) / b1
-  t <- qt(1-0.5*alpha, n - 2)
-  conf <- t * sxhats
-  result <- list("Prediction"=xs,"Standard Error"=sxhats,
-    "Confidence"=conf, "Confidence Limits"=c(xs - conf, xs + conf))
-  return(result)
-}
diff --git a/branches/0.1/chemCal/R/lod.R b/branches/0.1/chemCal/R/lod.R
deleted file mode 100644
index 5b74418..0000000
--- a/branches/0.1/chemCal/R/lod.R
+++ /dev/null
@@ -1,55 +0,0 @@
-lod <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default", tol = "default")
-{
-  UseMethod("lod")
-}
-
-lod.default <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default", tol = "default")
-{
-  stop("lod is only implemented for univariate lm objects.")
-}
-
-lod.lm <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default", tol = "default")
-{
-  if (length(object$weights) > 0) {
-    stop(paste(
-      "\nThe detemination of a lod from calibration models obtained by",
-      "weighted linear regression requires confidence intervals for",
-      "predicted y values taking into account weights for the x values",
-      "from which the predictions are to be generated.",
-      "This is not supported by the internally used predict.lm method.",
-      sep = "\n"
-    ))
-  }
-  xname <- names(object$model)[[2]]
-  xvalues <- object$model[[2]]
-  yname <- names(object$model)[[1]]
-  newdata <- data.frame(0)
-  names(newdata) <- xname
-  y0 <- predict(object, newdata, interval = "prediction", 
-    level = 1 - 2 * alpha)
-  yc <- y0[[1,"upr"]]
-  if (method == "din") {
-    y0.d <- predict(object, newdata, interval = "prediction",
-      level = 1 - 2 * beta)
-    deltay <- y0.d[[1, "upr"]] - y0.d[[1, "fit"]]
-    lod.y <- yc + deltay 
-    lod.x <- inverse.predict(object, lod.y)$Prediction
-  } else {
-    f <- function(x) {
-      newdata <- data.frame(x)
-      names(newdata) <- xname
-      pi.y <- predict(object, newdata, interval = "prediction",
-        level = 1 - 2 * beta)
-      yd <- pi.y[[1,"lwr"]]
-      (yd - yc)^2
-    }
-    if (tol == "default") tol = min(xvalues[xvalues !=0]) / 1000
-    lod.x <- optimize(f, interval = c(0, max(xvalues) * 10), tol = tol)$minimum
-    newdata <- data.frame(x = lod.x)
-    names(newdata) <- xname
-    lod.y <-  predict(object, newdata)
-  }
-  lod <- list(lod.x, lod.y)
-  names(lod) <- c(xname, yname)
-  return(lod)
-}
diff --git a/branches/0.1/chemCal/R/loq.R b/branches/0.1/chemCal/R/loq.R
deleted file mode 100644
index f832265..0000000
--- a/branches/0.1/chemCal/R/loq.R
+++ /dev/null
@@ -1,41 +0,0 @@
-loq <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto", 
-  var.loq = "auto", tol = "default")
-{
-  UseMethod("loq")
-}
-
-loq.default <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
-  var.loq = "auto", tol = "default")
-{
-  stop("loq is only implemented for univariate lm objects.")
-}
-
-loq.lm <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
-  var.loq = "auto", tol = "default")
-{
-  if (length(object$weights) > 0 && var.loq == "auto" && w.loq == "auto") {
-    stop(paste("If you are using a model from weighted regression,",
-      "you need to specify a reasonable approximation for the",
-      "weight (w.loq) or the variance (var.loq) at the",
-      "limit of quantification"))
-  }
-  xname <- names(object$model)[[2]]
-  xvalues <- object$model[[2]]
-  yname <- names(object$model)[[1]]
-  f <- function(x) {
-    newdata <- data.frame(x = x)
-    names(newdata) <- xname
-    y <- predict(object, newdata)
-    p <- inverse.predict(object, rep(y, n), ws = w.loq, 
-        var.s = var.loq, alpha = alpha)
-    (p[["Prediction"]] - k * p[["Confidence"]])^2
-  }
-  if (tol == "default") tol = min(xvalues[xvalues !=0]) / 1000
-  loq.x <- optimize(f, interval = c(0, max(xvalues) * 10), tol = tol)$minimum
-  newdata <- data.frame(x = loq.x)
-  names(newdata) <- xname
-  loq.y <- predict(object, newdata)
-  loq <- list(loq.x, loq.y)
-  names(loq) <- c(xname, yname)
-  return(loq)
-}
diff --git a/branches/0.1/chemCal/TODO b/branches/0.1/chemCal/TODO
deleted file mode 100644
index 1fc44a7..0000000
--- a/branches/0.1/chemCal/TODO
+++ /dev/null
@@ -1,5 +0,0 @@
-- lod(): At the moment, it is not possible to calculate an lod for the
-  case of more than one replicates (m is fixed to 1). The formula
-  for the prediction of y from mean(x) in Massart et al, p. 433
-  could be used to generalize this.
-- Write methods for nonlinear calibration functions
diff --git a/branches/0.1/chemCal/data/din32645.rda b/branches/0.1/chemCal/data/din32645.rda
deleted file mode 100644
index 4e2913a..0000000
--- a/branches/0.1/chemCal/data/din32645.rda
+++ /dev/null
diff --git a/branches/0.1/chemCal/data/massart97ex1.rda b/branches/0.1/chemCal/data/massart97ex1.rda
deleted file mode 100644
index 1a6fd4b..0000000
--- a/branches/0.1/chemCal/data/massart97ex1.rda
+++ /dev/null
diff --git a/branches/0.1/chemCal/data/massart97ex3.rda b/branches/0.1/chemCal/data/massart97ex3.rda
deleted file mode 100644
index 7f60f3a..0000000
--- a/branches/0.1/chemCal/data/massart97ex3.rda
+++ /dev/null
diff --git a/branches/0.1/chemCal/man/calplot.lm.Rd b/branches/0.1/chemCal/man/calplot.lm.Rd
deleted file mode 100644
index b60a032..0000000
--- a/branches/0.1/chemCal/man/calplot.lm.Rd
+++ /dev/null
@@ -1,69 +0,0 @@
-\name{calplot}
-\alias{calplot}
-\alias{calplot.default}
-\alias{calplot.lm}
-\title{Plot calibration graphs from univariate linear models}
-\description{
-	Produce graphics of calibration data, the fitted model as well
-  as confidence, and, for unweighted regression, prediction bands. 
-}
-\usage{
-  calplot(object, xlim = c("auto", "auto"), ylim = c("auto", "auto"),
-    xlab = "Concentration", ylab = "Response", alpha=0.05, varfunc = NULL)
-}
-\arguments{
-  \item{object}{
-    A univariate model object of class \code{\link{lm}} or 
-    \code{\link[MASS:rlm]{rlm}} 
-    with model formula \code{y ~ x} or \code{y ~ x - 1}.
-  }
-  \item{xlim}{
-    The limits of the plot on the x axis.
-  }
-  \item{ylim}{
-    The limits of the plot on the y axis.
-  }
-  \item{xlab}{
-    The label of the x axis.
-  }
-  \item{ylab}{
-    The label of the y axis.
-  }
-  \item{alpha}{
-    The error tolerance level for the confidence and prediction bands. Note that this 
-    includes both tails of the Gaussian distribution, unlike the alpha and beta parameters
-    used in \code{\link{lod}} (see note below).
-  }
-  \item{varfunc}{
-    The variance function for generating the weights in the model.
-    Currently, this argument is ignored (see note below).
-  }
-}
-\value{
-  A plot of the calibration data, of your fitted model as well as lines showing
-  the confidence limits. Prediction limits are only shown for models from
-  unweighted regression.
-} 
-\note{
-  Prediction bands for models from weighted linear regression require weights
-  for the data, for which responses should be predicted. Prediction intervals
-  using weights e.g. from a variance function are currently not supported by
-  the internally used function \code{\link{predict.lm}}, therefore,
-  \code{calplot} does not draw prediction bands for such models.
-  
-  It is possible to compare the \code{\link{calplot}} prediction bands with the
-  \code{\link{lod}} values if the \code{lod()} alpha and beta parameters are
-  half the value of the \code{calplot()} alpha parameter.
-  
-}
-\examples{
-data(massart97ex3)
-m <- lm(y ~ x, data = massart97ex3)
-calplot(m)
-}
-\author{
-  Johannes Ranke 
-  \email{jranke@uni-bremen.de} 
-  \url{http://www.uft.uni-bremen.de/chemie/ranke}
-}
-\keyword{regression}
diff --git a/branches/0.1/chemCal/man/chemCal-package.Rd b/branches/0.1/chemCal/man/chemCal-package.Rd
deleted file mode 100644
index fb09cb1..0000000
--- a/branches/0.1/chemCal/man/chemCal-package.Rd
+++ /dev/null
@@ -1,16 +0,0 @@
-\name{chemCal-package}
-\alias{chemCal-package}
-\docType{package}
-\title{
-  Calibration functions for analytical chemistry
-}
-\description{
-  See \url{../DESCRIPTION}
-}
-\details{
-  There is a package vignette located in \url{../doc/chemCal.pdf}.
-}
-\author{
-  Author and Maintainer: Johannes Ranke <jranke@uni-bremen.de>
-}
-\keyword{manip}
diff --git a/branches/0.1/chemCal/man/din32645.Rd b/branches/0.1/chemCal/man/din32645.Rd
deleted file mode 100644
index cacbf07..0000000
--- a/branches/0.1/chemCal/man/din32645.Rd
+++ /dev/null
@@ -1,61 +0,0 @@
-\name{din32645}
-\docType{data}
-\alias{din32645}
-\title{Calibration data from DIN 32645}
-\description{
-  Sample dataset to test the package.
-}
-\usage{data(din32645)}
-\format{
-  A dataframe containing 10 rows of x and y values.
-}
-\examples{
-data(din32645)
-m <- lm(y ~ x, data = din32645)
-calplot(m)
-
-## Prediction of x with confidence interval
-(prediction <- inverse.predict(m, 3500, alpha = 0.01))
-
-# This should give 0.07434 according to test data from Dintest, which 
-# was collected from Procontrol 3.1 (isomehr GmbH) in this case
-round(prediction$Confidence,5)
-
-## Critical value:
-(crit <- lod(m, alpha = 0.01, beta = 0.5))
-
-# According to DIN 32645, we should get 0.07 for the critical value
-# (decision limit, "Nachweisgrenze")
-round(crit$x, 2)
-# and according to Dintest test data, we should get 0.0698 from
-round(crit$x, 4)
-
-## Limit of detection (smallest detectable value given alpha and beta)
-# In German, the smallest detectable value is the "Erfassungsgrenze", and we
-# should get 0.14 according to DIN, which we achieve by using the method 
-# described in it:
-lod.din <- lod(m, alpha = 0.01, beta = 0.01, method = "din")
-round(lod.din$x, 2)
-
-## Limit of quantification
-# This accords to the test data coming with the test data from Dintest again, 
-# except for the last digits of the value cited for Procontrol 3.1 (0.2121)
-(loq <- loq(m, alpha = 0.01))
-round(loq$x,4)
-
-# A similar value is obtained using the approximation 
-# LQ = 3.04 * LC (Currie 1999, p. 120)
-3.04 * lod(m,alpha = 0.01, beta = 0.5)$x
-}
-\references{
-  DIN 32645 (equivalent to ISO 11843), Beuth Verlag, Berlin, 1994
-
-  Dintest. Plugin for MS Excel for evaluations of calibration data. Written
-  by Georg Schmitt, University of Heidelberg. 
-  \url{http://www.rzuser.uni-heidelberg.de/~df6/download/dintest.htm}
-
-  Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including
-  detection and quantification capabilities (IUPAC Recommendations 1995). 
-  Analytica Chimica Acta 391, 105 - 126.
-}
-\keyword{datasets}
diff --git a/branches/0.1/chemCal/man/inverse.predict.Rd b/branches/0.1/chemCal/man/inverse.predict.Rd
deleted file mode 100644
index 347d670..0000000
--- a/branches/0.1/chemCal/man/inverse.predict.Rd
+++ /dev/null
@@ -1,69 +0,0 @@
-\name{inverse.predict}
-\alias{inverse.predict}
-\alias{inverse.predict.lm}
-\alias{inverse.predict.rlm}
-\alias{inverse.predict.default}
-\title{Predict x from y for a linear calibration}
-\usage{inverse.predict(object, newdata, \dots,
-  ws, alpha=0.05, var.s = "auto")
-}
-\arguments{
-  \item{object}{
-    A univariate model object of class \code{\link{lm}} or 
-    \code{\link[MASS:rlm]{rlm}} 
-    with model formula \code{y ~ x} or \code{y ~ x - 1}.
-  }
-  \item{newdata}{ 
-    A vector of observed y values for one sample.
-  }
-  \item{\dots}{
-    Placeholder for further arguments that might be needed by 
-    future implementations.
-  }
-  \item{ws}{ 
-    The weight attributed to the sample. This argument is obligatory
-    if \code{object} has weights.
-  }
-  \item{alpha}{
-    The error tolerance level for the confidence interval to be reported.
-  }
-  \item{var.s}{
-    The estimated variance of the sample measurements. The default is to take
-    the residual standard error from the calibration and to adjust it
-    using \code{ws}, if applicable. This means that \code{var.s}
-    overrides \code{ws}.
-  }
-}
-\value{
-  A list containing the predicted x value, its standard error and a
-  confidence interval.
-}
-\description{
-  This function predicts x values using a univariate linear model that has been
-  generated for the purpose of calibrating a measurement method. Prediction
-  intervals are given at the specified confidence level.
-  The calculation method was taken from Massart et al. (1997). In particular,
-  Equations 8.26 and 8.28 were combined in order to yield a general treatment
-  of inverse prediction for univariate linear models, taking into account 
-  weights that have been used to create the linear model, and at the same
-  time providing the possibility to specify a precision in sample measurements
-  differing from the precision in standard samples used for the calibration.
-  This is elaborated in the package vignette.
-}
-\note{
-  The function was validated with examples 7 and 8 from Massart et al. (1997).
-}
-\references{
-  Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J.,
-  Smeyers-Verbeke, J. (1997) Handbook of Chemometrics and Qualimetrics: Part A,
-  p. 200
-}
-\examples{
-# This is example 7 from Chapter 8 in Massart et al. (1997)
-data(massart97ex1)
-m <- lm(y ~ x, data = massart97ex1)
-inverse.predict(m, 15)        #  6.1 +- 4.9
-inverse.predict(m, 90)        # 43.9 +- 4.9
-inverse.predict(m, rep(90,5)) # 43.9 +- 3.2
-}
-\keyword{manip}
diff --git a/branches/0.1/chemCal/man/lod.Rd b/branches/0.1/chemCal/man/lod.Rd
deleted file mode 100644
index 04aac1f..0000000
--- a/branches/0.1/chemCal/man/lod.Rd
+++ /dev/null
@@ -1,88 +0,0 @@
-\name{lod}
-\alias{lod}
-\alias{lod.lm}
-\alias{lod.rlm}
-\alias{lod.default}
-\title{Estimate a limit of detection (LOD)}
-\usage{
-  lod(object, \dots, alpha = 0.05, beta = 0.05, method = "default", tol = "default")
-}
-\arguments{
-  \item{object}{
-    A univariate model object of class \code{\link{lm}} or 
-    \code{\link[MASS:rlm]{rlm}} 
-    with model formula \code{y ~ x} or \code{y ~ x - 1}, 
-    optionally from a weighted regression.
-  }
-  \item{\dots}{
-    Placeholder for further arguments that might be needed by 
-    future implementations.
-  }
-  \item{alpha}{
-    The error tolerance for the decision limit (critical value).
-  }
-  \item{beta}{
-    The error tolerance beta for the detection limit.
-  }
-  \item{method}{
-    The \dQuote{default} method uses a prediction interval at the LOD
-    for the estimation of the LOD, which obviously requires
-    iteration. This is described for example in Massart, p. 432 ff.
-    The \dQuote{din} method uses the prediction interval at
-    x = 0 as an approximation.
-  }
-  \item{tol}{
-    When the \dQuote{default} method is used, the default tolerance
-    for the LOD on the x scale is the value of the smallest non-zero standard
-    divided by 1000. Can be set to a numeric value to override this.
-  }
-}
-\value{
-  A list containig the corresponding x and y values of the estimated limit of
-  detection of a model used for calibration.  
-}
-\description{
-  The decision limit (German: Nachweisgrenze) is defined as the signal or
-  analyte concentration that is significantly different from the blank signal
-  with a first order error alpha (one-sided significance test).
-  The detection limit, or more precise, the minimum detectable value 
-  (German: Erfassungsgrenze), is then defined as the signal or analyte
-  concentration where the probability that the signal is not detected although
-  the analyte is present (type II or false negative error), is beta (also a
-  one-sided significance test).
-}
-\note{
-  - The default values for alpha and beta are the ones recommended by IUPAC.
-  - The estimation of the LOD in terms of the analyte amount/concentration
-    xD from the LOD in the signal domain SD is done by simply inverting the
-    calibration function (i.e. assuming a known calibration function).
-  - The calculation of a LOD from weighted calibration models requires
-    a weights argument for the internally used \code{\link{predict.lm}}
-    function, which is currently not supported in R.
-}
-\references{
-  Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J., 
-  Smeyers-Verbeke, J. (1997) Handbook of Chemometrics and Qualimetrics: Part A,
-  Chapter 13.7.8 
-
-  J. Inczedy, T. Lengyel, and A.M. Ure (2002) International Union of Pure and
-  Applied Chemistry Compendium of Analytical Nomenclature: Definitive Rules.
-  Web edition.
-
-  Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including
-  detection and quantification capabilities (IUPAC Recommendations 1995). 
-  Analytica Chimica Acta 391, 105 - 126.
-}
-\examples{
-data(din32645)
-m <- lm(y ~ x, data = din32645)
-lod(m) 
-
-# The critical value (decision limit, German Nachweisgrenze) can be obtained
-# by using beta = 0.5:
-lod(m, alpha = 0.01, beta = 0.5)
-}
-\seealso{
-  Examples for \code{\link{din32645}}  
-}
-\keyword{manip}
diff --git a/branches/0.1/chemCal/man/loq.Rd b/branches/0.1/chemCal/man/loq.Rd
deleted file mode 100644
index 082cf34..0000000
--- a/branches/0.1/chemCal/man/loq.Rd
+++ /dev/null
@@ -1,82 +0,0 @@
-\name{loq}
-\alias{loq}
-\alias{loq.lm}
-\alias{loq.rlm}
-\alias{loq.default}
-\title{Estimate a limit of quantification (LOQ)}
-\usage{
-  loq(object, \dots, alpha = 0.05, k = 3, n = 1, w.loq = "auto",
-    var.loq = "auto", tol = "default")
-}
-\arguments{
-  \item{object}{
-    A univariate model object of class \code{\link{lm}} or 
-    \code{\link[MASS:rlm]{rlm}} 
-    with model formula \code{y ~ x} or \code{y ~ x - 1}, 
-    optionally from a weighted regression. If weights are specified
-    in the model, either \code{w.loq} or \code{var.loq} have to 
-    be specified.
-  }
-  \item{alpha}{
-    The error tolerance for the prediction of x values in the calculation.
-  }
-  \item{\dots}{
-    Placeholder for further arguments that might be needed by 
-    future implementations.
-  }
-  \item{k}{ 
-    The inverse of the maximum relative error tolerated at the
-    desired LOQ.
-  }
-  \item{n}{
-    The number of replicate measurements for which the LOQ should be
-    specified.
-  }
-  \item{w.loq}{
-    The weight that should be attributed to the LOQ. Defaults
-    to one for unweighted regression, and to the mean of the weights
-    for weighted regression. See \code{\link{massart97ex3}} for 
-    an example how to take advantage of knowledge about the 
-    variance function.
-  }
-  \item{var.loq}{
-    The approximate variance at the LOQ. The default value is 
-    calculated from the model.
-  }
-  \item{tol}{
-    The default tolerance for the LOQ on the x scale is the value of the
-    smallest non-zero standard divided by 1000. Can be set to a 
-    numeric value to override this.
-  }
-}
-\value{
-  The estimated limit of quantification for a model used for calibration.
-}
-\description{
-  The limit of quantification is the x value, where the relative error
-  of the quantification given the calibration model reaches a prespecified
-  value 1/k. Thus, it is the solution of the equation
-    \deqn{L = k c(L)}{L = k * c(L)}
-  where c(L) is half of the length of the confidence interval at the limit L
-  (DIN 32645, equivalent to ISO 11843). c(L) is internally estimated by
-  \code{\link{inverse.predict}}, and L is obtained by iteration. 
-}
-\note{
-  - IUPAC recommends to base the LOQ on the standard deviation of the signal
-    where x = 0. 
-  - The calculation of a LOQ based on weighted regression is non-standard
-    and therefore not tested. Feedback is welcome.
-}
-\examples{
-data(massart97ex3)
-attach(massart97ex3)
-m <- lm(y ~ x)
-loq(m)
-
-# We can get better by using replicate measurements
-loq(m, n = 3)
-}
-\seealso{
-  Examples for \code{\link{din32645}}  
-}
-\keyword{manip}
diff --git a/branches/0.1/chemCal/man/massart97ex1.Rd b/branches/0.1/chemCal/man/massart97ex1.Rd
deleted file mode 100644
index 44e1b85..0000000
--- a/branches/0.1/chemCal/man/massart97ex1.Rd
+++ /dev/null
@@ -1,17 +0,0 @@
-\name{massart97ex1}
-\docType{data}
-\alias{massart97ex1}
-\title{Calibration data from Massart et al. (1997), example 1}
-\description{
-  Sample dataset from p. 175 to test the package.
-}
-\usage{data(massart97ex1)}
-\format{
-  A dataframe containing 6 observations of x and y data.
-}
-\source{
-  Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J.,
-  Smeyers-Verbeke, J. (1997) Handbook of Chemometrics and Qualimetrics: Part A,
-  Chapter 8.
-}
-\keyword{datasets}
diff --git a/branches/0.1/chemCal/man/massart97ex3.Rd b/branches/0.1/chemCal/man/massart97ex3.Rd
deleted file mode 100644
index efdcf02..0000000
--- a/branches/0.1/chemCal/man/massart97ex3.Rd
+++ /dev/null
@@ -1,51 +0,0 @@
-\name{massart97ex3}
-\docType{data}
-\alias{massart97ex3}
-\title{Calibration data from Massart et al. (1997), example 3}
-\description{
-  Sample dataset from p. 188 to test the package.
-}
-\usage{data(massart97ex3)}
-\format{
-  A dataframe containing 6 levels of x values with 5
-  observations of y for each level.
-}
-\examples{
-data(massart97ex3)
-attach(massart97ex3)
-yx <- split(y, x)
-ybar <- sapply(yx, mean)
-s <- round(sapply(yx, sd), digits = 2)
-w <- round(1 / (s^2), digits = 3)
-weights <- w[factor(x)]
-m <- lm(y ~ x, w = weights)
-calplot(m)
-
-# The following concords with the book p. 200
-inverse.predict(m, 15, ws = 1.67)  # 5.9 +- 2.5
-inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
-
-# The LOD is only calculated for models from unweighted regression
-# with this version of chemCal
-m0 <- lm(y ~ x) 
-lod(m0)
-
-# Limit of quantification from unweighted regression
-loq(m0)
-
-# For calculating the limit of quantification from a model from weighted
-# regression, we need to supply weights, internally used for inverse.predict
-# If we are not using a variance function, we can use the weight from
-# the above example as a first approximation (x = 15 is close to our
-# loq approx 14 from above).
-loq(m, w.loq = 1.67)
-# The weight for the loq should therefore be derived at x = 7.3 instead
-# of 15, but the graphical procedure of Massart (p. 201) to derive the 
-# variances on which the weights are based is quite inaccurate anyway. 
-}
-\source{
-  Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J.,
-  Smeyers-Verbeke, J. (1997) Handbook of Chemometrics and Qualimetrics: Part A,
-  Chapter 8.
-}
-\keyword{datasets}
diff --git a/branches/0.1/chemCal/tests/din32645.R b/branches/0.1/chemCal/tests/din32645.R
deleted file mode 100644
index e5ffed7..0000000
--- a/branches/0.1/chemCal/tests/din32645.R
+++ /dev/null
@@ -1,7 +0,0 @@
-require(chemCal)
-data(din32645)
-m <- lm(y ~ x, data = din32645)
-inverse.predict(m, 3500, alpha = 0.01)
-lod <- lod(m, alpha = 0.01, beta = 0.5)
-lod(m, alpha = 0.01, beta = 0.01) 
-loq <- loq(m, alpha = 0.01)
diff --git a/branches/0.1/chemCal/tests/din32645.Rout.save b/branches/0.1/chemCal/tests/din32645.Rout.save
deleted file mode 100644
index 7c9e55d..0000000
--- a/branches/0.1/chemCal/tests/din32645.Rout.save
+++ /dev/null
@@ -1,48 +0,0 @@
-
-R version 3.1.0 (2014-04-10) -- "Spring Dance"
-Copyright (C) 2014 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-> require(chemCal)
-Loading required package: chemCal
-> data(din32645)
-> m <- lm(y ~ x, data = din32645)
-> inverse.predict(m, 3500, alpha = 0.01)
-$Prediction
-[1] 0.1054792
-
-$`Standard Error`
-[1] 0.02215619
-
-$Confidence
-[1] 0.07434261
-
-$`Confidence Limits`
-[1] 0.03113656 0.17982178
-
-> lod <- lod(m, alpha = 0.01, beta = 0.5)
-> lod(m, alpha = 0.01, beta = 0.01) 
-$x
-[1] 0.132909
-
-$y
-       1 
-3765.025 
-
-> loq <- loq(m, alpha = 0.01)
-> 
-> proc.time()
-   user  system elapsed 
-  0.472   0.302   0.354 
diff --git a/branches/0.1/chemCal/tests/massart97.R b/branches/0.1/chemCal/tests/massart97.R
deleted file mode 100644
index 00f837f..0000000
--- a/branches/0.1/chemCal/tests/massart97.R
+++ /dev/null
@@ -1,25 +0,0 @@
-require(chemCal)
-data(massart97ex1)
-m <- lm(y ~ x, data = massart97ex1)
-inverse.predict(m, 15)        #  6.1 +- 4.9
-inverse.predict(m, 90)        # 43.9 +- 4.9
-inverse.predict(m, rep(90,5)) # 43.9 +- 3.2
-
-data(massart97ex3)
-attach(massart97ex3)
-yx <- split(y, x)
-ybar <- sapply(yx, mean)
-s <- round(sapply(yx, sd), digits = 2)
-w <- round(1 / (s^2), digits = 3)
-weights <- w[factor(x)]
-m <- lm(y ~ x, w = weights)
-#calplot(m)
-
-inverse.predict(m, 15, ws = 1.67)  # 5.9 +- 2.5
-inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
-
-m0 <- lm(y ~ x) 
-lod(m0)
-
-loq(m0)
-loq(m, w.loq = 1.67)
diff --git a/branches/0.1/chemCal/tests/massart97.Rout.save b/branches/0.1/chemCal/tests/massart97.Rout.save
deleted file mode 100644
index ce99c30..0000000
--- a/branches/0.1/chemCal/tests/massart97.Rout.save
+++ /dev/null
@@ -1,128 +0,0 @@
-
-R version 3.1.0 (2014-04-10) -- "Spring Dance"
-Copyright (C) 2014 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-> require(chemCal)
-Loading required package: chemCal
-> data(massart97ex1)
-> m <- lm(y ~ x, data = massart97ex1)
-> inverse.predict(m, 15)        #  6.1 +- 4.9
-$Prediction
-[1] 6.09381
-
-$`Standard Error`
-[1] 1.767278
-
-$Confidence
-[1] 4.906751
-
-$`Confidence Limits`
-[1]  1.187059 11.000561
-
-> inverse.predict(m, 90)        # 43.9 +- 4.9
-$Prediction
-[1] 43.93983
-
-$`Standard Error`
-[1] 1.767747
-
-$Confidence
-[1] 4.908053
-
-$`Confidence Limits`
-[1] 39.03178 48.84788
-
-> inverse.predict(m, rep(90,5)) # 43.9 +- 3.2
-$Prediction
-[1] 43.93983
-
-$`Standard Error`
-[1] 1.141204
-
-$Confidence
-[1] 3.168489
-
-$`Confidence Limits`
-[1] 40.77134 47.10832
-
-> 
-> data(massart97ex3)
-> attach(massart97ex3)
-> yx <- split(y, x)
-> ybar <- sapply(yx, mean)
-> s <- round(sapply(yx, sd), digits = 2)
-> w <- round(1 / (s^2), digits = 3)
-> weights <- w[factor(x)]
-> m <- lm(y ~ x, w = weights)
-> #calplot(m)
-> 
-> inverse.predict(m, 15, ws = 1.67)  # 5.9 +- 2.5
-$Prediction
-[1] 5.865367
-
-$`Standard Error`
-[1] 0.8926109
-
-$Confidence
-[1] 2.478285
-
-$`Confidence Limits`
-[1] 3.387082 8.343652
-
-> inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
-$Prediction
-[1] 44.06025
-
-$`Standard Error`
-[1] 2.829162
-
-$Confidence
-[1] 7.855012
-
-$`Confidence Limits`
-[1] 36.20523 51.91526
-
-> 
-> m0 <- lm(y ~ x) 
-> lod(m0)
-$x
-[1] 5.407085
-
-$y
-       1 
-13.63911 
-
-> 
-> loq(m0)
-$x
-[1] 13.97764
-
-$y
-      1 
-30.6235 
-
-> loq(m, w.loq = 1.67)
-$x
-[1] 7.346195
-
-$y
-       1 
-17.90777 
-
-> 
-> proc.time()
-   user  system elapsed 
-  0.529   0.327   0.443 
diff --git a/branches/0.1/chemCal/vignettes/chemCal-001.pdf b/branches/0.1/chemCal/vignettes/chemCal-001.pdf
deleted file mode 100644
index ef9e332..0000000
--- a/branches/0.1/chemCal/vignettes/chemCal-001.pdf
+++ /dev/null
diff --git a/branches/0.1/chemCal/vignettes/chemCal-002.pdf b/branches/0.1/chemCal/vignettes/chemCal-002.pdf
deleted file mode 100644
index 2b56f14..0000000
--- a/branches/0.1/chemCal/vignettes/chemCal-002.pdf
+++ /dev/null
diff --git a/branches/0.1/chemCal/vignettes/chemCal.Rnw b/branches/0.1/chemCal/vignettes/chemCal.Rnw
deleted file mode 100644
index 30e0331..0000000
--- a/branches/0.1/chemCal/vignettes/chemCal.Rnw
+++ /dev/null
@@ -1,130 +0,0 @@
-\documentclass[a4paper]{article}
-%\VignetteIndexEntry{Short manual for the chemCal package}
-\usepackage{hyperref}
-
-\title{Basic calibration functions for analytical chemistry}
-\author{Johannes Ranke}
-
-\begin{document}
-\maketitle
-
-The \texttt{chemCal} package was first designed in the course of a lecture and lab
-course on "analytics of organic trace contaminants" at the University of Bremen
-from October to December 2004. In the fall 2005, an email exchange with 
-Ron Wehrens led to the belief that it would be desirable to implement the
-inverse prediction method given in \cite{massart97} since it also covers the
-case of weighted regression. Studies of the IUPAC orange book and of DIN 32645
-as well as publications by Currie and the Analytical Method Committee of the 
-Royal Society of Chemistry and a nice paper by Castillo and Castells provided
-further understanding of the matter.
-
-At the moment, the package consists of four functions, working on univariate
-linear models of class \texttt{lm} or \texttt{rlm}, plus to datasets for
-validation.
-
-A \href{http://bugs.r-project.org/bugzilla3/show_bug.cgi?id=8877}{bug
-report (PR\#8877)} and the following e-mail exchange on the r-devel mailing list about
-prediction intervals from weighted regression entailed some further studies
-on this subject. However, I did not encounter any proof or explanation of the
-formula cited below yet, so I can't really confirm that Massart's method is correct.
-
-When calibrating an analytical method, the first task is to generate a suitable
-model. If we want to use the \texttt{chemCal} functions, we will have to restrict
-ourselves to univariate, possibly weighted, linear regression so far.
-
-Once such a model has been created, the calibration can be graphically
-shown by using the \texttt{calplot} function:
-
-<<echo=TRUE,fig=TRUE>>=
-library(chemCal)
-data(massart97ex3)
-m0 <- lm(y ~ x, data = massart97ex3)
-calplot(m0)
-@
-
-As we can see, the scatter increases with increasing x. This is also
-illustrated by one of the diagnostic plots for linear models 
-provided by R: 
-
-<<echo=TRUE,fig=TRUE>>=
-plot(m0,which=3)
-@
-
-Therefore, in Example 8 in \cite{massart97} weighted regression
-is proposed which can be reproduced by
-
-<<>>=
-attach(massart97ex3)
-yx <- split(y, x)
-ybar <- sapply(yx, mean)
-s <- round(sapply(yx, sd), digits = 2)
-w <- round(1 / (s^2), digits = 3)
-weights <- w[factor(x)]
-m <- lm(y ~ x, w = weights)
-@
-
-If we now want to predict a new x value from measured y values,
-we use the \texttt{inverse.predict} function:
-
-<<>>=
-inverse.predict(m, 15, ws=1.67)
-inverse.predict(m, 90, ws = 0.145)
-@
-
-The weight \texttt{ws} assigned to the measured y value has to be 
-given by the user in the case of weighted regression, or alternatively,
-the approximate variance \texttt{var.s} at this location.
-
-\section*{Theory for \texttt{inverse.predict}}
-Equation 8.28 in \cite{massart97} gives a general equation for predicting the
-standard error $s_{\hat{x_s}}$ for an x value predicted from measurements of y
-according to the linear calibration function $ y = b_0 + b_1 \cdot x$:
-
-\begin{equation}
-s_{\hat{x_s}} = \frac{s_e}{b_1} \sqrt{\frac{1}{w_s m} + \frac{1}{\sum{w_i}} +
-    \frac{(\bar{y_s} - \bar{y_w})^2 \sum{w_i}}
-        {{b_1}^2 \left( \sum{w_i} \sum{w_i {x_i}^2} - 
-            {\left( \sum{ w_i x_i } \right)}^2 \right) }}
-\end{equation}
-
-with
-
-\begin{equation}
-s_e = \sqrt{ \frac{\sum w_i (y_i - \hat{y_i})^2}{n - 2}}
-\end{equation}
-
-where $w_i$ is the weight for calibration standard $i$, $y_i$ is the mean $y$
-value (!) observed for standard $i$, $\hat{y_i}$ is the estimated value for
-standard $i$, $n$ is the number calibration standards, $w_s$ is the weight
-attributed to the sample $s$, $m$ is the number of replicate measurements of
-sample $s$, $\bar{y_s}$ is the mean response for the sample, 
-$\bar{y_w} = \frac{\sum{w_i y_i}}{\sum{w_i}}$ is the weighted mean of responses
-$y_i$, and $x_i$ is the given $x$ value for standard $i$.
-
-The weight $w_s$ for the sample should be estimated or calculated in accordance
-to the weights used in the linear regression. 
-
-I adjusted the above equation in order to be able to take a different
-precisions in standards and samples into account. In analogy to Equation 8.26
-from \cite{massart97} we get
-
-\begin{equation}
-s_{\hat{x_s}} = \frac{1}{b_1} \sqrt{\frac{{s_s}^2}{w_s m} + 
-    {s_e}^2 \left( \frac{1}{\sum{w_i}} +
-        \frac{(\bar{y_s} - \bar{y_w})^2 \sum{w_i}}
-            {{b_1}^2 \left( \sum{w_i} \sum{w_i {x_i}^2} - {\left( \sum{ w_i x_i } \right)}^2 \right) } \right) }
-\end{equation}
-
-where I interpret $\frac{{s_s}^2}{w_s}$ as an estimator of the variance at location
-$\hat{x_s}$, which can be replaced by a user-specified value using the argument
-\texttt{var.s} of the function \texttt{inverse.predict}.
-
-\begin{thebibliography}{1}
-\bibitem{massart97}
-Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J.,
-Smeyers-Verbeke, J. 
-\newblock Handbook of Chemometrics and Qualimetrics: Part A,
-\newblock Elsevier, Amsterdam, 1997
-\end{thebibliography}
-
-\end{document}
diff --git a/branches/0.1/chemCal/vignettes/chemCal.pdf b/branches/0.1/chemCal/vignettes/chemCal.pdf
deleted file mode 100644
index 935b160..0000000
--- a/branches/0.1/chemCal/vignettes/chemCal.pdf
+++ /dev/null
diff --git a/branches/0.1/chemCal/vignettes/chemCal.tex b/branches/0.1/chemCal/vignettes/chemCal.tex
deleted file mode 100644
index 6326126..0000000
--- a/branches/0.1/chemCal/vignettes/chemCal.tex
+++ /dev/null
@@ -1,169 +0,0 @@
-\documentclass[a4paper]{article}
-%\VignetteIndexEntry{Short manual for the chemCal package}
-\usepackage{hyperref}
-
-\title{Basic calibration functions for analytical chemistry}
-\author{Johannes Ranke}
-
-\usepackage{Sweave}
-\begin{document}
-\maketitle
-
-The \texttt{chemCal} package was first designed in the course of a lecture and lab
-course on "analytics of organic trace contaminants" at the University of Bremen
-from October to December 2004. In the fall 2005, an email exchange with 
-Ron Wehrens led to the belief that it would be desirable to implement the
-inverse prediction method given in \cite{massart97} since it also covers the
-case of weighted regression. Studies of the IUPAC orange book and of DIN 32645
-as well as publications by Currie and the Analytical Method Committee of the 
-Royal Society of Chemistry and a nice paper by Castillo and Castells provided
-further understanding of the matter.
-
-At the moment, the package consists of four functions, working on univariate
-linear models of class \texttt{lm} or \texttt{rlm}, plus to datasets for
-validation.
-
-A \href{http://bugs.r-project.org/bugzilla3/show_bug.cgi?id=8877}{bug
-report (PR\#8877)} and the following e-mail exchange on the r-devel mailing list about
-prediction intervals from weighted regression entailed some further studies
-on this subject. However, I did not encounter any proof or explanation of the
-formula cited below yet, so I can't really confirm that Massart's method is correct.
-
-When calibrating an analytical method, the first task is to generate a suitable
-model. If we want to use the \texttt{chemCal} functions, we will have to restrict
-ourselves to univariate, possibly weighted, linear regression so far.
-
-Once such a model has been created, the calibration can be graphically
-shown by using the \texttt{calplot} function:
-
-\begin{Schunk}
-\begin{Sinput}
-> library(chemCal)
-> data(massart97ex3)
-> m0 <- lm(y ~ x, data = massart97ex3)
-> calplot(m0)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{chemCal-001}
-
-As we can see, the scatter increases with increasing x. This is also
-illustrated by one of the diagnostic plots for linear models 
-provided by R: 
-
-\begin{Schunk}
-\begin{Sinput}
-> plot(m0,which=3)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{chemCal-002}
-
-Therefore, in Example 8 in \cite{massart97} weighted regression
-is proposed which can be reproduced by
-
-\begin{Schunk}
-\begin{Sinput}
-> attach(massart97ex3)
-> yx <- split(y, x)
-> ybar <- sapply(yx, mean)
-> s <- round(sapply(yx, sd), digits = 2)
-> w <- round(1 / (s^2), digits = 3)
-> weights <- w[factor(x)]
-> m <- lm(y ~ x, w = weights)
-\end{Sinput}
-\end{Schunk}
-
-If we now want to predict a new x value from measured y values,
-we use the \texttt{inverse.predict} function:
-
-\begin{Schunk}
-\begin{Sinput}
-> inverse.predict(m, 15, ws=1.67)
-\end{Sinput}
-\begin{Soutput}
-$Prediction
-[1] 5.865367
-
-$`Standard Error`
-[1] 0.8926109
-
-$Confidence
-[1] 2.478285
-
-$`Confidence Limits`
-[1] 3.387082 8.343652
-\end{Soutput}
-\begin{Sinput}
-> inverse.predict(m, 90, ws = 0.145)
-\end{Sinput}
-\begin{Soutput}
-$Prediction
-[1] 44.06025
-
-$`Standard Error`
-[1] 2.829162
-
-$Confidence
-[1] 7.855012
-
-$`Confidence Limits`
-[1] 36.20523 51.91526
-\end{Soutput}
-\end{Schunk}
-
-The weight \texttt{ws} assigned to the measured y value has to be 
-given by the user in the case of weighted regression, or alternatively,
-the approximate variance \texttt{var.s} at this location.
-
-\section*{Theory for \texttt{inverse.predict}}
-Equation 8.28 in \cite{massart97} gives a general equation for predicting the
-standard error $s_{\hat{x_s}}$ for an x value predicted from measurements of y
-according to the linear calibration function $ y = b_0 + b_1 \cdot x$:
-
-\begin{equation}
-s_{\hat{x_s}} = \frac{s_e}{b_1} \sqrt{\frac{1}{w_s m} + \frac{1}{\sum{w_i}} +
-    \frac{(\bar{y_s} - \bar{y_w})^2 \sum{w_i}}
-        {{b_1}^2 \left( \sum{w_i} \sum{w_i {x_i}^2} - 
-            {\left( \sum{ w_i x_i } \right)}^2 \right) }}
-\end{equation}
-
-with
-
-\begin{equation}
-s_e = \sqrt{ \frac{\sum w_i (y_i - \hat{y_i})^2}{n - 2}}
-\end{equation}
-
-where $w_i$ is the weight for calibration standard $i$, $y_i$ is the mean $y$
-value (!) observed for standard $i$, $\hat{y_i}$ is the estimated value for
-standard $i$, $n$ is the number calibration standards, $w_s$ is the weight
-attributed to the sample $s$, $m$ is the number of replicate measurements of
-sample $s$, $\bar{y_s}$ is the mean response for the sample, 
-$\bar{y_w} = \frac{\sum{w_i y_i}}{\sum{w_i}}$ is the weighted mean of responses
-$y_i$, and $x_i$ is the given $x$ value for standard $i$.
-
-The weight $w_s$ for the sample should be estimated or calculated in accordance
-to the weights used in the linear regression. 
-
-I adjusted the above equation in order to be able to take a different
-precisions in standards and samples into account. In analogy to Equation 8.26
-from \cite{massart97} we get
-
-\begin{equation}
-s_{\hat{x_s}} = \frac{1}{b_1} \sqrt{\frac{{s_s}^2}{w_s m} + 
-    {s_e}^2 \left( \frac{1}{\sum{w_i}} +
-        \frac{(\bar{y_s} - \bar{y_w})^2 \sum{w_i}}
-            {{b_1}^2 \left( \sum{w_i} \sum{w_i {x_i}^2} - {\left( \sum{ w_i x_i } \right)}^2 \right) } \right) }
-\end{equation}
-
-where I interpret $\frac{{s_s}^2}{w_s}$ as an estimator of the variance at location
-$\hat{x_s}$, which can be replaced by a user-specified value using the argument
-\texttt{var.s} of the function \texttt{inverse.predict}.
-
-\begin{thebibliography}{1}
-\bibitem{massart97}
-Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J.,
-Smeyers-Verbeke, J. 
-\newblock Handbook of Chemometrics and Qualimetrics: Part A,
-\newblock Elsevier, Amsterdam, 1997
-\end{thebibliography}
-
-\end{document}