Fix URLs in README, convert to roxygenv0.2.3

- The roxygen conversion was done using Rd2roxygen
- Also edit _pkgdown.yml to group the reference
- Use markdown bullet lists for lod and loq docs

11 files changed, 327 insertions, 300 deletions

diff --git a/man/calplot.Rd b/man/calplot.Rd
new file mode 100644
index 0000000..440d469
--- /dev/null
+++ b/man/calplot.Rd
@@ -0,0 +1,73 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/calplot.R
+\name{calplot}
+\alias{calplot}
+\alias{calplot.default}
+\alias{calplot.lm}
+\title{Plot calibration graphs from univariate linear models}
+\usage{
+calplot(
+  object,
+  xlim = c("auto", "auto"),
+  ylim = c("auto", "auto"),
+  xlab = "Concentration",
+  ylab = "Response",
+  legend_x = "auto",
+  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{legend_x}{An optional numeric value for adjusting the x coordinate of
+the legend.}
+
+\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.
+}
+\description{
+Produce graphics of calibration data, the fitted model as well as
+confidence, and, for unweighted regression, prediction bands.
+}
+\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
+}
diff --git a/man/calplot.lm.Rd b/man/calplot.lm.Rd
deleted file mode 100644
index 39f20de..0000000
--- a/man/calplot.lm.Rd
+++ /dev/null
@@ -1,72 +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", legend_x = "auto",
-    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{legend_x}{
-    An optional numeric value for adjusting the x coordinate of the legend.
-  }
-  \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} 
-}
-\keyword{regression}
diff --git a/man/din32645.Rd b/man/din32645.Rd
index ffcbaed..a8e6a31 100644
--- a/man/din32645.Rd
+++ b/man/din32645.Rd
@@ -1,15 +1,17 @@
-\name{din32645}
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/chemCal-package.R
 \docType{data}
+\name{din32645}
 \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.
+A dataframe containing 10 rows of x and y values.
+}
+\description{
+Sample dataset to test the package.
 }
 \examples{
+
 m <- lm(y ~ x, data = din32645)
 calplot(m)
 
@@ -45,16 +47,17 @@ 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
+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. Formerly available from
-  the Website of the University of Heidelberg.
+Dintest. Plugin for MS Excel for evaluations of calibration data. Written by
+Georg Schmitt, University of Heidelberg. Formerly available from the Website
+of the University of Heidelberg.
 
-  Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including
-  detection and quantification capabilities (IUPAC Recommendations 1995). 
-  Analytica Chimica Acta 391, 105 - 126.
+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/man/inverse.predict.Rd b/man/inverse.predict.Rd
index 373623e..08c24d7 100644
--- a/man/inverse.predict.Rd
+++ b/man/inverse.predict.Rd
@@ -1,67 +1,72 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/inverse.predict.lm.R
 \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")
+\usage{
+inverse.predict(
+  object,
+  newdata,
+  ...,
+  ws = "auto",
+  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}.
-  }
+\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.
+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.
+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.
+}
+\details{
+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
 }
 \note{
-  The function was validated with examples 7 and 8 from Massart et al. (1997). 
-  Note that the behaviour of inverse.predict changed with chemCal version
-  0.2.1. Confidence intervals for x values obtained from calibrations with
-  replicate measurements did not take the variation about the means into account.
-  Please refer to the vignette for details.}
-\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
+The function was validated with examples 7 and 8 from Massart et al.
+(1997).  Note that the behaviour of inverse.predict changed with chemCal
+version 0.2.1. Confidence intervals for x values obtained from calibrations
+with replicate measurements did not take the variation about the means into
+account.  Please refer to the vignette for details.
 }
 \examples{
+
 # This is example 7 from Chapter 8 in Massart et al. (1997)
 m <- lm(y ~ x, data = massart97ex1)
 inverse.predict(m, 15)        #  6.1 +- 4.9
@@ -84,5 +89,10 @@ m3.means <- lm(y ~ x, w = weights, data = massart97ex3.means)
 inverse.predict(m3.means, 15, ws = 1.67)  # 5.9 +- 2.5
 inverse.predict(m3.means, 90, ws = 0.145) # 44.1 +- 7.9
 
+
+}
+\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
 }
-\keyword{manip}
diff --git a/man/lod.Rd b/man/lod.Rd
index ce32670..05107f3 100644
--- a/man/lod.Rd
+++ b/man/lod.Rd
@@ -1,3 +1,5 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/lod.R
 \name{lod}
 \alias{lod}
 \alias{lod.lm}
@@ -5,83 +7,82 @@
 \alias{lod.default}
 \title{Estimate a limit of detection (LOD)}
 \usage{
-  lod(object, \dots, alpha = 0.05, beta = 0.05, method = "default", tol = "default")
+lod(
+  object,
+  ...,
+  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.
-  }
+\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.  
+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).
+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.
+* 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.
 }
 \examples{
+
 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)
+
+}
+\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.
 }
 \seealso{
-  Examples for \code{\link{din32645}}  
+Examples for \code{\link{din32645}}
 }
-\keyword{manip}
diff --git a/man/loq.Rd b/man/loq.Rd
index c247f34..390d3a8 100644
--- a/man/loq.Rd
+++ b/man/loq.Rd
@@ -1,3 +1,5 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/loq.R
 \name{loq}
 \alias{loq}
 \alias{loq.lm}
@@ -5,76 +7,74 @@
 \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")
+loq(
+  object,
+  ...,
+  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.
-  }
+\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{\dots}{Placeholder for further arguments that might be needed by
+future implementations.}
+
+\item{alpha}{The error tolerance for the prediction of x values in the
+calculation.}
+
+\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.
+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. 
+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.
+* 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{
+
 m <- lm(y ~ x, data = massart97ex1)
 loq(m)
 
 # We can get better by using replicate measurements
 loq(m, n = 3)
+
 }
 \seealso{
-  Examples for \code{\link{din32645}}  
+Examples for \code{\link{din32645}}
 }
-\keyword{manip}
diff --git a/man/massart97ex1.Rd b/man/massart97ex1.Rd
index 44e1b85..d154a9c 100644
--- a/man/massart97ex1.Rd
+++ b/man/massart97ex1.Rd
@@ -1,17 +1,18 @@
-\name{massart97ex1}
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/chemCal-package.R
 \docType{data}
+\name{massart97ex1}
 \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.
+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.
+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.
+}
+\description{
+Sample dataset from p. 175 to test the package.
 }
 \keyword{datasets}
diff --git a/man/massart97ex3.Rd b/man/massart97ex3.Rd
index d7f8d00..284a435 100644
--- a/man/massart97ex3.Rd
+++ b/man/massart97ex3.Rd
@@ -1,16 +1,23 @@
-\name{massart97ex3}
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/chemCal-package.R
 \docType{data}
+\name{massart97ex3}
 \alias{massart97ex3}
 \title{Calibration data from Massart et al. (1997), example 3}
-\description{
-  Sample dataset from p. 188 to test the package.
-}
-\usage{massart97ex3}
 \format{
-  A dataframe containing 6 levels of x values with 5
-  observations of y for each level.
+A dataframe containing 6 levels of x values with 5 observations of y
+for each level.
+}
+\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.
+}
+\description{
+Sample dataset from p. 188 to test the package.
 }
 \examples{
+
 # For reproducing the results for replicate standard measurements in example 8,
 # we need to do the calibration on the means when using chemCal > 0.2
 weights <- with(massart97ex3, {
@@ -45,10 +52,6 @@ loq(m3.means, 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/man/rl95_cadmium.Rd b/man/rl95_cadmium.Rd
index 7ee4222..8e0b02c 100644
--- a/man/rl95_cadmium.Rd
+++ b/man/rl95_cadmium.Rd
@@ -1,16 +1,19 @@
-\name{rl95_cadmium}
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/chemCal-package.R
 \docType{data}
+\name{rl95_cadmium}
 \alias{rl95_cadmium}
-\title{Cadmium concentrations measured by AAS as reported by Rocke and Lorenzato (1995)}
-\description{
-  Dataset reproduced from Table 1 in Rocke and Lorenzato (1995).
-}
+\title{Cadmium concentrations measured by AAS as reported by Rocke and Lorenzato
+(1995)}
 \format{
-  A dataframe containing four replicate observations for each 
-  of the six calibration standards.
+A dataframe containing four replicate observations for each of the
+six calibration standards.
 }
 \source{
-  Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for
-  measurement error in analytical chemistry. Technometrics 37(2), 176-184.
+Rocke, David M. und Lorenzato, Stefan (1995) A two-component model
+for measurement error in analytical chemistry. Technometrics 37(2), 176-184.
+}
+\description{
+Dataset reproduced from Table 1 in Rocke and Lorenzato (1995).
 }
 \keyword{datasets}
diff --git a/man/rl95_toluene.Rd b/man/rl95_toluene.Rd
index 21fea0f..1f8836a 100644
--- a/man/rl95_toluene.Rd
+++ b/man/rl95_toluene.Rd
@@ -1,18 +1,20 @@
-\name{rl95_toluene}
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/chemCal-package.R
 \docType{data}
+\name{rl95_toluene}
 \alias{rl95_toluene}
 \title{Toluene amounts measured by GC/MS as reported by Rocke and Lorenzato (1995)}
-\description{
-  Dataset reproduced from Table 4 in Rocke and Lorenzato (1995). The toluene
-  amount in the calibration samples is given in picograms per 100 µL.
-  Presumably this is the volume that was injected into the instrument.
-}
 \format{
-  A dataframe containing four replicate observations for each
-  of the six calibration standards.
+A dataframe containing four replicate observations for each of the
+six calibration standards.
 }
 \source{
-  Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for
-  measurement error in analytical chemistry. Technometrics 37(2), 176-184.
+Rocke, David M. und Lorenzato, Stefan (1995) A two-component model
+for measurement error in analytical chemistry. Technometrics 37(2), 176-184.
+}
+\description{
+Dataset reproduced from Table 4 in Rocke and Lorenzato (1995). The toluene
+amount in the calibration samples is given in picograms per 100 µL.
+Presumably this is the volume that was injected into the instrument.
 }
 \keyword{datasets}
diff --git a/man/utstats14.Rd b/man/utstats14.Rd
index ec41bd5..1b739d4 100644
--- a/man/utstats14.Rd
+++ b/man/utstats14.Rd
@@ -1,18 +1,21 @@
-\name{utstats14}
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/chemCal-package.R
 \docType{data}
+\name{utstats14}
 \alias{utstats14}
 \title{Example data for calibration with replicates from University of Toronto}
-\description{
-  Dataset read into R from 
-  \url{https://sites.chem.utoronto.ca/chemistry/coursenotes/analsci/stats/files/example14.xls}.
-}
 \format{
-  A tibble containing three replicate observations of the response for five
-  calibration concentrations.
+A tibble containing three replicate observations of the response for
+five calibration concentrations.
 }
 \source{
-  David Stone and Jon Ellis (2011) Statistics in Analytical Chemistry. Tutorial website
-  maintained by the Departments of Chemistry, University of Toronto.
-  \url{https://sites.chem.utoronto.ca/chemistry/coursenotes/analsci/stats/index.html}
+David Stone and Jon Ellis (2011) Statistics in Analytical Chemistry.
+Tutorial website maintained by the Departments of Chemistry, University of
+Toronto.
+\url{https://sites.chem.utoronto.ca/chemistry/coursenotes/analsci/stats/index.html}
+}
+\description{
+Dataset read into R from
+\url{https://sites.chem.utoronto.ca/chemistry/coursenotes/analsci/stats/files/example14.xls}.
 }
 \keyword{datasets}