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

1 files changed, 55 insertions, 55 deletions

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}