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+---
+output: github_document
+---
 
 <!-- README.md is generated from README.rmd. Please edit that file -->
 
+
+
 # chemCal - Calibration functions for analytical chemistry
 
 <!-- badges: start -->
-
 [![](https://www.r-pkg.org/badges/version/chemCal)](https://cran.r-project.org/package=chemCal)
-[![Build
-Status](https://app.travis-ci.com/jranke/chemCal.svg?token=Sq9VuYWyRz2FbBLxu6DK&branch=main)](https://app.travis-ci.com/jranke/chemCal)
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 <!-- badges: end -->
 
 ## Overview
 
-chemCal is an R package providing some basic functions for conveniently
-working with linear calibration curves with one explanatory variable.
+chemCal is an R package providing some basic functions for conveniently working
+with linear calibration curves with one explanatory variable.
 
 ## Installation
 
-From within [R](https://www.r-project.org/), get the official chemCal
-release using
+From within [R][r-project], get the official chemCal release using
+
 
 ``` r
 install.packages("chemCal")
@@ -28,12 +30,13 @@ install.packages("chemCal")
 
 ## Usage
 
-chemCal works with univariate linear models of class `lm`. Working with
-one of the datasets coming with chemCal, we can produce a calibration
-plot using the `calplot` function:
+chemCal works with univariate linear models of class `lm`. Working with one of
+the datasets coming with chemCal, we can produce a calibration plot using the
+`calplot` function:
 
 ### Plotting a calibration
 
+
 ``` r
 library(chemCal)
 m0 <- lm(y ~ x, data = massart97ex3)
@@ -44,8 +47,9 @@ calplot(m0)
 
 ### LOD and LOQ
 
-If you use unweighted regression, as in the above example, we can
-calculate a Limit Of Detection (LOD) from the calibration data.
+If you use unweighted regression, as in the above example, we can calculate a
+Limit Of Detection (LOD) from the calibration data.
+
 
 ``` r
 lod(m0)
@@ -55,16 +59,16 @@ lod(m0)
 #> $y
 #> [1] 13.63911
 ```
-
-This is the minimum detectable value (German: Erfassungsgrenze),
-i.e. the value where the probability that the signal is not detected
-although the analyte is present is below a specified error tolerance
-beta (default is 0.05 following the IUPAC recommendation).
+This is the minimum detectable value (German: Erfassungsgrenze), i.e. the
+value where the probability that the signal is not detected although the
+analyte is present is below a specified error tolerance beta (default is 0.05
+following the IUPAC recommendation).
 
 You can also calculate the decision limit (German: Nachweisgrenze), i.e.
-the value that is significantly different from the blank signal with an
-error tolerance alpha (default is 0.05, again following IUPAC
-recommendations) by setting beta to 0.5.
+the value that is significantly different from the blank signal
+with an error tolerance alpha (default is 0.05, again following
+IUPAC recommendations) by setting beta to 0.5.
+
 
 ``` r
 lod(m0, beta = 0.5)
@@ -76,9 +80,9 @@ lod(m0, beta = 0.5)
 ```
 
 Furthermore, you can calculate the Limit Of Quantification (LOQ), being
-defined as the value where the relative error of the quantification
-given the calibration model reaches a prespecified value (default is
-1/3).
+defined as the value where the relative error of the quantification given the
+calibration model reaches a prespecified value (default is 1/3).
+
 
 ``` r
 loq(m0)
@@ -91,9 +95,10 @@ loq(m0)
 
 ### Confidence intervals for measured values
 
-Finally, you can get a confidence interval for the values measured using
-the calibration curve, i.e. for the inverse predictions using the
-function `inverse.predict`.
+Finally, you can get a confidence interval for the values
+measured using the calibration curve, i.e. for the inverse
+predictions using the function `inverse.predict`.
+
 
 ``` r
 inverse.predict(m0, 90)
@@ -110,8 +115,9 @@ inverse.predict(m0, 90)
 #> [1] 40.70952 47.17014
 ```
 
-If you have replicate measurements of the same sample, you can also give
-a vector of numbers.
+If you have replicate measurements of the same sample,
+you can also give a vector of numbers.
+
 
 ``` r
 inverse.predict(m0, c(91, 89, 87, 93, 90))
@@ -130,5 +136,8 @@ inverse.predict(m0, c(91, 89, 87, 93, 90))
 
 ## Reference
 
-You can use the R help system to view documentation, or you can have a
-look at the [online documentation](https://pkgdown.jrwb.de/chemCal/).
+You can use the R help system to view documentation, or you can
+have a look at the [online documentation][pd-site].
+
+[r-project]: https://www.r-project.org/
+[pd-site]: https://pkgdown.jrwb.de/chemCal/