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1 files changed, 37 insertions, 47 deletions

diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,43 +1,39 @@
----
-output: github_document
----
 
 <!-- README.md is generated from README.rmd. Please edit that file -->
 
-
-
 # chemCal - Calibration functions for analytical chemistry
 
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+
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-[![Build Status](https://travis-ci.com/jranke/chemCal.svg?branch=master)](https://app.travis-ci.com/github/jranke/chemCal)
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 ## 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][r-project], get the official chemCal release using
+From within [R](https://www.r-project.org/), get the official chemCal
+release using
 
-
-```r
+``` r
 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
+``` r
 library(chemCal)
 m0 <- lm(y ~ x, data = massart97ex3)
 calplot(m0)
@@ -47,11 +43,10 @@ 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
+``` r
 lod(m0)
 #> $x
 #> [1] 5.407085
@@ -59,18 +54,18 @@ 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).
 
-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.
+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.
 
-```r
+``` r
 lod(m0, beta = 0.5)
 #> $x
 #> [1] 2.720388
@@ -80,11 +75,11 @@ 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
+``` r
 loq(m0)
 #> $x
 #> [1] 9.627349
@@ -95,12 +90,11 @@ 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
+``` r
 inverse.predict(m0, 90)
 #> $Prediction
 #> [1] 43.93983
@@ -115,11 +109,10 @@ 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
+``` r
 inverse.predict(m0, c(91, 89, 87, 93, 90))
 #> $Prediction
 #> [1] 43.93983
@@ -136,8 +129,5 @@ 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][pd-site].
-
-[r-project]: https://www.r-project.org/
-[pd-site]: 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](https://pkgdown.jrwb.de/chemCal/).