Update Readme links and fix typos

mikemc · Jul 24, 2019 · f0f8fef · f0f8fef
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 The metacal package provides tools for bias estimation and calibration in
 marker-gene and metagenomics sequencing experiments. It implements the methods
 described in [McLaren MR, Willis AD, Callahan BJ
-(2019)](https://www.biorxiv.org/content/10.1101/559831v1) and is used for the
+(2019)](https://www.biorxiv.org/content/10.1101/559831v2) and is used for the
 analysis associated with that manuscript, available at the [manuscript's
-repository](https://github.com/mikemc/2019-bias-manuscript).
+repository](https://github.com/mikemc/mgs-bias-manuscript).
 
 ## Installation
 
@@ -30,10 +30,10 @@ protocols.
 
 It is currently not possible to calibrate the composition of a natural
 community without making strong and untested assumptions about bias being the
-same for constructed and natural samples and about the efficiciencies of taxa
-not in the controls (e.g., approximating them by that of the closest relative
-or the average average efficiency). For this and other limitations described in
-the Discussion of our manuscript, calibration as a practical method to obtain
+same for constructed and natural samples and about the efficiencies of taxa not
+in the controls (e.g., approximating them by that of the closest relative or
+the average efficiency). For this and other limitations described in the
+Discussion of our manuscript, calibration as a practical method to obtain
 quantitatively accurate composition measurements is not currently feasible
 using this or any package. However, calibration using a hypothesized bias
 (perhaps partially informed by experimental measurement) can still be useful to

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diff --git a/vignettes/tutorial.Rmd b/vignettes/tutorial.Rmd
@@ -238,7 +238,7 @@ bias.pw0
 ## Evaluate the bias estimate visually
 
 Plotting the bias estimate against the observed errors is useful to get a sense
-of the strength of bias versus noise in the control measurements were and how
+of the strength of bias versus noise in the control measurements and how
 precisely the bias is estimated. When the control samples vary in their actual
 compositions, such plots can also allow us to confirm that the bias remains
 consistent across samples with different compositions.  Since bias acts