Merge pull request #73 from lhdjung/devel

Devel

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: scrutiny
 Title: Error Detection in Science
-Version: 0.4.0
+Version: 0.5.0
 Authors@R: c(
     person(given = "Lukas",
            family = "Jung",

README.md

@@ -66,20 +66,20 @@ pigs1
 # GRIM-testing for data frames:
 grim_map(pigs1)
 #> # A tibble: 12 × 4
-#>    x         n consistency ratio
-#>    <chr> <dbl> <lgl>       <dbl>
-#>  1 7.22     32 TRUE         0.68
-#>  2 4.74     25 FALSE        0.75
-#>  3 5.23     29 FALSE        0.71
-#>  4 2.57     24 FALSE        0.76
-#>  5 6.77     27 FALSE        0.73
-#>  6 2.68     28 TRUE         0.72
-#>  7 7.01     29 FALSE        0.71
-#>  8 7.38     26 TRUE         0.74
-#>  9 3.14     27 FALSE        0.73
-#> 10 6.89     31 FALSE        0.69
-#> 11 5.00     25 TRUE         0.75
-#> 12 0.24     28 FALSE        0.72
+#>    x         n consistency probability
+#>    <chr> <dbl> <lgl>             <dbl>
+#>  1 7.22     32 TRUE               0.68
+#>  2 4.74     25 FALSE              0.75
+#>  3 5.23     29 FALSE              0.71
+#>  4 2.57     24 FALSE              0.76
+#>  5 6.77     27 FALSE              0.73
+#>  6 2.68     28 TRUE               0.72
+#>  7 7.01     29 FALSE              0.71
+#>  8 7.38     26 TRUE               0.74
+#>  9 3.14     27 FALSE              0.73
+#> 10 6.89     31 FALSE              0.69
+#> 11 5.00     25 TRUE               0.75
+#> 12 0.24     28 FALSE              0.72
 ```
 
 Test percentages instead of means:
@@ -99,14 +99,14 @@ pigs2
 grim_map(pigs2, percent = TRUE)
 #> ℹ `x` converted from percentage
 #> # A tibble: 6 × 4
-#>   x         n consistency ratio
-#>   <chr> <dbl> <lgl>       <dbl>
-#> 1 0.674   150 FALSE        0.85
-#> 2 0.542   150 FALSE        0.85
-#> 3 0.540   150 TRUE         0.85
-#> 4 0.698   150 FALSE        0.85
-#> 5 0.681   150 FALSE        0.85
-#> 6 0.554   150 FALSE        0.85
+#>   x         n consistency probability
+#>   <chr> <dbl> <lgl>             <dbl>
+#> 1 0.674   150 FALSE              0.85
+#> 2 0.542   150 FALSE              0.85
+#> 3 0.540   150 TRUE               0.85
+#> 4 0.698   150 FALSE              0.85
+#> 5 0.681   150 FALSE              0.85
+#> 6 0.554   150 FALSE              0.85
 ```
 
 You can choose how the means are reconstructed for testing — below,

vignettes/related.Rmd

@@ -18,7 +18,7 @@ Software for error detection goes well beyond scrutiny. This vignette presents b
 
 Please contact me if you know about relevant software that isn't listed here (email: jung-lukas\@gmx.net).
 
--   For good reason, [statcheck](https://michelenuijten.shinyapps.io/statcheck-web/) by Sacha Epskamp and Michèle Nuijten is the best-known error detection software. It reconstructs *p*-values and tests them for consistency with their respective statistic, such as *t* or *F*. Even better, it operates on PDF files automatically, enabling users to scan [massive amounts](https://link.springer.com/article/10.3758/s13428-015-0664-2) of published articles. Steve Haroz built a [simple edition](https://steveharoz.shinyapps.io/statchecksimple/) of the statcheck web app.
+-   For good reason, [statcheck](https://michelenuijten.shinyapps.io/statcheck-web/) by Sacha Epskamp and Michèle Nuijten is the best-known error detection software. It reconstructs *p*-values and tests them for consistency with their respective statistic, such as *t* or *F*. Even better, it operates on PDF files automatically, enabling users to scan [massive amounts](https://link.springer.com/article/10.3758/s13428-015-0664-2) of published articles. Steve Haroz built a [simple edition](https://steveharoz.shinyapps.io/statchecksimple) of the statcheck web app.
 
 -   James Heathers' [SPRITE](https://peerj.com/preprints/26968v1/) algorithm reconstructs possible distributions of raw data from summary statistics. James also wrote a [light introduction](https://medium.com/hackernoon/introducing-sprite-and-the-case-of-the-carthorse-child-58683c2bfeb) to SPRITE. For R users, it was implemented in [rsprite2](https://lukaswallrich.github.io/rsprite2/) by Lukas Wallrich, building up on code by Nick Brown. Jordan Anaya developed a [Python-based SPRITE app](http://www.prepubmed.org/sprite/).
 

vignettes/rounding-options.Rmd

@@ -40,7 +40,7 @@ If the decimal portion to be cut off by rounding is 5, how do these technologies
 
 -   [SPSS](https://sr.ipsosinteractive.com/SPSSMR/Reporter/doc/en/help/ref_rounding.htm) rounds to even by default, but users may choose to round up instead.
 
--   [Matlab](https://de.mathworks.com/help/matlab/ref/round.html) rounds away from zero by default (i.e., it rounds up if the input is positive). However, other rounding procedures can be chosen.
+-   Matlab rounds away from zero by default (i.e., it rounds up if the input is positive). However, other rounding procedures can be chosen.
 
 -   In [SAS](https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/lefunctionsref/p0tj6cmga7p8qln1ejh6ebevm0c9.htm), `ROUND()` rounds up, and `ROUNDE()` rounds to even. Both have a small tolerance. `ROUNDZ()` rounds to even without a tolerance.
 

vignettes/wrangling.Rmd

@@ -34,7 +34,7 @@ The only solution I know of is to work with strings --- namely, strings that can
 
 #### Automated
 
-Several R packages help to extract tables from PDF. I recommend tabulizer (not currently on CRAN; see [installation notes](https://docs.ropensci.org/tabulizer/#installation)). There are also the [pdftables](https://expersso.r-universe.dev/pdftables#) and [pdftools](https://ropensci.r-universe.dev/pdftools#) packages.
+Several R packages help to extract tables from PDF. I recommend tabulizer (not currently on CRAN; see [installation notes](https://stackoverflow.com/questions/70036429/having-issues-installing-tabulizer-package-in-r)). There are also the [pdftables](https://expersso.r-universe.dev/pdftables#) and [pdftools](https://ropensci.r-universe.dev/pdftools#) packages.
 
 Using tabulizer requires Java to be installed. When it works well, tabulizer is a great tool for importing tables quickly and efficiently. It automatically captures values as strings, so trailing zeros are treated just like other digits.