Generating Tables

It is quite easy to generate tables of outputs for the various concurve functions and in different formats. Here we show how to do this with a simple example. First, we’ll simulate some fake data as usual, compare the means, and then produce a confidence function of the outputs.

library(concurve)
#> Please see the documentation on https://stat.lesslikely.com/concurve/ or by typing `help(concurve)`
GroupA <- rnorm(500)
GroupB <- rnorm(500)
RandomData <- data.frame(GroupA, GroupB)

intervalsdf <- curve_mean(GroupA, GroupB,
  data = RandomData, method = "default"
)

The results are now stored in intervalsdf. Using the curve_table() function, we can now produce a high quality table with several values of interest.

(x <- curve_table(data = intervalsdf[[1]], format = "image"))

Lower Limit	Upper Limit	Interval Width	Interval Level (%)	CDF	P-value	S-value (bits)
0.038	0.079	0.040	25.0	0.625	0.750	0.415
0.016	0.101	0.085	50.0	0.750	0.500	1.000
-0.014	0.131	0.146	75.0	0.875	0.250	2.000
-0.023	0.139	0.162	80.0	0.900	0.200	2.322
-0.033	0.149	0.182	85.0	0.925	0.150	2.737
-0.046	0.163	0.208	90.0	0.950	0.100	3.322
-0.066	0.182	0.248	95.0	0.975	0.050	4.322
-0.084	0.200	0.284	97.5	0.988	0.025	5.322
-0.105	0.222	0.326	99.0	0.995	0.010	6.644

Here we specified the format as “image”, which will give us just that. We can also specify other options such as

(z <- curve_table(intervalsdf[[1]], format = "latex"))

	Lower Limit	Upper Limit	Interval Width	Interval Level (%)	CDF	P-value	S-value (bits)
2501	0.038	0.079	0.040	25.0	0.625	0.750	0.415
5001	0.016	0.101	0.085	50.0	0.750	0.500	1.000
7501	-0.014	0.131	0.146	75.0	0.875	0.250	2.000
8001	-0.023	0.139	0.162	80.0	0.900	0.200	2.322
8501	-0.033	0.149	0.182	85.0	0.925	0.150	2.737
9001	-0.046	0.163	0.208	90.0	0.950	0.100	3.322
9501	-0.066	0.182	0.248	95.0	0.975	0.050	4.322
9751	-0.084	0.200	0.284	97.5	0.988	0.025	5.322
9901	-0.105	0.222	0.326	99.0	0.995	0.010	6.644

which is useful for inserting the output into a TeX document, and we can also specify options such as

(df <- curve_table(intervalsdf[[1]], format = "data.frame"))
#>      Lower Limit Upper Limit Interval Width Interval Level (%)   CDF P-value
#> 2501       0.038       0.079          0.040               25.0 0.625   0.750
#> 5001       0.016       0.101          0.085               50.0 0.750   0.500
#> 7501      -0.014       0.131          0.146               75.0 0.875   0.250
#> 8001      -0.023       0.139          0.162               80.0 0.900   0.200
#> 8501      -0.033       0.149          0.182               85.0 0.925   0.150
#> 9001      -0.046       0.163          0.208               90.0 0.950   0.100
#> 9501      -0.066       0.182          0.248               95.0 0.975   0.050
#> 9751      -0.084       0.200          0.284               97.5 0.988   0.025
#> 9901      -0.105       0.222          0.326               99.0 0.995   0.010
#>      S-value (bits)
#> 2501          0.415
#> 5001          1.000
#> 7501          2.000
#> 8001          2.322
#> 8501          2.737
#> 9001          3.322
#> 9501          4.322
#> 9751          5.322
#> 9901          6.644

The options “pptx” and “docx” can also be specified as format options, but specifying these will open those programs if they are installed, which may not be ideal for all because no everyone has access.

Cite R Packages

Please remember to cite the packages that you use.

citation("concurve")
#> To cite package 'concurve' in publications use:
#> 
#>   Rafi Z, Vigotsky A (2026). _concurve: Computes and Plots
#>   Compatibility (Confidence) Intervals, P-Values, S-Values, &
#>   Likelihood Intervals to Form Consonance, Surprisal, & Likelihood
#>   Functions_. R package version 3.0.5,
#>   <https://CRAN.R-project.org/package=concurve>.
#> 
#>   Rafi Z, Greenland S (2020). "Semantic and Cognitive Tools to Aid
#>   Statistical Science: Replace Confidence and Significance by
#>   Compatibility and Surprise." _BMC Medical Research Methodology_,
#>   *20*, 244. ISSN 1471-2288. doi:10.1186/s12874-020-01105-9
#>   <https://doi.org/10.1186/s12874-020-01105-9>.
#>   <https://doi.org/10.1186/s12874-020-01105-9>.
#> 
#> To see these entries in BibTeX format, use 'print(<citation>,
#> bibtex=TRUE)', 'toBibtex(.)', or set
#> 'options(citation.bibtex.max=999)'.
citation("flextable")
#> To cite package 'flextable' in publications use:
#> 
#>   Gohel D, Skintzos P (2026). _flextable: Functions for Tabular
#>   Reporting_. R package version 0.10.0,
#>   <https://ardata-fr.github.io/flextable-book/>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {flextable: Functions for Tabular Reporting},
#>     author = {David Gohel and Panagiotis Skintzos},
#>     year = {2026},
#>     note = {R package version 0.10.0},
#>     url = {https://ardata-fr.github.io/flextable-book/},
#>   }
citation("officer")
#> To cite package 'officer' in publications use:
#> 
#>   Gohel D, Moog S, Heckmann M (2026). _officer: Manipulation of
#>   Microsoft Word and PowerPoint Documents_. R package version 0.7.6,
#>   <https://ardata-fr.github.io/officeverse/>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {officer: Manipulation of Microsoft Word and PowerPoint Documents},
#>     author = {David Gohel and Stefan Moog and Mark Heckmann},
#>     year = {2026},
#>     note = {R package version 0.7.6},
#>     url = {https://ardata-fr.github.io/officeverse/},
#>   }

Cite R Packages

References