Computational Provenance & Reproducibility Record

RAISINS· Correlation Analysis Module

This Computational Provenance Record documents the statistical computing environment, software dependencies, computational provenance, and bibliographic references associated with the RAISINS Correlation Analysis module. It is intended to support computational reproducibility and software transparency. Detailed statistical methodology, mathematical derivations, and user guidance are provided separately in the official module documentation.

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1 Module Metadata

Parameter	Specification
Module	Correlation Analysis
Module Version	`1.0.0`
DOI	10.5281/zenodo.21321191
Document Type	Computational workflow
Statistical Engine	R
R Version	`4.5.2`
Reproducibility	Execution Environment: Posit Connect · GCR · renv-locked

2 Statistical Dependency Manifest

Package	Version	Repository	Core Statistical Functions
Hmisc	`5.2-5`	CRAN	`rcorr()`
stats	`4.5.2`	Base R	`cor()`

3 Statistical Function Registry

Analytical Role	Primary Function(s)
Correlation coefficients	`stats::cor()`
Correlation significance	`Hmisc::rcorr()`

4 Default Methods & Parameters

Analysis	Step / Parameter	Default Method / Value
Correlation Analysis	Coefficient	`cor()` with `method = "pearson"` (default), or `"spearman"` / `"kendall"`; missing values handled by `use = "pairwise.complete.obs"`
	Significance	P-value matrix from `Hmisc::rcorr()` for Pearson and Spearman; Kendall p-values are not produced in the matrix view and are shown as `NA`; decision threshold α (default 0.05)
	Star codes	`*` p ≤ 0.001, `` p ≤ 0.01, `*` p ≤ 0.05

5 R Code for Key Analytical Steps

The code blocks below demonstrate the exact computation behind each reported result using the mtcars dataset from the datasets package. All blocks are illustrative and non-executing (eval = FALSE).

5.1 Correlation Matrix

data(mtcars)
corr_method <- "pearson"          # or "spearman", "kendall"
corr_mat <- cor(mtcars,
                method = corr_method,
                use    = "pairwise.complete.obs")
round(corr_mat, 2)

5.2 Significance and Star Codes

# P-value matrix for Pearson and Spearman via Hmisc::rcorr()
rc <- Hmisc::rcorr(as.matrix(mtcars), type = "pearson")  # or "spearman"
rc$r        # correlation coefficients
rc$P        # p-value matrix (NA on the diagonal)

# star codes applied at alpha cut points; overall alpha defaults to 0.05
# Kendall does not return a p-value matrix here, so those cells are set to NA
alpha <- 0.05
stars <- ifelse(rc$P <= 0.001, "***",
         ifelse(rc$P <= 0.01,  "**",
         ifelse(rc$P <= 0.05,  "*", "")))
stars

Explore the entire Correlation Analysis module in preview mode using our demo datasets. To submit suggestions or report a workflow issue, please use the discussion section below, or visit the official RAISINS website.

6 RAISINS Native Statistical Framework

RAISINS uses R for all its statistical computations. Every package used to generate major results is listed and demonstrated with examples, so results can be reproduced independently. These results are then organized and formatted on the RAISINS website along with visualisation to make them easier to use and interpret. RAISINS also has its own custom-built statistical tools for managing workflows, validating results, and generating reports. Details of these are not fully covered here, they’re shared with outside researchers only on request, and are subject to licensing terms.

7 Package References

R Core Team. (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Harrell, F. E. (2025). Hmisc: Harrell Miscellaneous (R package version 5.2-5). https://doi.org/10.32614/CRAN.package.Hmisc

Allaire, J. J., Xie, Y., Dervieux, C., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., Wickham, H., Cheng, J., Chang, W., & Iannone, R. (2026). rmarkdown: Dynamic Documents for R (R package version 2.31). https://github.com/rstudio/rmarkdown

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