0.6.0

corrp 0.6.0

Dedicated version for the publication of the corrp package in the JOSS.

• Add VignetteBuilder: knitr to DESCRIPTION
• Add usefull error message for required parameters.
• Fix C++ Astar method.
• Run benchmarks, expand the paper to include statements on resource-intensive options, and incorporate an enhanced version of energy::dcorT.test. Also, change the data used in the paper.
• Update paper:
  • Give a more detail explanation of ACCA algorithm.
  • Strenghted statement of need.
  • Make a map of correlation → R method.
  • Provide a brief remark on the symmetry of the correlation matrix.
• Update README.md according with changes in the paper and functions.

Methods Added

• Added method set_arguments: Assigns provided arguments from the args_list to the parent environment. If an argument is inside the arguments of the methods that calculate statistics, it assigns it on the parent environment, and removes the argument from the list.
• Added method assert_required_argument: Ensures that a required argument is provided. If the argument is missing, it throws an error with a clear message.

Methods Altered

• Altered messages and make *.args lists be able to alter arguments (p.value, comp, "alternative", "num.s", "rk") of methods: .corlm, .cramersvp, .dcorp, .corperp, .micorp, .uncorp, .corpps.
• Update the .corpps method to support p-value testing (p-test), which is disabled by default due to its slow performance. When p-test is not performed, the isig value is set to NA. p-test can be run assigning an element ptest = TRUE to pps.args argument.

Documentation

• Enhanced the documentation for corrp and corr_fun by including examples, refining the pair type section with additional details and references, and providing a more comprehensive explanation of the output format and its interpretation.
• Improved the documentation for corr_rm by adding examples and providing a clearer explanation of the c parameter.
• Improved the documentation for acca by adding examples and providing a more detailed explanation in the description.
• Added examples of usage in the documentation for: acca, best_acca, corrp, corr_rm, corr_matrix, corr_fun, ptest, sil_acca.
• Fix grammar and ensure package style cohesion.

0.3.0

0.3.0

• Added C++ implementations of Average correlation clustering algorithm and the Average Silhouette width;
• acca New function to clustering correlations;
• sil_acca Computes the Average Silhouette width to ACCA clusters;
• best_acca Find the optimal number of ACCA clusters;
• Checks ok.

0.2.0

• Changed package name corrP to corrp ;
• Changelog file created ;
• License file GLP3 created;
• Added new correlations types analysis: pps ; dcor ; mic ; uncoef;
• corrp function output has a new class clist with index matrix and data values;
• corr_fun: New function to calculate correlation type inferences to pair of variables;
• corr_matrix: New function to create correlation matrix ;
• corr_rm: New function to remove highly correlated variables from a data.frame;
• Added verbose param to corrp and corr_fun functions ;
• Added testthat unit tests;
• Checks ok;
• Fixed some bugs in function'sand documentations;

0.1.1

Details

The data.frame is allowed to have columns of these four classes: integer, numeric, factor and character. The character column is considered as categorical variable.

In this new package the correlation is automatically computed according to the variables types:

• integer/numeric pair: Pearson correlation test ;
• integer/numeric - factor/categorical pair: correlation coefficient or squared root of R^2 coefficient of linear regression;
• factor/categorical pair: cramersV a measure of association between two nominal .

Also, the statistical significance of all correlation’s values in the matrix are tested. If the statistical tests do not obtain a significance level lower than p.value param the null hypothesis can’t be rejected and by default, the correlation between the variable pair will be zero.

Example:

library(corrP)
# run correlation in parallel backend
air_cor = corrP(airquality,parallel = TRUE, n.cores = 4, p.value = 0.05)
corrplot::corrplot(air_cor)
corrgram::corrgram(air_cor)

Another package function rh_corrP can remove highly correlated variables from data.frames using the CorrP matrix.

air_cor = corrP(airquality)
 airqualityH = rh_corrP(df=airquality,corrmat=air_cor,cutoff=0.5)

setdiff(colnames(airquality),(colnames( airqualityH )))

[1] "Ozone" "Temp"

The CoorP package is still very new, but it is already capable of providing some interesting features. In the next versions we will be including some types of plots to be made with corrP correlation matrix .