Changelog

Source: NEWS.md

LikertMakeR (development version)

LikertMakeR 2.0.0 (March 2026)

This release introduces major improvements to the internal algorithms used to generate synthetic rating-scale data and correlation matrices.

Breaking changes

• makeItemsScale() has been rewritten and one parameter has been removed. Existing code that used the removed argument, variance, will need to be updated.

• makeCorrAlpha() has been completely redesigned - previously flagged in development version 1.5.0. Some parameters have changed, and the internal algorithm for generating correlation matrices has been replaced to guarantee an assumed underlying single-factor structure.

New features

• Added reliability(), a new function for computing reliability statistics for rating-scale data - previously flagged in development version 1.5.0.

• Added alpha_sensitivity(), a new function that computes how Cronbach’s alpha changes as a function of scale design parameters.

Improvements

• makeCorrAlpha() is now substantially faster and more stable when generating correlation matrices that satisfy a target Cronbach’s alpha.

• alpha() has additional integrity checks

• Improved numerical stability and diagnostics in correlation-matrix generation.

• Various documentation improvements and code cleanup.

LikertMakeR 1.5.0 (February 2026)

Improvements

Major Improvements to makeCorrAlpha()

New constructive correlation generator

makeCorrAlpha() has been completely re-engineered. The previous swap-based approach for achieving positive-definite correlation matrices has been replaced with a constructive one-factor generator.

The previous algorithm:

• was slow
• was unable to process large number of items (rare, but sometimes wanted)
• frequently gave results that were more consistent with multi-factor scales
  • e.g., eigenvalues may give two or more values > 1,

The new algorithm:

• runs more than 150 times faster than the previous version (3000 times faster for large k).
• Guarantees positive definiteness without post-hoc repair.
• Produces stable behaviour across large numbers of items (tested up to k = 40).
• Maintains alpha accuracy within ±0.005 when alpha_noise = 0.
• Uses adaptive dispersion control to ensure feasibility at high variance levels.

This results in substantially improved numerical stability and smoother behaviour across the parameter space.

precision argument removed and replaced with alpha_noise argument

A new argument, alpha_noise, has been introduced to provide optional random variation around the requested Cronbach’s alpha.

By default (alpha_noise = 0), the function reproduces the specified alpha deterministically. When a small positive value is supplied, the function will generate slightly different reliability values across runs.

This is particularly useful in teaching and simulation contexts where you may wish to demonstrate natural variation rather than produce the exact same matrix each time. The noise is applied in a way that ensures the resulting alpha always remains within valid bounds.

Deprecated argument

• sort_cors is now deprecated. It served no useful purpose.

Backward compatibility

For most use cases, behaviour remains consistent with previous versions. However, users who relied on the previous swap-based construction may observe smoother and more stable correlation structures under the new implementation.

summated parameter added to makeItemsScale() function

Added to specify whether the given scale is a summated scale (e.g. 4-20 in integer increments for four 5-point items) or an averaged scale (e.g., 1-5 in 0.25 increments).

Maintenance

• Tutorials added to the website

  • infographic to be removed from CRAN

  • blog_01 (quick-start tutorial) generates a dataframe.

  • blog_2 (t-test tutorial) “reproduces”” published data, runs t-test, effect-sizes, and visualisation.

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LikertMakeR 1.4.0 (February 2026)

CRAN release: 2026-02-11

Improvements

• New reliability() function: Calculates various reliability statistics:

  • Cronbach’s alpha ($\alpha$)

  • McDonald’s omega-total ($\omega_t$)

  • Guttman’s lambda 6 ($\lambda 6$)

  • Zumbo’s ordinal alpha (Cronbach’s alpha from polychoric correlations)

  • ordinal-omega-total (McDonald’s omega total from polychoric correlations)

  • coefficient H

  with bootstrapped Confidence Intervals for each as an option.

• correlateScales() function runs 3 to 8 times faster

  • due to some pre-calculation and vectorisation replacing “for loops”

Maintenance

• Fixed typos and simplified examples.

• removed redundant makeItems() function

• reduced length of README file and moved most content to vignettes

LikertMakeR 1.3.0 (2025-11-24)

CRAN release: 2025-11-26

Improvements

• New makeScalesRegression() function: Generates synthetic rating-scale data that replicates reported regression results, and then returns

  1. a data frame that provides the requested statistical properties and

  2. a correlation matrix and summary moments of the data frame, plus

  3. diagnostic statistics, including comparison of target values against achieved values.

• makeScales() function replaces makeItems() function:

  • I finally worked out how to turn a single value into a vector of length k.
  • Embarrassingly straightforward.

• Updated version of makeCorrAlpha() function produces a more “natural-looking” correlation matrix, plus diagnostics:

  • previous version sorted correlations in the correlation matrix to improve likelihood of extracting a positive-definite matrix. Fast, but unnatural results.
  • I have applied a slightly faster algorithm for rearranging the correlations in a draft matrix to produce one that is positive-definite.
    
  • Additional parameter sort_cors = FALSE. If TRUE, results are similar to the earlier version of makeCorrAlpha(). A little faster but not as “natural-looking”.
  • Additional parameter diagnostics = FALSE. If TRUE, returns a list containing the correlation matrix and a diagnostics list (target/achieved alpha, average inter-item correlation, eigenvalues, PD flag, and key arguments). If FALSE (default), returns the correlation matrix only.

• Updated version of lfast() function

  • runs slightly faster

Maintenance

• new vignette for the new function makeScalesRegression().

• updated examples for makeScales() function.

• updated badges in readme file.

LikertMakeR 1.2.0 (2025-10-10)

CRAN release: 2025-10-09

Improvements

• New makeRepeated() function : takes summary statistics that are reported in a typical repeated-measures ANOVA study, and then returns

  1. a correlation matrix of the vectors of repeated measures and

  2. a data frame based on the correlation matrix and summary moments, plus

  3. diagnostic statistics, including possible F-statistics based on information provided.

• #lfast_validation# vignette shows that #LikertMaker# does a remarkably good job of replicating real rating-scale data.

Maintenance

• Vignettes are too large with so many images, so CRAN files include only the #LikertMakeR_vignette# file. Two vignettes that validate lfast() and makeCorrLoadings() appear only in the package website.

LikertMakeR 1.1.0 (2025-05-26)

CRAN release: 2025-05-30

Improvements

• new makePaired() function: takes summary statistics from a paired-sample t-test and produces a data frame of rating-scale data that would deliver such summary statistics

• lcor() function rewrite: previous version used a very systematic swapping of values in each column to minimise the difference between data correlation and a target correlation matrix. This algorithm had the effect of causing extreme values in each column to be highly-correlated (or lowly correlated as applicable), and leaving middle-values relatively uncorrelated. This property was probably not noticeable in most cases but was apparent when the range of scale values was great.

Maintenance

• Vignettes minor updates.

LikertMakeR 1.0.2 (2025-04-25)

Improvements

• Some test examples updated.

Maintenance

• Vignettes updated.

LikertMakeR 1.0.1 (2025-04-07)

Improvements

• Vignettes are now properly registered and included in the build.
• Improved documentation: two vignettes now illustrate package usage:
  • LikertMakeR vignette
  • makeCorrLoadings validation
• Updated DESCRIPTION metadata to comply with CRAN requirements.

Maintenance

• Switched vignette engine to knitr::rmarkdown for better compatibility with CRAN and development tools.

LikertMakeR 1.0.0 (2025-04-03)

CRAN release: 2025-04-04

makeCorrLoadings() function added

makeCorrLoadings() generates a correlation matrix of inter-item correlations based on item factor loadings as might be seen in Exploratory Factor Analysis (EFA) or a Structural Equation Model (SEM).

Such a correlation matrix can be applied to the function to generate synthetic data with those predefined factor structures.

LikertMakeR 1.0.0 (2025-01-08)

CRAN release: 2025-04-04

update version number to correct major.minor.patch format

No update from V 0.4.5.

This will be the new numbered for submission to CRAN

LikertMakeR 0.4.5 (2025-01-07)

makePaired() function added

makePaired() generates a dataframe of two paired vectors to emulate data for a paired-sample t-test

LikertMakeR 0.4.0 (2024-11-17)

CRAN release: 2024-11-19

target Cronbach’s Alpha added to makeItemsScale() function

generated scale items now defined by a target Cronbach’s Alpha, as well as by variance within each scale item. This latest version adds a little randomness to the selection of candidate row vectors.

LikertMakeR 0.3.0 (2024-05-18)

CRAN release: 2024-05-19

more randomness in swaps task to makeCorrAlpha() function

correlation matrix usually has values sorted lowest to highest. This happens less often

LikertMakeR 0.2.6 (2024-05-11)

added ‘precision’ parameter to makeCorrAlpha() function

‘precision’ adds random variation around the target Cronbach’s Alpha. Default = ‘0’ (no variation giving Alpha exact to two decimal places)

LikertMakeR 0.2.5 (2024-04-20)

added correlateScales() function

Create a dataframe of correlated scales from different dataframes of scale items

LikertMakeR 0.2.2 (2024-03-31)

added makeItemsScale() function

Generate rating-scale items from a given summated scale

LikertMakeR 0.2.0 (2024-03-02)

CRAN release: 2024-03-02

For submission to CRAN

Faster and more accurate functions: lcor() & lfast()

These replace the old lcor() & lfast() with the previous lcor_C() & lfast_R()

LikertMakeR 0.1.9 (2024-02-11)

Added a new functions: makeCorrAlpha(), makeItems(), alpha(), eigenvalues()

• makeCorrAlpha() constructs a random correlation matrix of given dimensions and predefined Cronbach’s Alpha.

• makeItems() generates synthetic rating-scale data with predefined first and second moments and a predefined correlation matrix

• alpha() calculate Cronbach’s Alpha from a given correlation matrix or a given dataframe

• eigenvalues() calculates eigenvalues of a correlation matrix with an optional scree plot

LikertMakeR 0.1.7 (2024-02-02)

Added a new function: lcor_C()

• lcor_C() is a C++ implementation of the lcor() function. It should run considerably faster than lcor(). When I’m confident that lcor_C() works as well or better than lcor(), then I shall replace lcor() with the C++ implementation in an update to CRAN.

LikertMakeR 0.1.6 (2024-01-18)

• Made code and examples more tidy - this makes code a few nanoseconds faster

• Added some further in-line comments.

• setting up for some C++ mods to make lcor() faster, and to introduce make_items() function.

LikertMakeR 0.1.5 (2022-12-20)

CRAN release: 2022-12-22

Initial CRAN release

• Added references to DESCRIPTION file and expanded citations to vignettes

• Reduced runtime by setting target to zero instead of -Inf.

• Specified one thread instead of attempting Parallel