correlation matrix usually has values sorted lowest to highest. This happens less often
‘precision’ adds random variation around the target Cronbach’s Alpha. Default = ‘0’ (no variation giving Alpha exact to two decimal places)
Create a dataframe of correlated scales from different dataframes of scale items
Generate rating-scale items from a given summated scale
Faster and more accurate functions: lcor() & lfast()
These replace the old lcor() & lfast() with the previous lcor_C() & lfast_R()
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
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.
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