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.
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