No update from V 0.4.5.
This will be the new numbered for submission to CRAN
makePaired() generates a dataframe of two paired vectors to emulate data for a paired-sample t-test
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()
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makeCorrAlpha() constructs a random correlation matrix of given dimensions and predefined Cronbach's Alpha.
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makeItems() generates synthetic rating-scale data with predefined first and second moments and a predefined correlation matrix
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alpha() calculate Cronbach's Alpha from a given correlation matrix or a given dataframe
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eigenvalues() calculates eigenvalues of a correlation matrix with an optional scree plot
- 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.
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Made code and examples more tidy - this makes code a few nanoseconds faster
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Added some further in-line comments.
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setting up for some C++ mods to make lcor() faster, and to introduce make_items() function.
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Added references to DESCRIPTION file and expanded citations to vignettes
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Reduced runtime by setting target to zero instead of -Inf.
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Specified one thread instead of attempting Parallel