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Something that was discussed during the vacineff challenge is whether we want vaccineff functions to be able to deal with grouped data out of the box.
One example would be to compute vaccine effectiveness by country in a dataset including multiple countries.
Pros of computing by groups:
code could be slightly more efficient than requiring the user to create a loop since we can use vectorized operations
fewer lines of code for users
Cons of computing by groups:
necessity to add a new argument, which makes the package slightly more difficult to learn
slightly more lines of code in package
No matter what is chosen, there should be an example showing how effectiveness can be computed for multiple groups.
The text was updated successfully, but these errors were encountered:
I held off on addressing this issue until we had a well-defined structure for the package. However, it’s a good and relevant idea, and we’ll work on it after submitting to CRAN.
Something that was discussed during the vacineff challenge is whether we want vaccineff functions to be able to deal with grouped data out of the box.
One example would be to compute vaccine effectiveness by country in a dataset including multiple countries.
Pros of computing by groups:
Cons of computing by groups:
No matter what is chosen, there should be an example showing how effectiveness can be computed for multiple groups.
The text was updated successfully, but these errors were encountered: