consider evaluating predictive cdfs at a vector of category boundaries rather than looping over them

[elray1]

looking at this code: https://github.com/reichlab/idforecastutils/blob/b137497b0bbe8c59210dcecb37214ad133740d52/R/get_pmf_forecasts_from_quantile.R#L91-L120

It seems like we should be able to avoid the for loops here by:

• evaluating cdfs that come from distfromq at the vector of all endpoints in columns like truth_df_filtered[[paste0("crit", seq_len(num_cat - 1), sep="")]]
• appending a 1 to the end of that vector (and maybe a 0 to the beginning)
• taking the diff of the result to get from "cumulative category probabilities" to the pmf values
• you may need to unnest the results at the end

It may be helpful to split all of that functionality out into a helper function

[elray1]

You may be able to adapt some ideas in this code: https://github.com/Infectious-Disease-Modeling-Hubs/example-complex-forecast-hub/blob/main/internal-data-raw/create_model_output_data.R