tidymodels/desirability2

Desirability Functions for Multiparameter Optimization

desirability2's Issues

Use of `use_data` argument

I don't find it intuitive what use_data did without looking at the source. Maybe add an example, or note that use_data is only used if low, high, or target is missing.

More descriptive documentation of what scale, scale_low, and scale_high does

Right now it says the following in the documentation

desirability2/R/in-line.R

Lines 16 to 17 in b8547b2

	#' @param scale,scale_low,scale_high A single numeric value to rescale the
	#' desirability function.

{desirability} wrote

Values less than one make the criteria more difficult to satisfy while values greater than one make it easier.

In addition, is it not documented what the difference between scale, scale_low, and scale_high are.

I would like to see documented how exactly the scale affects the desirability, which as far as I can tell is just a power

Inconsistent return for check functions

Some check functions return invisible(NULL) and some return invisible(TRUE). Ideally these should all return the same.

Upkeep for desirability2

2023

Necessary:

• Update copyright holder in DESCRIPTION: person(given = "Posit Software, PBC", role = c("cph", "fnd"))
• Double check license file uses '[package] authors' as copyright holder. Run use_mit_license()
• Update email addresses *@rstudio.com -> *@posit.co
• Update logo (https://github.com/rstudio/hex-stickers); run use_tidy_logo()
• usethis::use_tidy_coc()
• usethis::use_tidy_github_actions()

Optional:

• Review 2022 checklist to see if you completed the pkgdown updates
• Prefer pak::pak("org/pkg") over devtools::install_github("org/pkg") in README
• Consider running use_tidy_dependencies() and/or replace compat files with use_standalone()
• use_standalone("r-lib/rlang", "types-check") instead of home grown argument checkers
• Add alt-text to pictures, plots, etc; see https://posit.co/blog/knitr-fig-alt/ for examples

`scale` transformation is limited to powers

I haven't read too much about desirability functions. But is there a reason why the scaling is limited to powers and not any order-preserving transformation that maps the region [0, 1] to [0, 1]?

No guard for scale argument

There is no checking for the scale argument so you could end up with weird things happening.

library(desirability2)

d_max(5, 0, 10, scale = 1)
#> [1] 0.5

d_max(5, 0, 10, scale = 1:2)
#> Warning in out[middle] <- ((x[middle] - low)/(high - low))^scale: number of
#> items to replace is not a multiple of replacement length
#> [1] 0.5

d_max(5, 0, 10, scale = -2)
#> [1] 4

d_max(5, 0, 10, scale = NA)
#> [1] NA

^{Created on 2021-10-16 by the reprex package (v2.0.1)}