format_long_hazards: Generate long format hazards data for conditional density...
In benkeser/intermed:

Generate long format hazards data for conditional density estimation

format_long_hazards(
  A,
  W,
  wts = rep(1, length(A)),
  type = c("equal_range", "equal_mass"),
  n_bins = NULL,
  breaks = NULL
)

`A`	The `numeric` vector or similar of the observed values of an intervention for a group of observational units of interest.
`W`	A `data.frame`, `matrix`, or similar giving the values of baseline covariates (potential confounders) for the observed units whose observed intervention values are provided in the previous argument.
`wts`	A `numeric` vector of observation-level weights. The default is to weight all observations equally.
`type`	A `character` indicating the strategy to be used in creating bins along the observed support of the intervention `A`. For bins of equal range, use "equal_range" and consider consulting the documentation of `ggplot2::cut_interval` for more information. To ensure each bins has the same number of points, use "equal_mass" and consult the documentation of `ggplot2::cut_number` for details.
`n_bins`	Only used if `type` is set to `"equal_range"` or `"equal_mass"`. This `numeric` value indicates the number of bins that the support of the intervention `A` is to be divided into.
`breaks`	A `numeric` vector of break points to be used in dividing up the support of `A`. This is passed as a `...` argument to `base::cut.default` by either `cut_interval` or `cut_number`.