sampled_rums: Format RUM data base on resampled tows Function calls mlogit
In peterkuriyama/ch4:

Format RUM data base on resampled tows Function calls mlogit

sampled_rums(data_in = filt_clusts, the_port = "ASTORIA / WARRENTON",
  min_year = 2010, max_year = 2012, risk_coefficient = 1, ndays = 60,
  focus_year = 2012, nhauls_sampled = 50, seed = 300, ncores, rev_scale,
  model_type = "no_bycatch", net_cost, habit_distance, return_hauls = FALSE)

`data_in`	Data going in to the function; default is filt_clusts
`the_port`	Port of focus; Default is Astoria
`min_year`	Minimum year used to filter the data
`max_year`	Maximum year used to filter the data, also RUM data is filtered to be from the max_year
`risk_coefficient`	Coefficient to adjust the quota prices up or down, feeding into net revenue calculations
`ndays`	Number of previous days data to use in revenue expectations
`focus_year`	Year to focus on for the models
`nhauls_sampled`	Number of hauls to sample from the full data set
`seed`	Seed for sampling tows
`ncores`	Number of cores to use
`rev_scale`	Scale the revenue by this factor
`net_cost`	Type of netting of costs;
`habit_distance`	Distance of spatiotemporal filter
`return_hauls`	Option to return the hauls before processing dummys, defaults to FALSE
`trip_dists`	Distances covered by each trip