new_set: Create new parameter set
In Michael-Stevens-27/silverblaze: Geographic Profiling in R

Create a new parameter set within an rgeoprofile_project. The new parameter set becomes the active set once created.

new_set(
  project,
  spatial_prior = NULL,
  source_model = "uniform",
  name = "(no name)",
  sigma_model = "single",
  dispersal_model = "normal",
  sigma_prior_mean = 1,
  sigma_prior_sd = 1,
  expected_popsize_model = "single",
  expected_popsize_prior_mean = 1000,
  expected_popsize_prior_sd = 20,
  sentinel_radius = 0.2,
  n_binom = FALSE,
  alpha_prior_mean = 1,
  alpha_prior_sd = 100,
  weight_prior = 1
)

`project`	an rgeoprofile_project, as produced by the function `rgeoprofile_project()`.
`spatial_prior`	a raster file defining the spatial prior. Precision values are taken from this raster if it is defined.
`source_model`	choose prior type for source locations. Pick from "uniform" (default), "normal" (bivariate normal), "kernel" (KDE based on positive data) or "manual" (the current value of the raster)
`name`	an optional name for the parameter set.
`sigma_model`	set as `"single"` to assume the same dispersal distance for all sources, or `"independent"` to assume an independently drawn dispersal distance for each source.
`dispersal_model`	distribute points via a "normal", "cauchy" or "laplace" model
`sigma_prior_mean`	the prior mean of the parameter sigma (km).
`sigma_prior_sd`	the prior standard deviation of the parameter sigma (km). Set to 0 to use a fixed value for sigma (fixed at `sigma_prior_mean`).
`expected_popsize_model`	set as `"single"` to assume the same number of events for all sources, or `"independent"` to assume an independently drawn number of events for each source.
`expected_popsize_prior_mean`	the prior mean of the expected total population size.
`expected_popsize_prior_sd`	the prior standard deviation of the expected total population size. Set to 0 to use a fixed value (fixed at `expected_popsize_prior_mean`).
`sentinel_radius`	the observation radius of sentinel sites.
`n_binom`	set to true or false, decide if a negative binomial model should be run for a set of over-dispersed count data.
`alpha_prior_mean`	the prior mean alpha.
`alpha_prior_sd`	the prior standard deviation of alpha.
`weight_prior`	control the prior on weights for a point-pattern model