gam_jacknife: Fit jacknife GAM analysis.
In jpwrobinson/funk: James Robinson's functions, attributed where stolen

This function runs a drop-one jacknife to estimate relative variable importance.

gam_jacknife(
  dataset,
  exp.names,
  indicator,
  family,
  base_k = -1,
  smoother = "cr",
  n.param.max = 3
)

`dataset`	is a data frame of response and explanatory covariates
`exp.names`	is a vector of explanatory covariate names, matching colnames of dataset
`indicator`	is the name of the focal response variable
`family`	specifies the fitted distribution (e.g. 'gaussian', 'poisson', 'binomial')
`base_k`	is the number of knots for each smoother term. Defaults to -1, where mgcv uses generalized cross-validation
`smoother`	is the spline basis, defaults to 'cr'
`n.param.max`	is the maximum number of fitted explanatory covariates, defaults to 3