createRcsSettings: Create Restricted Cubic Spline Settings
In rekkasA/SmoothHte: Fit smooth risk-based absolute benefit predictions

Creates the settings for fitting a restricted cubic spline smooth estimate of absolute benefit

createRcsSettings(
  nKnots = 3,
  method = "lrm.fit",
  model = FALSE,
  x = FALSE,
  y = FALSE,
  linear.predictors = TRUE,
  se.fit = FALSE,
  penalty = 0,
  tol = 1e-07,
  strata.penalty = 0,
  var.penalty = "simple",
  scale = FALSE
)

`nKnots`	Number of knots. Default is 5. The minimum value is 3.
`method`	Name of fitting function. Only allowable choice at present is lrm.fit.
`model`	Causes the model frame to be returned in the fit object
`x`	Causes the expanded design matrix (with missings excluded) to be returned under the name x. For print, an object created by lrm.
`y`	causes the response variable (with missings excluded) to be returned under the name y.
`linear.predictors`	Causes the predicted X beta (with missings excluded) to be returned under the name linear.predictors. When the response variable has more than two levels, the first intercept is used.
`se.fit`	Causes the standard errors of the fitted values to be returned under the name se.fit.
`penalty`	The penalty factor subtracted from the log likelihood is 0.5 β' P β, where β is the vector of regression coefficients other than intercept(s), and P is penalty factors * penalty.matrix and penalty.matrix is defined below. The default is penalty=0 implying that ordinary unpenalized maximum likelihood estimation is used. If penalty is a scalar, it is assumed to be a penalty factor that applies to all non-intercept parameters in the model. Alternatively, specify a list to penalize different types of model terms by differing amounts. The elements in this list are named simple, nonlinear, interaction and nonlinear.interaction. If you omit elements on the right of this series, values are inherited from elements on the left. Examples: penalty=list(simple=5, nonlinear=10) uses a penalty factor of 10 for nonlinear or interaction terms. penalty=list(simple=0, nonlinear=2, nonlinear.interaction=4) does not penalize linear main effects, uses a penalty factor of 2 for nonlinear or interaction effects (that are not both), and 4 for nonlinear interaction effects.
`tol`	Singularity criterion (see lrm.fit)
`strata.penalty`	Scalar penalty factor for the stratification factor, for the experimental strat variable
`var.penalty`	The type of variance-covariance matrix to be stored in the var component of the fit when penalization is used. The default is the inverse of the penalized information matrix. Specify var.penalty="sandwich" to use the sandwich estimator (see below under var), which limited simulation studies have shown yields variances estimates that are too low.
`scale`	Set to TRUE to subtract means and divide by standard deviations of columns of the design matrix before fitting, and to back-solve for the un-normalized covariance matrix and regression coefficients. This can sometimes make the model converge for very large sample sizes where for example spline or polynomial component variables create scaling problems leading to loss of precision when accumulating sums of squares and crossproducts.