twFSA: twFSA
In rFSA: Feasible Solution Algorithm for Finding Best Subsets and Interactions

A function for termwise feasiblity

twFSA(
  formula,
  data,
  fitfunc = lm,
  fixvar = NULL,
  quad = FALSE,
  cores = 1,
  criterion = AIC,
  minmax = "min",
  checkfeas = NULL,
  var4int = NULL,
  min.nonmissing = 1,
  ...
)

`formula`	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
`data`	a data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model.
`fitfunc`	the method that should be used to fit the model. For Example: lm, glm, or other methods that rely on formula, data, and other inputs.
`fixvar`	variable(s) to fix in the model. Usually a covariate that should always be included (Example: Age, Sex). Will still consider it with interactions. Default is NULL.
`quad`	Include quadratic terms or not. Logical.
`cores`	number of cores to use while running. Note: Windows can only use 1 core. See mclapply for details. If function detects a Windows user it will automatically set cores=1.
`criterion`	which criterion function to either maximize or minimize. For linear models one can use: r.squared, adj.r.squared, cv5.lmFSA (5 Fold Cross Validation error), cv10.lmFSA (10 Fold Cross Validation error), apress (Allen's Press Statistic), int.p.val (Interaction P-value), AIC, BIC.
`minmax`	whether to minimize or maximize the criterion function
`checkfeas`	vector of variables that could be a feasible solution. These variables will be used as the last random start.
`var4int`	specification of which variables to check for marginal feasiblilty. Default is NULL
`min.nonmissing`	the combination of predictors will be ignored unless this many of observations are not missing
`...`	other arguments passed to fitfunc.