time_table_leaps: Best subset (linear) regression on a time.table
In rossklin/dynpan: Dynamics estimation for multiple time series

Perform a best subsets regression procedure on data stored in time.table(s). Produces one linear regression subset (the one with the lowest residual sum of squares) for each subset size of the covariates and each component of the dependent variable.

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time_table_leaps(x, y = NULL, idxs = NULL, use.auxiliary = FALSE,
  input.cols = NULL, output.cols = NULL, ..., modelfun = function(x)
  polySane(raw = TRUE, x, degree = 2), has.no.na = FALSE)

`x`	`time.table` that contains predictors and, optionally, dependent variable(s).
`y`	`time.table` containing dependent variable(s).
`idxs`	index/time values to include, defaults to all complete cases
`use.auxiliary`	whether to include auxiliary values
`input.cols`	column(s) of `x` to use for computing covariate(s)
`output.cols`	column(s) of `x` or `y` to use as dependent varaiable(s)
`...`	additional arguments to pass to `modelfun`
`modelfun`	function that produces the actual covariates used in the linear regression
`has.no.na`	whether user guarantees `x`/`y` contian no `NA` values