linear_crt: DTT using a fixed linear statistic
In stephenbates19/digitaltwins: Causal inference for SNPs

Returns p-values from the DTT using a linear feature importance measure.

linear_crt(H, H_parent, anc, Y, beta, group, d, adjust = NULL,
  test_idx = 1:nrow(H), family = "gaussian", n_reps = 100,
  genotypes = TRUE, verbose = FALSE, parallel = FALSE,
  recomb_thresh = 1e-08, f_stat = FALSE)

`H`	an (2n x p) matrix of the subject haplotypes. It is assumed rows 1,2 belong to subject 1, rows 3,4 belong to subject 2, etc.
`H_parent`	and (n_2 x p) matrix of the parental haplotypes
`anc`	a (2n x 2) table of ancestries. anc[i, 1] and and[i, 2] give the rows of H_parent corresponding to the parents of row i of the haplotypes H
`Y`	observed responses, a vector of length n or a matrix of dimension (n x k)
`beta`	feature importance directions, fit on from an ind data set. A vector of length p+1 or a matrix of dimension ((p+1) x k). e.g. The output of get_beta_glment. The first coefficient is assumed to be an intercept.
`group`	a list of of indices of the group to test. Should be a continuous region, such as 10:20.
`d`	a vector of length p of genentic distances
`adjust`	a vector of length n or a matrix of dimension (n x k), giving the contribution of the other chromosomes to the likelihood. I.e. adjust = X the fitted coefficients.
`test_idx`	a set of indices (between 1 and 2n) used to compute the test statistics
`family`	type of regression. Either "gaussian" or "binomial". If "guassian", correlation is used as a feature importance statistic. If "binomial", logistic log-likelihood is used as a feature importance statistic.
`n_reps`	number of repitions of the CRT to carry out
`genotypes`	Defaults to TRUE. If FALSE, all rows are assumed to be independent (i.e. no two haplotypes are from the same individual).
`verbose`	if TRUE, prints various diagnostic messages to console
`parallel`	requires doMC to be registered (default: FALSE)
`recomb_thresh`	threshold of probability of recombination events to ignore. Lower values will have potentially higher power at increased computational cost.
`f_stat`	whether or not to use the f-statistic for continuous responses. The f-statistic may be much slower for groups with many nonzero coefficients.