This function performs influence curve-based estimation of the effect of an
exposure on biological expression values associated with a given biomarker,
controlling for a user-specified set of baseline covariates.

exp_biomarkertmle(Y, A, W, g_lib, Q_lib, cv_folds, ...)

## Arguments

Y |
A `numeric` vector of expression values for a given biomarker. |

A |
A `numeric` vector of discretized exposure vector (e.g., from
a design matrix whose effect on expression values is of interest. |

W |
A `Matrix` of `numeric` values corresponding to baseline
covariates to be marginalized over in the estimation process. |

g_lib |
A `character` vector identifying the library of learning
algorithms to be used in fitting the propensity score P[A = a | W]. |

Q_lib |
A `character` vector identifying the library of learning
algorithms to be used in fitting the outcome regression E[Y | A, W]. |

cv_folds |
A `numeric` scalar indicating how many folds to use in
performing targeted minimum loss estimation. Cross-validated estimates are
more robust, allowing relaxing of theoretical conditions and construction
of conservative variance estimates. |

... |
Additional arguments passed to `drtmle` in
computing the targeted minimum loss estimator of the average treatment
effect. |

## Value

TMLE-based estimate of the relationship between biomarker expression
and changes in an exposure variable, computed iteratively and saved in the
`tmleOut`

slot in a `biotmle`

object.