--- a
+++ b/man/exp_biomarkertmle.Rd
@@ -0,0 +1,42 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/biotmle.R
+\name{exp_biomarkertmle}
+\alias{exp_biomarkertmle}
+\title{TMLE procedure using ATE for Biomarker Identication from Exposure}
+\usage{
+exp_biomarkertmle(Y, A, W, g_lib, Q_lib, cv_folds, ...)
+}
+\arguments{
+\item{Y}{A \code{numeric} vector of expression values for a given biomarker.}
+
+\item{A}{A \code{numeric} vector of discretized exposure vector (e.g., from
+a design matrix whose effect on expression values is of interest.}
+
+\item{W}{A \code{Matrix} of \code{numeric} values corresponding to baseline
+covariates to be marginalized over in the estimation process.}
+
+\item{g_lib}{A \code{character} vector identifying the library of learning
+algorithms to be used in fitting the propensity score P[A = a | W].}
+
+\item{Q_lib}{A \code{character} vector identifying the library of learning
+algorithms to be used in fitting the outcome regression E[Y | A, W].}
+
+\item{cv_folds}{A \code{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.}
+
+\item{...}{Additional arguments passed to \code{\link[drtmle]{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
+ \code{tmleOut} slot in a \code{biotmle} object.
+}
+\description{
+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.
+}