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+++ b/man/biomarkertmle.Rd
@@ -0,0 +1,97 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/biotmle.R
+\name{biomarkertmle}
+\alias{biomarkertmle}
+\title{Biomarker Evaluation with Targeted Minimum Loss Estimation of the ATE}
+\usage{
+biomarkertmle(
+  se,
+  varInt,
+  normalized = TRUE,
+  ngscounts = FALSE,
+  bppar_type = BiocParallel::MulticoreParam(),
+  bppar_debug = FALSE,
+  cv_folds = 1,
+  g_lib = c("SL.mean", "SL.glm", "SL.bayesglm"),
+  Q_lib = c("SL.mean", "SL.bayesglm", "SL.earth", "SL.ranger"),
+  ...
+)
+}
+\arguments{
+\item{se}{A \code{SummarizedExperiment} containing microarray expression
+or next-generation sequencing data in the \code{assays} slot and a matrix
+of phenotype-level data in the \code{colData} slot.}
+
+\item{varInt}{A \code{numeric} indicating the column of the design matrix
+corresponding to the treatment or outcome of interest (in the
+\code{colData} slot of the \code{SummarizedExperiment} argument "se").}
+
+\item{normalized}{A \code{logical} indicating whether the data included in
+the \code{assay} slot of the input \code{SummarizedExperiment} object has
+been normalized externally. The default is set to \code{TRUE} with the
+expectation that an appropriate normalization method has been applied. If
+set to \code{FALSE}, median normalization is performed for microarray data.}
+
+\item{ngscounts}{A \code{logical} indicating whether the data are counts
+generated from a next-generation sequencing experiment (e.g., RNA-seq). The
+default setting assumes continuous expression measures as generated by
+microarray platforms.}
+
+\item{bppar_type}{A parallelization option specified by \code{BiocParallel}.
+Consult the manual page for \code{\link[BiocParallel]{BiocParallelParam}}
+for possible types and their descriptions. The default for this argument is
+\code{\link[BiocParallel]{MulticoreParam}}, for multicore evaluation.}
+
+\item{bppar_debug}{A \code{logical} indicating whether or not to rely upon
+pkg{BiocParallel}. Setting this argument to \code{TRUE}, replaces the call
+to \code{\link[BiocParallel]{bplapply}} by a call to \code{lapply}, which
+significantly reduces the overhead of debugging. Note that invoking this
+option overrides all other parallelization arguments.}
+
+\item{cv_folds}{A \code{numeric} scalar indicating how many folds to use in
+performing targeted minimum loss estimation. Cross-validated estimates have
+been demonstrated to allow relaxation of certain theoretical conditions and
+and accommodate the construction of more conservative variance estimates.}
+
+\item{g_lib}{A \code{character} vector specifying the library of machine
+learning algorithms for use in fitting the propensity score P(A = a | W).}
+
+\item{Q_lib}{A \code{character} vector specifying the library of machine
+learning algorithms for use in fitting the outcome regression E[Y | A,W].}
+
+\item{...}{Additional arguments to be passed to \code{\link[drtmle]{drtmle}}
+in computing the targeted minimum loss estimator of the average treatment
+effect.}
+}
+\value{
+S4 object of class \code{biotmle}, inheriting from
+ \code{SummarizedExperiment}, with additional slots \code{tmleOut} and
+ \code{call}, among others, containing TML estimates of the ATE of exposure
+ on biomarker expression.
+}
+\description{
+Computes the causal target parameter defined as the difference between the
+biomarker expression values under treatment and those same values under no
+treatment, using Targeted Minimum Loss Estimation.
+}
+\examples{
+library(dplyr)
+library(biotmleData)
+library(SuperLearner)
+library(SummarizedExperiment)
+data(illuminaData)
+
+colData(illuminaData) <- colData(illuminaData) \%>\%
+  data.frame() \%>\%
+  mutate(age = as.numeric(age > median(age))) \%>\%
+  DataFrame()
+benz_idx <- which(names(colData(illuminaData)) \%in\% "benzene")
+
+biomarkerTMLEout <- biomarkertmle(
+  se = illuminaData[1:2, ],
+  varInt = benz_idx,
+  bppar_type = BiocParallel::SerialParam(),
+  g_lib = c("SL.mean", "SL.glm"),
+  Q_lib = c("SL.mean", "SL.glm")
+)
+}