From 9b93edf0b53574e353be4a1c590682716ddb430c Mon Sep 17 00:00:00 2001
From: naeemkh <khoshnevis.naeem@gmail.com>
Date: Mon, 18 Dec 2023 14:41:48 -0500
Subject: [PATCH] update docs

---
 man/estimate_cerf_gp.Rd   | 15 ++++++++++++++-
 man/estimate_cerf_nngp.Rd | 15 ++++++++++++++-
 2 files changed, 28 insertions(+), 2 deletions(-)

diff --git a/man/estimate_cerf_gp.Rd b/man/estimate_cerf_gp.Rd
index c19d3f9..c9caf6b 100644
--- a/man/estimate_cerf_gp.Rd
+++ b/man/estimate_cerf_gp.Rd
@@ -19,7 +19,8 @@ estimate_cerf_gp(
 \arguments{
 \item{data}{A data.frame of observation data.}
 
-\item{w}{A vector of exposure level to compute CERF.}
+\item{w}{A vector of exposure level to compute CERF (please also see the
+notes).}
 
 \item{gps_m}{An S3 gps object including:
 gps: A data.frame of GPS vectors.
@@ -67,6 +68,18 @@ Estimates the conditional exposure response function (cerf) using Gaussian
 Process (gp). The function tune the best match (the lowest covariate balance)
 for the provided set of hyperparameters.
 }
+\note{
+Please note that \code{w} is a vector representing a grid of exposure levels at
+which the CERF is to be estimated. This grid can include both observed and
+hypothetical values of the exposure variable. The purpose of defining this
+grid is to provide a structured set of points across the exposure spectrum
+for estimating the CERF. This approach is essential in nonparametric models
+like Gaussian Processes (GPs), where the CERF is evaluated at specific points
+to understand the relationship between the exposure and outcome variables
+across a continuum. It facilitates a comprehensive analysis by allowing
+practitioners to examine the effect of varying exposure levels, including
+those not directly observed in the dataset.
+}
 \examples{
 \donttest{
 set.seed(129)
diff --git a/man/estimate_cerf_nngp.Rd b/man/estimate_cerf_nngp.Rd
index 33d5ef5..2e2560f 100644
--- a/man/estimate_cerf_nngp.Rd
+++ b/man/estimate_cerf_nngp.Rd
@@ -20,7 +20,8 @@ estimate_cerf_nngp(
 \arguments{
 \item{data}{A data.frame of observation data.}
 
-\item{w}{A vector of exposure level to compute CERF.}
+\item{w}{A vector of exposure level to compute CERF (please also see the
+notes).}
 
 \item{gps_m}{An S3 gps object including:
 gps: A data.frame of GPS vectors.
@@ -72,6 +73,18 @@ Estimates the conditional exposure response function (cerf) using
 the nearest neighbor (nn) Gaussian Process (gp). The function tune the best
 match (the lowest covariate balance) for the provided set of hyperparameters.
 }
+\note{
+Please note that \code{w} is a vector representing a grid of exposure levels at
+which the CERF is to be estimated. This grid can include both observed and
+hypothetical values of the exposure variable. The purpose of defining this
+grid is to provide a structured set of points across the exposure spectrum
+for estimating the CERF. This approach is essential in nonparametric models
+like Gaussian Processes (GPs), where the CERF is evaluated at specific points
+to understand the relationship between the exposure and outcome variables
+across a continuum. It facilitates a comprehensive analysis by allowing
+practitioners to examine the effect of varying exposure levels, including
+those not directly observed in the dataset.
+}
 \examples{
 
 \donttest{