---
title: "The qgcomp package: g-computation on exposure quantiles"
author: "Alexander Keil"
date: "`r Sys.Date()`"
#output: rmarkdown::pdf_document 
output: rmarkdown::html_vignette  
vignette: >
  %\VignetteIndexEntry{The qgcomp package: list of vignettes}
  %\VignetteEngine{knitr::knitr}
  \usepackageutf8{inputenc}
---

```{r invisibles, echo=FALSE, results='markup', message=FALSE}
library("knitr")
#library("gWQS")
```



## Introduction<a name="intro"></a>
`qgcomp` is a package to implement g-computation for analyzing the effects of exposure
mixtures. Quantile g-computation yields estimates of the effect of increasing
all exposures by one quantile, simultaneously. This, it estimates a "mixture
effect" useful in the study of exposure mixtures such as air pollution, diet,
and water contamination.

Help for this package is available in the help files for individual functions, or in the package vignettes.

The basic vignette can be viewed in R/Rstudio by running the following
```{r v1, echo=TRUE, results='markup', message=FALSE}
vignette("qgcomp-basic-vignette", "qgcomp")
```
The basic vignette covers:

1. Introduction
2. How to use the `qgcomp` package
3. Example 1: linear model
4. Example 2: conditional odds ratio, marginal odds ratio in a logistic model(#ex-logistic)
5. Example 3: adjusting for covariates, plotting estimates
6. Example 4: non-linearity (and non-homogeneity)
7. Example 5: comparing model fits and further exploring non-linearity
8. Example 6: miscellaneous other ways to allow non-linearity
9. FAQ

The advanced vignette can be viewed in R/Rstudio by running the following
```{r v2, echo=TRUE, results='markup', message=FALSE}
vignette("qgcomp-advanced-vignette", "qgcomp")
```
The advanced vignette covers:

1. Example 7: time-to-event analysis and parallel processing
2. Example 8: clustering
3. Example 9: partial effects
4. Example 10: multinomial outcomes
5. Example 11: sample weighting from, e.g. NHANES
6. Missing data, limits of detection and multiple imputation

