moving sticker around

README.Rmd

@@ -15,7 +15,7 @@ knitr::opts_chunk$set(
 )
 ```
 
-# R/`IsoXshift` <img src="man/figures/IsoXshift_sticker.png" style="float:right; height:200px;">
+# R/`IsoXshift`
 
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 [![R-CMD-check](https://github.com/blind-contours/IsoXshift/workflows/R-CMD-check/badge.svg)](https://github.com/blind-contours/IsoXshift/actions)
@@ -36,6 +36,9 @@ __Authors:__ [David McCoy](https://davidmccoy.org)
 
 ## What's `IsoXshift`?
 
+![](https://github.com/blind-contours/IsoXshift/blob/main/IsoXshift_sticker.png)
+
+
 The `IsoXshift` R package offers an approach which identifies the minimum effort intervention on two exposures which, if the population were given these intervention levels, would result in a target outcome. This parameter reflects the most synergistic interaction or set of interactions in a mixed exposure. The target parameter is similar to isobolic interactions used in toxicology studies where researchers investigate how much simultaneous dose of two exposures results in a target outcome, like cancer or cell death in cultures. 
 
 From a policy perspective, this parameter represents the most efficient intervention that can be done on a mixed exposure to get to a desired outcome. This is because, for a collection of possible interventions or changes to pollutants, for example, we find the exposure set that most efficiently results in an expected outcome close to our desired outcome. Efficient here means the exposure(s) need to be shifted the least to get to a desired outcome, like pre-industry levels of thyroid cancer etc. 

README.md

@@ -1,7 +1,7 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# R/`IsoXshift` <img src="man/figures/IsoXshift_sticker.png" style="float:right; height:200px;">
+# R/`IsoXshift` 
 
 <!-- badges: start -->
 
@@ -30,6 +30,9 @@ license](https://img.shields.io/badge/license-MIT-brightgreen.svg)](https://open
 
 ## What’s `IsoXshift`?
 
+![](https://github.com/blind-contours/IsoXshift/blob/main/IsoXshift_sticker.png)
+
+
 The `IsoXshift` R package offers an approach which identifies the
 minimum effort intervention on two exposures which, if the population
 were given these intervention levels, would result in a target outcome.