update readme

README.Rmd

@@ -1,5 +1,5 @@
 ---
-title: "local sensitivity analysis with mrgsolve"
+title: ""
 output: github_document
 ---
 
@@ -8,7 +8,16 @@ knitr::opts_chunk$set(comment = '.', message=FALSE, warning = FALSE,
                       fig.path="man/figures/README-")
 ```
 
-# Model
+# vera
+<!-- badges: start -->
+[![Travis build status](https://travis-ci.org/kylebaron/vera.svg?branch=master)](https://travis-ci.org/kylebaron/vera)
+<!-- badges: end -->
+
+## Overview
+local sensitivity analysis in R with mrgsolve
+
+
+## Model
 Load the vera package and a PBPK model from the mrgsolve package.  We 
 decrease the tolerance a bit as well as the maximum step size.
 ```{r,message=FALSE}
@@ -21,7 +30,7 @@ mod <- modlib(
 ) %>% obsonly
 ```
 
-# Scenario
+## Scenario
 Create a function that uses the model to simulate a certain scenario.  For now, 
 we just simulate a single dose.
 ```{r}
@@ -37,7 +46,7 @@ d <- ev(amt = 100)
 fun(param(mod),d) %>% plot(Cp+Amu~time)
 ```
 
-# Sensitivity analysis
+## Sensitivity analysis
 
 Use `vera::lsa()`.  We pick the __parameters__ that we want to fiddle with 
 (`par`):
@@ -58,7 +67,7 @@ Also specify the __output__ that we want to look at (`var`):
 out <- lsa(mod, fun, par = "Kpli,Kpmu,Kpad,BW", var = "Cp,Amu", dose = d)
 ```
 
-# Ouput
+## Ouput
 
 The output is long and ready to send in to `ggplot2`.  
 ```{r}
@@ -71,7 +80,7 @@ There is a default plotting method as well.
 plot(out)
 ```
 
-# Plot with ggplot2
+## Plot with ggplot2
 ```{r}
 library(ggplot2)
 
@@ -86,6 +95,6 @@ filter(out, par=="Kpmu") %>%
 
 <hr>
 
-# More info
+## More info
 
 See [inst/doc/about.md (on GitHub only)](inst/doc/about.md) for more details.

README.md

@@ -1,7 +1,17 @@
-local sensitivity analysis with mrgsolve
-================
 
-# Model
+# vera
+
+<!-- badges: start -->
+
+[![Travis build
+status](https://travis-ci.org/kylebaron/vera.svg?branch=master)](https://travis-ci.org/kylebaron/vera)
+<!-- badges: end -->
+
+## Overview
+
+local sensitivity analysis in R with mrgsolve
+
+## Model
 
 Load the vera package and a PBPK model from the mrgsolve package. We
 decrease the tolerance a bit as well as the maximum step size.
@@ -16,7 +26,7 @@ mod <- modlib(
 ) %>% obsonly
 ```
 
-# Scenario
+## Scenario
 
 Create a function that uses the model to simulate a certain scenario.
 For now, we just simulate a single dose.
@@ -36,7 +46,7 @@ fun(param(mod),d) %>% plot(Cp+Amu~time)
 
 ![](man/figures/README-unnamed-chunk-2-1.png)<!-- -->
 
-# Sensitivity analysis
+## Sensitivity analysis
 
 Use `vera::lsa()`. We pick the **parameters** that we want to fiddle
 with (`par`):
@@ -58,7 +68,7 @@ Also specify the **output** that we want to look at (`var`):
 out <- lsa(mod, fun, par = "Kpli,Kpmu,Kpad,BW", var = "Cp,Amu", dose = d)
 ```
 
-# Ouput
+## Ouput
 
 The output is long and ready to send in to `ggplot2`.
 
@@ -82,7 +92,7 @@ plot(out)
 
 ![](man/figures/README-unnamed-chunk-5-1.png)<!-- -->
 
-# Plot with ggplot2
+## Plot with ggplot2
 
 ``` r
 library(ggplot2)
@@ -99,7 +109,7 @@ filter(out, par=="Kpmu") %>%
 
 <hr>
 
-# More info
+## More info
 
 See [inst/doc/about.md (on GitHub only)](inst/doc/about.md) for more
 details.