DSAIRM

R Package - Dynamical Systems Approach to Immune Response Modeling

https://github.com/ahgroup/dsairm

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R Package - Dynamical Systems Approach to Immune Response Modeling

README.Rmd

---
output: github_document
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/"
)
```

# DSAIRM 


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**DSAIRM - Dynamical Systems Approach to Immune Response Modeling**

## Description
DSAIRM is an R package containing a set of simulation models (apps) that teach within-host infection dynamics and immune response modeling from a dynamical system perspective. 

All models can be explored through a graphical user interface, no reading or writing code is required. Each app comes with documentation and instructions which teach important concepts of within-host and immune response modeling, and how to use simulation models to understand such concepts. 

It is also possible to go beyond the graphical interface and directly access and modify all simulations to adapt them to your needs.

## Getting Started

While the main idea is to install the R package and use it locally, if you want to get a quick glimpse at the package to see if this package is for you, you can give it [a quick try online, without having to install it](https://shiny.ovpr.uga.edu/DSAIRM/). If you like what you see, you can install it and start using it with these 3 commands:
``` 
install.packages('DSAIRM')
library('DSAIRM')
dsairmmenu()
```
For an introduction to the package, step-by-step instructions on getting started, and more information on the different ways you can use the package [see the _Get Started_ tutorial (vignette)](https://ahgroup.github.io/DSAIRM/articles/DSAIRM.html).


## Further information

* The [package tutorial (vignette)](https://ahgroup.github.io/DSAIRM/articles/DSAIRM.html) contains detailed instructions on the different ways the package can be used.
* [I published a paper describing the package](https://doi.org/10.1186/s12865-019-0321-0). The package has since been updated and changed, but the paper still describes the overall idea and context well.  
* Several live-coding video recordings showing the different ways of using DSAIRM are available on the website [Simulation Modeling in Immunology (SMI)](https://andreashandel.github.io/SMIcourse/). See the _DSAIRM_ section under _Modeling_. That website also contains a lot of other potentially interesting materials.
* I have full solutions and quiz sheets for all of the __What to do__ tasks for each app. If you are an instructor using this package as part of a class, email me if you are interested in having access to the materials.
* Contributions to the package are very welcome! If you want to take a deeper look at the package, see [this Markdown file](https://github.com/ahgroup/DSAIRM/blob/main/auxiliary/docsfordevelopers/documentation.md) which provides further information on the details of the package structure. I'd be excited to receive any contributions from individuals who want to help improve the package. If you plan to develop new apps, or make other substantial contributions, it might be best to get in touch with me first.
* A companion package to this one, called _Dynamical Systems Approaches to Infectious Disease Epidemiology (DSAIDE)_, focuses on models at the population level. It has the same structure as DSAIRM. [See the DSAIDE site for more information.](https://ahgroup.github.io/DSAIDE/)


## Citation and Contributors

If the package does in any way help you with your work such that it warrants citing it, please cite [this publication in BMC Immunology](https://doi.org/10.1186/s12865-019-0321-0). 

This R package is developed and maintained by [Andreas Handel](https://www.andreashandel.com/). A full list of contributors and a Bibtex entry for the citation [can be found here](https://ahgroup.github.io/DSAIRM/authors.html).

This project was/is partially supported by NIH grants U19AI117891, U01AI150747, R01AI170116, R25AI147391 and R25GM089694.

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