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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
<img src="man/figures/logo.png" align="right" />
# EndTB
<!-- badges: start -->
[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
<!-- badges: end -->
The goal of EndTB is to provide a convinient set of established TB models for
the simulation, estimation, and projection of Tuberculosis Epidemic.
Moreover
- the models are implemented in C++ which provides fast simulations.
- the models are called within the [TMB
framework](https://kaskr.github.io/adcomp/Introduction.html), allowing advanced
and fast parameter estimation with emprical Bayes approach.
## Installation
You can install the development version of EndTB like so:
```{r eval=FALSE}
remotes::install_github('kklot/EndTB')
```
Documents of the package and functions can be view as normal R's documentation
or online at [EndTB.](https://kklot.github.io/EndTB)
## Example
This is a basic example which shows you how to start a model:
```{r}
library(EndTB)
TB <- TBM$new(c(sigma = 2))
```
where the default parameters $\sigma$ was replaced with 2.
Steady-state of the model without treatments and no population growth
for that set of parameters can be ploted with.
```{r plot}
TB$plot()
```
List of states to be plotted can be supplied into the arguments of the `plot`
function above.
Further details can be read in **References** in the top navigation. For
example, [the
documentation of `plot`](https://kklot.github.io/EndTB/reference/TBM.html#method-plot-).
Check out model's parameters and states for Moldova model.
```{r para}
EndTB:::states_moldova
EndTB:::pars_moldova
```
## TODO
- [x] 1st revision of the WHO's model (and currently the only)
- [ ] Revise model to Argentina data
- [ ] Add fitting example
- [ ] Add other model variations