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README.Rmd
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---
output: github_document
---
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[](https://github.com/EPPIcenter/moire/actions/workflows/R-CMD-check.yaml)
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```{r setup, echo = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", fig.path = "man/figures/")
```
# moire <img src="man/figures/logo.svg" align="right"/>
`moire` is a package implementing an MCMC based approach to estimating complexity of infection (COI), also sometimes referred to as multiplicity of infection (MOI), population allele frequencies, and within-host relatedness from polyallelic genomics data.
## Installation
`moire` can be installed either using our r-universe repository (preferred)
```{r, eval = FALSE}
# Install from r-universe
install.packages("moire", repos = c("https://eppicenter.r-universe.dev", "https://cloud.r-project.org"))
```
or from GitHub using `remotes`
```{r, eval = FALSE}
# Install development version from Github
remotes::install_github("EPPIcenter/moire")
```
## Usage
moire supports loading data from either a long format `data.frame` using `load_long_form_data()` or from a wide format `data.frame` using `load_delimited_data()`.
```{r, eval = FALSE}
df <- read.csv("your_data.csv")
data <- load_long_form_data(df)
# With data in appropriate format, run MCMC as follows
mcmc_results <- moire::run_mcmc(data, is_missing = data$is_missing)
```
## Manuscript
[](https://doi.org/10.5281/zenodo.10092403)
The paper describing our method may be found [here](https://doi.org/10.1093/bioinformatics/btae619)