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-# BarBay
+# BarBay.jl
 
-[![Build Status](https://github.com/mrazomej/BarBay.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/mrazomej/BarBay.jl/actions/workflows/CI.yml?query=branch%3Amain)
+[![codecov](https://codecov.io/gh/mrazomej/BarBay.jl/graph/badge.svg?token=W2JREEH8O8)](https://codecov.io/gh/mrazomej/BarBay.jl)
 
-Welcome to the `GitHub` repository for `BarBay.jl`, a software package to perform Bayesian inference of relative fitness on high-throughput barcode sequencing data!
+BarBay.jl is a Julia package for performing Bayesian inference of relative fitness from high-throughput barcode sequencing data. The package implements robust statistical models to analyze competitive fitness assays and estimate relative fitness values with quantified uncertainty.
 
-Please visit the [documentation](https://mrazomej.github.io/BarBay.jl) to learn more about the package and how to use it.
+## Features
 
-Please open an issue on this repository if you have any comments or want to contribute.
+- 🧬 Analyze barcode sequencing data from competitive fitness assays
+- 📊 Support for multiple experimental designs:
+    - Single condition experiments
+    - Multiple experimental replicates
+    - Multiple environmental conditions
+    - Multiple genotype groupings
+- 📈 Bayesian inference using:
+    - Variational inference with ADVI
+- 🎯 Built-in support for:
+    - Population mean fitness estimation
+    - Mutant fitness effects
+    - Hierarchical models for replicates and genotypes
+    - Environment-specific fitness effects
+
+## Installation
+
+```julia
+using Pkg
+Pkg.add("BarBay")
+```
+
+## Quick Start
+
+Here's a simple example of analyzing fitness data from a single condition
+experiment:
+
+```julia
+using BarBay
+using CSV
+using DataFrames
+
+# Load your data (must be in tidy format)
+data = DataFrame(
+    :barcode => [...],    # Barcode identifiers
+    :time => [...],       # Time points
+    :count => [...],      # Raw counts
+    :neutral => [...]     # Boolean indicating neutral lineages
+)
+
+# Run Bayesian inference using ADVI
+results = BarBay.vi.advi(
+    data = data,
+    model = BarBay.model.fitness_normal,  # Basic fitness model
+    outputname = "fitness_results"        # Optional: save to CSV
+)
+
+# The results DataFrame contains posterior distributions for:
+# - Population mean fitness
+# - Individual mutant fitness effects
+# - Associated uncertainty measurements
+```
+
+For more complex experimental designs:
+
+```julia
+# Multiple replicates
+results = BarBay.vi.advi(
+    data = data,
+    model = BarBay.model.replicate_fitness_normal,
+    rep_col = :replicate  # Column indicating replicates
+)
+
+# Multiple environments
+results = BarBay.vi.advi(
+    data = data,
+    model = BarBay.model.multienv_fitness_normal,
+    env_col = :environment  # Column indicating environments
+)
+
+# Multiple genotypes
+results = BarBay.vi.advi(
+    data = data,
+    model = BarBay.model.genotype_fitness_normal,
+    genotype_col = :genotype  # Column indicating genotypes
+)
+```
+
+## Documentation
+
+For detailed documentation, tutorials, and API reference, please visit our
+[documentation page](https://mrazomej.github.io/BarBay.jl/stable/).
+
+## Contributing
+
+We welcome contributions! Please feel free to:
+
+- Open issues for bug reports or feature requests
+- Submit pull requests
+- Suggest improvements to documentation
+- Share example use cases
+
+## Citation
+
+If you use BarBay.jl in your research, please cite:
+
+Razo-Mejia, M., Mani, M. & Petrov, D. Bayesian inference of relative fitness on
+high-throughput pooled competition assays. *PLoS Comput Biol* 20,
+[e1011937](https://dx.plos.org/10.1371/journal.pcbi.1011937) (2024).
+
+## License
+
+BarBay.jl is released under the MIT License.
+
+## Contact
+
+Please open an issue on this repository if you have any questions, comments, or
+would like to contribute.
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