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README.md

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+# distimate
+
+`distimate` is a Rust crate that provides probability distributions specifically
+designed for estimation and risk analysis scenarios. It offers implementations
+of various distributions that are commonly used in project management, cost
+estimation, and other fields where uncertainty needs to be modeled.
+
+## Features
+
+- Implementations of PERT, Triangular, and Normal distributions tailored for
+  estimation purposes
+- Consistent API across all distributions
+- Support for common statistical operations (mean, variance, skewness, etc.)
+- Sampling capabilities for Monte Carlo simulations
+- Range-respecting implementations that work with min, likely (mode), and max estimates
+
+## Installation
+
+Add this to your `Cargo.toml`:
+
+```toml
+[dependencies]
+distimate = "<desired version>"
+```
+
+## Usage
+
+Here's a quick example of how to use the PERT distribution:
+
+```rust
+use distimate::prelude::*;
+use distimate::Pert;
+
+fn main() {
+    let pert = Pert::new(1.0, 2.0, 3.0).unwrap();
+    println!("Mean: {}", pert.mean().unwrap());
+    println!("Variance: {}", pert.variance().unwrap());
+    println!("95th percentile: {}", pert.inverse_cdf(0.95));
+}
+```
+
+## Distributions
+
+### PERT Distribution
+
+The PERT (Program Evaluation and Review Technique) distribution is commonly used
+in project management and risk analysis. It's defined by minimum, most likely
+(mode), and maximum values.
+
+```rust
+let pert = Pert::new(1.0, 2.0, 3.0).unwrap();
+```
+
+### Triangular Distribution
+
+The Triangular distribution is often used when limited sample data is available.
+It's also defined by minimum, most likely (mode), and maximum values.
+
+```rust
+let triangular = Triangular::new(1.0, 2.0, 3.0).unwrap();
+```
+
+### Normal Distribution
+
+The Normal (Gaussian) distribution is implemented with modifications to respect
+a given range. It's suitable for natural phenomena and when values are expected
+to be symmetrically distributed around a mean.
+
+```rust
+let normal = Normal::new(1.0, 2.0, 3.0).unwrap();
+```
+
+## Common Operations
+
+All distributions implement common traits for statistical operations:
+
+- `Distribution`: Provides `mean()`, `variance()`, `skewness()`, and `entropy()`
+- `Median`: Provides `median()`
+- `Mode`: Provides `mode()`
+- `Continuous`: Provides `pdf()` and `ln_pdf()`
+- `ContinuousCDF`: Provides `cdf()` and `inverse_cdf()`
+- `Min` and `Max`: Provide `min()` and `max()`
+
+Additionally, all distributions can be sampled using the `sample()` method,
+which is useful for Monte Carlo simulations.
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request.
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file
+for details.