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:

[dependencies]
distimate = "<desired version>"

Usage

Here's a quick example of how to use the PERT distribution:

use distimate::prelude::*;
use distimate::Pert;

fn main() {
    // Create a PERT distribution for a task estimated to take
    // between 1 and 3 days, most likely 2 days
    let task_duration = Pert::new(1.0, 2.0, 3.0).unwrap();

    println!("Expected duration: {:.2} days", task_duration.expected_value());
    println!("Optimistic estimate (P5): {:.2} days", task_duration.optimistic_estimate());
    println!("Pessimistic estimate (P95): {:.2} days", task_duration.pessimistic_estimate());
    println!("Probability of completing within 2.5 days: {:.2}%",
             task_duration.probability_of_completion(2.5) * 100.0);
    println!("Risk of exceeding 2.5 days: {:.2}%",
             task_duration.risk_of_overrun(2.5) * 100.0);

    let (lower, upper) = task_duration.confidence_interval(0.9);
    println!("90% Confidence Interval: {:.2} to {:.2} days", lower, upper);
}

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.

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.

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.

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 file for details.