Crate pramana 

Pramana

प्रमाण (Sanskrit: proof, measure, evidence) — Statistics and probability library for the AGNOS ecosystem.

Built on hisab for mathematical primitives.

Modules

Module	Description
distribution	Probability distributions (Normal, Uniform, Exponential, Poisson, Binomial, Bernoulli, Gamma, Beta, Chi-Squared, Student-t, F, Cauchy, Weibull, Multivariate Normal)
descriptive	Descriptive statistics, KDE, correlation matrix, PCA
hypothesis	Hypothesis testing (t-tests, chi-squared) and confidence intervals
regression	Linear, polynomial, and logistic regression
bayesian	Bayesian inference and naive Bayes classification
combinatorics	Factorials, permutations, combinations, Stirling approximation
monte_carlo	Monte Carlo integration, Metropolis-Hastings MCMC, Gibbs sampling
markov	Markov chains, Hidden Markov Models (Forward, Viterbi, Baum-Welch)
timeseries	Time series: moving average, exponential smoothing, autocorrelation, ARIMA

Quick Start

use pramana::{descriptive, distribution::{Normal, Distribution}, SimpleRng};

// Descriptive statistics
let data = [1.0, 2.0, 3.0, 4.0, 5.0];
let m = descriptive::mean(&data).unwrap();
assert!((m - 3.0).abs() < 1e-10);

// Fit and sample from a normal distribution
let s = descriptive::std_dev(&data).unwrap();
let normal = Normal::new(m, s).unwrap();
let mut rng = SimpleRng::new(42);
let sample = normal.sample(&mut rng);
assert!(sample.is_finite());

Hypothesis Testing

use pramana::hypothesis;

let data = [10.0, 10.1, 9.9, 10.2, 9.8, 10.0, 10.1, 9.9];
let result = hypothesis::t_test_one_sample(&data, 0.0, 0.05).unwrap();
assert!(result.reject); // mean clearly differs from 0

Monte Carlo

use pramana::{monte_carlo, SimpleRng};

let mut rng = SimpleRng::new(42);
let pi = monte_carlo::monte_carlo_pi(100_000, &mut rng).unwrap();
assert!((pi - std::f64::consts::PI).abs() < 0.1);

Re-exports

pub use error::PramanaError;

pub use rng::Rng;

pub use rng::SimpleRng;

pub use hisab;

Modules

bayesian

Bayesian inference and naive Bayes classification.

bridge

Cross-crate bridges — primitive-value conversions from other AGNOS science crates. Cross-crate bridges — convert primitive values from other AGNOS science crates into pramana statistics parameters and vice versa.

combinatorics

Combinatorial functions: factorial, permutations, combinations, Stirling’s approximation.

descriptive

Descriptive statistics and kernel density estimation.

distribution

Probability distributions.

error

Error types for pramana.

hypothesis

Hypothesis testing and confidence intervals.

integration

Integration APIs for downstream consumers (soorat rendering). Integration APIs for downstream consumers.

markov

Markov chains and Hidden Markov Models.

monte_carlo

Monte Carlo methods: integration, simulation, and MCMC.

regression

Linear and polynomial regression.

rng

Random number generation trait and a simple deterministic PRNG.

timeseries

Time series analysis: moving average, exponential smoothing, autocorrelation, ARIMA.

Type Aliases

Result

Convenience alias for Result<T, PramanaError>.