Crate statoxide 

StatOxide: High-performance statistical computing library

This is the main entry point for the StatOxide library, providing a unified API for all statistical computing functionality.

Overview

StatOxide provides:

• Core data structures: Series, DataFrame, Formula
• Statistical functions: Descriptive statistics, distributions, tests
• Statistical models: Linear regression, GLM, mixed effects, time series
• Time series analysis: ARIMA, GARCH, forecasting, decomposition
• Linear algebra: Matrix operations, solvers, decompositions
• Utilities: Random generation, validation, numerical methods

Quick Start

use std::collections::HashMap;
use statoxide::{
    DataFrame, Series, Formula,
    stats::{mean, std, correlation},
    models::{GLM, GLMModelBuilder, Family, Link},
    tsa::{TimeSeries, ARIMA},
    ndarray::Array1,
};

// Create a DataFrame
let mut columns = HashMap::new();
columns.insert("x".to_string(), Series::new("x", Array1::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0])));
columns.insert("y".to_string(), Series::new("y", Array1::from_vec(vec![2.0, 4.0, 5.0, 4.0, 5.0])));
let df = DataFrame::from_series(columns).unwrap();

// Parse a formula
let formula = Formula::parse("y ~ x + x^2").unwrap();

// Compute statistics
let x_series = df.column("x").unwrap();
let x_data = x_series.data().to_owned(); // Convert view to owned array
let avg = statoxide::stats::mean(&x_data).unwrap();
println!("Mean of x: {}", avg);

Modules

• core - Core data structures and formula parsing
• models - Statistical models (regression, GLM, mixed effects, etc.)
• stats - Statistical functions and tests
• tsa - Time series analysis
• linalg - Linear algebra utilities
• utils - General utilities

Re-exports

pub use ndarray;

Modules

core

Core data structures and formula parsing

correlation

Correlation measures and related statistics

data

Data structures for statistical computing

error

Error types for so-core

formula

R-style formula parser and design matrix builder

linalg

models

prelude

stats

tsa

utils

Structs

ARIMA

ARIMA model

DataFrame

A DataFrame represents a collection of named Series (columns)

Formula

A formula expression (response ~ predictors)

GARCH

GARCH model

GLM

Generalized Linear Model

GLMModelBuilder

GLM model configuration and builder

GLMResults

Results from fitting a Generalized Linear Model

Series

A Series represents a single column of data with a name and dtype

TimeSeries

Time series data structure

Enums

Error

Core error type

Family

Distribution families for GLM

Link

Link functions for GLM

Functions

correlation

Compute correlation coefficient (Pearson)

inv

Compute matrix inverse using the default backend

matmul

Compute matrix multiplication: C = A * B using the default backend

mean

Compute mean of an array

random_normal_array

Generate random array from normal distribution

softmax

Softmax function for probability distribution

solve

Solve linear system A * x = b using the default backend

standardize

Standardize array (z-score normalization)

std

Compute standard deviation

variance

Compute variance with given degrees of freedom adjustment

version

Returns the version of StatOxide

Type Aliases

Result

Result type alias for convenience