basic statistics library.
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A Rust library for fitting statistical models
Statistical modeling for Rust — OLS/WLS regression, GLM, survival analysis, ARIMA/VAR, nonparametric tests, and more. A statsmodels-style library.
A Rust library for performing Oaxaca-Blinder decomposition on Polars DataFrames, with support for categorical variables and bootstrapped standard errors.
High-performance LOWESS (Locally Weighted Scatterplot Smoothing)
LOWESS (Locally Weighted Scatterplot Smoothing)
Computes efficiently the correlation (Pearson, Spearman or Kendall) and the p-value (two-sided) between all the pairs from two datasets
Comprehensive time-series analysis in Rust — decomposition, ARIMA, anomaly detection, spectral analysis
ANCOM differential abundance (Mandal et al 2015) — scikit-bio skbio.stats.composition.ancom equivalent: per-feature W statistic from pairwise log-ratio one-way ANOVA, with the tau/theta detection cutoff
High-performance statistical computing library written in Rust, exposed to Python via PyO3
High-performance statistical computing library written in Rust, exposed to Python via PyO3
High-performance statistical computing library written in Rust, exposed to Python via PyO3
High-performance statistical computing library written in Rust, exposed to Python via PyO3
No description provided.
No description provided.
No description provided.