bootstrap-resampling

bayesian bootstrapping in python

**curve_fit_utils** is a Python module containing useful tools for curve fitting

Generate error bars and perform binning analysis using jackknife or bootstrap resampling. Calculate average and error in quantum Monte Carlo data (or other data) and on functions of averages (such as fluctuations, skew, and kurtosis).

These projects were part of the course Data Analysis (ECE Department, AUTH, 7th semester).

Bootstrap resampling tools for model uncertainty analysis

R script for agglomerative hierarchical cluster analysis of bathymetric profiles: z-score scaling, Euclidean and correlation distance, average-linkage hclust, dendextend/magrittr dendrogram styling, tanglegram comparison and pvclust AU/BP bootstrap p-values. Mariana Trench; Lemenkova (2020), Carpathian JECE 13(1):5-12.

Applied data science in Python: statistical inference, regression, machine learning, NLP, and deep learning. Each project states a question, shows the work, and reports what the data can't support.

Jackknife & bootstrap resampling in Fortran with python bindings

MATLAB code for quality control of peak frequency estimates via bootstrapping.

Implementation of LOWESS (Locally Weighted Scatterplot Smoothing) algorithm with bootstrap confidence intervals for nonparametric regression and data smoothing in Python.

Jackknife with R to estimate the bias of a statistic

Empirical simulation study comparing K-Means vs. GMM under geometric stress tests (anisotropy, heteroscedasticity, overlap). Demonstrates the bias-variance trade-off in unsupervised learning using Monte Carlo and Bootstrap resampling.

Browser-based and Python tool for robust SWOT–AHP prioritization with bootstrap uncertainty, scenario sensitivity, TOWS strategy translation, and SPI ranking. No installation required.

Statistical A/B test analysis of a real 90K-player mobile game experiment: SRM check, two-proportion z-tests, bootstrap resampling, segment analysis, and a peeking-problem simulation

Statistical analysis of salary differences based on job mode (remote vs. in-person) and education levels using Welch’s t-test, ANOVA, and bootstrapping.

Python package for the statistical analysis of lattice field theory data based on bootstrap and jackknife methods.

Tools for julia programming of statistical analysis

End-to-end Python research pipeline replicating "Beyond Benchmark Rankings": treats LLM selection as portfolio construction rather than leaderboard ranking. Estimates marginal utility, informational novelty, and correlated-failure risk per model, then runs budget-feasible greedy optimization to select LLM ensembles.