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Curso de Modelos No paramétricos y de Regresión impartido en la Facultad de Ciencias semestre 2026-2

Extended diagnostic and visualization tools for Cox proportional hazard models in R

Multivariate statistical analysis assignments using R — Goodness of Fit, ANOVA, Regression, PCA, Clustering & Classification.

One-sample Kolmogorov-Smirnov goodness-of-fit test.

Perform a chi-square goodness-of-fit test.

Group project for Statistical Inference Methods II. Moving beyond simple descriptive statistics, the study focuses on applying various hypothesis tests and inference techniques to draw specific conclusions.

R package to perform goodness-of-fit tests for capture-recapture models (and various manipulations)

Table 1 and diagnostics from complex survey designs

End-to-End Python scalable forensic accounting toolkit implementing Benford's Law analysis for FTSE financial data. Delivers automated anomaly detection with Chi-Squared/MAD testing, comprehensive validation pipelines, and risk-based prioritization of investigative resources. Replicates Ausloos et al.'s (2025) methodology with full reproducibility.

Tests for rotational symmetry on the hypersphere. Software companion for "On optimal tests for rotational symmetry against new classes of hyperspherical distributions"

Limited information goodness-of-fit tests for ordinal factor models

a C++11 library for lattice QCD data analysis

A Python Package for the Four-Parameter Beta Distribution and Likelihood-Based Estimation

Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

Limited Information Goodness of Fit Tests for Binary Factor Models

An example of the Monte Carlo test with the Kolmogorov Smirnov test

Evaluating the probabilistic forecasts between competitive forecasters

This analysis is part of our comprehensive statistics course project. This chapter encompasses critical topics such as goodness of fit., test for independence, and contingency tables with Yates correction. These concepts are essential for examining data relationships and validating statistical models.