obulezi-oj.r-universe.dev

[obulezi-oj] DRLAP2 0.1.1

oj.obulezi@unizik.edu.ng (Okechukwu J. Obulezi) — Tue, 25 Aug 2026 15:10:16 GMT

Implements Maximum Likelihood Estimation (MLE) and Bayesian Markov Chain Monte Carlo (MCMC) sampling algorithms for progressive censoring models, with support for dynamic reinforcement learning environment simulation and accelerated computational routines written in C++.

[obulezi-oj] fuzzyurn 0.1.0

oj.obulezi@unizik.edu.ng (Okechukwu J. Obulezi) — Fri, 21 Aug 2026 13:30:20 GMT

Implements Generalized Non-Central Fuzzy Dynamic Hypergeometric Processes. Includes discrete Markov chain sampling under dynamic weight decay and continuous fuzzy membership maps, infinitesimal generator evaluation, weak convergence to Itô diffusion SDEs, numerical solutions for Fokker-Planck PDEs, stationary Gibbs-Boltzmann densities, and Azuma-Hoeffding concentration bounds.

[obulezi-oj] robustrcp 0.1.0

oj.obulezi@unizik.edu.ng (Okechukwu J. Obulezi) — Thu, 20 Aug 2026 15:30:02 GMT

Implements robust ratio-cum-product estimators using auxiliary medians for estimating the population mean under simple random sampling without replacement (SRSWOR). Provides analytical optimal tuning parameters, bias, Mean Squared Error (MSE), and Percent Relative Efficiency (PRE) evaluations.

[obulezi-oj] weatherMRJD 0.1.1

oj.obulezi@unizik.edu.ng (Okechukwu J. Obulezi) — Thu, 20 Aug 2026 15:20:02 GMT

Provides statistical tools for analyzing weather patterns, temperature anomalies, and climate risk. Implements Markov regime-switching jump diffusion (MRJD) models to capture abrupt shifts, extreme weather events, and structural breaks in environmental time series data. Estimates model parameters using maximum likelihood estimation and offers utility functions for simulating regime-dependent stochastic processes. The regime-switching methodology is based on Hamilton (1989) "Analysis of Time Series Subject to Changes in Regime" .

[obulezi-oj] PINNProgCens 0.1.0

oj.obulezi@unizik.edu.ng (Okechukwu J. Obulezi) — Sun, 09 Aug 2026 08:10:15 GMT

Implementation of Physics-Informed Neural Networks ('PINN') for lifetime estimation under progressive Type-II censoring schemes. Combines parametric baseline hazards with physical differential degradation models.

[obulezi-oj] ProgVine 0.1.0

oj.obulezi@unizik.edu.ng (Okechukwu J. Obulezi) — Sun, 09 Aug 2026 08:10:10 GMT

Implements Progressive Regularized Vine Copula (Prog-Vine) frameworks for high-dimensional dependent competing risks with masked failure causes under Progressive Type-II Censoring. Fits Weibull marginals, estimates pair-copula trees using Expectation-Maximization (EM) algorithms, computes Louis observed information confidence intervals, and implements Data Augmentation Gibbs Samplers for Bayesian credible intervals.

[obulezi-oj] DropCens 0.1.0

oj.obulezi@unizik.edu.ng (Okechukwu J. Obulezi) — Sun, 09 Aug 2026 08:00:17 GMT

Implements distributionally robust optimization algorithms for progressive Type-II censored Weibull lifetime data under Wasserstein ambiguity balls. Provides primal-dual convex optimization solvers and robust inference bounds.

[obulezi-oj] ghcclm 0.1.0

oj.obulezi@unizik.edu.ng (Okechukwu J. Obulezi) — Sat, 08 Aug 2026 13:00:06 GMT

Implements generalized hybrid contrast coding methods for K-level categorical predictors in linear models as established by Obulezi (2026)[cite: 3]. The package automates design matrix construction mixing dummy indicators and composite group contrasts, checks rank constraints via singular value decomposition, computes closed-form OLS parameter mappings, and estimates robust heteroscedastic covariance matrices[cite: 3].

[obulezi-oj] pcsclr 0.1.1

oj.obulezi@unizik.edu.ng (Okechukwu J. Obulezi) — Thu, 30 Jul 2026 18:54:11 GMT

Implements simulation, numerical maximum likelihood estimation via fourth-order Runge-Kutta path optimization, and high-speed Bayesian Markov Chain Monte Carlo (MCMC) samplers for Weibull lifetimes under progressive censoring setups with competitive latent risks. Both point estimation and interval estimation are provided for the model parameters.