Statistical Entropy Paper and Simulations

I am preparing a research article on the estimation of several entropy measures for the Inverse Power Half-Logistic distribution under an Upper Record Ranked Set Sampling scheme. The work must develop Bayesian, non-Bayesian and expected Bayesian estimators, assess them through a thorough Monte-Carlo study, and demonstrate their practical value on real data. You will write the complete manuscript and supply every line of code that leads to the reported numbers and figures. My preferred workflow uses both R and Python, so feel free to split the analysis between, for example, tidyverse / rstan in R and numpy / scipy / matplotlib in Python, provided the results match. The paper should follow the structure: Abstract, Literature Review, Methodology, Conclusion, and be typeset in LaTeX (with a clean .tex source) so that equations and proofs are presented clearly. Originality is critical; no AI-generated text or derivations will be accepted. The mathematical sections must show each step of the derivations and cite supporting literature where needed. For the simulation, design scenarios that explore small, medium and large sample sizes, report bias, MSE and coverage of credible/confidence intervals, then discuss the findings in the Results subsection. Finally, verify the proposed estimators on at least one publicly available dataset, explaining preprocessing and interpreting the calculated entropy values. Deliverables (acceptance criteria): • LaTeX manuscript ready for journal submission • All R and Python scripts, organised and documented so I can rerun everything with a single command • Generated tables, figures and any supplementary material in a clearly labelled folder • A concise reproducibility guide (README) When you send your bid, attach a detailed project proposal outlining the theoretical roadmap, simulation plan, and tentative timeline.