Polynomial Maximization Method for Non-Gaussian Regression R package EstemPMM version 0.4.0 Implements the Polynomial Maximization Method ('PMM') for parameter estimation in linear and time series models when error distributions deviate from normality. The 'PMM2' variant achieves lower variance parameter estimates compared to ordinary least squares ('OLS') when errors exhibit significant skewness. The 'PMM3' variant (S=3) targets symmetric platykurtic error distributions, reducing variance when excess kurtosis is negative. Includes automatic method selection ('pmm_dispatch'), linear regression, 'AR'/'MA'/'ARMA'/'ARIMA' models, and bootstrap inference. Methodology described in Zabolotnii, Warsza, and Tkachenko (2018) <doi:10.1007/978-3-319-77179-3_75>, Zabolotnii, Tkachenko, and Warsza (2022) <doi:10.1007/978-3-031-03502-9_37>, and Zabolotnii, Tkachenko, and Warsza (2023) <doi:10.1007/978-3-031-25844-2_21>, and Zabolotnii (2025) <doi:10.48550/arXiv.2511.07059>. Software Depends: R (>= 3.5.0) Imports: methods, stats, graphics, utils Suggests: dplyr, ggplot2, gridExtra, testthat (>= 3.0.0), rmarkdown, knitr, MASS, numDeriv Serhii Zabolotnii <zabolotniua@gmail.com> Comprehensive R Archive Network (CRAN) Serhii Zabolotnii [aut, cre] (ORCID: <https://orcid.org/0000-0003-0242-2234>) GPL-3 2026-05-29 application/tgz https://CRAN.R-project.org/package=EstemPMM doi:10.32614/CRAN.package.EstemPMM