{ "_id": "6a197609acfb0bcc41df181d", "Package": "EstemPMM", "Type": "Package", "Title": "Polynomial Maximization Method for Non-Gaussian Regression", "Version": "0.4.0", "Date": "2026-05-28", "Authors@R": "person(\"Serhii\", \"Zabolotnii\",\nemail = \"zabolotniua@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-0242-2234\"))", "Description": "Implements the Polynomial Maximization Method ('PMM') for\nparameter estimation in linear and time series models when\nerror distributions deviate from normality. The 'PMM2' variant\nachieves lower variance parameter estimates compared to\nordinary least squares ('OLS') when errors exhibit significant\nskewness. The 'PMM3' variant (S=3) targets symmetric\nplatykurtic error distributions, reducing variance when excess\nkurtosis is negative. Includes automatic method selection\n('pmm_dispatch'), linear regression, 'AR'/'MA'/'ARMA'/'ARIMA'\nmodels, and bootstrap inference. Methodology described in\nZabolotnii, Warsza, and Tkachenko (2018)\n, Zabolotnii, Tkachenko, and\nWarsza (2022) , and\nZabolotnii, Tkachenko, and Warsza (2023)\n, and Zabolotnii (2025)\n.", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "Roxygen": "list(markdown = TRUE)", "Config/testthat/edition": "3", "URL": "https://github.com/SZabolotnii/EstemPMM", "BugReports": "https://github.com/SZabolotnii/EstemPMM/issues", "VignetteBuilder": "knitr", "Config/roxygen2/version": "8.0.0", "RoxygenNote": "7.3.3", "Collate": "'data.R' 'optimized_direct_pmm2.R' 'pmm_base_classes.R'\n'pmm2_classes.R' 'pmm2_common.R' 'pmm2_inference.R'\n'pmm2_ma_estimator.R' 'pmm2_main.R' 'pmm2_monte_carlo.R'\n'pmm2_package.R' 'pmm2_ts_design.R' 'pmm2_ts_main.R'\n'pmm2_ts_methods.R' 'pmm2_unified.R' 'pmm2_utils.R'\n'pmm3_classes.R' 'pmm3_dispatch.R' 'pmm3_main.R'\n'pmm3_solver.R' 'pmm3_ts_classes.R' 'pmm3_ts_main.R'\n'pmm3_ts_methods.R' 'pmm3_utils.R' 'pmm_show_methods.R'\n'pmm_unified_api.R' 'sarimax_wrapper.R'", "Repository": "https://szabolotnii.r-universe.dev", "Date/Publication": "2026-05-29 07:03:35 UTC", "RemoteUrl": "https://github.com/szabolotnii/estempmm", "RemoteRef": "HEAD", "RemoteSha": "718925a6ddcdf4599f7a0c60fc24cc021f3db5c9", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-29 09:47:43 UTC", "User": "root" }, "Author": "Serhii Zabolotnii [aut, cre] (ORCID:\n)", "Maintainer": "Serhii Zabolotnii ", "MD5sum": "17f037791a5a8fa5a2066100e7fec100", "_user": "szabolotnii", "_type": "src", "_file": "EstemPMM_0.4.0.tar.gz", "_fileid": "660022a7e4748d92b209d5be522c69a5fd2aeb44062706c0a992346d85cb268c", "_filesize": 4075494, "_sha256": "660022a7e4748d92b209d5be522c69a5fd2aeb44062706c0a992346d85cb268c", "_created": "2026-05-29T09:47:43.000Z", "_published": "2026-05-29T11:18:33.165Z", "_distro": "noble", "_jobs": [ { "job": 78477468930, "time": 128, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7287906449" }, { "job": 78477468972, "time": 143, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7287911215" }, { "job": 78477468943, "time": 92, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7289415444" }, { "job": 78477468939, "time": 90, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7289378974" }, { "job": 78476975423, "time": 189, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7287866914" }, { "job": 78477468927, "time": 96, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7287897442" }, { "job": 78477468981, "time": 116, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7287902979" }, { "job": 78477468967, "time": 120, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7287904183" }, { "job": 78477468979, "time": 122, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7287904430" } ], "_buildurl": "https://github.com/r-universe/szabolotnii/actions/runs/26630230906", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/szabolotnii/estempmm", "_commit": { "id": "718925a6ddcdf4599f7a0c60fc24cc021f3db5c9", "author": "serhiizabolotnii ", "committer": "serhiizabolotnii ", "message": "build: rebuild EstemPMM_0.4.0.tar.gz with 80 new tests included\n\nRe-run of R CMD build after adding test-v040-new-features.R.\nThe tarball now ships:\n * 695 tests (695 PASS, 0 FAIL, 1 SKIP) incl. the 80 new 0.4.0\n feature tests covering unified API, show() methods, PMMdispatch\n S3 class, and ci_method / bias in both inference functions.\n * inst/notes/pmm3_vcov_derivation.md.\nR CMD check --as-cran: 0 errors, 0 warnings, 1 environmental NOTE\n(unable to verify current time).\n\nCo-Authored-By: Claude Opus 4.7 (1M context) \n", "time": 1780038215 }, "_maintainer": { "name": "Serhii Zabolotnii", "email": "zabolotniua@gmail.com", "login": "szabolotnii", "linkedin": "in/serhii-zabolotnii-45a95432", "orcid": "0000-0003-0242-2234", "description": "Professor Department of Computer Engineering and Information Technologies", "uuid": 135612547 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "methods", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "dplyr", "role": "Suggests" }, { "package": "ggplot2", "role": "Suggests" }, { "package": "gridExtra", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "MASS", "role": "Suggests" }, { "package": "numDeriv", "role": "Suggests" } ], "_owner": "szabolotnii", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-43", "n": 36 }, { "week": "2025-44", "n": 2 }, { "week": "2025-45", "n": 6 }, { "week": "2025-46", "n": 9 }, { "week": "2025-47", "n": 19 }, { "week": "2026-12", "n": 4 }, { "week": "2026-15", "n": 2 }, { "week": "2026-18", "n": 1 }, { "week": "2026-20", "n": 1 }, { "week": "2026-22", "n": 3 } ], "_tags": [ { "name": "v0.3.0", "date": "2026-03-20" } ], "_stars": 0, "_contributors": [ { "user": "claude", "count": 47, "uuid": 81847 }, { "user": "szabolotnii", "count": 23, "uuid": 135612547 } ], "_userbio": { "uuid": 135612547, "type": "user", "name": "Serhii Zabolotnii", "description": "Professor Department of Computer Engineering and Information Technologies" }, "_downloads": { "count": 459, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/EstemPMM" }, "_devurl": "https://github.com/szabolotnii/estempmm", "_searchresults": 50, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/EstemPMM.html", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/szabolotnii/estempmm", "_realowner": "szabolotnii", "_cranurl": true, "_releases": [ { "version": "0.1.1", "date": "2025-11-07" }, { "version": "0.3.1", "date": "2026-04-07" }, { "version": "0.3.2", "date": "2026-05-15" } ], "_exports": [ "ar_pmm2", "ar_pmm3", "arima_pmm2", "arima_pmm3", "arma_pmm2", "arma_pmm3", "coef", "compare_ar_methods", "compare_arima_methods", "compare_arma_methods", "compare_ma_methods", "compare_sar_methods", "compare_ts_methods", "compare_with_ols", "compute_moments", "compute_moments_pmm3", "confint", "create_sar_matrix", "create_sarma_matrix", "fitted", "lm_pmm2", "lm_pmm3", "ma_pmm2", "ma_pmm3", "nobs", "plot", "plot_pmm2_bootstrap", "pmm_ar", "pmm_arima", "pmm_arma", "pmm_dispatch", "pmm_gamma6", "pmm_kurtosis", "pmm_lm", "pmm_ma", "pmm_sarima", "pmm_skewness", "pmm2_inference", "pmm2_monte_carlo_compare", "pmm2_nonlinear_iterative", "pmm2_nonlinear_onestep", "pmm2_variance_factor", "pmm2_variance_matrices", "pmm3_variance_factor", "pmm3_variance_matrices", "predict", "residuals", "sar_pmm2", "sarima_pmm2", "sarma_pmm2", "show", "sma_pmm2", "summary", "test_symmetry", "ts_pmm2", "ts_pmm2_inference", "ts_pmm3", "vcov" ], "_datasets": [ { "name": "auto_mpg", "title": "Auto MPG Dataset", "object": "auto_mpg", "class": [ "data.frame" ], "fields": [ "mpg", "cylinders", "displacement", "horsepower", "weight", "acceleration", "model_year", "origin", "car_name" ], "rows": 398, "table": true, "tojson": true }, { "name": "DCOILWTICO", "title": "WTI Crude Oil Prices", "object": "DCOILWTICO", "class": [ "data.frame" ], "fields": [ "observation_date", "DCOILWTICO" ], "rows": 1305, "table": true, "tojson": true }, { "name": "djia2002", "title": "Dow Jones Industrial Average Daily Data (July-December 2002)", "object": "djia2002", "class": [ "data.frame" ], "fields": [ "date", "close", "change" ], "rows": 127, "table": true, "tojson": true } ], "_help": [ { "page": "ar_pmm2", "title": "Fit an AR model using PMM2 (wrapper)", "topics": [ "ar_pmm2" ] }, { "page": "ar_pmm3", "title": "Fit an AR model using PMM3", "topics": [ "ar_pmm3" ] }, { "page": "arima_pmm2", "title": "Fit an ARIMA model using PMM2 (wrapper)", "topics": [ "arima_pmm2" ] }, { "page": "arima_pmm3", "title": "Fit an ARIMA model using PMM3", "topics": [ "arima_pmm3" ] }, { "page": "ARIMAPMM2-class", "title": "S4 class for storing PMM2 ARIMA model results", "topics": [ "ARIMAPMM2-class" ] }, { "page": "ARIMAPMM3-class", "title": "S4 class for PMM3 ARIMA model results", "topics": [ "ARIMAPMM3-class" ] }, { "page": "arma_pmm2", "title": "Fit an ARMA model using PMM2 (wrapper)", "topics": [ "arma_pmm2" ] }, { "page": "arma_pmm3", "title": "Fit an ARMA model using PMM3", "topics": [ "arma_pmm3" ] }, { "page": "ARMAPMM2-class", "title": "S4 class for storing PMM2 ARMA model results", "topics": [ "ARMAPMM2-class" ] }, { "page": "ARMAPMM3-class", "title": "S4 class for PMM3 ARMA model results", "topics": [ "ARMAPMM3-class" ] }, { "page": "ARPMM2-class", "title": "S4 class for storing PMM2 AR model results", "topics": [ "ARPMM2-class" ] }, { "page": "ARPMM3-class", "title": "S4 class for PMM3 AR model results", "topics": [ "ARPMM3-class" ] }, { "page": "auto_mpg", "title": "Auto MPG Dataset", "topics": [ "auto_mpg" ] }, { "page": "BasePMM2-class", "title": "Virtual S4 class for the PMM2 model family", "topics": [ "BasePMM2-class" ] }, { "page": "BasePMM3-class", "title": "Virtual S4 class for the PMM3 model family", "topics": [ "BasePMM3-class" ] }, { "page": "coef-PMM2fit-method", "title": "Extract coefficients from PMM2fit object", "topics": [ "coef,PMM2fit-method" ] }, { "page": "coef-PMM3fit-method", "title": "Extract coefficients from PMM3fit object", "topics": [ "coef,PMM3fit-method" ] }, { "page": "coef-SARPMM2-method", "title": "Extract coefficients from SARPMM2 object", "topics": [ "coef,SARPMM2-method" ] }, { "page": "coef-SMAPMM2-method", "title": "Extract coefficients from SMAPMM2 object", "topics": [ "coef,SMAPMM2-method" ] }, { "page": "coef-TS2fit-method", "title": "Extract coefficients from TS2fit object", "topics": [ "coef,TS2fit-method" ] }, { "page": "coef-TS3fit-method", "title": "Extract coefficients from TS3fit object", "topics": [ "coef,TS3fit-method" ] }, { "page": "compare_ar_methods", "title": "Compare AR methods", "topics": [ "compare_ar_methods" ] }, { "page": "compare_arima_methods", "title": "Compare ARIMA methods", "topics": [ "compare_arima_methods" ] }, { "page": "compare_arma_methods", "title": "Compare ARMA methods", "topics": [ "compare_arma_methods" ] }, { "page": "compare_ma_methods", "title": "Compare MA methods", "topics": [ "compare_ma_methods" ] }, { "page": "compare_sar_methods", "title": "Compare SAR model estimation methods", "topics": [ "compare_sar_methods" ] }, { "page": "compare_ts_methods", "title": "Compare PMM2 with classical time series estimation methods", "topics": [ "compare_ts_methods" ] }, { "page": "compare_with_ols", "title": "Compare PMM2 with OLS", "topics": [ "compare_with_ols" ] }, { "page": "compute_moments", "title": "Calculate moments and cumulants of error distribution", "topics": [ "compute_moments" ] }, { "page": "compute_moments_pmm3", "title": "Compute central moments for PMM3 from residuals", "topics": [ "compute_moments_pmm3" ] }, { "page": "compute_pmm2_components", "title": "Compute PMM2 weights and components", "topics": [ "compute_pmm2_components" ] }, { "page": "confint-PMM2fit-method", "title": "Confidence intervals for PMM2fit coefficients", "topics": [ "confint,PMM2fit-method" ] }, { "page": "confint-PMM3fit-method", "title": "Confidence intervals for PMM3fit coefficients", "topics": [ "confint,PMM3fit-method" ] }, { "page": "confint-TS2fit-method", "title": "Confidence intervals for TS2fit AR model coefficients", "topics": [ "confint,TS2fit-method" ] }, { "page": "confint-TS3fit-method", "title": "Confidence intervals for TS3fit AR model coefficients", "topics": [ "confint,TS3fit-method" ] }, { "page": "create_sar_matrix", "title": "Create design matrix for seasonal AR model", "topics": [ "create_sar_matrix" ] }, { "page": "create_sarma_matrix", "title": "Create design matrix for seasonal ARMA model", "topics": [ "create_sarma_matrix" ] }, { "page": "DCOILWTICO", "title": "WTI Crude Oil Prices", "topics": [ "DCOILWTICO" ] }, { "page": "djia2002", "title": "Dow Jones Industrial Average Daily Data (July-December 2002)", "topics": [ "djia2002" ] }, { "page": "fitted-PMM2fit-method", "title": "Extract fitted values from PMM2fit object", "topics": [ "fitted,PMM2fit-method" ] }, { "page": "fitted-PMM3fit-method", "title": "Extract fitted values from PMM3fit object", "topics": [ "fitted,PMM3fit-method" ] }, { "page": "fitted-TS2fit-method", "title": "Extract fitted values from TS2fit object", "topics": [ "fitted,TS2fit-method" ] }, { "page": "fitted-TS3fit-method", "title": "Extract fitted values from TS3fit object", "topics": [ "fitted,TS3fit-method" ] }, { "page": "format.PMMdispatch", "title": "Format method for PMMdispatch objects", "topics": [ "format.PMMdispatch" ] }, { "page": "get_sarimax_jacobian", "title": "Calculate SARIMAX Jacobian (Numerical)", "topics": [ "get_sarimax_jacobian" ] }, { "page": "get_sarimax_residuals", "title": "Calculate SARIMAX Residuals", "topics": [ "get_sarimax_residuals" ] }, { "page": "lm_pmm2", "title": "PMM2: Main function for PMM2 (S=2)", "topics": [ "lm_pmm2" ] }, { "page": "lm_pmm3", "title": "PMM3: Fit linear model using Polynomial Maximization Method (S=3)", "topics": [ "lm_pmm3" ] }, { "page": "logLik.PMM2fit", "title": "Extract log-likelihood from PMM2fit object", "topics": [ "logLik.PMM2fit" ] }, { "page": "logLik.PMM3fit", "title": "Extract log-likelihood from PMM3fit object", "topics": [ "logLik.PMM3fit" ] }, { "page": "logLik.TS2fit", "title": "Extract log-likelihood from TS2fit object", "topics": [ "logLik.TS2fit" ] }, { "page": "logLik.TS3fit", "title": "Extract log-likelihood from TS3fit object", "topics": [ "logLik.TS3fit" ] }, { "page": "ma_pmm2", "title": "Fit an MA model using PMM2 (wrapper)", "topics": [ "ma_pmm2" ] }, { "page": "ma_pmm3", "title": "Fit an MA model using PMM3", "topics": [ "ma_pmm3" ] }, { "page": "MAPMM2-class", "title": "S4 class for storing PMM2 MA model results", "topics": [ "MAPMM2-class" ] }, { "page": "MAPMM3-class", "title": "S4 class for PMM3 MA model results", "topics": [ "MAPMM3-class" ] }, { "page": "nobs-PMM2fit-method", "title": "Number of observations in PMM2fit object", "topics": [ "nobs,PMM2fit-method" ] }, { "page": "nobs-PMM3fit-method", "title": "Number of observations in PMM3fit object", "topics": [ "nobs,PMM3fit-method" ] }, { "page": "nobs-TS2fit-method", "title": "Number of observations in TS2fit object", "topics": [ "nobs,TS2fit-method" ] }, { "page": "nobs-TS3fit-method", "title": "Number of observations in TS3fit object", "topics": [ "nobs,TS3fit-method" ] }, { "page": "plot_pmm2_bootstrap", "title": "Plot bootstrap distributions for PMM2 fit", "topics": [ "plot_pmm2_bootstrap" ] }, { "page": "plot-PMM2fit-missing-method", "title": "Plot diagnostic plots for PMM2fit object", "topics": [ "plot,PMM2fit,missing-method" ] }, { "page": "plot-PMM3fit-missing-method", "title": "Plot diagnostic plots for PMM3fit object", "topics": [ "plot,PMM3fit,missing-method" ] }, { "page": "plot-TS2fit-missing-method", "title": "Build diagnostic plots for TS2fit objects", "topics": [ "plot,TS2fit,missing-method" ] }, { "page": "plot-TS3fit-missing-method", "title": "Plot diagnostic plots for TS3fit object", "topics": [ "plot,TS3fit,missing-method" ] }, { "page": "pmm_ar", "title": "Fit an AR model with the polynomial maximization method", "topics": [ "pmm_ar" ] }, { "page": "pmm_arima", "title": "Fit an ARIMA model with the polynomial maximization method", "topics": [ "pmm_arima" ] }, { "page": "pmm_arma", "title": "Fit an ARMA model with the polynomial maximization method", "topics": [ "pmm_arma" ] }, { "page": "pmm_dispatch", "title": "Automatic PMM method selection", "topics": [ "pmm_dispatch" ] }, { "page": "pmm_gamma6", "title": "Compute sixth-order cumulant coefficient gamma6", "topics": [ "pmm_gamma6" ] }, { "page": "pmm_kurtosis", "title": "Calculate kurtosis from data", "topics": [ "pmm_kurtosis" ] }, { "page": "pmm_lm", "title": "Fit a linear model with the polynomial maximization method", "topics": [ "pmm_lm" ] }, { "page": "pmm_ma", "title": "Fit an MA model with the polynomial maximization method", "topics": [ "pmm_ma" ] }, { "page": "pmm_sarima", "title": "Fit a seasonal ARIMA model with the polynomial maximization method", "topics": [ "pmm_sarima" ] }, { "page": "pmm_skewness", "title": "Calculate skewness from data", "topics": [ "pmm_skewness" ] }, { "page": "pmm2_inference", "title": "Bootstrap inference for PMM2 fit", "topics": [ "pmm2_inference" ] }, { "page": "pmm2_monte_carlo_compare", "title": "Monte Carlo comparison of PMM2 estimation methods", "topics": [ "pmm2_monte_carlo_compare" ] }, { "page": "pmm2_nonlinear_iterative", "title": "Universal PMM2 estimator (Iterative)", "topics": [ "pmm2_nonlinear_iterative" ] }, { "page": "pmm2_nonlinear_onestep", "title": "Universal PMM2 estimator (One-step Global)", "topics": [ "pmm2_nonlinear_onestep" ] }, { "page": "pmm2_variance_factor", "title": "Calculate theoretical skewness, kurtosis coefficients and variance reduction factor", "topics": [ "pmm2_variance_factor" ] }, { "page": "pmm2_variance_matrices", "title": "Calculate theoretical variance matrices for OLS and PMM2", "topics": [ "pmm2_variance_matrices" ] }, { "page": "PMM2fit-class", "title": "S4 class for storing PMM2 regression model results", "topics": [ "PMM2fit-class" ] }, { "page": "pmm3_variance_factor", "title": "Calculate PMM3 theoretical variance reduction factor", "topics": [ "pmm3_variance_factor" ] }, { "page": "pmm3_variance_matrices", "title": "Calculate theoretical variance matrices for OLS and PMM3", "topics": [ "pmm3_variance_matrices" ] }, { "page": "PMM3fit-class", "title": "S4 class for PMM3 regression fit results", "topics": [ "PMM3fit-class" ] }, { "page": "PMMfit-class", "title": "Virtual root S4 class for PMM fit objects", "topics": [ "PMMfit-class" ] }, { "page": "PMMtsfit-class", "title": "Virtual S4 class for PMM time-series fit objects", "topics": [ "PMMtsfit-class" ] }, { "page": "predict-PMM2fit-method", "title": "Prediction method for PMM2fit objects", "topics": [ "predict,PMM2fit-method" ] }, { "page": "predict-PMM3fit-method", "title": "Predict method for PMM3fit objects", "topics": [ "predict,PMM3fit-method" ] }, { "page": "predict-TS2fit-method", "title": "Prediction method for TS2fit objects", "topics": [ "predict,TS2fit-method" ] }, { "page": "predict-TS3fit-method", "title": "Predict method for TS3fit objects", "topics": [ "predict,TS3fit-method" ] }, { "page": "print.PMMdispatch", "title": "Print method for PMMdispatch objects", "topics": [ "print.PMMdispatch" ] }, { "page": "residuals-PMM2fit-method", "title": "Extract residuals from PMM2fit object", "topics": [ "residuals,PMM2fit-method" ] }, { "page": "residuals-PMM3fit-method", "title": "Extract residuals from PMM3fit object", "topics": [ "residuals,PMM3fit-method" ] }, { "page": "residuals-TS2fit-method", "title": "Extract residuals from TS2fit object", "topics": [ "residuals,TS2fit-method" ] }, { "page": "residuals-TS3fit-method", "title": "Extract residuals from TS3fit object", "topics": [ "residuals,TS3fit-method" ] }, { "page": "sar_pmm2", "title": "Fit Seasonal AR model using PMM2 method", "topics": [ "sar_pmm2" ] }, { "page": "sarima_pmm2", "title": "Fit a Seasonal ARIMA model using PMM2 method", "topics": [ "sarima_pmm2" ] }, { "page": "SARIMAPMM2-class", "title": "S4 class for Seasonal ARIMA model results with PMM2", "topics": [ "SARIMAPMM2-class" ] }, { "page": "sarma_pmm2", "title": "Fit a Seasonal ARMA model using PMM2 method", "topics": [ "sarma_pmm2" ] }, { "page": "SARMAPMM2-class", "title": "S4 class for Seasonal ARMA model results with PMM2", "topics": [ "SARMAPMM2-class" ] }, { "page": "SARPMM2-class", "title": "S4 class for Seasonal AR model results with PMM2", "topics": [ "SARPMM2-class" ] }, { "page": "show-PMM2fit-method", "title": "Show method for PMM2fit objects", "topics": [ "show,PMM2fit-method" ] }, { "page": "show-PMM3fit-method", "title": "Show method for PMM3fit objects", "topics": [ "show,PMM3fit-method" ] }, { "page": "show-TS2fit-method", "title": "Show method for TS2fit objects (and subclasses)", "topics": [ "show,TS2fit-method" ] }, { "page": "show-TS3fit-method", "title": "Show method for TS3fit objects (and subclasses)", "topics": [ "show,TS3fit-method" ] }, { "page": "sma_pmm2", "title": "Fit a Seasonal MA model using PMM2", "topics": [ "sma_pmm2" ] }, { "page": "SMAPMM2-class", "title": "S4 class for Seasonal MA PMM2 results", "topics": [ "SMAPMM2-class" ] }, { "page": "solve_pmm2_step", "title": "PMM2 step solver", "topics": [ "solve_pmm2_step" ] }, { "page": "summary-PMM2fit-method", "title": "Generic summary method for PMM2fit objects", "topics": [ "summary,PMM2fit-method" ] }, { "page": "summary-PMM3fit-method", "title": "Summary method for PMM3fit objects", "topics": [ "summary,PMM3fit-method" ] }, { "page": "summary-SARIMAPMM2-method", "title": "Generic summary method for SARIMAPMM2 objects", "topics": [ "summary,SARIMAPMM2-method" ] }, { "page": "summary-SARMAPMM2-method", "title": "Generic summary method for SARMAPMM2 objects", "topics": [ "summary,SARMAPMM2-method" ] }, { "page": "summary-SARPMM2-method", "title": "Summary method for SARPMM2 objects", "topics": [ "summary,SARPMM2-method" ] }, { "page": "summary-SMAPMM2-method", "title": "Summary method for SMAPMM2 objects", "topics": [ "summary,SMAPMM2-method" ] }, { "page": "summary-TS2fit-method", "title": "Generic summary method for TS2fit objects", "topics": [ "summary,TS2fit-method" ] }, { "page": "summary-TS3fit-method", "title": "Summary method for TS3fit objects", "topics": [ "summary,TS3fit-method" ] }, { "page": "summary.PMMdispatch", "title": "Summary method for PMMdispatch objects", "topics": [ "summary.PMMdispatch" ] }, { "page": "test_symmetry", "title": "Test whether residuals are sufficiently symmetric for PMM3", "topics": [ "test_symmetry" ] }, { "page": "ts_pmm2", "title": "Fit a time series model using the PMM2 method", "topics": [ "ts_pmm2" ] }, { "page": "ts_pmm2_inference", "title": "Bootstrap inference for PMM2 time series models", "topics": [ "ts_pmm2_inference" ] }, { "page": "ts_pmm3", "title": "Fit a time series model using PMM3", "topics": [ "ts_pmm3" ] }, { "page": "TS2fit-class", "title": "S4 class for PMM2 time-series fit results", "topics": [ "TS2fit-class" ] }, { "page": "TS3fit-class", "title": "S4 class for PMM3 time-series fit results", "topics": [ "TS3fit-class" ] }, { "page": "vcov-PMM2fit-method", "title": "Variance-covariance matrix for PMM2fit object", "topics": [ "vcov,PMM2fit-method" ] }, { "page": "vcov-PMM3fit-method", "title": "Variance-covariance matrix for PMM3fit objects", "topics": [ "vcov,PMM3fit-method" ] }, { "page": "vcov-TS2fit-method", "title": "Variance-covariance matrix for TS2fit AR models", "topics": [ "vcov,TS2fit-method" ] }, { "page": "vcov-TS3fit-method", "title": "Variance-covariance matrix for TS3fit AR models", "topics": [ "vcov,TS3fit-method" ] } ], "_readme": "https://github.com/szabolotnii/estempmm/raw/HEAD/README.md", "_rundeps": [], "_vignettes": [ { "source": "bootstrap_inference.Rmd", "filename": "bootstrap_inference.html", "title": "Bootstrap Inference for PMM2 Models", "author": "Serhii Zabolotnii", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Bootstrap Basics", "The Bootstrap Principle", "Setup", "Part 1: Bootstrap for Linear Regression", "Basic Example: Simple Linear Regression", "Understanding Bootstrap Distributions", "Part 2: Confidence Interval Methods", "Percentile Method", "Bias-Corrected (BC) Method", "Comparison with OLS", "Part 3: Hypothesis Testing", "Testing Individual Coefficients", "Testing Equality of Coefficients", "Part 4: Bootstrap for Time Series Models", "AR Model Bootstrap", "ARMA Model Bootstrap", "Part 5: Choosing Bootstrap Parameters", "Number of Bootstrap Samples (B)", "Parallel Bootstrap", "Part 6: Diagnostic Checks", "Convergence of Bootstrap Estimates", "Bootstrap Distribution Visualization", "Part 7: Practical Guidelines", "When to Use Bootstrap for PMM2", "Common Pitfalls and Solutions", "Part 8: Advanced Topics", "Studentized Bootstrap", "Block Bootstrap for Time Series", "Summary", "Best Practices Checklist", "References", "Next Steps" ], "created": "2025-10-23 14:14:47", "modified": "2025-10-23 14:14:47", "commits": 1 }, { "source": "pmm2_introduction.Rmd", "filename": "pmm2_introduction.html", "title": "Introduction to PMM2: Polynomial Maximization Method", "author": "Serhii Zabolotnii", "engine": "knitr::rmarkdown", "headings": [ "The Problem: When OLS Falls Short", "Practical Implications", "The Solution: Polynomial Maximization Method (PMM2)", "Theoretical Foundation", "Getting Started with EstemPMM", "Example 1: Simple Linear Regression with Skewed Errors", "Data Generation", "Visualize Error Distribution", "Model Fitting: PMM2 vs OLS", "Bootstrap Inference for Variance Comparison", "Example 2: Multiple Regression", "Example 3: Polynomial Regression", "Diagnostic Plots", "Comparing PMM2 with OLS: Monte Carlo Evidence", "When to Use PMM2", "Practical Recommendations", "Next Steps", "Summary", "References" ], "created": "2025-10-23 14:14:47", "modified": "2026-05-14 14:03:53", "commits": 2 }, { "source": "pmm2_time_series.Rmd", "filename": "pmm2_time_series.html", "title": "PMM2 for Time Series: AR, MA, ARMA, ARIMA, and Seasonal Models", "author": "Serhii Zabolotnii", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Setup", "Part 1: Autoregressive (AR) Models", "AR(1) Model with Skewed Innovations", "Estimate AR(1) Model: PMM2 vs CSS", "AR(2) Model", "Part 2: Moving Average (MA) Models", "MA(1) Model", "MA(2) Model", "Part 3: ARMA Models", "ARMA(1,1) Model", "Diagnostic Plots for ARMA Models", "Part 4: ARIMA Models with Differencing", "ARIMA(1,1,1) Model", "Fitting ARIMA Models", "Part 5: Bootstrap Inference for Time Series", "Part 6: Comparing Methods with Monte Carlo", "Part 7: Prediction with Time Series Models", "Practical Guidelines", "When to Use PMM2 for Time Series", "Model Selection Strategy", "Computational Considerations", "Summary Statistics: Efficiency Gains", "Advanced Topics", "Custom Innovation Distributions", "Part 8: Seasonal Models (SAR and SMA)", "Seasonal Autoregressive (SAR) Models", "Seasonal Moving Average (SMA) Models", "Comparing Seasonal Methods", "Practical Applications of Seasonal Models", "Conclusion", "Next Steps", "References" ], "created": "2025-10-23 14:14:47", "modified": "2025-11-13 15:22:06", "commits": 3 }, { "source": "pmm3_time_series.Rmd", "filename": "pmm3_time_series.html", "title": "PMM3 for Time Series: AR, MA, ARMA, and ARIMA Models", "author": "Serhii Zabolotnii", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "When PMM3 vs PMM2 vs OLS?", "Setup", "Part 1: AR Models with PMM3", "AR(1) with Uniform Innovations", "Fit AR(1): PMM3 vs MLE", "AR(2) Model", "Part 2: MA Models with PMM3", "MA(1) Model", "MA(2) Model", "Part 3: ARMA Models", "ARMA(1,1) Model", "Diagnostic Plots", "Part 4: ARIMA Models with Differencing", "ARIMA(1,1,0) Model", "Fit ARIMA(1,1,0)", "ARIMA(1,1,1) Model", "Part 5: Forecasting with PMM3 Models", "Forecasting ARMA Models", "Part 6: Method Selection with pmm_dispatch()", "Part 7: Real-Data Example — DJIA 2002", "Analyze Innovation Properties", "Fit AR(1) with All Three Methods", "Part 8: Real-Data Example — WTI Crude Oil Prices", "Innovation Analysis", "Fit and Compare", "Part 9: Adaptive Mode", "Part 10: Model Comparison via AIC", "Practical Guidelines", "When to Use PMM3 for Time Series", "Diagnostic Workflow", "Computational Notes", "Efficiency Summary", "Available Functions", "Next Steps", "References" ], "created": "2026-03-20 13:38:28", "modified": "2026-03-20 13:38:28", "commits": 1 }, { "source": "pmm3_symmetric_errors.Rmd", "filename": "pmm3_symmetric_errors.html", "title": "PMM3: Linear Regression for Symmetric Platykurtic Errors", "author": "Serhii Zabolotnii", "engine": "knitr::rmarkdown", "headings": [ "The Problem: When OLS and PMM2 Are Not Enough", "Where Platykurtic Errors Arise", "The Solution: PMM3 (Polynomial Maximization Method, S=3)", "The Newton-Raphson Algorithm", "Getting Started", "Example 1: Simple Regression with Uniform Errors", "Data Generation", "Visualize Error Distribution", "Automatic Method Selection", "Fit PMM3", "Compare with OLS", "Example 2: Three-Way Comparison (OLS vs PMM2 vs PMM3)", "Example 3: Real Data Diagnostic — Auto MPG Horsepower", "Fit Quadratic Model", "Dispatcher Recommendation", "Visualize the OLS Fit", "Example 4: Real Data — Auto MPG (Linear, PMM2 Case)", "Example 5: Multiple Regression", "Example 6: No-Intercept Model", "Interpreting the PMM3 Summary", "Adaptive Mode", "Diagnostic Plots", "Utility Functions", "Testing Symmetry", "Computing Moments", "Sixth-Order Cumulant", "Variance Factor", "When to Use PMM3", "Decision Workflow", "Efficiency Summary (Monte Carlo Evidence)", "Next Steps", "References" ], "created": "2026-03-20 09:40:00", "modified": "2026-05-14 14:03:53", "commits": 3 }, { "source": "seasonal_models.Rmd", "filename": "seasonal_models.html", "title": "Seasonal Time Series Models with PMM2", "author": "Serhii Zabolotnii", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Load Package", "Part 1: Seasonal Autoregressive (SAR) Models", "Understanding SAR Models", "Example: SAR(1,1)_12 with Monthly Data", "Seasonal Patterns Visualization", "Fitting SAR Models: PMM2 vs Classical Methods", "Understanding PMM2 Efficiency", "Residual Diagnostics", "Part 2: Seasonal Moving Average (SMA) Models", "Understanding SMA Models", "Example: SMA(1)_4 with Quarterly Data", "Quarterly Seasonal Pattern", "Fitting SMA Models", "SMA Convergence Behavior", "Part 3: Monte Carlo Evidence for Seasonal Models", "SAR Monte Carlo Comparison", "Part 4: SARMA and SARIMA Models", "SARMA(p,P,q,Q)_s Models", "SARIMA Models with Differencing", "Part 5: Model Comparison and Selection", "Comparing Different Methods", "Information Criteria for Model Selection", "Part 6: Real-World Example - Airline Passengers", "Modeling Strategy", "Part 7: Practical Guidelines", "When to Use Seasonal Models", "Choosing Between Seasonal Models", "PMM2 vs Classical Methods", "Diagnostic Checklist", "Part 8: Advanced Topics", "Multiple Seasonal Periods", "Seasonal Adjustment", "Bootstrap Inference for Seasonal Models", "Summary", "References", "Next Steps" ], "created": "2026-05-14 14:03:53", "modified": "2026-05-14 14:03:53", "commits": 1 } ], "_score": 6.176091259055681, "_indexed": true, "_nocasepkg": "estempmm", "_universes": [ "szabolotnii" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.4.0", "date": "2026-05-29T09:49:48.000Z", "distro": "noble", "commit": "718925a6ddcdf4599f7a0c60fc24cc021f3db5c9", "fileid": "24de59472b06759e3fc4dd3ede025f3436b438f69aa8953537a118a57bee2bac", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/szabolotnii/actions/runs/26630230906" }, { "r": "4.6.0", "os": "linux", "version": "0.4.0", "date": "2026-05-29T09:49:58.000Z", "distro": "noble", "commit": "718925a6ddcdf4599f7a0c60fc24cc021f3db5c9", "fileid": "d40a726988a30d9059031e5ae959578d2aa4f641932147b875d755afaa870cd0", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/szabolotnii/actions/runs/26630230906" }, { "r": "4.5.3", "os": "mac", "version": "0.4.0", "date": "2026-05-29T11:17:57.000Z", "commit": "718925a6ddcdf4599f7a0c60fc24cc021f3db5c9", "fileid": "c85de191af9777477553990923b045602dc4427f07de63327d2f27c4101132b1", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/szabolotnii/actions/runs/26630230906" }, { "r": "4.6.0", "os": "mac", "version": "0.4.0", "date": "2026-05-29T11:15:49.000Z", "commit": "718925a6ddcdf4599f7a0c60fc24cc021f3db5c9", "fileid": "24233f98c88a62f6f7a74a07867c4f95900ee2101d4010717ca3a889247e3bc9", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/szabolotnii/actions/runs/26630230906" }, { "r": "4.6.0", "os": "wasm", "version": "0.4.0", "date": "2026-05-29T09:49:44.000Z", "commit": "718925a6ddcdf4599f7a0c60fc24cc021f3db5c9", "fileid": "367c7039636d839e8d06f253024895c171653db868cdd1584e531b2db00cf99b", "status": "success", "buildurl": "https://github.com/r-universe/szabolotnii/actions/runs/26630230906" }, { "r": "4.7.0", "os": "win", "version": "0.4.0", "date": "2026-05-29T09:49:15.000Z", "commit": "718925a6ddcdf4599f7a0c60fc24cc021f3db5c9", "fileid": "6785951a9aa1faed7de90e92223ed73b6241bc1a857b5dc63f00385df1079a91", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/szabolotnii/actions/runs/26630230906" }, { "r": "4.5.3", "os": "win", "version": "0.4.0", "date": "2026-05-29T09:49:21.000Z", "commit": "718925a6ddcdf4599f7a0c60fc24cc021f3db5c9", "fileid": "d4b324dbaf539ca55dbcde31964635caa69fc43d7c1e36ad67e53d39ab0bfef8", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/szabolotnii/actions/runs/26630230906" }, { "r": "4.6.0", "os": "win", "version": "0.4.0", "date": "2026-05-29T09:49:18.000Z", "commit": "718925a6ddcdf4599f7a0c60fc24cc021f3db5c9", "fileid": "427f17b49a7d3ea12204a99329415dd5be7613eeb7f9cf38c7fcbf9af8ef18e7", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/szabolotnii/actions/runs/26630230906" } ] }