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DESCRIPTION (latest release date, Suggests list
for packages used in the demos).R CMD check --as-cran (now
warning-free after installing qpdf)..R) and
included them in inst/doc for distribution..Rbuildignore and .gitignore,
keeping only files required for CRAN.New Features: - lm_pmm2() - Linear
regression estimation using Polynomial Maximization Method (S=2) -
ar_pmm2() - Autoregressive (AR) time series modeling with
PMM2 - ma_pmm2() - Moving Average (MA) time series modeling
with PMM2 - arma_pmm2() - ARMA time series modeling with
PMM2 - arima_pmm2() - ARIMA time series modeling with PMM2
- pmm2_inference() - Bootstrap inference for linear models
- ts_pmm2_inference() - Bootstrap inference for time series
models - Statistical utilities: pmm_skewness(),
pmm_kurtosis(), compute_moments() - Comparison
functions: compare_with_ols(),
compare_ts_methods(), compare_ar_methods(),
compare_ma_methods(), compare_arma_methods(),
compare_arima_methods()
S4 Classes: - PMM2fit - Results
container for linear regression models - TS2fit - Base
class for time series results - ARPMM2,
MAPMM2, ARMAPMM2, ARIMAPMM2 -
Specialized time series result classes
Methods: - summary() - Model summary
statistics - coef() - Extract coefficients -
fitted() - Fitted values - predict() -
Predictions for new data - residuals() - Model residuals -
plot() - Diagnostic plots
Documentation: - Comprehensive Roxygen2
documentation for all exported functions - README with theoretical
background and basic usage examples - Demonstration script
pmm2_demo_runner.R showing practical applications
Module Organization: - R/pmm2_main.R -
Primary PMM2 fitting functions - R/pmm2_classes.R - S4
class definitions - R/pmm2_utils.R - Utility functions for
moment computation and optimization - R/pmm2_ts_design.R -
Time series design matrix construction
Dependencies: - Core: methods,
stats, graphics, utils -
Optional: MASS (for advanced statistical functions,
available in Suggests)
Quality Assurance: - Unit tests covering core PMM2 functionality - Edge case handling for numerical stability - Convergence diagnostics and warnings
0.2.0 (PMM3 Ready Architecture): - PMM3 implementation (S=3 polynomial methods) - Refactored base classes supporting method extensibility - Vignette documentation with practical use cases - Enhanced bootstrap procedures for small samples - GitHub Actions CI/CD integration
0.3.0 (Advanced Methods): - Adaptive PMM order selection - Robust variance estimation - Model selection criteria (AIC/BIC for PMM) - Generalized Linear Models (GLM) with PMM
1.0.0 (Stable API): - API stabilization and backward compatibility guarantee - Extended performance benchmarks - Specialized applications (econometrics, biostatistics)
If you use EstemPMM in your research, please cite the relevant publications:
For Linear Regression (lm_pmm2): Zabolotnii S., Warsza Z.L., Tkachenko O. (2018) Polynomial Estimation of Linear Regression Parameters for the Asymmetric PDF of Errors. In: Szewczyk R., Zieliński C., Kaliczyńska M. (eds) Automation 2018. AUTOMATION 2018. Advances in Intelligent Systems and Computing, vol 743. Springer, Cham. https://doi.org/10.1007/978-3-319-77179-3_75
For Autoregressive Models (ar_pmm2): Zabolotnii S., Tkachenko O., Warsza Z.L. (2022) Application of the Polynomial Maximization Method for Estimation Parameters of Autoregressive Models with Asymmetric Innovations. In: Szewczyk R., Zieliński C., Kaliczyńska M. (eds) Automation 2022. AUTOMATION 2022. Advances in Intelligent Systems and Computing, vol 1427. Springer, Cham. https://doi.org/10.1007/978-3-031-03502-9_37
For Moving Average Models (ma_pmm2): Zabolotnii S., Tkachenko O., Warsza Z.L. (2023) Polynomial Maximization Method for Estimation Parameters of Asymmetric Non-gaussian Moving Average Models. In: Szewczyk R., et al. (eds) Automation 2023. AUTOMATION 2023. Lecture Notes in Networks and Systems, vol 630. Springer, Cham.
Algorithm Stability: - Regularization parameter automatically adjusted for ill-conditioned systems - Step size limiting prevents divergence in optimization - Convergence history tracking for diagnostics
Numerical Considerations: - Moment estimation uses robust methods to handle outliers - Design matrices constructed with numerical stability in mind - NA/Inf values detected and handled appropriately
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.