Pacote BTSR

Repository URL: 
https://cran.r-project.org/web/packages/BTSR/index.html

Simulate, estimate and forecast a wide range of regression-based dynamic models for bounded time series, covering the most commonly applied models in the literature. The main calculations are done in 'FORTRAN', which translates into very fast algorithms.

References

  • Bayer FM, Bayer DM, Pumi G (2017). “Kumaraswamy autoregressive moving average models for double bounded environmental data.” Journal of Hydrology, 555, 385–396. doi:10.1016/j.jhydrol.2017.10.006.
  • Pumi G, Valk M, Bisognin C, Bayer FM, Prass TS (2019). “Beta autoregressive fractionally integrated moving average models.” Journal of Statistical Planning and Inference, 200, 196–212. doi:10.1016/j.jspi.2018.10.001.
  • Pumi G, Prass TS, Souza RR (2021). “A dynamic model for double bounded time series with chaotic driven conditional averages.” Scandinavian Journal of Statistics, 48(1), 68–86. doi:10.1111/sjos.12439. 
  • Pumi G, Prass TS, Taufemback CG (2024). “Unit-Weibull autoregressive moving average models.” TEST, 33, 204–229. doi:10.1007/s11749023008938. 
  • Pumi G, Prass TS, Taufemback CG (2024). “Publisher Correction: Unit-Weibull autoregressive moving average models.” TEST, 33, 358–359. doi:10.1007/s11749023009057. 
  • Pumi G, Matsuoka DH, Prass TS (2025). “A GARMA Framework for Unit-Bounded Time Series Based on the Unit-Lindley Distribution with Application to Renewable Energy Data.” doi:10.48550/arXiv.2504.07351. 
  • Pumi G, Matsuoka DH, Prass TS, Palm BG (2025). “A Matsuoka-Based GARMA Model for Hydrological Forecasting: Theory, Estimation, and Applications.” doi:10.48550/arXiv.2502.18645. 
  • Prass TS, Pumi G, Taufemback CG, Carlos JH (2025). “Positive time series regression models: theoretical and computational aspects.” Computational Statistics, 40, 1185–1215. doi:10.1007/s0018002401531z