References

Published

Last Updated on December, 2024

liste <- 
  readLines('hmm.bib') |> 
  as_tibble() |> 
  filter(stringr::str_detect(value, pattern = '@|keywords')) |> 
  rowid_to_column() %>% 
  mutate(int_part = floor((rowid-1)/2), parity = rowid %% 2) %>% 
  select(-rowid) %>% 
  pivot_wider( values_from = value, names_from = parity, names_prefix = "key") |> 
  mutate(key1 = stringr::str_remove(key1, pattern = '@[:alnum:]+\\{')) |> 
  mutate(key1 = stringr::str_remove(key1, pattern = ','))

Warning in readLines("hmm.bib"): incomplete final line found on 'hmm.bib'

maf_liste <- liste %>% 
  filter(str_detect(key0, 'MAF')) %>% 
  select(key1) %>% 
  pull()

linear_liste <- liste %>% 
  filter(!str_detect(key0, 'MAF')) %>% 
  select(key1) %>% 
  pull()
  
NoCite(bib = myBib, linear_liste)

Some useful references to dig further in HMM

Akaike, H. (1973). “Information theory as an extension of the maximum likelihood principle. Á In: Petrov, BN and Csaki, F”. In: Second International Symposium on Information Theory. Akademiai Kiado, Budapest, pp. 276Á281.

Biernacki, C., G. Celeux, and G. Govaert (2000). “Assessing a mixture model for clustering with the integrated completed likelihood”. In: IEEE transactions on pattern analysis and machine intelligence 22.7, pp. 719-725.

Cappe Oliverand Moulines, E. and T. Ryden (2005). Inference in Hidden Markov Models. Springer.

Carpenter, B., A. Gelman, M. D. Hoffman, et al. (2017). “Stan: A probabilistic programming language”. In: Journal of statistical software 76.1.

de Valpine, P., D. Turek, C. Paciorek, et al. (2017). “Programming with models: writing statistical algorithms for general model structures with NIMBLE”. In: Journal of Computational and Graphical Statistics 26 (2), pp. 403-413. DOI: 10.1080/10618600.2016.1172487.

Freitas, C., C. Lydersen, M. A. Fedak, et al. (2008). “A simple new algorithm to filter marine mammal Argos locations”. In: Marine Mammal Science 24.2, pp. 315-325.

Guédon, Y. (2007). “Exploring the state sequence space for hidden Markov and semi-Markov chains”. In: Computational Statistics & Data Analysis 51.5, pp. 2379-2409.

Jammalamadaka, S. R. and A. Sengupta (2001). Topics in circular statistics. Vol. 5. world scientific.

Lunn, D. J., A. Thomas, N. Best, et al. (2000). “WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility”. In: Statistics and computing 10.4, pp. 325-337.

Michelot, T., R. Langrock, and T. A. Patterson (2016). “moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models”. In: Methods in Ecology and Evolution 7.11, pp. 1308-1315. DOI: 10.1111/2041-210X.12578. eprint: https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.12578. URL: https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.12578.

Morales, J. M., D. T. Haydon, J. Frair, et al. (2004). “Extracting more out of relocation data: building movement models as mixtures of random walks”. In: Ecology 85.9, pp. 2436-2445.

Patterson, T. A., L. Thomas, C. Wilcox, et al. (2008). “State–space models of individual animal movement”. In: Trends in Ecology & Evolution 23.2, pp. 87 - 94. ISSN: 0169-5347. DOI: https://doi.org/10.1016/j.tree.2007.10.009. URL: http://www.sciencedirect.com/science/article/pii/S0169534707003588.

Rabiner, L. R. (1989). “A tutorial on hidden Markov models and selected applications in speech recognition”. In: Proceedings of the IEEE 77.2, pp. 257-286.