References

liste <- 
    readLines('spatstat.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 = ',')) 
  
statspat_liste <- liste %>% 
  filter(str_detect(key0, 'spatstat')) %>% 
  select(key1) %>% 
  pull()

NoCite(bib = myBib, statspat_liste)

Pour approfondir ou compléter le cours

Spatial and spatio-temporal models with R-INLA (2013). “Spatial and spatio-temporal models with R-INLA”. In: Spatial and spatio-temporal epidemiology 4, pp. 33-49.

Bayesian inference with INLA (2020). Bayesian inference with INLA. Chapman and Hall/CRC.

Cressie, N. (2015). Statistics for spatial data. John Wiley & Sons.

Gaetan, C., X. Guyon, and others (2010). Vol. 90. Springer.