Playing around w/ awesome @R_nimble to fit multievent #HMM capture-recapture models to data - Code and data here https://t.co/2oHvsplahv Comments welcome 🤗 pic.twitter.com/tA89r80YYV
— Olivier Gimenez 🖖 (@oaggimenez) December 2019
New paper by #SarahBenhaiem & @LMarescot 🤩👏 "Robustness of Eco-Epidemiological Capture-Recapture Parameter Estimates to Variation in Infection State Uncertainty" https://t.co/ZvNghUHUUY #HMM #multievent #TMB #WomenInSTEM #carnivorepapersbywomen #heynas #serengeti pic.twitter.com/VT6iStk5UW
— Olivier Gimenez 🍉 (@oaggimenez) 28 août 2018
Following my attempts to fit a HMM model to [capture-recapture data with Rcpp](http://localhost:1313/post/multievent_in_rcpp/) and to [occupancy data with ADMB](http://localhost:1313/post/occupancy_in_admb/), a few colleagues suggested TMB as a potential alternative for several reasons (fast, allows for parallel computations, works with R, accomodates spatial stuff, easy implementation of random effects, and probably other reasons that I don't know).
Following my previous post on [using ADMB to fit hidden Markov models](https://oliviergimenez.github.io/post/occupancy_in_admb/), I took some time to learn how to use Rcpp ([Eddelbuettel & Francois 2011](https://www.jstatsoft.org/article/view/v040i08); [Eddelbuettel 2013](http://www.springer.com/us/book/9781461468677)), a package that gives friendly access to the power of C++ and increase the speed of your R programs. Kudos to Dirk Eddelbuettel, Romain Francois and their colleagues, Rcpp is awesome!