HMM

Review of HMMs in ecology

Our new preprint on HMMs for ecology, or how we''’ve been using HMMs for ever without even knowing it.

Nina's PhD defense on modelling population dynamics in presence of hybridization

🇮🇹🇫🇷 @NSantostasi succesfully defended her PhD on modelling population dynamics in presence of hybridization between @SapienzaRoma & @umontpellier #WomenInSTEM #HMM #CaptureRecapture #MatrixModels #IndividualBasedModels #WildlifeManagement pic.twitter.com/grqiuP9xMG — Olivier Gimenez 🖖 (@oaggimenez) February 21020

Weed demography and hidden Markov models

☘️🚜🍇 New #preprint led by @kazakou_elena in which we assess the effect of management practices on weed demography w/ hidden Markov models #HMM and #RandomEffects #WomenInSTEM #WomenInScience #OpenBUGS pic.twitter.com/xH5mQiMzd8 — Olivier Gimenez 🖖 (@oaggimenez) January 9, 2020

Nimble and HMM

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 on Inferring animal social networks with imperfect detection

New paper on "Inferring animal social networks with imperfect detection" w/ colleagues from 🇦🇷 and #LorenaMansilla our 🇨🇱 PhD student #SocialNetworks #CaptureRecapture #StateSpaceModel #rstats #BayesianStatistics - Download PDF freely here https://t.

Modeling the demography of species providing extended parental care: A capturerecapture approach with a case study on Polar Bears

New preprint by awesome #SarahCubaynes "Modeling the demography of species providing extended parental care: A #capturerecapture approach with a case study on #Polar Bears" https://t.co/x1H0X7GyKn #demography #rstats #multistate #WomenInSTEM #WomenInScience pic.

How to estimate the prevalence of hybrids

How many 🐺🐶 hybrids are there?! @NSantostasi shows how to estimate prevalence w/ #HMM imperfect detection in her @Ecol_Evol #Euring17 paper https://t.co/KwiHB2ZBpX #openaccess 🤝 work w/ @SapienzaRoma @ISPRA_Press @wolfappcenter @umontpellier @CNRS_OccitaniE @INEE_CNRS 🇮🇹🇫🇷 pic.

New paper!

🤩🐺 New paper by unstoppable @LouvrierJulie "Accounting for #misidentification and #heterogeneity in #occupancy studies using #HMMs" https://t.co/UqJfbkFjbW #teamboulet #openaccess @EcoHMM #carnivorepapersbywomen @ONCFS_officiel @umontpellier @IsiteMUSE @CNRSenLR @INEE_CNRS pic.twitter.com/YGgPNjzRUE — Olivier Gimenez 🥐 (@oaggimenez) 19 septembre18

Fitting dynamic occupancy models with TMB

Following my recent attempt to [fit a HMM model to capture-recapture data with TMB](https://oliviergimenez.github.io/post/multievent_in_tmb/) and the rather estonishing outcome (the code was 300 time faster than the equivalent R code!), I was curious to add TMB to the [list of options I tried to fit dynamic occupancy models](https://oliviergimenez.github.io/post/occupancy_in_admb/). Well, the least I can say is that TMB is fast, damn fast!

Fitting HMM/multievent capture-recapture models with TMB

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).