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helske
KFAS:Kalman Filter and Smoother for Exponential Family State Space Models
State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient functions for Kalman filtering, smoothing, forecasting, and simulation of multivariate exponential family state space models, with observations from Gaussian, Poisson, binomial, negative binomial, and gamma distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.
Maintained by Jouni Helske. Last updated 7 months ago.
dynamic-linear-modelexponential-familyfortrangaussian-modelsstate-spacetime-seriesopenblas
64 stars 10.97 score 242 scripts 16 dependents
ianjonsen
bsam:Bayesian State-Space Models for Animal Movement
Tools to fit Bayesian state-space models to animal tracking data. Models are provided for location filtering, location filtering and behavioural state estimation, and their hierarchical versions. The models are primarily intended for fitting to ARGOS satellite tracking data but options exist to fit to other tracking data types. For Global Positioning System data, consider the 'moveHMM' package. Simplified Markov Chain Monte Carlo convergence diagnostic plotting is provided but users are encouraged to explore tools available in packages such as 'coda' and 'boa'.
Maintained by Ian Jonsen. Last updated 9 months ago.
17 stars 5.42 score 31 scripts