Packaged: 2017-07-19 19:21:15 UTC; brettmcclintock

Packaged: 2017-08-15 21:40:55 UTC; brettmcclintock

Packaged: 2017-09-20 17:50:16 UTC; brettmcclintock

Packaged: 2017-10-19 23:12:05 UTC; brettmcclintock

Description: Extended tools for analyzing telemetry data using generalized hidden Markov models. These include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2017) <arXiv:1710.03786>.
Suggests: testthat, setRNG, knitr, splines, splines2
Packaged: 2017-12-08 20:47:21 UTC; brettmcclintock

survival, qdapRegex, geosphere, LaplacesDemon, prodlim
Packaged: 2018-02-27 16:56:06 UTC; brettmcclintock

Description: Extended tools for analyzing telemetry data using generalized hidden Markov models. These include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2017) <doi:10.1111/2041-210X.12995>.
survival, qdapRegex, geosphere, LaplacesDemon, prodlim, dplyr,
Packaged: 2018-05-21 23:22:13 UTC; brettmcclintock

Description: Extended tools for analyzing telemetry data using generalized hidden Markov models. These include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.
Suggests: testthat, setRNG, knitr, splines, splines2 (>= 0.2.8)
Packaged: 2018-06-19 23:27:16 UTC; brettmcclintock