Suggests: covr, testthat, knitr, rmarkdown

Title: A Package for Reconstructing Animal Paths from Solar Geolocation
person("Eldar", "Rakhimberdiev", email = "eldar@nioz.nl", role = c("aut","cre")),
person("Anatoly", "Saveliev", role = "aut"),
person("Julia", "Karagicheva", role = "aut"),
person("Simeon", "Lisovski", role = "ctb"))
Description: Spatio-temporal locations of an animal are computed
from annotated data with a hidden Markov  model via particle
filter algorithm. The package is relatively robust to varying
The hidden Markov model is described in Movement Ecology (Rakhimberdiev et al., 2015)<doi:10.1186/s40462-015-0062-5>,
general package description is in the Methods in Ecology and Evolution (Rakhimberdiev et al., 2017)<doi:10.1111/2041-210X.12765>
and package accuracy assessed in the Journal of Avian Biology (Rakhimberdiev et al. 2016)<doi:10.1111/jav.00891>.

Title: Reconstruct Animal Paths from Solar Geolocation Loggers Data
person("Eldar", "Rakhimberdiev", email = "e.rakhimberdiev@rug.nl", role = c("aut","cre")),
The hidden Markov model is described in Movement Ecology (Rakhimberdiev et al., 2015) <doi:10.1186/s40462-015-0062-5>,
general package description is in the Methods in Ecology and Evolution (Rakhimberdiev et al., 2017) <doi:10.1111/2041-210X.12765>
and package accuracy assessed in the Journal of Avian Biology (Rakhimberdiev et al. 2016) <doi:10.1111/jav.00891>.

fields, maptools, methods, mgcv, nlme, parallel, RcppArmadillo,

Imports: bit, ggsn, ggmap, ggplot2, CircStats, circular, fields,
maptools, methods, mgcv, nlme, parallel, RcppArmadillo, rgdal,

Title: Frequentist Model Averaging Distribution
Authors@R: c(person("Barry L.", "Nelson", role = c("aut"), email = "nelsonb@northwestern.edu"),
person("Xi", "Jiang", role = c("aut","cre"), email = "xijiang2020@u.northwestern.edu"))
Description: Creation of an input model (fitted distribution) via the frequentist model averaging (FMA) approach and generate random-variates from the distribution specified by "myfit" which is the fitted input model via the FMA approach. See W. X. Jiang and B. L. Nelson (2018), "Better Input Modeling via Model Averaging," Proceedings of the 2018 Winter Simulation Conference, IEEE Press, 1575-1586.
Imports: fitdistrplus, STAR, EnvStats, extraDistr, MASS, quadprog