Title: Estimating the Parameters of a Continuous-Time Markov Chain from
Description: Functions for estimating Markov generator matrices from discrete-time observations. The implemented approaches comprise diagonal adjustment, weighted adjustment and quasi-optimization of matrix logarithm based candidate solutions, an expectation-maximization algorithm as well as a Gibbs sampler.
Imports: Rcpp (>= 0.12.6), expm, numDeriv

Imports: Rcpp (>= 0.12.10), coda, expm, numDeriv

Author: Marius Pfeuffer [aut,cre], Greig Smith [ctb], Goncalo dos Reis [ctb]
Imports: Rcpp (>= 0.12.13), coda, expm, numDeriv
Suggests: knitr, rmarkdown, R.rsp, markovchain

Imports: Rcpp (>= 0.12.17), coda, expm, numDeriv

Author: Marius Pfeuffer [aut,cre], Greig Smith [ctb], Goncalo dos Reis [ctb], Linda Moestel [ctb], Matthias Fischer [ctb]

Title: Modeling Animal Movement with Continuous-Time Discrete-Space
Depends: R (>= 2.10), crawl, raster, Matrix
Description: Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework.  As described in Hanks et al. (2015), this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.

Description: Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework.  As described in Hanks et al. (2015) <DOI:10.1214/14-AOAS803> , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.

Depends: R (>= 2.10), raster, Matrix, fda

Depends: R (>= 2.10), raster, Matrix, fda, gdistance, sp

Title: Collaborative Targeted Maximum Likelihood Estimation
Authors@R: c(person("Cheng", "Ju", email = "jucheng1992@gmail.com", role = c("aut", "cre")),person("Susan", "Gruber", role = c("aut")),person("Richard", "Wyss", role = c("ctb")),person("Jenny","Haggstrom", role = c("ctb")),person("Mark", "van der Laan", role = c("aut", "ths")))
Description: Implements the general template for collaborative targeted maximum likelihood estimation. It also provides several commonly used C-TMLE instantiation, like the vanilla/scalable variable-selection C-TMLE (Ju et al. (2017) <doi:10.1177/0962280217729845>) and the glmnet-C-TMLE algorithm (Ju et al. (2017) <arXiv:1706.10029>).
Depends: R (>= 2.14.0), SuperLearner, tmle, glmnet, stats