Analyze biotic homogenization of landscapes
Tools for assessing exotic species' contributions to
landscape homogeneity using average pairwise Jaccard similarity
and an analytical approximation derived in Harris et al. (2011,
"Occupancy is nine-tenths of the law," The American
Naturalist). Also includes a randomization method for assessing
sources of model error.

Function for drawing the coastline of the United Kingdom

Function for drawing the coastline of the British Isles

Bipartite Longitudinal Influence Network (BLIN) Estimation
c(person("Frank W. Marrs", email="frank.marrs@colostate.edu", role=c("aut", "cre")), person("Benjamin W. Campbell", role=c("aut")), person("Bailey K. Fosdick", role=c("aut")), person("Skyler J. Cranmer", role=c("aut")), person("Tobias B{\"o}hmelt", role=c("aut")))
Estimate influence networks from longitudinal bipartite relational data, where the longitudinal relations are continuous. The outputs are estimates of weighted influence networks among each actor type in the data set. The generative model is the Bipartite Longitudinal Influence Network (BLIN) model, a linear autoregressive model for these type of data. The supporting paper is ``Inferring Influence Networks from Longitudinal Bipartite Relational Data'', which is in preparation by the same authors. The model may be estimated using maximum likelihood methods and Bayesian methods. For more detail on methods, see Marrs et. al. <arXiv:1809.03439>.
glmnet, stats, Matrix, MASS, abind, graphics, mvtnorm
Frank W. Marrs [aut, cre],
Benjamin W. Campbell [aut],
Bailey K. Fosdick [aut],
Skyler J. Cranmer [aut],
Tobias B{"o}hmelt [aut]

c(person(given = "Lukas",
family = "Baumann",
role = c("aut"),
email = "baumann@imbi.uni-heidelberg.de"),
person(given = "Maximilian",
family = "Pilz",
role = c("aut", "cre"),
email = "pilz@imbi.uni-heidelberg.de"),
person(given = "Institute of Medical Biometry and Informatics - University of Heidelberg",
role = c("cph")))
Computation of key characteristics and plots for blinded sample size recalculation.
Continuous as well as binary endpoints are supported in superiority and non-inferiority trials.
The implemented methods include the approaches by
Lu, K. (2019) <doi:10.1002/pst.1737>,
Kieser, M. and Friede, T. (2000) <doi:10.1002/(SICI)1097-0258(20000415)19:7%3C901::AID-SIM405%3E3.0.CO;2-L>,
Friede, T. and Kieser, M. (2004) <doi:10.1002/pst.140>,
Friede, T., Mitchell, C., Mueller-Veltern, G. (2007) <doi:10.1002/bimj.200610373>, and
Friede, T. and Kieser, M. (2011) <doi:10.3414/ME09-01-0063>.
test_statistics.R methods.R Student.R blindrecalc.R
ChiSquare.R ChiSquare_helper.R FarringtonManning.R
FarringtonManning_helper.R RcppExports.R
Lukas Baumann [aut],
Maximilian Pilz [aut, cre],
Institute of Medical Biometry and Informatics - University of
Heidelberg [cph]