c(person("Pein", "Florian", role = c("aut", "cre"), email = "fp366@cam.ac.uk"),
person("Thomas", "Hotz", role = "ctb", email = "thomas.hotz@tu-ilmenau.de"),
person("Inder", "Tecuapetla-Gómez", role = "ctb", email = "itecuapetla@conabio.gob.mx"),
person("Timo", "Aspelmeier", role = "ctb", email = "timo.aspelmeier@mathematik.uni-goettingen.de"))

c(person("Pein", "Florian", role = c("aut", "cre"), email = "fp366@cam.ac.uk"),
person("Timo", "Aspelmeier", role = "ctb", email = "timo.aspelmeier@mathematik.uni-goettingen.de"))
stepR (>= 2.1.0), lowpassFilter, stats, methods
Implements the model-free multiscale idealisation approaches: Jump-Segmentation by MUltiResolution Filter (JSMURF) <doi:10.1109/TNB.2013.2284063>, JUmp Local dEconvolution Segmentation filter (JULES) <doi:10.1109/TNB.2018.2845126> and Heterogeneous Idealization by Local testing and DEconvolution (HILDE) <arXiv:2008.02658>. Further details on how to use them are given in the accompanying vignette.
Pein Florian [aut, cre],
Timo Aspelmeier [ctb]

c(person("Pein", "Florian", role = c("aut", "cre"), email = "f.pein@lancaster.ac.uk"),
person("Timo", "Aspelmeier", role = "ctb", email = "timo.aspelmeier@mathematik.uni-goettingen.de"))

Get description of images from Clarifai API. For more information,
see http://clarifai.com. Clarifai uses a large deep learning cloud to come
up with descriptive labels of the things in an image. It also provides how
confident it is about each of the labels.

Get description of images from Clarifai API. For more information,
see <http://clarifai.com>. Clarifai uses a large deep learning cloud to come
up with descriptive labels of the things in an image. It also provides how
confident it is about each of the labels.

Distribution-Free Tests of Non-Nested Models
c(person("Dave", "Armstrong", email="dave@quantoid.net",
role=c("aut", "cre")),
person("Brenton", "Kenkel", email="brenton.kenkel@@gmail.com",
role=c("aut")))
Implementation of Clarke's distribution-free test of
non-nested models.  Currently supported model functions are:
lm(), glm() ('binomial', 'poisson', 'negative binomial' links),
polr() ('MASS'), clm() ('ordinal'), and multinom() ('nnet').
For more information on the test, see Clarke (2007)
<doi:10.1093/pan/mpm004>.
Dave Armstrong [aut, cre],
Brenton Kenkel [aut]