Calculates a degree of spatial association between regionalizations
or categorical maps using the information-theoretical V-measure
(Nowosad and Stepinski (2018) <doi:10.17605/OSF.IO/RCJH7>). It also
offers an R implementation of the MapCurve method
(Hargrove et al. (2006) <doi:10.1007/s10109-006-0025-x>).
c(person("Jakub", "Nowosad",
email = "nowosad.jakub@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-1057-3721")),
person("Tomasz", "Stepinski",
role = c("aut")),
person("Space Informatics Lab", role = c("cph"))
)
sf, entropy, tidyr, dplyr, rlang, tibble
Jakub Nowosad [aut, cre] (<https://orcid.org/0000-0002-1057-3721>),
Tomasz Stepinski [aut],
Space Informatics Lab [cph]

Calculates a degree of spatial association between regionalizations
or categorical maps using the information-theoretical V-measure
(Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>). It also
offers an R implementation of the MapCurve method
(Hargrove et al. (2006) <doi:10.1007/s10109-006-0025-x>).

dplyr, entropy, raster, rlang, sf, tibble, tidyr

testthat, covr, knitr, rmarkdown, methods

Provides SABRE functionality from within R
sabreR is a package for the statistical analysis of
multi-process random effect response data. These responses can
take the form of binary, ordinal, count and linear recurrent
events. The response sequences can be of different types. Such
multi-process data is common in many research areas, e.g. the
analysis of work and life histories. sabreR may be used for
analyisng longitudinal datasets surveys either with recurrent
information collected over time or with a clustered sampling
scheme.

Multivariate Generalized Linear Mixed Models

Semiparametric empirical likelihood ratio
based test of changepoint with one-change or epidemic alternatives
with data-based model diagnostic

Semiparametric empirical likelihood ratio
based tests of change-point with one-change or epidemic alternatives
with data-based model diagnostic are contained.

Detection of Fixations in Eye-Tracking Data
c(person("Titus", "von der Malsburg", email="malsburg@posteo.de",
role=c("aut", "cph", "cre")))
Functions for detecting eye fixations in raw eye-tracking
data.  The detection is done using a velocity-based algorithm for
saccade detection proposed by Ralf Engbert and Reinhold Kliegl in
2003.  The algorithm labels segments as saccades when the velocity of
the eye movement exceeds a certain threshold.  Anything between two
saccades is considered a fixation.  Thus the algorithm is not
appropriate for data containing episodes of smooth pursuit eye
movements.
Titus von der Malsburg <malsburg@posteo.de>