Multiple moderation analysis for two-instance repeated measures designs, with up to three simultaneous moderators (dichotomous and/or continuous) with additive or multiplicative relationship. Includes analyses of simple slopes and conditional effects at (automatically determined or manually set) values of the moderator(s), as well as an implementation of the Johnson-Neyman procedure for determining regions of significance in single moderator models. Based on Montoya, A. K. (2018) "Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models" <doi:10.3758/s13428-018-1088-6> .

Barbosa A.M., Brown J.A., Jimenez-Valverde A., Real R.
Analyses species distribution models and evaluates their performance. It includes functions for performing variation partitioning, calculating several measures of model discrimination and calibration, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots.

graphics, grDevices, stats, methods, terra

Analyses species distribution models and evaluates their performance. It includes functions for performing variation partitioning, calculating several measures of model discrimination and calibration, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots. Initially described in Barbosa et al. (2013) <doi:10.1111/ddi.12100>.

Community Structure Detection via Modularity Maximization
The algorithms implemented here are used to detect the community structure of a network.
These algorithms follow different approaches, but are all based on the concept of modularity maximization.

c(person("Miroslav","Šiman", role=c("aut")),
person("Pavel","Boček", role=c("aut", "cre"), email="bocek@utia.cas.cz"))
Miroslav Šiman [aut],
Pavel Boček [aut, cre]
The Institute of Information Theory and Automation of the
Czech Academy of Sciences Pod Vodárenskou věží 4, CZ-182 08
Prague 8, Czech Republic email: utia@utia.cas.cz, phone: +420
266 053 111
Contains basic tools for performing
multiple-output quantile regression and computing
regression quantile contours by means of directional
regression quantiles. In the location case, one can thus
obtain halfspace depth contours in two to six dimensions.
quantile regression, multivariate regression, semiparametric
regression, nonparametric regression, data depth, regression
depth, halfspace depth, Tukey depth, depth contour,
multivariate quantile, regression quantile, quantile-based
inference
addItem.R findItem.R delItem.R addRow.R findRow.R checkArray.R
checkCTechSTu.R getCTechSTM1u.R getCTechSTM2u.R getCharSTM1u.R
getCharSTM2u.R findOptimalBasisM1FromScratch.R
findOptimalBasisM2FromScratch.R compContourM1u.R
compContourM2u.R evalContour.R