Performs Invariant Coordinate Selection (ICS) (Tyler, Critchley, Duembgen and Oja (2009) <doi:10.1111/j.1467-9868.2009.00706.x>) and especially ICS for multivariate outlier detection with application to quality control (Archimbaud, Nordhausen, Ruiz-Gazen (2018) <doi:10.1016/j.csda.2018.06.011>) using a shiny app.

We construct the explicit form of clamped cubic interpolating spline (both uniform - knots are equidistant and non-uniform - knots are arbitrary). Using this form, we propose a linear regression model suitable for real data smoothing.

Currently using the proportional hazards (PH) model. More methods under other semiparametric regression models will be included in later versions.

For different linear dimension reduction methods like principal components analysis (PCA), independent components analysis (ICA) and supervised linear dimension reduction tests and estimates for the number of interesting components (ICs) are provided.

Functions for computing the global and local Gaussian density estimates based on the ICV bandwidth. See the article of Savchuk, O.Y., Hart, J.D., Sheather, S.J. (2010). Indirect cross-validation for density estimation. Journal of the American Statistical Association, 105(489), 415-423 <doi:10.1198/jasa.2010.tm08532>.

Functions for converting time series of spatial abundance or density 
    data in raster format to vector fields of population movement using the digital 
    image correlation technique. More specifically, the functions in the package 
    compute cross-covariance using discrete fast Fourier transforms for computational 
    efficiency. Vectors in vector fields point in the direction of highest two 
    dimensional cross-covariance. The package has a novel implementation of the 
    digital image correlation algorithm that is designed to detect persistent 
    directional movement when image time series extend beyond a sequence of 
    two raster images.

Computes and tests individual (species, phylogenetic and functional) diversity-area relationships, i.e., how species-, phylogenetic- and functional-diversity varies with spatial scale around the individuals of some species in a community. See applications of these methods in Wiegand et al. (2007) <doi:10.1073/pnas.0705621104> or Chacon-Labella et al. (2016) <doi:10.1007/s00442-016-3547-z>.