Package: GeoXp
Type: Package
Title: Interactive exploratory spatial data analysis
Encoding: latin1
Version: 1.6.2
Date: 2013-08-14
Author: Yves Aragon, Thibault Laurent, Lauriane Robidou, Anne
Ruiz-Gazen, Christine Thomas-Agnan
Maintainer: Thibault Laurent <thibault.laurent@univ-tlse1.fr>
Depends: R (>= 2.14.0), quantreg, rgl, spdep
Imports: KernSmooth, rgeos, robustbase, splancs, stats, tcltk
Suggests: maptools, MatrixModels, RANN
Description: GeoXp is a tool for researchers in spatial statistics,
spatial econometrics, geography, ecology etc allowing to link
dynamically statistical plots with elementary maps. This
coupling consists in the fact that the selection of a zone on
the map results in the automatic highlighting of the
corresponding points on the statistical graph or reversely the
selection of a portion of the graph results in the automatic
highlighting of the corresponding points on the map. GeoXp
includes tools from different areas of spatial statistics
including geostatistics as well as spatial econometrics and
point processes. Besides elementary plots like boxplots,
histograms or simple scatterplot, GeoXp also couples with maps
Moran scatterplots, variogram cloud, Lorentz Curves,...In order
to make the most of the multidimensionality of the data, GeoXp
includes some dimension reduction techniques such as PCA.
License: GPL (>= 2)
URL: http://www.jstatsoft.org/v47/i02/paper
Packaged: 2013-08-22 15:38:29 UTC; laurent
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-08-22 20:16:22

person("Ha-Young", "Shin", email = "hayoung.shin@gmail.com", role = c("aut", "cre")),
Description: Provides a gradient descent algorithm to find a geodesic relationship between real-valued independent variables and a manifold-valued dependent variable (i.e. geodesic regression). Available manifolds are Euclidean space, the sphere, and Kendall's 2-dimensional shape space. Besides the standard least-squares loss, the least absolute deviations, Huber, and Tukey biweight loss functions can also be used to perform robust geodesic regression. Functions to help choose appropriate cutoff parameters to maintain high efficiency for the Huber and Tukey biweight estimators are included. The k-sphere is a k-dimensional manifold: we represent it as a sphere of radius 1 and center 0 embedded in (k+1)-dimensional space. Kendall's 2D shape space with K landmarks is of real dimension 2K-4; preshapes are represented as complex K-vectors with mean 0 and magnitude 1. Details are described in Shin, H.-Y. and Oh, H.-S. (2020) <arXiv:2007.04518>. Also see Fletcher, P. T. (2013) <doi:10.1007/s11263-012-0591-y> and Kim, H. J., Adluru, N., Collins, M. D., Chung, M. K., Bendin, B. B., Johnson, S. C., Davidson, R. J. and Singh, V. (2014) <doi:10.1109/CVPR.2014.352>.