ggplot2, knitr, PerformanceAnalytics, stats, SuppDists,
truncdist, VGAM, xtable, graphics, utils, rmarkdown

An Extended Mallows Model and Its Hierarchical Version for
Ranked Data Aggregation
For multiple full/partial ranking lists, R package 'ExtMallows' can (1) detect whether the input ranking lists are over-correlated, and (2) use the Mallows model or extended Mallows model to integrate the ranking lists, and (3) use hierarchical extended Mallows model for rank integration if there are groups of over-correlated ranking lists.
Han Li, Minxuan Xu, Jun S. Liu and Xiaodan Fan

By using a multispectral image and ESRI shapefile (Point/Polygon), a data table will be generating for classification or regression. The data table will be contained by band wise raster values and class ids.

By using a multispectral image and ESRI shapefile (Point/Polygon), a data table will be generated for classification or regression. The data table will be contained by band wise raster values and class ids.

By using a multispectral image and ESRI shapefile (Point/ Line/ Polygon), a data table will be generated for classification, regression or other processing. The data table will be contained by band wise raster values and shapefile ids (User Defined).

c(person("Boris","Beranger", role = "aut", email="borisberanger@gmail.com"),
person("Simone","Padoan", role = c("cre","aut"), email="simone.padoan@unibocconi.it"),
person("Giulia", "Marcon", role = "aut", email="giuliamarcongm@gmail.com"),
person("Steven G.", "Johnson", role = "ctb", comment = "Author of included cubature fragments"))
Boris Beranger [aut],
Simone Padoan [cre, aut],
Giulia Marcon [aut],
Steven G. Johnson [ctb] (Author of included cubature fragments)
numDeriv, evd, sn, CompRandFld, quadprog, copula, nloptr,
gtools, mvtnorm, rlist, fda
A set of procedures for modelling parametrically and non-parametrically the dependence structure of multivariate extreme-values is provided. The statistical inference is performed with non-parametric estimators, likelihood-based estimators and Bayesian techniques. Adapts the methodologies derived in Beranger et al. (2019) <arxiv:1904.08251>, Beranger et al. (2017) <doi:10.1111/sjos.12240>, Beranger and Padoan (2015) <arxiv:1508.05561>, Marcon et al. (2017) <doi:10.1002/sta4.145>, Marcon et al. (2017) <doi:10.1016/j.jspi.2016.10.004> and Marcon et al. (2016) <doi:10.1214/16-EJS1162>. It also refers to the works of Bortot (2010) <https://pdfs.semanticscholar.org/b0dc/1cb608d35bf515c76e39aacc14b4de82e281.pdf>, Padoan (2011) <doi:10.1016/j.jmva.2011.01.014>, Cooley et al. (2010) <doi:10.1016/j.jmva.2010.04.007>, Husler and Reiss (1989) <doi:10.1016/0167-7152(89)90106-5>, Engelke et al. (2015) <doi:10.1111/rssb.12074>, Coles and Tawn (1991) <doi:10.1111/j.2517-6161.1991.tb01830.x>, Nikoloulopoulos et al. (2011) <doi:10.1007/s10687-008-0072-4>, Opitz (2013) <doi:10.1016/j.jmva.2013.08.008>, Tawn (1990) <doi:10.2307/2336802>, Azzalini (1985) <https://www.jstor.org/stable/pdf/4615982.pdf>, Azzalini and Capitanio (2014) <doi:10.1017/CBO9781139248891>, Azzalini (2003) <doi:10.1111/1467-9469.00322>, Azzalini and Capitanio (1999) <doi:10.1111/1467-9868.00194>, Azzalini and Dalla Valle (1996) <doi:10.1093/biomet/83.4.715>, Einmahl et al. (2013) <doi:10.1007/s10687-012-0156-z>, Naveau et al (2009) <doi:10.1093/biomet/asp001> and Heffernan and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>.