Nikhil Garge, Barry Eggleston and Georgiy Bobashev
This package implements random forest method for model
based recursive partitioning. The mob() function, developed by
Zeileis et al (2008), within party package, is modified to
construct model-based decision trees based on random forests
methodology. The main input function mobForestAnalysis() takes
all input parameters to construct trees, compute out-of-bag
errors, predictions, and overall accuracy of forest. The
algorithm performs parallel computation using clusterApply()
function within 'parallel' package.

Nikhil Garge [aut], Barry Eggleston [aut], Georgiy Bobashev [aut], Benjamin Carper [cre], Kasey Jones [ctb, cre], Torsten Hothorn [ctb], Kurt Hornik [ctb], Carolin Strobl [ctb], Achim Zeileis [ctb]
Functions to implements random forest method for model based
recursive partitioning. The mob() function, developed by Zeileis et al. (2008),
within 'party' package, is modified to construct model-based decision trees based
on random forests methodology. The main input function mobforest.analysis() takes
all input parameters to construct trees, compute out-of-bag errors, predictions,
and overall accuracy of forest. The algorithm performs parallel computation
using cluster functions within 'parallel' package.
testthat (>= 1.0.2), mlbench (>= 2.1), lattice

Calculates Transition Matrices and Mobility Indices
Measures mobility in a population through transition matrices and mobility indices. Relative, mixed, and absolute transition matrices are supported. The Prais-Bibby, Absolute Movement, Origin Specific, and Weighted Group Mobility indices are supported. Example income and grade data are included.

Models Bivariate Dependence and Produces Bivariate Return
Periods
person(given = "Alois",
family = "Tilloy",
role = c("aut", "cre"),
email = "alois.tilloy@kcl.ac.uk",
comment = c(ORCID = "https://orcid.org/0000-0002-5881-0642"))
Models the dependence between two variables in the extremes, select most
relevant models, estimate bivariate return periods and create bivariate level curves.
Methods used in the package are described in the following reference:
Tilloy, Malamud, Winter and Joly-Laugel (2020) <doi:10.5194/nhess-20-2091-2020>
Supporting references for the conditional extremes model, Jt-KDE model and for copula modelling are the following:
Heffernan and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>
Cooley, Thibaud, Castillo and Wehner (2019) <doi:10.1007/s10687-019-00348-0>
Nelsen (2006) <doi:10.1007/0-387-28678-0>.
stats, copBasic, grDevices, ks, lattice, SpatialExtremes, zoo,
copula, ggplot2, viridis,