person("Magnus Dehli", "Vigeland", , "m.d.vigeland@medisin.uio.no", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-9134-4962"))

Irregular-lattice based ensemble empirical mode decomposition
R (>= 2.11.0), fields, deldir, rgeos, sp, spdep
A data-driven and adaptive hierarchical-scale decomposition method for irregular-lattice field (represented by polygons).

An R Package for Implementing Belief Functions
Kuang Zhou <kzhoumath@163.com>; Arnaud Martin
<arnaud.martin@univ-rennes1.fr>
This package contains several basic functions to implement belief
functions.

Some basic functions to  implement belief functions including: transformation between belief functions using the method introduced by Philippe Smets (arXiv:1304.1122 [cs.AI]), evidence combination,  evidence discounting, decision-making, and constructing masses. Currently,  thirteen combination rules and five decision rules are supported. It can also be used to generate different types of random masses when working on belief combination and conflict management.

Some basic functions to implement belief functions including:
transformation between belief functions using the method introduced by
Philippe Smets (arXiv:1304.1122 [cs.AI]), evidence combination, evidence
discounting, decision-making, and constructing masses. Currently, thirteen
combination rules and five decision rules are supported. It can also be
used to generate different types of random masses when working on belief
combination and conflict management.

Some basic functions to implement belief functions including:
transformation between belief functions using the method introduced by
Philippe Smets <arXiv:1304.1122>, evidence combination, evidence
discounting, decision-making, and constructing masses. Currently, thirteen
combination rules and six decision rules are supported. It can also be
used to generate different types of random masses when working on belief
combination and conflict management.

Some basic functions to implement belief functions including:
transformation between belief functions using the method introduced by
Philippe Smets (arXiv:1304.1122 [cs.AI]), evidence combination, evidence
discounting, decision-making, and constructing masses. Currently, thirteen
combination rules and six decision rules are supported. It can also be
used to generate different types of random masses when working on belief
combination and conflict management.