Description: 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.
Packaged: 2015-05-15 10:44:59 UTC; zhoukuang

Description: 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

Philippe Smets <arXiv:1304.1122>, evidence combination, evidence
combination rules and six decision rules are supported. It can also be
Packaged: 2021-01-06 02:25:35 UTC; zhoukuang

Description: Implementation of some (simple) Individual Based Models and methods
to create new ones, particularly for population dynamics models (reproduction,
mortality and movement). The basic operations for the simulations are

Title: Connect to Your IBM 'Acoustic' Data with 'R'
Description: Authentication can be the most difficult part about
working with a new API. 'ibmAcousticR' facilitates making a
connection to the IBM 'Acoustic' email campaign management API
and executing various queries. The IBM 'Acoustic' API

Title: Connect to Your 'IBM Acoustic' Data
connection to the 'IBM Acoustic' email campaign management API
and executing various queries. The 'IBM Acoustic' API

Title: Toolkits to Develop Individual-Based Models in Infectious
Authors@R: person("Sai Thein Than", "Tun", email = "theinthantun.sai@gmail.com", role = c("aut", "cre"))
Description: It provides a generic set of tools for initializing a synthetic
population with each individual in specific disease states, and
making transitions between those disease states according to the rates
calculated on each timestep. Additional functions will follow for changing
attributes on demographic, health belief and movement.
Maintainer: Sai Thein Than Tun <theinthantun.sai@gmail.com>