Description: R build of Rothermel's (1972) model for surface fire rate of spread with some additional utilities (uncertainty propagation, standard fuel model selection, fuel model optimization by genetic algorithm) and sample datasets.

Title: Simplification of Decision Tables using Rough Sets
Description: Rough Sets were introduced by Zdzislaw Pawlak on his book "Rough Sets: Theoretical Aspects of Reasoning About Data". Rough Sets provide a formal method to approximate crisp sets when the set-element belonging relationship is either known or undetermined. This enables the use of Rough Sets for reasoning about incomplete or contradictory knowledge. A decision table is a prescription of the decisions to make given some conditions. Such decision tables can be reduced without losing prescription ability. This package provides the classes and methods for knowledge reduction from decision tables as presented in the chapter 7 of the aforementioned book. This package provides functions for calculating the both the discernibility matrix and the essential parts of decision tables.

Title: Data Analysis Using Rough Set and Fuzzy Rough Set Theories.
Author: Lala Septem Riza, Andrzej Janusz, Chris Cornelis, Francisco
Herrera, Dominik Slezak, and Jose Manuel Benitez
Description: This package provides comprehensive implementations of the rough
set theory (RST) and the fuzzy rough set theory (FRST), and integrates
these two theories into a single package. It provides implementations, not
only for the basic concepts of RST and FRST, but also most common methods
based on them for handling some tasks such as discretization, feature
selection, instance selection, rule induction, and prediction. RST was
introduced by Zdzislaw Pawlak in 1982 as a sophisticated mathematical tool
based on indiscernibility relations to model and process imprecise or
incomplete information. It works on symbolic-valued datasets for tackling
the data analysis problems. By using the indiscernibility relation for
objects/instances, RST does not require additional parameters to analyze
the data. FRST is an extension of RST. The FRST combines concepts of
vagueness and indiscernibility that are expressed with fuzzy sets (as