Description: A hybrid method of feature selection which combines both filter and wrapper methods. The first level involves feature reduction based on some of the important filter methods while the second level involves feature subset selection as in a wrapper method. Comparative analysis with the existing feature selection packages shows this package results in higher classification accuracy, reduced processing time and improved data handling capacity.
Packaged: 2017-09-25 10:25:08 UTC; yamini.pandari

person("Yamini", "Pandari", email = "yamini.pandari@latentview.com", role = c("aut", "cre")),
person("Prashanth", "Thangavel", email = "prashanth.thangavel@latentview.com", role = "aut"),
person("Hemanth", "Senthamaraikannan", email = "hemanth.senthamaraikannan@latentview.com", role = "aut"),
person("Sivaranjani", "Jagadeeswaran", email = "sivaranjani.jagadeeswaran@latentview.com", role = "aut"),
person("Thirumaalavan", "Elumalai", email = "thirumaalavan.elumalai@latentview.com", role = "aut"))
Packaged: 2017-10-11 08:58:47 UTC; yamini.pandari

person("Yamini", "Pandari", email = "yamini.pandari@gmail.com", role = c("aut", "cre")),
person("Sivaranjani", "Jagadeeswaran", email = "sivaranjani.jagadeeswaran@latentview.com", role = "aut"))
Packaged: 2019-06-11 09:33:29 UTC; yamini.pandari

Title: Implementation of the Hybrid Monte Carlo and Multipoint Hybrid
Description: This package is an R implementation of the Hybrid Monte
Carlo and Multipoint Hybrid Monte Carlo sampling techniques
described in Liu (2001): "Monte Carlo Strategies in Computing".

Title: Hybrid Bayesian Networks Using R and JAGS
Author: Jarrod E. Dalton <daltonj@ccf.org> and Benjamin Nutter <benjamin.nutter@gmail.com>
Description: Facilities for easy implementation of hybrid Bayesian networks
using R. Bayesian networks are directed acyclic graphs representing joint
probability distributions, where each node represents a random variable and
each edge represents conditionality. The full joint distribution is
therefore factorized as a product of conditional densities, where each node
is assumed to be independent of its non-descendents given information on its
parent nodes. Since exact, closed-form algorithms are computationally
burdensome for inference within hybrid networks that contain a combination
of continuous and discrete nodes, particle-based approximation techniques
like Markov Chain Monte Carlo are popular.  We provide a user-friendly
interface to constructing these networks and running inference using the 'rjags' package.
Econometric analyses (maximum expected utility under competing policies,
value of information) involving decision and utility nodes are also
Imports: ArgumentCheck, broom (>= 0.3.7), DiagrammeR (>= 0.8), plyr,
dplyr, graph, gRbase, magrittr, stats, stringr, utils