Packaged: 2022-04-20 08:01:14 UTC; Manuele.Leonelli

Title: Bagged k-Nearest Neighbors Survival Prediction
Maintainer: Marvin N. Wright <wright@imbs.uni-luebeck.de>
Description: Implements a bootstrap aggregated (bagged) version of
the k-nearest neighbors survival probability prediction method (Lowsky et
al. 2013). In addition to the bootstrapping of training samples, the
features can be subsampled in each baselearner to break the correlation
between them. The Rcpp package is used to speed up the computation.
Imports: prodlim, pec, Rcpp (>= 0.11.2), parallel, methods

Maintainer: Marvin N. Wright <marv@wrig.de>

Title: Bayesian Estimation for Normal Heteroscedastic Nonlinear
Author: Nicolas Molano-Gonzalez, Marta Corrales Bossio, Maria Fernanda Zarate, Edilberto Cepeda-Cuervo.
Description: Implementation of Bayesian estimation in normal heteroscedastic nonlinear regression Models following Cepeda-Cuervo, (2001).

Title: Bayesian Networks & Path Analysis
Suggests: bnlearn, gdata, graph, lavaan, parallel, plyr, Rgraphviz,
Author: Elias Carvalho, Joao R N Vissoci, Luciano Andrade, Emerson P Cabrera, Julio C Nievola
Description: We proposed a hybrid approach using the computational and statistical  resources
of the Bayesian Networks to learn a network structure from a data set using 4 different
algorithms and the robustness of the statistical methods present in the Structural Equation
Modeling to check the goodness of fit from model over data. We built an intermediate
algorithm to join the features of 'bnlearn' and 'lavaan' R packages. The Bayesian Networks
structure learning algorithms used were 'Hill-Climbing', 'Max-Min Hill-Climbing', 'Restricted

Package: bnpa
Type: Package
Title: Bayesian Networks & Path Analysis
Version: 0.3.0
Imports: bnlearn, fastDummies, lavaan, Rgraphviz, semPlot, xlsx
Author: Elias Carvalho, Joao R N Vissoci, Luciano Andrade, Wagner Machado, Emerson P Cabrera, Julio C Nievola
Maintainer: Elias Carvalho <ecacarva@gmail.com>
Description: This project aims to enable the method of Path Analysis to infer causalities
from data. For this we propose a hybrid approach, which uses Bayesian network
structure learning algorithms from data to create the input file for creation of a
PA model. The process is performed in a semi-automatic way by our intermediate
algorithm, allowing novice researchers to create and evaluate their own PA models
from a data set. The references used for this project are:
Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press. <doi:10.1017/S0269888910000275>.
Nagarajan, R., Scutari, M., & Lèbre, S. (2013). Bayesian networks in r. Springer, 122, 125-127. Scutari, M., & Denis, J. B. <doi:10.1007/978-1-4614-6446-4>.
Scutari M (2010). Bayesian networks: with examples in R. Chapman and Hall/CRC. <doi:10.1201/b17065>.
Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1 - 36. <doi:10.18637/jss.v048.i02>.
URL: https://sites.google.com/site/bnparp/.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-08-01 01:57:45 UTC; elias
Repository: CRAN
Date/Publication: 2019-08-01 23:20:02 UTC