bnlearn, dplyr, ggplot2, gRain, gRbase, graphics, matrixcalc,
purrr, qgraph, RColorBrewer, reshape2, rlang, tidyr

An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. References: H. Chan, A. Darwiche (2002) <doi:10.1613/jair.967>; C. Goergen, M. Leonelli (2020) <ArXiv:1809.10794>; M. Leonelli, R. Ramanathan, R.L. Wilkerson (2021) <ArXiv:2107.11785>.
bnlearn, dplyr, ggplot2, gRain, gRbase, graphics, purrr,
qgraph, RColorBrewer, reshape2, rlang, tidyr

Bagged k-Nearest Neighbors Survival Prediction
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.
prodlim, pec, Rcpp (>= 0.11.2), parallel, methods

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

bnlearn, gdata, graph, lavaan, parallel, plyr, Rgraphviz,
stats, semPlot, qgraph
Elias Carvalho, Joao R N Vissoci, Luciano Andrade, Emerson P Cabrera, Julio C Nievola
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
Maximization' and 'Tabu Search'.