preprocessedData

Delete rows of a dataset which contains anomalous values.

Learning, manipulation and evaluation of mixtures of truncated basis functions
(MoTBFs), which include mixtures of polynomials (MOPs) and mixtures of truncated
exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian
networks (I. Pérez-Bernabé, A. Salmerón, H. Langseth (2015) <doi:10.1007/978-3-319-20807-7_36>; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) <doi:10.1016/j.ijar.2013.09.012>; I. Pérez-Bernabé, A. Fernández, R. Rumí, A. Salmerón (2016) <doi:10.1007/s10618-015-0429-7>). The package provides functionality for learning univariate, multivariate and conditional densities, with the possibility of incorporating prior knowledge. Structural learning of hybrid Bayesian networks is also provided. A set of useful tools is provided, including plotting, printing and likelihood evaluation. This package makes use of S3 objects, with two new classes called 'motbf' and 'jointmotbf'.

Ana D. Maldonado

MoTBFs

Learning Hybrid Bayesian Networks using Mixtures of Truncated
Basis Functions

Inmaculada Pérez-Bernabé

Antonio Salmerón

Thomas D.  Nielsen

Ángel T.  Sáez-Ruiz

preprocessedData function

<dl><dt>data</dt>
<dd>A dataset of class <code>"matrix"</code> or <code>"data.frame"</code>,</dd>
<dt>strangeElements</dt>
<dd>A <code>"character"</code> string which contains the elements to remove.</dd></dl>

Arguments

Data cleaning — preprocessedData

<dl>

<dt>data</dt>
<dd>A dataset of class <code>"matrix"</code> or <code>"data.frame"</code>,</dd>


<dt>strangeElements</dt>
<dd>A <code>"character"</code> string which contains the elements to remove.</dd>

</dl>

preprocessedData: Data cleaning

Description

Usage

Arguments