Flexible Data Simulation Using the Multivariate Normal Distribution

This R package can be used to generate artificial data conditionally on pre-specified (simulated or user-defined) relationships between the variables and/or observations. Each observation is drawn from a multivariate Normal distribution where the mean vector and covariance matrix reflect the desired relationships. Outputs can be used to evaluate the performances of variable selection, graphical modelling, or clustering approaches by comparing the true and estimated structures (B Bodinier et al (2021) ).

Stability-enHanced Approaches using Resampling Procedures

In stability selection (N Meinshausen, P Bühlmann (2010) ) and consensus clustering (S Monti et al (2003) ), resampling techniques are used to enhance the reliability of the results. In this package, hyper-parameters are calibrated by maximising model stability, which is measured under the null hypothesis that all selection (or co-membership) probabilities are identical (B Bodinier et al (2023a) and B Bodinier et al (2023b) ). Functions are readily implemented for the use of LASSO regression, sparse PCA, sparse (group) PLS or graphical LASSO in stability selection, and hierarchical clustering, partitioning around medoids, K means or Gaussian mixture models in consensus clustering.

Load Test Shiny Applications

Assesses the number of concurrent users 'shiny' applications are capable of supporting, and for directing application changes in order to support a higher number of users. Provides facilities for recording 'shiny' application sessions, playing recorded sessions against a target server at load, and analyzing the resulting metrics.

Tool Kit to Implement a W.A.S.P.A.S. Based Multi-Criteria Decision Analysis Solution

Provides a set of functions to implement decision-making systems based on the W.A.S.P.A.S. method (Weighted Aggregated Sum Product Assessment), Chakraborty and Zavadskas (2012) . So this package offers functions that analyze and validate the raw data, which must be entered in a determined format; extract specific vectors and matrices from this raw database; normalize the input data; calculate rankings by intermediate methods; apply the lambda parameter for the main method; and a function that does everything at once. The package has an example database called choppers, with which the user can see how the input data should be organized so that everything works as recommended by the decision methods based on multiple criteria that this package solves. Basically, the data are composed of a set of alternatives, which will be ranked, a set of choice criteria, a matrix of values for each Alternative-Criterion relationship, a vector of weights associated with the criteria, since certain criteria are considered more important than others, as well as a vector that defines each criterion as cost or benefit, this determines the calculation formula, as there are those criteria that we want the highest possible value (e.g. durability) and others that we want the lowest possible value (e.g. price).

Structural Equation Modeling with Deep Neural Network and Machine Learning

Training and validation of a custom (or data-driven) Structural Equation Models using layer-wise Deep Neural Networks or node-wise Machine Learning algorithms, which extend the fitting procedures of the 'SEMgraph' R package .

Provenance Visualizer

Displays provenance graphically for provenance collected by the 'rdt' or 'rdtLite' packages, or other tools providing compatible PROV JSON output. The exact format of the JSON created by 'rdt' and 'rdtLite' is described in < https://github.com/End-to-end-provenance/ExtendedProvJson>. More information about rdtLite and associated tools is available at < https://github.com/End-to-end-provenance/> and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics, .

Network Analysis and Causal Inference Through Structural Equation Modeling

Estimate networks and causal relationships in complex systems through Structural Equation Modeling. This package also includes functions to import, weight, manipulate, and fit biological network models within the Structural Equation Modeling framework proposed in Grassi M, Palluzzi F, Tarantino B (2022) .

Record Linkage Functions for Linking and Deduplicating Data Sets

Provides functions for linking and deduplicating data sets. Methods based on a stochastic approach are implemented as well as classification algorithms from the machine learning domain. For details, see our paper "The RecordLinkage Package: Detecting Errors in Data" Sariyar M / Borg A (2010) .

Creates Adjacency Matrices for Lineage Searches

Creates and manages a provenance graph corresponding to the provenance created by the 'rdtLite' package, which collects provenance from R scripts. 'rdtLite' is available on CRAN. The provenance format is an extension of the W3C PROV JSON format (< https://www.w3.org/Submission/2013/SUBM-prov-json-20130424/>). The extended JSON provenance format is described in < https://github.com/End-to-end-provenance/ExtendedProvJson>.

National Road Safety Observatory (ONSV) Style for 'ggplot2' Graphics

Helps to create 'ggplot2' charts in the style used by the National Road Safety Observatory (ONSV). The package includes functions to customize 'ggplot2' objects with new theme and colors.