Web Application Framework for R

Makes it incredibly easy to build interactive web applications with R. Automatic "reactive" binding between inputs and outputs and extensive prebuilt widgets make it possible to build beautiful, responsive, and powerful applications with minimal effort.

Epimed Solutions Collection for Data Editing, Analysis, and Benchmark of Health Units

Collection of functions related to benchmark with prediction models for data analysis and editing of clinical and epidemiological data.

Compare Provenance Collections to Explain Changed Script Outputs

Inspects provenance collected by the 'rdt' or 'rdtLite' packages, or other tools providing compatible PROV JSON output created by the execution of a script, and find differences between two provenance collections. Factors under examination included the hardware and software used to execute the script, versions of attached libraries, use of global variables, modified inputs and outputs, and changes in main and sourced scripts. Based on detected changes, 'provExplainR' can be used to study how these factors affect the behavior of the script and generate a promising diagnosis of the causes of different script results. 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, .

Object Pooling

Enables the creation of object pools, which make it less computationally expensive to fetch a new object. Currently the only supported pooled objects are 'DBI' connections.

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.

Datasets for Agresti and Finlay's "Statistical Methods for the Social Sciences"

Datasets used in "Statistical Methods for the Social Sciences" (SMSS) by Alan Agresti and Barbara Finlay.

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).

Create Dashboards with 'Shiny'

Create dashboards with 'Shiny'. This package provides a theme on top of 'Shiny', making it easy to create attractive dashboards.