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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,
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.
Stability-enHanced Approaches using Resampling Procedures
In stability selection (N Meinshausen, P Bühlmann (2010)
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)
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.
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
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)
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 for importing,
weight, manipulate, and fit biological network models within the
Structural Equation Modeling framework as outlined in the Supplementary Material of
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)