A generator for synthetic, multivariate & heterogeneous datasteams with probabilistically repeating patterns.
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Updated
Feb 8, 2019 - R
A generator for synthetic, multivariate & heterogeneous datasteams with probabilistically repeating patterns.
Simulation of Partition-of-Unity copulas in R, e.g. for the purpose of modeling risk or for the creation of synthetic data based on restricted datasets
HR analysis including work around ONS Wellbeing questions.
An app to compare local data with public demographic data.
Make Gapminder-style synthetic datasets
Synthetic data generation for DataSHIELD
Synthesize dataverse data based on DDI metadata
Reproducing and extending results from PrOCTOR paper (Predicting Odds of Clinical Trial Outcomes using Random forest) via gradient boosting methods
dsSynthetic library to generate synthetic data in DataSHIELD
Simple synthetic data from data frames, tibbles, data.tables and lists
Simulation framework for realistic large-scale individual-level health data generation
Generate synthetic longitudinal correlated data using distributions and correlations from real-world observational data
Generate synthetic data that is as fresh as the real thing
Generates synthetic data to apply simulations for causal inference
Partially Synthetic Data Generation for Recommender systems
R Package to generate synthetic data.
Access Synthetic CDISC Data from Archive Packages
R package for Cardiovascular Risk Dataset and Data generation script
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