Below are the packages included in HADES. For each package a link is provided with more information, including instructions on how to install and use the package.
-
New-user cohort studies using large-scale regression for propensity and outcome models.Learn more…
-
Self-Controlled Case Series analysis using few or many predictors, includes splines for age and seasonality.Learn more…
-
A self-controlled cohort design, where time preceding exposure is used as control.Learn more…
-
Routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study.Learn more…
-
Build and evaluate predictive models for user-specified outcomes, using a wide array of machine learning algorithms.Learn more…
-
Performing patient level prediction using deep learningLearn more…
-
Building and validating ensemble patient-level predictive models.Learn more…
-
Various types of characterizations of a target and outcome cohort.Learn more…
-
Develop and manipulate complex cohort definitions in RLearn more…
-
An R wrapper for Circe, a library for creating cohort definitions, expressing them as JSON, SQL, or Markdown.Learn more…
-
Instantiating cohorts in a database based on a set of cohort definitions.Learn more…
-
The OHDSI Phenotype Library: a collection of community-maintained pre-definined cohorts.Learn more…
-
Generate a wide set of diagnostics to evaluate cohort definitions against databases in the CDM.Learn more…
-
Semi-automated evaluation of cohorts, producing metrics suchs as sensitivity, specificity, and positive and negative preditive value.Learn more…
-
Visually explore all individual-level data of patients in a cohortLearn more…
-
Generate descriptive statistics on an entire OMOP CDM databaseLearn more…
-
Expose and evaluate observational data quality.Learn more…
-
Use negative control exposure-outcome pairs to profile and calibrate a particular analysis design.Learn more…
-
Use real data and established reference sets as well as simulations injected in real data to evaluate the performance of methods.Learn more…
-
Storing very large data objects on a local drive, while still making it possible to manipulate the data in an efficient manner.Learn more…
-
A large scale k-nearest neighbor classifier using the Lucene search engine.Learn more…
-
Broken Adaptive Ridge Regression with Cyclops.Learn more…
-
Highly efficient implementation of regularized logistic, Poisson and Cox regression.Learn more…
-
Connect directly to a wide range of database platforms, including SQL Server, Oracle, and PostgreSQL.Learn more…
-
A standard CDM dataset for testing and demonstration purposes that runs on an embedded SQLite database.Learn more…
-
Automatically extract large sets of features for user-specified cohorts using data in the CDM.Learn more…
-
Hydrating package skeletons into executable R study packages based on specifications in JSON format.Learn more…
-
Performing L0-based regressions using CyclopsLearn more…
-
Securely sharing (large) files between OHDSI collaborators.Learn more…
-
Contains shiny modules that can be used within shiny result interfacesLearn more…
-
Support for parallel computation with logging to console, disk, or e-mail.Learn more…
-
A lightweight utility for data migrations allowing old results to work with new shiny apps and data visualisations.Learn more…
-
Interact with OHDSI WebAPI web services.Learn more…
-
Interactively view analysis results across different types of analyses.Learn more…
-
Generate SQL on the fly for the various SQL dialects.Learn more…