kalamri2

allofus

R package to streamline use of the AllofUs researcher workbench

multi_ethnic

mutlti_ethnic_analysis

aouFI

Helper functions for Roux AoU Frailty Project

PROSPER_analysis

allofus

:exclamation: This is a read-only mirror of the CRAN R package repository. allofus — Interface for 'All of Us' Researcher Workbench. Homepage: https://roux-ohdsi.github.io/allofus/, https://github.com/roux-ohdsi/allofus Report bugs for this package: https://github.com/roux-ohdsi/allofus/issues

datasets

A collection of datasets of ML problem solving

plyranges

A grammar of genomic data transformation

Antipsychotic_BNF_project

R script for BNF calculation of dataset to monitor antipsychotic prescribing over time

asdfree

analyze survey data for free

Import-MAUDE-Files

SAS Programs used to import the FDA's Manufacturer and User Device Experience (MAUDE) files.

canvas-ios

Canvas iOS apps

FAERS

fuzzyfaers

Functions to extract drug records from the FAERS (FDA Adverse Event Reporting System) database

MEPS

This repository provides example code for loading and analyzing data from AHRQ's Medical Expenditure Panel Survey (MEPS). More information about the survey and access to public use data files is available on our website

fine-flow-tool-kit

The FINE Flow Tool Kit provides a set of Python utilities that demonstrates an implementation of the FINE team classification process, and the associated FINE flow equations.

Disproportionality-Measures-with-SAS

PRR, RR, IC, EBGM (MGPS) and more (including data prep)

fda_faers_dl_extract

Downloads and extracts FDA FAERS data and generates a sql script to read the data into a MYSQL database through MYSQL shell.

faerslib

Python tools for working with data from the FDA Adverse Event Reporting System.

FAERS-and-CMS-Data-Mining

Import-MAUDE-Files

SAS Programs used to import the FDA's Manufacturer and User Device Experience (MAUDE) files.

joineR

R package for fitting joint models to time-to-event and longitudinal data

mlss2015_bnp_tutorial

DirichletProcessMixtureModel

The Dirichlet Process Mixture Model provides a fully Bayesian analysis for the inference of clusters and their parameters.

similarity_based_imputation

Similarity-based Imputation of Missing Data in Electronic Health Records

Rcpp_RcppArmadillo_tutorial

A tutorial from R interfacing to C, Rcpp to RcppArmadillo and RcppParallel

bnp_tutorial

Missing-data-in-EHR

Missing data/longitudinal data analysis/Latent class analysis/Bayesian Methods

Bsvygmm

R package to fit a Bayesian growth mixture model to complex survey data

Bayesian_DP_Gibbs

MCMC for Dirichlet Process mixture model and missing data imputations

edp-extras

Supplemental Material for "Functional clustering methods for longitudinal data with application to electronic health records"