tab - Create Summary Tables for Statistical Reports

Contains functions for creating various types of summary tables, e.g. comparing characteristics across levels of a categorical variable and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards models. Functions are available to handle data from simple random samples as well as complex surveys.

Last updated 4 years ago

manuscriptsreportsreproducible-researchstatisticstables

6.97 score 2 stars 9 dependents 86 scripts 781 downloads

accelerometry - Functions for Processing Accelerometer Data

A collection of functions that perform operations on time-series accelerometer data, such as identify non-wear time, flag minutes that are part of an activity bout, and find the maximum 10-minute average count value. The functions are generally very flexible, allowing for a variety of algorithms to be implemented. Most of the functions are written in C++ for efficiency.

Last updated 6 years ago

accelerometerexercisemoving-averagephysical-activitysedentary-lifewearable-devicescpp

6.62 score 6 stars 5 dependents 31 scripts 505 downloads

dvmisc - Convenience Functions, Moving Window Statistics, and Graphics

Collection of functions for running and summarizing statistical simulation studies, creating visualizations (e.g. CART Shiny app, histograms with fitted probability mass/density functions), calculating moving-window statistics efficiently, and performing common computations.

Last updated 4 years ago

aicbmihistogramsmiscellaneouscpp

6.18 score 1 stars 8 dependents 125 scripts 632 downloads

stocks - Stock Market Analysis

Functions for analyzing and visualizing stock market data. Main features are loading and aligning historical data, calculating performance metrics for individual funds or portfolios (e.g. annualized growth, maximum drawdown, Sharpe/Sortino ratio), and creating graphs.

Last updated 5 years ago

investment-analysisportfolio-constructionportfolio-optimizationsharpe-ratiostock-markettime-seriescpp

4.63 score 22 stars 39 scripts 173 downloads

pooling - Fit Poolwise Regression Models

Functions for calculating power and fitting regression models in studies where a biomarker is measured in "pooled" samples rather than for each individual. Approaches for handling measurement error follow the framework of Schisterman et al. (2010) <doi:10.1002/sim.3823>.

Last updated 5 years ago

assay-modelingbiomarkersefficiencyepidemiologymaximum-likelihoodmeasurement-errorpooling

3.60 score 80 scripts 271 downloads