bets.covid19 - The BETS Model for Early Epidemic Data

Implements likelihood inference for early epidemic analysis. BETS is short for the four key epidemiological events being modeled: Begin of exposure, End of exposure, time of Transmission, and time of Symptom onset. The package contains a dataset of the trajectory of confirmed cases during the coronavirus disease (COVID-19) early outbreak. More detail of the statistical methods can be found in Zhao et al. (2020) <arXiv:2004.07743>.

Last updated 5 years ago

2019-ncov

4.43 score 27 stars 2 scripts 182 downloads

mr.raps - Two Sample Mendelian Randomization using Robust Adjusted Profile Score

Mendelian randomization is a method of identifying and estimating a confounded causal effect using genetic instrumental variables. This packages implements methods for two-sample Mendelian randomization with summary statistics by using Robust Adjusted Profile Score (RAPS). References: Qingyuan Zhao, Jingshu Wang, Jack Bowden, Dylan S. Small. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. <arXiv:1801.09652>.

Last updated 1 months ago

archivedpackagesr-universe

2.88 score 5 stars 1 dependents 168 downloads

seismic - Predict Information Cascade by Self-Exciting Point Process

An implementation of self-exciting point process model for information cascades, which occurs when many people engage in the same acts after observing the actions of others (e.g. post resharings on Facebook or Twitter). It provides functions to estimate the infectiousness of an information cascade and predict its popularity given the observed history. See http://snap.stanford.edu/seismic/ for more information and datasets.

Last updated 3 years ago

2.00 score 6 scripts 178 downloads