Packaged: 2022-04-01 18:42:35 UTC; leenachoi

Title: A max-margin supervised Sparse Topical Coding Model
Author: Jun Zhu <junzhu@cs.cmu.edu>, Aykut FIRAT <aykut@mit.edu>
Description: This is a C++ implementation of Sparse Topical Coding
(STC), a model of discrete data which is fully described in Zhu
et al. (2011) (http://www.cs.cmu.edu/~junzhu/stc/stc.pdf). It
can be used for multi-class classification and describing
documents with underlying sparse topics.

Title: Large Scale Single Mediator Hypothesis Testing
Description: A collection of methods for large scale single mediator hypothesis
testing. The six included methods for testing the mediation effect are Sobel's
test, Max P test, joint significance test under the composite null hypothesis,
high dimensional mediation testing, divide-aggregate composite null test,
and Sobel's test under the composite null hypothesis. Du, J., Zhou, X., Hao, W.,
Liu, Y., Smith, J. A., & Mukherjee, B (2022) "Methods for Large-scale Single
Mediator Hypothesis Testing: Possible Choices and Comparisons."
Imports: HDMT, locfdr, qqman, qvalue, fdrtool

Title: Bayesian Methods for Identifying the Most Harmful Medication
Description: This package implements two distinct but related
statistical approaches to the problem of identifying the
combinations of medication error characteristics that are more
likely to result in harm: 1) Bayesian hierarchical models with
optimal Bayesian ranking on the log odds of harm, and 2) an
empirical Bayes model that estimates the ratio of the observed
count of harm to the count that would be expected if error
characteristics and harm were independent. In addition, for the
Bayesian hierarchical model, the package provides functions to
assess the sensitivity of results to different specifications

Description: Two distinct but related statistical approaches to the problem of identifying the combinations of medication error characteristics that are more likely to result in harm are implemented in this package: 1) a Bayesian hierarchical model with optimal Bayesian ranking on the log odds of harm, and 2) an empirical Bayes model that estimates the ratio of the observed count of harm to the count that would be expected if error characteristics and harm were independent. In addition, for the Bayesian hierarchical model, the package provides functions to assess the sensitivity of results to different specifications of the random effects distributions.