# Michael Sachs

#### 8 packages on CRAN

cosinor is a set of simple functions that transforms longitudinal data to estimate the cosinor linear model as described in Tong (1976). Methods are given to summarize the mean, amplitude and acrophase, to predict the mean annual outcome value, and to test the coefficients.

Most ROC curve plots obscure the cutoff values and inhibit interpretation and comparison of multiple curves. This attempts to address those shortcomings by providing plotting and interactive tools. Functions are provided to generate an interactive ROC curve plot for web use, and print versions. A Shiny application implementing the functions is also included.

Contains the core methods for the evaluation of principal surrogates in a single clinical trial. Provides a flexible interface for defining models for the risk given treatment and the surrogate, the models for integration over the missing counterfactual surrogate responses, and the estimation methods. Estimated maximum likelihood and pseudo-score can be used for estimation, and the bootstrap for inference. A variety of post-estimation summary methods are provided, including print, summary, plot, and testing.

ClinicalTrials.gov is a registry and results database of publicly and privately supported clinical studies of human participants conducted around the world (see <https://clinicaltrials.gov/> for more information). Users can search for information about and results from those trials. This provides a set of functions to interact with the search and download features. Results are downloaded to temporary directories and returned as R objects.

A common way of validating a biological assay for is through a procedure, where m levels of an analyte are measured with n replicates at each level, and if all m estimates of the coefficient of variation (CV) are less than some prespecified level, then the assay is declared validated for precision within the range of the m analyte levels. Two limitations of this procedure are: there is no clear statistical statement of precision upon passing, and it is unclear how to modify the procedure for assays with constant standard deviation. We provide tools to convert such a procedure into a set of m hypothesis tests. This reframing motivates the m:n:q procedure, which upon completion delivers a 100q% upper confidence limit on the CV. Additionally, for a post-validation assay output of y, the method gives an ``effective standard deviation interval'' of log(y) plus or minus r, which is a 68% confidence interval on log(mu), where mu is the expected value of the assay output for that sample. Further, the m:n:q procedure can be straightforwardly applied to constant standard deviation assays. We illustrate these tools by applying them to a growth inhibition assay.

Custom template and output formats for use with rmarkdown. Produce Edward Tufte-style handouts in html formats with full support for rmarkdown features

Output formats and utilities for authoring books and technical documents with R Markdown.

Quickly and easily perform exploratory data analysis by uploading your data as a 'csv' file. Start generating insights using 'ggplot2' plots and 'table1' tables with descriptive stats, all using an easy-to-use point and click 'Shiny' interface.