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r-lib

usethis:Automate Package and Project Setup

Automate package and project setup tasks that are otherwise performed manually. This includes setting up unit testing, test coverage, continuous integration, Git, 'GitHub', licenses, 'Rcpp', 'RStudio' projects, and more.

Maintained by Jennifer Bryan. Last updated 26 days ago.

githubsetup

869 stars 17.54 score 5.6k scripts 336 dependents

atorus-research

Tplyr:A Traceability Focused Grammar of Clinical Data Summary

A traceability focused tool created to simplify the data manipulation necessary to create clinical summaries.

Maintained by Mike Stackhouse. Last updated 1 years ago.

pharmatables

95 stars 9.49 score 138 scripts 2 dependents

dipterix

shidashi:A Shiny Dashboard Template System

A template system based on 'AdminLTE3' (<https://adminlte.io/themes/v3/>) theme. Comes with default theme that can be easily customized. Developers can upload modified templates on 'Github', and users can easily download templates with 'RStudio' project wizard. The key features of the default template include light and dark theme switcher, resizing graphs, synchronizing inputs across sessions, new notification system, fancy progress bars, and card-like flip panels with back sides, as well as various of 'HTML' tool widgets.

Maintained by Zhengjia Wang. Last updated 1 years ago.

10 stars 4.72 score 35 scripts 1 dependents

fvafrcu

packager:Create, Build and Maintain Packages

Helper functions for package creation, building and maintenance. Designed to work with a build system such as 'GNU make' or package 'fakemake' to help you to conditionally work through the stages of package development (such as spell checking, linting, testing, before building and checking a package).

Maintained by Andreas Dominik Cullmann. Last updated 1 years ago.

3.70 score 2 scripts

jordanrvl

esmtools:Preprocessing Experience Sampling Method (ESM) Data

Tailored explicitly for Experience Sampling Method (ESM) data, it contains a suite of functions designed to simplify preprocessing steps and create subsequent reporting. It empowers users with capabilities to extract critical insights during preprocessing, conducts thorough data quality assessments (e.g., design and sampling scheme checks, compliance rate, careless responses), and generates visualizations and concise summary tables tailored specifically for ESM data. Additionally, it streamlines the creation of informative and interactive preprocessing reports, enabling researchers to transparently share their dataset preprocessing methodologies. Finally, it is part of a larger ecosystem which includes a framework and a web gallery (<https://preprocess.esmtools.com/>).

Maintained by Jordan Revol. Last updated 1 years ago.

2 stars 2.00 score 7 scripts

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