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rigr

rigr: Regression, Inference, and General Data Analysis Tools in R - Published in JOSS (2022)

https://github.com/statdivlab/rigr

Science Score: 95.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in JOSS metadata
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    2 of 8 committers (25.0%) from academic institutions
  • ○
    Institutional organization owner
  • ✓
    JOSS paper metadata
    Published in Journal of Open Source Software

Scientific Fields

Engineering Computer Science - 40% confidence
Last synced: 6 months ago · JSON representation

Repository

Regression, Inference, and General Data Analysis Tools for R

Basic Info
  • Host: GitHub
  • Owner: statdivlab
  • License: other
  • Language: R
  • Default Branch: main
  • Size: 5.84 MB
Statistics
  • Stars: 10
  • Watchers: 5
  • Forks: 3
  • Open Issues: 19
  • Releases: 1
Created almost 5 years ago · Last pushed 6 months ago
Metadata Files
Readme License Code of conduct

README.Rmd 

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```



[![CRAN status](https://www.r-pkg.org/badges/version/rigr)](https://CRAN.R-project.org/package=rigr) [![R-CMD-check](https://github.com/statdivlab/rigr/workflows/R-CMD-check/badge.svg)](https://github.com/statdivlab/rigr/actions) [![codecov](https://codecov.io/gh/statdivlab/rigr/branch/main/graph/badge.svg)](https://app.codecov.io/gh/statdivlab/rigr)



# `rigr`: Regression, Inference, and General Data Analysis Tools for R

## Introduction

`rigr` is an `R` package to streamline data analysis in `R`. Learning both `R` and introductory statistics at the same time can be challenging, and so we created `rigr` to facilitate common data analysis tasks and enable learners to focus on statistical concepts.

`rigr`, formerly known as [`uwIntroStats`](https://CRAN.R-project.org/package=uwIntroStats), provides easy-to-use interfaces for descriptive statistics, one- and two-sample inference, and regression analyses. `rigr` output includes key information while omitting unnecessary details that can be confusing to beginners. Heteroskedasticity-robust ("sandwich") standard errors are returned by default, and multiple partial F-tests and tests for contrasts are easy to specify. A single regression function (`regress()`) can fit both linear models, generalized linear models, and proportional hazards models, allowing students to more easily make connections between different classes of models.

## Installation

You can install the stable release of `rigr` from CRAN as follows:

    install.packages("rigr")

You can install the development version of `rigr` from GitHub using the code below. The installment is through the `R` package `remotes`.

```{r, echo=FALSE}
  # run the following code to make sure that the remote package it installed
  if (!requireNamespace("remotes", quietly = TRUE)) {
    install.package("remotes")
  }
  # install the package
  remotes::install_github("statdivlab/rigr")
```

If this produces an error, please run `install.packages("remotes")` first then try the above line again.

`rigr` is maintained by the [StatDivLab](https://statdivlab.github.io/), but relies on community support to log issues and implement new features. Is there a method you would like to have implemented? Please submit a pull request or start a [discussion](https://github.com/statdivlab/rigr/discussions/)!

## Documentation

Examples of how to use the main functions in `rigr` are provided in three vignettes. One details the `regress` function and its utilities, one details the `descrip` function for descriptive statistics, and the third details functions used for one- and two-sample inference, including `ttest`, `wilcoxon`, and `proptest`.

## Humans

Maintainer: [Amy Willis](https://statdivlab.github.io/)

Authors: [Scott S Emerson](http://www.emersonstatistics.com/), [Brian D Williamson](https://bdwilliamson.github.io/), [Charles Wolock](https://cwolock.github.io/), [Taylor Okonek](https://taylorokonek.github.io/), [Yiqun T Chen](https://yiqunchen.github.io/), [Jim Hughes](https://www.biostat.washington.edu/people/james-hughes), [Amy Willis](https://statdivlab.github.io/), [Andrew J Spieker](https://www.vumc.org/biostatistics/person/andrew-spieker) and Travis Y Hee Wai.

## Issues

If you encounter any **bugs**, please [file an issue](https://github.com/statdivlab/rigr/issues/). Better yet, [submit a pull request](https://github.com/statdivlab/rigr/pulls/)!

Do you have a **question**? Please first check out the vignettes, then please post on the [Discussions](https://github.com/statdivlab/rigr/discussions/).

## Code of Conduct

Please note that the rigr project is released with a [Contributor Code of Conduct](https://statdivlab.github.io/rigr/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

Owner

  • Name: Statistical Diversity Lab
  • Login: statdivlab
  • Kind: organization
  • Location: United States of America

The StatDivLab develops rigorous statistical methods to analyze microbiome and biodiversity data

JOSS Publication

rigr: Regression, Inference, and General Data Analysis Tools in R
Published
December 26, 2022
DOI
Volume 7, Issue 80, Page 4847
Authors
Yiqun T. Chen ORCID
Department of Biostatistics, University of Washington, Seattle, USA, Data Science Institute & Department of Biomedical Data Science, Stanford University, Stanford, USA
Brian D. Williamson ORCID
Kaiser Permanente Washington Health Research Institute, Seattle, USA
Taylor Okonek
Department of Biostatistics, University of Washington, Seattle, USA
Charles J. Wolock
Department of Biostatistics, University of Washington, Seattle, USA
Andrew J. Spieker
Vanderbilt University Medical Center, Nashville, USA
Travis Y. Hee Wai
VA Puget Sound Health Care System, Seattle, USA
James P. Hughes
Department of Biostatistics, University of Washington, Seattle, USA
Scott S. Emerson
Department of Biostatistics, University of Washington, Seattle, USA
Amy D. Willis ORCID
Department of Biostatistics, University of Washington, Seattle, USA
Editor
Øystein Sørensen ORCID
Tags
inference regression analysis data analysis robust standard errors
View PDF Review Thread Software Archive

GitHub Events

Total
  • Issues event: 5
  • Member event: 2
  • Issue comment event: 14
  • Push event: 11
  • Pull request review event: 1
  • Pull request event: 14
  • Create event: 3
Last Year
  • Issues event: 5
  • Member event: 2
  • Issue comment event: 14
  • Push event: 11
  • Pull request review event: 1
  • Pull request event: 14
  • Create event: 3

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 322
  • Total Committers: 8
  • Avg Commits per committer: 40.25
  • Development Distribution Score (DDS): 0.643
Past Year
  • Commits: 15
  • Committers: 2
  • Avg Commits per committer: 7.5
  • Development Distribution Score (DDS): 0.4
Top Committers
Name Email Commits
Charles Wolock c****k@g****m 115
Taylor Okonek 4****k 95
yiqunchen y****5@g****m 51
amy a****7@g****m 29
gthopkins 5****s 10
Sarah Teichman t****s@u****u 9
bdwilliamson b****6@u****u 8
Ameer D 3****D 5
Committer Domains (Top 20 + Academic)
uw.edu: 2

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 78
  • Total pull requests: 53
  • Average time to close issues: about 2 months
  • Average time to close pull requests: about 11 hours
  • Total issue authors: 14
  • Total pull request authors: 4
  • Average comments per issue: 1.72
  • Average comments per pull request: 0.42
  • Merged pull requests: 42
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 3
  • Pull requests: 15
  • Average time to close issues: less than a minute
  • Average time to close pull requests: about 20 hours
  • Issue authors: 3
  • Pull request authors: 2
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.27
  • Merged pull requests: 9
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • adw96 (31)
  • taylorokonek (15)
  • cwolock (12)
  • jphughes9 (6)
  • yiqunchen (2)
  • tomsing1 (2)
  • susannemay (2)
  • osorensen (2)
  • orvaquim (1)
  • krpaulson (1)
  • ngalanter (1)
  • bdwilliamson (1)
  • lytinoue (1)
  • viqule11 (1)
Pull Request Authors
  • yiqunchen (30)
  • svteichman (15)
  • gthopkins (6)
  • AmeerD (2)
Top Labels
Issue Labels
enhancement (19) bug (16) help wanted (9) documentation (6) high priority (5) low priority (4) easy (4) question (2) good first issue (1) more info needed (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 121 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 5
  • Total maintainers: 1
cran.r-project.org: rigr

Regression, Inference, and General Data Analysis Tools in R

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 121 Last month
Rankings
Stargazers count: 19.3%
Downloads: 23.1%
Dependent repos count: 24.0%
Average: 24.6%
Forks count: 27.8%
Dependent packages count: 28.8%
Maintainers (1)
adwillis@uw.edu
Last synced: 6 months ago

Dependencies

.github/workflows/R-CMD-check.yml actions
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  • actions/checkout master composite
  • actions/checkout v2 composite
  • actions/upload-artifact main composite
  • r-lib/actions/setup-pandoc v1 composite
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.github/workflows/code-coverage.yml actions
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.github/workflows/draft_pdf.yml actions
  • actions/checkout v2 composite
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  • openjournals/openjournals-draft-action master composite
.github/workflows/pkgdown.yaml actions
  • JamesIves/github-pages-deploy-action 4.1.4 composite
  • actions/checkout v2 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite
DESCRIPTION cran
  • R >= 3.5.0 depends
  • sandwich * imports
  • stats * imports
  • survival * imports
  • car * suggests
  • knitr * suggests
  • rmarkdown * suggests
  • testthat >= 3.0.0 suggests
  • tidyverse * suggests