R-universe search: topic:reproducible-research

ddsjoberg

gtsummary:Presentation-Ready Data Summary and Analytic Result Tables

Creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.

Maintained by Daniel D. Sjoberg. Last updated 3 days ago.

easy-to-use gt html5 regression-models reproducibility reproducible-research statistics summary-statistics summary-tables table1 tableone

1.1k stars 17.02 score 8.2k scripts 15 dependents

rapporter

pander:An R 'Pandoc' Writer

Contains some functions catching all messages, 'stdout' and other useful information while evaluating R code and other helpers to return user specified text elements (like: header, paragraph, table, image, lists etc.) in 'pandoc' markdown or several type of R objects similarly automatically transformed to markdown format. Also capable of exporting/converting (the resulting) complex 'pandoc' documents to e.g. HTML, 'PDF', 'docx' or 'odt'. This latter reporting feature is supported in brew syntax or with a custom reference class with a smarty caching 'backend'.

Maintained by Gergely Daróczi. Last updated 28 days ago.

literate-programming markdown pandoc pandoc-markdown reproducible-research rmarkdown cpp

297 stars 16.60 score 7.6k scripts 108 dependents

ropensci

targets:Dynamic Function-Oriented 'Make'-Like Declarative Pipelines

Pipeline tools coordinate the pieces of computationally demanding analysis projects. The 'targets' package is a 'Make'-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, <doi:10.21105/joss.00550>).

Maintained by William Michael Landau. Last updated 11 days ago.

data-science high-performance-computing make peer-reviewed pipeline r-targetopia reproducibility reproducible-research targets workflow

975 stars 15.18 score 4.6k scripts 22 dependents

hughjonesd

huxtable:Easily Create and Style Tables for LaTeX, HTML and Other Formats

Creates styled tables for data presentation. Export to HTML, LaTeX, RTF, 'Word', 'Excel', and 'PowerPoint'. Simple, modern interface to manipulate borders, size, position, captions, colours, text styles and number formatting. Table cells can span multiple rows and/or columns. Includes a 'huxreg' function for creation of regression tables, and 'quick_*' one-liners to print data to a new document.

Maintained by David Hugh-Jones. Last updated 24 days ago.

html huxtable latex microsoft-word powerpoint reproducible-research tables

323 stars 13.93 score 1.9k scripts 16 dependents

crsh

papaja:Prepare American Psychological Association Journal Articles with R Markdown

Tools to create dynamic, submission-ready manuscripts, which conform to American Psychological Association manuscript guidelines. We provide R Markdown document formats for manuscripts (PDF and Word) and revision letters (PDF). Helper functions facilitate reporting statistical analyses or create publication-ready tables and plots.

Maintained by Frederik Aust. Last updated 30 days ago.

apa apa-guidelines journal manuscript psychology reproducible-paper reproducible-research rmarkdown

663 stars 12.00 score 1.7k scripts 2 dependents

ropensci

drake:A Pipeline Toolkit for Reproducible Computation at Scale

A general-purpose computational engine for data analysis, drake rebuilds intermediate data objects when their dependencies change, and it skips work when the results are already up to date. Not every execution starts from scratch, there is native support for parallel and distributed computing, and completed projects have tangible evidence that they are reproducible. Extensive documentation, from beginner-friendly tutorials to practical examples and more, is available at the reference website <https://docs.ropensci.org/drake/> and the online manual <https://books.ropensci.org/drake/>.

Maintained by William Michael Landau. Last updated 4 months ago.

data-science drake high-performance-computing makefile peer-reviewed pipeline reproducibility reproducible-research ropensci workflow

1.3k stars 11.49 score 1.7k scripts 1 dependents

neuhausi

canvasXpress:Visualization Package for CanvasXpress in R

Enables creation of visualizations using the CanvasXpress framework in R. CanvasXpress is a standalone JavaScript library for reproducible research with complete tracking of data and end-user modifications stored in a single PNG image that can be played back. See <https://www.canvasxpress.org> for more information.

Maintained by Connie Brett. Last updated 1 hours ago.

analytics bioinformatics chart charting dash dashboard data-analytics data-science data-visualization genomics graphs javascript network network-visualization python reproducible-research shiny visualization

297 stars 11.28 score 145 scripts

openml

OpenML:Open Machine Learning and Open Data Platform

We provide an R interface to 'OpenML.org' which is an online machine learning platform where researchers can access open data, download and upload data sets, share their machine learning tasks and experiments and organize them online to work and collaborate with other researchers. The R interface allows to query for data sets with specific properties, and allows the downloading and uploading of data sets, tasks, flows and runs. See <https://www.openml.org/guide/api> for more information.

Maintained by Giuseppe Casalicchio. Last updated 10 months ago.

arff benchmarking benchmarking-suite classification data-science database dataset datasets machine-learning machine-learning-algorithms open-data open-science opendata openml openscience regression reproducible-research statistics

97 stars 11.04 score 7.1k scripts

yihui

litedown:A Lightweight Version of R Markdown

Render R Markdown to Markdown (without using 'knitr'), and Markdown to lightweight HTML or 'LaTeX' documents with the 'commonmark' package (instead of 'Pandoc'). Some missing Markdown features in 'commonmark' are also supported, such as raw HTML or 'LaTeX' blocks, 'LaTeX' math, superscripts, subscripts, footnotes, element attributes, and appendices, but not all 'Pandoc' Markdown features are (or will be) supported. With additional JavaScript and CSS, you can also create HTML slides and articles. This package can be viewed as a trimmed-down version of R Markdown and 'knitr'. It does not aim at rich Markdown features or a large variety of output formats (the primary formats are HTML and 'LaTeX'). Book and website projects of multiple input documents are also supported.

Maintained by Yihui Xie. Last updated 9 days ago.

litedown markdown r-markdown report-generator reproducible-research

201 stars 10.58 score 16 scripts 4 dependents

ropensci

rix:Reproducible Data Science Environments with 'Nix'

Simplifies the creation of reproducible data science environments using the 'Nix' package manager, as described in Dolstra (2006) <ISBN 90-393-4130-3>. The included `rix()` function generates a complete description of the environment as a `default.nix` file, which can then be built using 'Nix'. This results in project specific software environments with pinned versions of R, packages, linked system dependencies, and other tools. Additional helpers make it easy to run R code in 'Nix' software environments for testing and production.

Maintained by Bruno Rodrigues. Last updated 3 days ago.

nix peer-reviewed reproducibility reproducible-research

238 stars 10.54 score 67 scripts

predictiveecology

reproducible:Enhance Reproducibility of R Code

A collection of high-level, machine- and OS-independent tools for making reproducible and reusable content in R. The two workhorse functions are Cache() and prepInputs(). Cache() allows for nested caching, is robust to environments and objects with environments (like functions), and deals with some classes of file-backed R objects e.g., from terra and raster packages. Both functions have been developed to be foundational components of data retrieval and processing in continuous workflow situations. In both functions, efforts are made to make the first and subsequent calls of functions have the same result, but faster at subsequent times by way of checksums and digesting. Several features are still under development, including cloud storage of cached objects allowing for sharing between users. Several advanced options are available, see ?reproducibleOptions().

Maintained by Eliot J B McIntire. Last updated 1 months ago.

reproducibility reproducible-research

41 stars 10.52 score 122 scripts 15 dependents

ropensci

osfr:Interface to the 'Open Science Framework' ('OSF')

An interface for interacting with 'OSF' (<https://osf.io>). 'osfr' enables you to access open research materials and data, or create and manage your own private or public projects.

Maintained by Aaron Wolen. Last updated 9 months ago.

open-science osf reproducible-research

145 stars 10.18 score 588 scripts 3 dependents

ropensci

git2rdata:Store and Retrieve Data.frames in a Git Repository

The git2rdata package is an R package for writing and reading dataframes as plain text files. A metadata file stores important information. 1) Storing metadata allows to maintain the classes of variables. By default, git2rdata optimizes the data for file storage. The optimization is most effective on data containing factors. The optimization makes the data less human readable. The user can turn this off when they prefer a human readable format over smaller files. Details on the implementation are available in vignette("plain_text", package = "git2rdata"). 2) Storing metadata also allows smaller row based diffs between two consecutive commits. This is a useful feature when storing data as plain text files under version control. Details on this part of the implementation are available in vignette("version_control", package = "git2rdata"). Although we envisioned git2rdata with a git workflow in mind, you can use it in combination with other version control systems like subversion or mercurial. 3) git2rdata is a useful tool in a reproducible and traceable workflow. vignette("workflow", package = "git2rdata") gives a toy example. 4) vignette("efficiency", package = "git2rdata") provides some insight into the efficiency of file storage, git repository size and speed for writing and reading.

Maintained by Thierry Onkelinx. Last updated 2 months ago.

reproducible-research version-control

99 stars 10.03 score 216 scripts 4 dependents

bioc

pcaExplorer:Interactive Visualization of RNA-seq Data Using a Principal Components Approach

This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis.

Maintained by Federico Marini. Last updated 3 months ago.

immunooncology visualization rnaseq dimensionreduction principalcomponent qualitycontrol gui reportwriting shinyapps bioconductor principal-components reproducible-research rna-seq-analysis rna-seq-data shiny transcriptome user-friendly

56 stars 9.63 score 180 scripts

claudiozandonella

trackdown:Collaborative Editing of Rmd (or Quarto / Rnw) Documents in Google Drive

Collaborative writing and editing of R Markdown (or Quarto / Sweave) documents. The local .Rmd (or Quarto / .Rnw) is uploaded as a plain-text file to Google Drive. By taking advantage of the easily readable Markdown (or LaTeX) syntax and the well-known online interface offered by Google Docs, collaborators can easily contribute to the writing and editing process. After integrating all authors’ contributions, the final document can be downloaded and rendered locally.

Maintained by Claudio Zandonella Callegher. Last updated 2 years ago.

markdown reproducible-research

222 stars 8.49 score 69 scripts

bioc

GeneTonic:Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis

This package provides functionality to combine the existing pieces of the transcriptome data and results, making it easier to generate insightful observations and hypothesis. Its usage is made easy with a Shiny application, combining the benefits of interactivity and reproducibility e.g. by capturing the features and gene sets of interest highlighted during the live session, and creating an HTML report as an artifact where text, code, and output coexist. Using the GeneTonicList as a standardized container for all the required components, it is possible to simplify the generation of multiple visualizations and summaries.

Maintained by Federico Marini. Last updated 3 months ago.

gui geneexpression software transcription transcriptomics visualization differentialexpression pathways reportwriting genesetenrichment annotation go shinyapps bioconductor bioconductor-package data-exploration data-visualization functional-enrichment-analysis gene-expression pathway-analysis reproducible-research rna-seq-analysis rna-seq-data shiny transcriptome user-friendly

77 stars 8.28 score 37 scripts 1 dependents

ekstroem

dataMaid:A Suite of Checks for Identification of Potential Errors in a Data Frame as Part of the Data Screening Process

Data screening is an important first step of any statistical analysis. dataMaid auto generates a customizable data report with a thorough summary of the checks and the results that a human can use to identify possible errors. It provides an extendable suite of test for common potential errors in a dataset.

Maintained by Claus Thorn Ekstrøm. Last updated 3 years ago.

data-cleaning data-screening reproducible-research

143 stars 7.53 score 236 scripts

credibilitylab

groundhog:Version-Control for CRAN, GitHub, and GitLab Packages

Make R scripts reproducible, by ensuring that every time a given script is run, the same version of the used packages are loaded (instead of whichever version the user running the script happens to have installed). This is achieved by using the command groundhog.library() instead of the base command library(), and including a date in the call. The date is used to call on the same version of the package every time (the most recent version available at that date). Load packages from CRAN, GitHub, or Gitlab.

Maintained by Uri Simonsohn. Last updated 1 months ago.

reproducible-research

80 stars 7.45 score 264 scripts

hofnerb

papeR:A Toolbox for Writing Pretty Papers and Reports

A toolbox for writing 'knitr', 'Sweave' or other 'LaTeX'- or 'markdown'-based reports and to prettify the output of various estimated models.

Maintained by Benjamin Hofner. Last updated 4 years ago.

knitr latex r-language reporting reproducible reproducible-research sweave

30 stars 7.30 score 223 scripts 1 dependents

markvanderloo

lumberjack:Track Changes in Data

A framework that allows for easy logging of changes in data. Main features: start tracking changes by adding a single line of code to an existing script. Track changes in multiple datasets, using multiple loggers. Add custom-built loggers or use loggers offered by other packages. <doi:10.18637/jss.v098.i01>.

Maintained by Mark van der Loo. Last updated 10 months ago.

daff datascience logging reproducible-research

66 stars 7.13 score 68 scripts 1 dependents

ropensci

lightr:Read Spectrometric Data and Metadata

Parse various reflectance/transmittance/absorbance spectra file formats to extract spectral data and metadata, as described in Gruson, White & Maia (2019) <doi:10.21105/joss.01857>. Among other formats, it can import files from 'Avantes' <https://www.avantes.com/>, 'CRAIC' <https://www.microspectra.com/>, and 'OceanOptics'/'OceanInsight' <https://www.oceanoptics.com/> brands.

Maintained by Hugo Gruson. Last updated 2 months ago.

file-import reproducibility reproducible-research reproducible-science spectral-data spectroscopy

13 stars 7.11 score 11 scripts 2 dependents

nanxstats

liftr:Containerize R Markdown Documents for Continuous Reproducibility

Persistent reproducible reporting by containerization of R Markdown documents.

Maintained by Nan Xiao. Last updated 1 years ago.

containerization docker dynamic-documents knitr liftr reproducible-research reproducible-science rmarkdown statistical-computing

172 stars 7.03 score 21 scripts

vandomed

tab:Create Summary Tables for Statistical Reports

Contains functions for creating various types of summary tables, e.g. comparing characteristics across levels of a categorical variable and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards models. Functions are available to handle data from simple random samples as well as complex surveys.

Maintained by Dane R. Van Domelen. Last updated 4 years ago.

manuscripts reports reproducible-research statistics tables

2 stars 6.97 score 86 scripts 9 dependents

ropensci

geotargets:'Targets' Extensions for Geographic Spatial Formats

Provides extensions for various geographic spatial file formats, such as shape files and rasters. Currently provides support for the 'terra' geographic spatial formats. See the vignettes for worked examples, demonstrations, and explanations of how to use the various package extensions.

Maintained by Nicholas Tierney. Last updated 8 days ago.

geospatial pipeline r-targetopia raster reproducibility reproducible-research targets vector workflow

73 stars 6.79 score

bioc

ideal:Interactive Differential Expression AnaLysis

This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. Support for reproducibility of the whole analysis is provided by means of a template report which gets automatically compiled and can be stored/shared.

Maintained by Federico Marini. Last updated 3 months ago.

immunooncology geneexpression differentialexpression rnaseq sequencing visualization qualitycontrol gui genesetenrichment reportwriting shinyapps bioconductor differential-expression reproducible-research rna-seq rna-seq-analysis shiny user-friendly

29 stars 6.78 score 5 scripts

e-kotov

rJavaEnv:'Java' Environments for R Projects

Quickly install 'Java Development Kit (JDK)' without administrative privileges and set environment variables in current R session or project to solve common issues with 'Java' environment management in 'R'. Recommended to users of 'Java'/'rJava'-dependent 'R' packages such as 'r5r', 'opentripplanner', 'xlsx', 'openNLP', 'rWeka', 'RJDBC', 'tabulapdf', and many more. 'rJavaEnv' prevents common problems like 'Java' not found, 'Java' version conflicts, missing 'Java' installations, and the inability to install 'Java' due to lack of administrative privileges. 'rJavaEnv' automates the download, installation, and setup of the 'Java' on a per-project basis by setting the relevant 'JAVA_HOME' in the current 'R' session or the current working directory (via '.Rprofile', with the user's consent). Similar to what 'renv' does for 'R' packages, 'rJavaEnv' allows different 'Java' versions to be used across different projects, but can also be configured to allow multiple versions within the same project (e.g. with the help of 'targets' package). Note: there are a few extra steps for 'Linux' users, who don't have any 'Java' previously installed in their system, and who prefer package installation from source, rather then installing binaries from 'Posit Package Manager'. See documentation for details.

Maintained by Egor Kotov. Last updated 22 days ago.

environments java reproducibility reproducible-research

13 stars 6.74 score 7 scripts

frbcesab

rcompendium:Create a Package or Research Compendium Structure

Makes easier the creation of R package or research compendium (i.e. a predefined files/folders structure) so that users can focus on the code/analysis instead of wasting time organizing files. A full ready-to-work structure is set up with some additional features: version control, remote repository creation, CI/CD configuration (check package integrity under several OS, test code with 'testthat', and build and deploy website using 'pkgdown'). This package heavily relies on the R packages 'devtools' and 'usethis' and follows recommendations made by Wickham H. (2015) <ISBN:9781491910597> and Marwick B. et al. (2018) <doi:10.7287/peerj.preprints.3192v2>.

Maintained by Nicolas Casajus. Last updated 2 months ago.

reproducible-research research-compendium

40 stars 6.72 score 22 scripts

ethanbass

chromatographR:Chromatographic Data Analysis Toolset

Tools for high-throughput analysis of HPLC-DAD/UV chromatograms (or similar data). Includes functions for preprocessing, alignment, peak-finding and fitting, peak-table construction, data-visualization, etc. Preprocessing and peak-table construction follow the rough formula laid out in 'alsace' (Wehrens, R., Bloemberg, T.G., and Eilers P.H.C., 2015. <doi:10.1093/bioinformatics/btv299>. Alignment of chromatograms is available using parametric time warping (as implemented in the 'ptw' package) (Wehrens, R., Bloemberg, T.G., and Eilers P.H.C. 2015. <doi:10.1093/bioinformatics/btv299>) or variable penalty dynamic time warping (as implemented in 'VPdtw') (Clifford, D., & Stone, G. 2012. <doi:10.18637/jss.v047.i08>). Peak-finding uses the algorithm by Tom O'Haver <https://terpconnect.umd.edu/~toh/spectrum/PeakFindingandMeasurement.htm>. Peaks are then fitted to a gaussian or exponential-gaussian hybrid peak shape using non-linear least squares (Lan, K. & Jorgenson, J. W. 2001. <doi:10.1016/S0021-9673(01)00594-5>). See the vignette for more details and suggested workflow.

Maintained by Ethan Bass. Last updated 12 days ago.

bioinformatics cheminformatics chromatography gc-fid hplc hplc-dad hplc-pda hplv-uv metabolomics open-data open-science reproducibility reproducible-research

18 stars 6.36 score 8 scripts 1 dependents

rpahl

pipeflow:Lightweight, General-Purpose Data Analysis Pipelines

A lightweight yet powerful framework for building robust data analysis pipelines. With 'pipeflow', you initialize a pipeline with your dataset and construct workflows step by step by adding R functions. You can modify, remove, or insert steps and parameters at any stage, while 'pipeflow' ensures the pipeline's integrity. Overall, this package offers a beginner-friendly framework that simplifies and streamlines the development of data analysis pipelines by making them modular, intuitive, and adaptable.

Maintained by Roman Pahl. Last updated 3 months ago.

pipeline-tools reproducible-research

13 stars 6.35 score 19 scripts

rostools

prodigenr:Research Project Directory Generator

Create a project directory structure, along with typical files for that project. This allows projects to be quickly and easily created, as well as for them to be standardized. Designed specifically with scientists in mind (mainly bio-medical researchers, but likely applies to other fields).

Maintained by Luke Johnston. Last updated 3 months ago.

devtools open-science open-source project-management reproducibility reproducible-research reproducible-science rstudio usethis

43 stars 6.33 score 25 scripts

gesistsa

rang:Reconstructing Reproducible R Computational Environments

Resolve the dependency graph of R packages at a specific time point based on the information from various 'R-hub' web services <https://blog.r-hub.io/>. The dependency graph can then be used to reconstruct the R computational environment with 'Rocker' <https://rocker-project.org>.

Maintained by Chung-hong Chan. Last updated 2 months ago.

reproducibility reproducible-research

80 stars 6.32 score 13 scripts

ropensci

gittargets:Data Version Control for the Targets Package

In computationally demanding data analysis pipelines, the 'targets' R package (2021, <doi:10.21105/joss.02959>) maintains an up-to-date set of results while skipping tasks that do not need to rerun. This process increases speed and increases trust in the final end product. However, it also overwrites old output with new output, and past results disappear by default. To preserve historical output, the 'gittargets' package captures version-controlled snapshots of the data store, and each snapshot links to the underlying commit of the source code. That way, when the user rolls back the code to a previous branch or commit, 'gittargets' can recover the data contemporaneous with that commit so that all targets remain up to date.

Maintained by William Michael Landau. Last updated 8 months ago.

data-science data-version-control data-versioning reproducibility reproducible-research targets workflow

88 stars 5.99 score 11 scripts

ropensci

daiquiri:Data Quality Reporting for Temporal Datasets

Generate reports that enable quick visual review of temporal shifts in record-level data. Time series plots showing aggregated values are automatically created for each data field (column) depending on its contents (e.g. min/max/mean values for numeric data, no. of distinct values for categorical data), as well as overviews for missing values, non-conformant values, and duplicated rows. The resulting reports are shareable and can contribute to forming a transparent record of the entire analysis process. It is designed with Electronic Health Records in mind, but can be used for any type of record-level temporal data (i.e. tabular data where each row represents a single "event", one column contains the "event date", and other columns contain any associated values for the event).

Maintained by T. Phuong Quan. Last updated 7 months ago.

data-quality initial-data-analysis reproducible-research temporal-data time-series

36 stars 5.94 score 12 scripts

parklab

Nozzle.R1:Nozzle Reports

The Nozzle package provides an API to generate HTML reports with dynamic user interface elements based on JavaScript and CSS (Cascading Style Sheets). Nozzle was designed to facilitate summarization and rapid browsing of complex results in data analysis pipelines where multiple analyses are performed frequently on big data sets. The package can be applied to any project where user-friendly reports need to be created.

Maintained by Nils Gehlenborg. Last updated 10 years ago.

gehlenborglab html-report reproducible-research

68 stars 5.31 score 10 scripts 2 dependents

luciorq

condathis:Run Any CLI Tool on a 'Conda' Environment

Simplifies the execution of command line interface (CLI) tools within isolated and reproducible environments. It enables users to effortlessly manage 'Conda' environments, execute command line tools, handle dependencies, and ensure reproducibility in their data analysis workflows.

Maintained by Lucio Queiroz. Last updated 17 days ago.

bioinformatics conda reproducibility reproducible-research

10 stars 5.00 score 1 scripts

egarpor

goffda:Goodness-of-Fit Tests for Functional Data

Implementation of several goodness-of-fit tests for functional data. Currently, mostly related with the functional linear model with functional/scalar response and functional/scalar predictor. The package allows for the replication of the data applications considered in García-Portugués, Álvarez-Liébana, Álvarez-Pérez and González-Manteiga (2021) <doi:10.1111/sjos.12486>.

Maintained by Eduardo García-Portugués. Last updated 1 years ago.

functional-data-analysis goodness-of-fit reproducible-research statistics openblas cpp

10 stars 4.76 score 19 scripts 1 dependents

jatanrt

eprscope:Processing and Analysis of Electron Paramagnetic Resonance Data and Spectra in Chemistry

Processing, analysis and plottting of Electron Paramagnetic Resonance (EPR) spectra in chemistry. Even though the package is mainly focused on continuous wave (CW) EPR/ENDOR, many functions may be also used for the integrated forms of 1D PULSED EPR spectra. It is able to find the most important spectral characteristics like g-factor, linewidth, maximum of derivative or integral intensities and single/double integrals. This is especially important in spectral (time) series consisting of many EPR spectra like during variable temperature experiments, electrochemical or photochemical radical generation and/or decay. Package also enables processing of data/spectra for the analytical (quantitative) purposes. Namely, how many radicals or paramagnetic centers can be found in the analyte/sample. The goal is to evaluate rate constants, considering different kinetic models, to describe the radical reactions. The key feature of the package resides in processing of the universal ASCII text formats (such as '.txt', '.csv' or '.asc') from scratch. No proprietary formats are used (except the MATLAB EasySpin outputs) and in such respect the package is in accordance with the FAIR data principles. Upon 'reading' (also providing automatic procedures for the most common EPR spectrometers) the spectral data are transformed into the universal R 'data frame' format. Subsequently, the EPR spectra can be visualized and are fully consistent either with the 'ggplot2' package or with the interactive formats based on 'plotly'. Additionally, simulations and fitting of the isotropic EPR spectra are also included in the package. Advanced simulation parameters provided by the MATLAB-EasySpin toolbox and results from the quantum chemical calculations like g-factor and hyperfine splitting/coupling constants (a/A) can be compared and summarized in table-format in order to analyze the EPR spectra by the most effective way.

Maintained by Ján Tarábek. Last updated 18 hours ago.

chemistry data-analysis data-visualization epr esr fitting optimization programming-language reproducible-research scientific-plotting spectroscopy openjdk

4.76 score 7 scripts

dmolitor

tugboat:Build a Docker Image from a Directory or Project

Simple utilities to generate a Dockerfile from a directory or project, build the corresponding Docker image, and push the image to DockerHub.

Maintained by Daniel Molitor. Last updated 3 months ago.

docker reproducible-research

21 stars 4.72 score 4 scripts

pythonhealthdatascience

treat.sim:Nelson's Treatment Centre Simulation in Simmer

A discrete-event simulation of a simple urgent care treatment centre simulation from Nelson (2013). Implemented in R Simmer. The model is packaged to allow for easy experimentation, summary of results, and implementation in other software such as a Shiny interface.

Maintained by Thomas Monks. Last updated 8 months ago.

computer-simulation discrete-event-simulation health open-modelling open-science open-source r-language reproducible-research simmer

2 stars 4.48 score 5 scripts

dmolitor

jetty:Execute R in a 'Docker' Context

The goal of 'jetty' is to execute R functions and code snippets in an isolated R subprocess within a 'Docker' container and return the evaluated results to the local R session. 'jetty' can install necessary packages at runtime and seamlessly propagates errors and outputs from the 'Docker' subprocess back to the main session. 'jetty' is primarily designed for sandboxed testing and quick execution of example code.

Maintained by Daniel Molitor. Last updated 2 months ago.

docker reproducible-research

7 stars 4.38 score 23 scripts

rformassspectrometry

Metabonaut:Exploring and Analyzing LC-MS Data

This resource hosts tutorials and end-to-end workflows describing how to analyze LC-MS/MS data, from raw files to annotation, using Bioconductor packages.

Maintained by Philippine Louail. Last updated 2 days ago.

mass-spectrometry metabolomics reproducible-research workflow

4.38 score

ouhscbbmc

codified:Produce Standard/Formalized Demographics Tables

Augment clinical data with metadata to create output used in conventional publications and reports.

Maintained by Will Beasley. Last updated 1 years ago.

reproducible-research

3 stars 4.22 score 11 scripts

pakillo

rmdTemplates:A collection of Rmarkdown templates

A collection of Rmarkdown templates for writing scientific manuscripts, manuscript reviews, Beamer slides (metropolis theme), xaringan slides, and other Rmarkdown documents, with support for cross-references, citations and different bibliography styles.

Maintained by F. Rodriguez-Sanchez. Last updated 3 years ago.

reproducible-research rmarkdown rmarkdown-templates rstudio

129 stars 4.04 score 17 scripts

egarpor

sdetorus:Statistical Tools for Toroidal Diffusions

Implementation of statistical methods for the estimation of toroidal diffusions. Several diffusive models are provided, most of them belonging to the Langevin family of diffusions on the torus. Specifically, the wrapped normal and von Mises processes are included, which can be seen as toroidal analogues of the Ornstein-Uhlenbeck diffusion. A collection of methods for approximate maximum likelihood estimation, organized in four blocks, is given: (i) based on the exact transition probability density, obtained as the numerical solution to the Fokker-Plank equation; (ii) based on wrapped pseudo-likelihoods; (iii) based on specific analytic approximations by wrapped processes; (iv) based on maximum likelihood of the stationary densities. The package allows the replicability of the results in García-Portugués et al. (2019) <doi:10.1007/s11222-017-9790-2>.

Maintained by Eduardo García-Portugués. Last updated 1 years ago.

circular-statistics inference maximum-likelihood reproducible-research sde statistics toroidal-data openblas cpp

6 stars 3.95 score 9 scripts 1 dependents

illustratien

toolStability:Tool for Stability Indices Calculation

Tools to calculate stability indices with parametric, non-parametric and probabilistic approaches. The basic data format requirement for 'toolStability' is a data frame with 3 columns including numeric trait values, genotype,and environmental labels. Output format of each function is the dataframe with chosen stability index for each genotype. Function "table_stability" offers the summary table of all stability indices in this package. This R package toolStability is part of the main publication: Wang, Casadebaig and Chen (2023) <doi:10.1007/s00122-023-04264-7>. Analysis pipeline for main publication can be found on github: <https://github.com/Illustratien/Wang_2023_TAAG/tree/V1.0.0>. Sample dataset in this package is derived from another publication: Casadebaig P, Zheng B, Chapman S et al. (2016) <doi:10.1371/journal.pone.0146385>. For detailed documentation of dataset, please see on Zenodo <doi:10.5281/zenodo.4729636>. Indices used in this package are from: Döring TF, Reckling M (2018) <doi:10.1016/j.eja.2018.06.007>. Eberhart SA, Russell WA (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>. Eskridge KM (1990) <doi:10.2135/cropsci1990.0011183X003000020025x>. Finlay KW, Wilkinson GN (1963) <doi:10.1071/AR9630742>. Hanson WD (1970) Genotypic stability. <doi:10.1007/BF00285245>. Lin CS, Binns MR (1988) <https://cdnsciencepub.com/doi/abs/10.4141/cjps88-018>. Nassar R, Hühn M (1987). Pinthus MJ (1973) <doi:10.1007/BF00021563>. Römer T (1917). Shukla GK (1972). Wricke G (1962).

Maintained by Tien-Cheng Wang. Last updated 1 years ago.

analysis-package reproducible-research stability

1 stars 3.74 score 11 scripts

haghish

HMDA:Holistic Multimodel Domain Analysis for Exploratory Machine Learning

Holistic Multimodel Domain Analysis (HMDA) is a robust and transparent framework designed for exploratory machine learning research, aiming to enhance the process of feature assessment and selection. HMDA addresses key limitations of traditional machine learning methods by evaluating the consistency across multiple high-performing models within a fine-tuned modeling grid, thereby improving the interpretability and reliability of feature importance assessments. Specifically, it computes Weighted Mean SHapley Additive exPlanations (WMSHAP), which aggregate feature contributions from multiple models based on weighted performance metrics. HMDA also provides confidence intervals to demonstrate the stability of these feature importance estimates. This framework is particularly beneficial for analyzing complex, multidimensional datasets common in health research, supporting reliable exploration of mental health outcomes such as suicidal ideation, suicide attempts, and other psychological conditions. Additionally, HMDA includes automated procedures for feature selection based on WMSHAP ratios and performs dimension reduction analyses to identify underlying structures among features. For more details see Haghish (2025) <doi:10.13140/RG.2.2.32473.63846>.

Maintained by E. F. Haghish. Last updated 5 hours ago.

ensemble-feature-importance explainable-ai explainable-artificial-intelligence explainable-machine-learning explainable-ml exploratory-machine-learning exploratory-modelling feature-importance feature-selection-methods holistic-modeling holistic-multimodel-domain-analysis multimodel-ensemble reproducible-ai reproducible-research robust-feature-selection shapley-additive-explanations shapley-values transparent-ai weighted-mean-shap wmshap

1 stars 3.48 score

luciorq

dockerthis:Dockerthis: run any CLI tool on a Linux Container

Dockerthis is an R package that simplifies the execution of command line tools within isolated Docker containers.

Maintained by Lucio Rezende Queiroz. Last updated 6 months ago.

docker-container linux-containers reproducibility reproducible-research

1 stars 2.00 score 2 scripts