Computing Infrastructure

Function-oriented Make-like declarative pipelines for Statistics and data science are supported in the targets R package. As an extension to targets, the tarchetypes package provides convenient user-side functions to make targets easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the drake R package by Will Landau (2018) doi:10.21105/joss.00550.

Companion package to rOpenSci statistical software review project.

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).

Simple git client for R based on libgit2 https://libgit2.org with support for SSH and HTTPS remotes. All functions in gert use basic R data types (such as vectors and data-frames) for their arguments and return values. User credentials are shared with command line git through the git-credential store and ssh keys stored on disk or ssh-agent.

Tools to get and maintain a data repository from third-party data providers.

Automate rendering and cross-linking of Quarto books following a prescribed structure.

A framework to help construct R data packages in a reproducible manner. Potentially time consuming processing of raw data sets into analysis ready data sets is done in a reproducible manner and decoupled from the usual R CMD build process so that data sets can be processed into R objects in the data package and the data package can then be shared, built, and installed by others without the need to repeat computationally costly data processing. The package maintains data provenance by turning the data processing scripts into package vignettes, as well as enforcing documentation and version checking of included data objects. Data packages can be version controlled on GitHub, and used to share data for manuscripts, collaboration and reproducible research.

1. Finak, G., Mayer, B., Fulp, W., Obrecht, P., Sato, A., Chung, E., … Gottardo, R. (2018). DataPackageR: Reproducible data preprocessing, standardization and sharing using R/Bioconductor for collaborative data analysis. Gates Open Research, 2, 31. https://doi.org/10.12688/gatesopenres.12832.2

Plumber API to report package structure and function.

Manage jobs and builds on your Jenkins CI server https://jenkins.io/. Create and edit projects, schedule builds, manage the queue, download build logs, and much more.

Utilities to interact with the R-universe platform. Includes functions to manage local package repositories, as well as API wrappers for retrieving data and metadata about packages in r-universe.

Setup and retrieve HTTPS and SSH credentials for use with git and other services. For HTTPS remotes the package interfaces the git-credential utility which git uses to store HTTP usernames and passwords. For SSH remotes we provide convenient functions to find or generate appropriate SSH keys. The package both helps the user to setup a local git installation, and also provides a back-end for git/ssh client libraries to authenticate with existing user credentials.

Interface to the ZeroMQ lightweight messaging kernel (see https://zeromq.org/ for more information).

Bindings to libsodium https://doc.libsodium.org/: a modern, easy-to-use software library for encryption, decryption, signatures, password hashing and more. Sodium uses curve25519, a state-of-the-art Diffie-Hellman function by Daniel Bernstein, which has become very popular after it was discovered that the NSA had backdoored Dual EC DRBG.

Generates simple and beautiful one-page HTML reference manuals with package documentation. Math rendering and syntax highlighting are done server-side in R such that no JavaScript libraries are needed in the browser, which makes the documentation portable and fast to load.

Prism https://prismjs.com/ is a lightweight, extensible syntax highlighter, built with modern web standards in mind. This package provides server-side rendering in R using V8 such that no JavaScript library is required in the resulting HTML documents. Over 400 languages are supported.

Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the jagstargets R package is leverages targets and R2jags to ease this burden. jagstargets makes it super easy to set up scalable JAGS pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than targets alone. For the underlying methodology, please refer to the documentation of targets doi:10.21105/joss.02959 and JAGS (Plummer 2003) https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf.

BEAST2 (https://www.beast2.org) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. BEAST2 is commonly accompanied by BEAUti 2 (https://www.beast2.org), which, among others, allows one to install BEAST2 package. This package allows to work with BEAST2 packages from R.

BEAST2 (https://www.beast2.org) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. BEAST2 is a command-line tool. This package provides a way to call BEAST2 from an R function call.

Setup, run and analyze NetLogo (https://ccl.northwestern.edu/netlogo/) model simulations in R. nlrx experiments use a similar structure as NetLogos Behavior Space experiments. However, nlrx offers more flexibility and additional tools for running and analyzing complex simulation designs and sensitivity analyses. The user defines all information that is needed in an intuitive framework, using class objects. Experiments are submitted from R to NetLogo via XML files that are dynamically written, based on specifications defined by the user. By nesting model calls in future environments, large simulation design with many runs can be executed in parallel. This also enables simulating NetLogo experiments on remote high performance computing machines. In order to use this package, Java and NetLogo (>= 5.3.1) need to be available on the executing system.

1. Kaaronen, R. O., & Strelkovskii, N. (2019). Cultural Evolution of Sustainable Behaviours: Pro-Environmental Tipping Points in an Agent-Based Model. https://doi.org/10.31234/osf.io/w6dpa
2. Wesener, F., Szymczak, A., Rillig, M. C., & Tietjen, B. (2020). Stress priming affects fungal competition – evidence from a combined experimental and modeling study. https://doi.org/10.1101/2020.03.04.976357
3. Adams, R. I., Bhangar, S., Dannemiller, K. C., Eisen, J. A., Fierer, N., Gilbert, J. A., … Bibby, K. (2016). Ten questions concerning the microbiomes of buildings. Building and Environment, 109, 224–234. https://doi.org/10.1016/j.buildenv.2016.09.001
4. D’Orazio, M., Bernardini, G., & Quagliarini, E. (2020). Sustainable and resilient strategies for touristic cities against COVID-19: an agent-based approach. arXiv preprint arXiv:2005.12547. https://arxiv.org/pdf/2005.12547.pdf
5. Kopp, T., & Salecker, J. (2020). How traders influence their neighbours: Modelling social evolutionary processes and peer effects in agricultural trade networks. Journal of Economic Dynamics and Control, 117, 103944. https://doi.org/10.1016/j.jedc.2020.103944
6. Azizi, A., Mubayi, A., & Mubayi, A. (2020). The Impact of Individual’s Ecological Factors on the Dynamics of Alcohol Drinking among Arizona State University Students: An Application of the Survey Data-driven Agent-based Model. arXiv preprint arXiv:2011.01876 https://arxiv.org/abs/2011.01876.
7. Widyastuti, K., Imron, M. A., Pradopo, S. T., Suryatmojo, H., Sopha, B. M., Spessa, A., & Berger, U. (2020). PeatFire: an agent-based model to simulate fire ignition and spreading in a tropical peatland ecosystem. International Journal of Wildland Fire. https://doi.org/10.1071/wf19213
8. Dahirel, M., Bertin, A., Haond, M., Blin, A., Lombaert, E., Calcagno, V., … Vercken, E. (2020). Shifts from pulled to pushed range expansions caused by reduction of landscape connectivity. https://doi.org/10.1101/2020.05.13.092775
9. Ghoreishi, M., Razavi, S., & Elshorbagy, A. (2021). Understanding human adaptation to drought: agent-based agricultural water demand modeling in the Bow River Basin, Canada. Hydrological Sciences Journal, 66(3), 389–407. doi:10.1080/02626667.2021.1873344

This is a private template for use by rOpenSci packages. Please don’t use it for your own non-rOpenSci package.

Check whether a package is ready for submission to rOpenSci’s peer review system.

Static code analyses for R packages using the external code-tagging libraries ctags and gtags. Static analyses enable packages to be analysed very quickly, generally a couple of seconds at most. The package also provides access to a database generating by applying the main function to the full CRAN archive, enabling the statistical properties of any package to be compared with all other CRAN packages.

Tools for interacting with the Circle CI API (https://circleci.com/docs/api/v2/). Besides executing common tasks such as querying build logs and restarting builds, this package also helps setting up permissions to deploy from builds.

BEAST2 (https://www.beast2.org) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. mcbette allows to do a Bayesian model comparison over some site and clock models, using babette (https://github.com/ropensci/babette/).

Simple interface to query gitignore.io to fetch gitignore templates that can be included in the .gitignore file. More than 450 templates are currently available.

Access and tidy up data from the ODK Central API. ODK Central is a clearinghouse for digitally captured data https://docs.getodk.org/central-intro/. ODK Central and its API are documented at https://docs.getodk.org/.

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/.

Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the stantargets R package leverages targets and cmdstanr to ease these burdens. stantargets makes it super easy to set up scalable Stan pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than targets alone. stantargets can access all of cmdstanrs major algorithms (MCMC, variational Bayes, and optimization) and it supports both single-fit workflows and multi-rep simulation studies. For the statistical methodology, please refer to Stan’ documentation (Stan Development Team 2020) https://mc-stan.org/.

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.

Provides a way to describe common build and deployment workflows for R-based projects: packages, websites (e.g. blogdown, pkgdown), or data processing (e.g. research compendia). The recipe is described independent of the continuous integration tool used for processing the workflow (e.g. GitHub Actions or Circle CI). This package has been peer-reviewed by rOpenSci (v0.3.0.9004).

Acknowledge all contributors to a project via a single function call. The function appends to a README or other specified file(s) a table with names of all individuals who contributed via code or repository issues. The package also includes several additional functions to extract and quantify contributions to any repository.

Automatic testing of R packages via a simple YAML schema.

BEAST2 (https://www.beast2.org) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. BEAUti 2 (which is part of BEAST2) is a GUI tool that allows users to specify the many possible setups and generates the XML file BEAST2 needs to run. This package provides a way to create BEAST2 input files without active user input, but using R function calls instead.

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.

1. Corput, D. V. D. (2020). Locked in Syndrome Machine Learning Classification using Sentence Comprehension EEG Data. arXiv preprint arXiv:2006.12336 https://arxiv.org/pdf/2006.12336.pdf

Parsing (R)Markdown files with numerous regular expressions can be fraught with peril, but it does not have to be this way. Converting (R)Markdown files to XML using the commonmark package allows in-memory editing via of markdown elements via XPath through the extensible R6 class called yarn. These modified XML representations can be written to (R)Markdown documents via an xslt stylesheet which implements an extended version of GitHub-flavoured markdown so that you can tinker to your hearts content.

BEAST2 (https://www.beast2.org) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. BEAST2 is commonly accompanied by BEAUti 2, Tracer and DensiTree. babette provides for an alternative workflow of using all these tools separately. This allows doing complex Bayesian phylogenetics easily and reproducibly from R.

Convert R package documentation to a readthedocs website.

Interface to the libgit2 library, which is a pure C implementation of the Git core methods. Provides access to Git repositories to extract data and running some basic Git commands.

1. Blischak, J. D., Carbonetto, P., & Stephens, M. (2019). Creating and sharing reproducible research code the workflowr way. F1000Research, 8, 1749. https://doi.org/10.12688/f1000research.20843.1

Give advice about good practices when building R packages. Advice includes functions and syntax to avoid, package structure, code complexity, code formatting, etc.

Provide workflows and guidance for automatic translation of Markdown-based R content using DeepL API.

Provide functionality to download archives (backups) for all repositories in a GitHub organization (useful for backups!).

fellingdater is an R package that aims to facilitate the analysis and interpretation of tree-ring data from wooden cultural heritage objects and structures. The package standardizes the process of computing and combining felling date estimates, both for individual and groups of related tree-ring series.

Creating dendrochronological networks based on the similarity between tree-ring series or chronologies. The package includes various functions to compare tree-ring curves building upon the dplR package. The networks can be used to visualise and understand the relations between tree-ring curves. These networks are also very useful to estimate the provenance of wood as described in Visser (2021) DOI:10.5334/jcaa.79 or wood-use within a structure/context/site as described in Visser and Vorst (2022) DOI:10.1163/27723194-bja10014.

Make fake data that looks realistic, supporting addresses, person names, dates, times, colors, coordinates, currencies, digital object identifiers (DOIs), jobs, phone numbers, DNA sequences, doubles and integers from distributions and within a range.

Provides functionality to assert conditions that have to be met so that errors in data used in analysis pipelines can fail quickly. Similar to stopifnot() but more powerful, friendly, and easier for use in pipelines.

1. Petersen, A. H., & Ekstrøm, C. T. (2019). dataMaid: Your Assistant for Documenting Supervised Data Quality Screening in R. Journal of Statistical Software, 90(6). https://doi.org/10.18637/jss.v090.i06
2. van der Loo, M. P., & de Jonge, E. (2019). Data Validation Infrastructure for R. arXiv preprint arXiv:1912.09759. https://arxiv.org/pdf/1912.09759.pdf
3. Brick, C., McDowell, M., & Freeman, A. L. J. (2020). Risk communication in tables versus text: a registered report randomized trial on “fact boxes.” Royal Society Open Science, 7(3), 190876. https://doi.org/10.1098/rsos.190876
4. Goel, A., & Vitek, J. (2019). On the design, implementation, and use of laziness in R. Proceedings of the ACM on Programming Languages, 3(OOPSLA), 1–27. doi:10.1145/3360579

Convert natural language text into tokens. Includes tokenizers for shingled n-grams, skip n-grams, words, word stems, sentences, paragraphs, characters, shingled characters, lines, Penn Treebank, regular expressions, as well as functions for counting characters, words, and sentences, and a function for splitting longer texts into separate documents, each with the same number of words. The tokenizers have a consistent interface, and the package is built on the stringi and Rcpp packages for fast yet correct tokenization in UTF-8.

1. A. Mullen, L., Benoit, K., Keyes, O., Selivanov, D., & Arnold, J. (2018). Fast, Consistent Tokenization of Natural Language Text. Journal of Open Source Software, 3(23), 655. https://doi.org/10.21105/joss.00655
2. Pajo, J. (2018). Quantitative Falsification for Qualitative Findings. Social Science Computer Review, 089443931876795. https://doi.org/10.1177/0894439318767956
3. Casey, Jerome (2018). Text Analytics Techniques in the Digital World: a Sentiment Analysis Case Study of the Coverage of Climate Change on US News Networks. Irish Communication Review: Vol. 16: Iss. 1, Article 7. https://arrow.dit.ie/icr/vol16/iss1/7
4. Gye-Soo, K. 2018. Text Mining and Big Data Analysis in the Relational Database with R. International Journal of Trend in Research and Development. 4(5): 384-386. http://www.ijtrd.com/papers/IJTRD12170.pdf
5. Ficcadenti, V., Cerqueti, R., & Ausloos, M. (2019). A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2018.12.049
6. Calderone, A. (2019). A Computational Analysis of Natural Languages to Build a Sentence Structure Aware Artificial Neural Network. arXiv preprint arXiv:1906.05491 https://arxiv.org/pdf/1906.05491.pdf
7. Ulibarri, N., & Scott, T. A. (2019). Environmental hazards, rigid institutions, and transformative change: How drought affects the consideration of water and climate impacts in infrastructure management. Global Environmental Change, 59, 102005. https://doi.org/10.1016/j.gloenvcha.2019.102005
8. Claes, M., & Mäntylä, M. (2020). 20-MAD–20 Years of Issues and Commits of Mozilla and Apache Development. arXiv preprint arXiv:2003.14015. https://arxiv.org/pdf/2003.14015.pdf
9. Scott, T. A., Ulibarri, N., & Perez Figueroa, O. (2020). NEPA and National Trends in Federal Infrastructure Siting in the United States. Review of Policy Research. https://doi.org/10.1111/ropr.12399
10. Grassl, P., Schraffenberger, H., Zuiderveen Borgesius, F., & Buijzen, M. (2020, July 21). Dark and bright patterns in cookie consent requests. https://doi.org/10.31234/osf.io/gqs5h
11. López Galán, A., Chung, W.-S., & Marshall, N. J. (2020). Dynamic Courtship Signals and Mate Preferences in Sepia plangon. Frontiers in Physiology, 11. https://doi.org/10.3389/fphys.2020.00845
12. Brandão, L. A. C., Agrelli, A., Bernardo, L., Paparella, F., Moura, R., & Crovella, S. (2020). PlatCOVID: A Novel Web Tool to Analyze, Curate and Share COVID-19 Literature. doi:10.21203/rs.3.rs-42169/v1

Uses the node library is-my-json-valid or ajv to validate JSON against a JSON schema. Drafts 04, 06 and 07 of JSON schema are supported.

Tools to generate unique identifier codes and printable barcoded labels for the management of biological samples. The creation of unique ID codes and printable PDF files can be initiated by standard commands, user prompts, or through a GUI addin for R Studio. Biologically informative codes can be included for hierarchically structured sampling designs.

1. Walker, V. K., Das, P., Li, P., Lougheed, S. C., Moniz, K., Schott, S., … Koch, I. (2020). Identification of Arctic Food Fish Species for Anthropogenic Contaminant Testing Using Geography and Genetics. Foods, 9(12), 1824. https://doi.org/10.3390/foods9121824

Suite of tools for managing cached files, targeting use in other R packages. Uses rappdirs for cross-platform paths. Provides utilities to manage cache directories, including targeting files by path or by key; cached directories can be compressed and uncompressed easily to save disk space.

Implements a class and methods to work with sets, doing intersection, union, complementary sets, power sets, cartesian product and other set operations in a “tidy” way. These set operations are available for both classical sets and fuzzy sets. Import sets from several formats or from other several data structures.

Generate estimated body size distributions for populations or communities of birds, given either species ID or species’ mean body size. Designed to work naturally with the North American Breeding Bird Survey, or with any dataset of bird species, abundance, and/or mean size data.

BEAST2 (https://www.beast2.org) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. Tracer (https://github.com/beast-dev/tracer/) is a GUI tool to parse and analyze the files generated by BEAST2. This package provides a way to parse and analyze BEAST2 input files without active user input, but using R function calls instead.

Creates files and collects materials necessary to complete an rOpenSci package review. Review files are prepopulated with review package specific metadata. Review package source code is also cloned for local testing and inspection.

Provides tools for importing and working with bibliographic references. It greatly enhances the bibentry class by providing a class BibEntry which stores BibTeX and BibLaTeX references, supports UTF-8 encoding, and can be easily searched by any field, by date ranges, and by various formats for name lists (author by last names, translator by full names, etc.). Entries can be updated, combined, sorted, printed in a number of styles, and exported. BibTeX and BibLaTeX .bib files can be read into R and converted to BibEntry objects. Interfaces to NCBI Entrez, CrossRef, and Zotero are provided for importing references and references can be created from locally stored PDF files using Poppler. Includes functions for citing and generating a bibliography with hyperlinks for documents prepared with RMarkdown or RHTML.

An extension of knitr that adds flexibility in several ways. One common source of frustration with knitr is that it assumes the directory where the source file lives should be the working directory, which is often not true. ezknitr addresses this problem by giving you complete control over where all the inputs and outputs are, and adds several other convenient features to make rendering markdown/HTML documents easier.

Automate the Transformation of a Google Document into a Quarto Book source.

A simple to use summary function that can be used with pipes and displays nicely in the console. The default summary statistics may be modified by the user as can the default formatting. Support for data frames and vectors is included, and users can implement their own skim methods for specific object types as described in a vignette. Default summaries include support for inline spark graphs. Instructions for managing these on specific operating systems are given in the “Using skimr” vignette and the README.

1. Sinval, J., Marques-Pinto, A., Queirós, C., & Marôco, J. (2018). Work Engagement among Rescue Workers: Psychometric Properties of the Portuguese UWES. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.02229
2. Sinval, J., Pasian, S., Queirós, C., & Marôco, J. (2018). Brazil-Portugal Transcultural Adaptation of the UWES-9: Internal Consistency, Dimensionality, and Measurement Invariance. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.00353
3. Almeida, L. S., Pérez Fuentes, M. del C., Casanova, J. R., Gázquez Linares, J. J., & Molero Jurado, M. del M. (2018). Alcohol Expectancy-Adolescent Questionnaire (AEQ-AB): Validation for portuguese college students. Health and Addictions/Salud y Drogas, 18(2), 155. https://doi.org/10.21134/haaj.v18i2.389
4. António, N., de Almeida, A., & Nunes, L. (2018). Hotel booking demand datasets. Data in Brief. https://doi.org/10.1016/j.dib.2018.11.126
5. Sinval, J., Casanova, J. R., Marôco, J., & Almeida, L. S. (2018). University student engagement inventory (USEI): Psychometric properties. Current Psychology. https://doi.org/10.1007/s12144-018-0082-6
6. Rodrigues, S., Sinval, J., Queirós, C., Marôco, J., & Kaiseler, M. (2019). Transitioning from recruit to officer: An investigation of how stress appraisal and coping influence work engagement. International Journal of Selection and Assessment. https://doi.org/10.1111/ijsa.12238
7. Sinval, J., Sirgy, M. J., Lee, D.-J., & Marôco, J. (2019). The Quality of Work Life Scale: Validity Evidence from Brazil and Portugal. Applied Research in Quality of Life. https://doi.org/10.1007/s11482-019-09730-3
8. Nalborczyk, L., Grandchamp, R., Koster, E. H. W., Perrone-Bertolotti, M., & Loevenbruck, H. (2019). Can we decode phonetic features in inner speech using surface electromyography? https://doi.org/10.31234/osf.io/8v5yd
9. Correia, C. N., McLoughlin, K. E., Nalpas, N. C., Magee, D. A., Browne, J. A., Rue-Albrecht, K., … MacHugh, D. E. (2018). RNA Sequencing (RNA-Seq) Reveals Extremely Low Levels of Reticulocyte-Derived Globin Gene Transcripts in Peripheral Blood From Horses (Equus caballus) and Cattle (Bos taurus). Frontiers in Genetics, 9. https://doi.org/10.3389/fgene.2018.00278
10. Long, J. D., & Turner, D. (2020). Applied R in the Classroom. Australian Economic Review, 53(1), 139–157. https://doi.org/10.1111/1467-8462.12362
11. Sinval, J., & Marôco, J. (2020). Short Index of Job Satisfaction: Validity evidence from Portugal and Brazil. PLOS ONE, 15(4), e0231474. https://doi.org/10.1371/journal.pone.0231474
12. Lam, K.-L., Cheng, W.-Y., Su, Y., Li, X., Wu, X., Wong, K.-H., … Cheung, P. C.-K. (2020). Use of random forest analysis to quantify the importance of the structural characteristics of beta-glucans for prebiotic development. Food Hydrocolloids, 108, 106001. https://doi.org/10.1016/j.foodhyd.2020.106001
13. McKnelly, K. J., Howitz, W. J., Lam, S., & Link, R. D. (2020). Extraction on Paper Activity: An Active Learning Technique to Facilitate Student Understanding of Liquid–Liquid Extraction. Journal of Chemical Education, 97(7), 1960–1965. https://doi.org/10.1021/acs.jchemed.9b00975
14. Behrendt, I., Fasshauer, M., & Eichner, G. (2020). Gluten intake and metabolic health: conflicting findings from the UK Biobank. European Journal of Nutrition. https://doi.org/10.1007/s00394-020-02351-9
15. Aragão e Pina, J., Passos, A. M., Maynard, M. T., & Sinval, J. (2021). Self-efficacy, mental models and team adaptation: A first approach on football and futsal refereeing. Psychology of Sport and Exercise, 52, 101787. https://doi.org/10.1016/j.psychsport.2020.101787
16. España, S., Ochoa de Olza, M., Sala, N., Piulats, J. M., Ferrandiz, U., Etxaniz, O., … Font, A. (2020). PSA Kinetics as Prognostic Markers of Overall Survival in Patients with Metastatic Castration-Resistant Prostate Cancer Treated with Abiraterone Acetate. Cancer Management and Research, Volume 12, 10251–10260. https://doi.org/10.2147/cmar.s270392
17. Wadley, A. L., Venter, W. D. F., Moorhouse, M., Akpomiemie, G., Serenata, C., Hill, A., … Kamerman, P. R. (2020). High individual pain variability in people living with HIV: A graphical analysis. European Journal of Pain, 25(1), 160–170. https://doi.org/10.1002/ejp.1658
18. Wadley AL, Venter WDF, Moorhouse M, Akpomiemie G, Serenata C, Hill A, Sokhela S, Mqamelo N, Kamerman PR. High individual pain variability in people living with HIV: A graphical analysis. Eur J Pain 2020. https://doi.org/10.1002/ejp.1658
19. Schrag, N. F. D., Apley, M. D., Godden, S. M., Lubbers, B. V., & Singer, R. S. (2020). Antimicrobial use quantification in adult dairy cows – Part 1 – Standardized regimens as a method for describing antimicrobial use. Zoonoses and Public Health, 67(S1), 51–68. https://doi.org/10.1111/zph.12766
20. Nopp-Mayr, U., Reimoser, S., Reimoser, F., Sachser, F., Obermair, L., & Gratzer, G. (2020). Analyzing long-term impacts of ungulate herbivory on forest-recruitment dynamics at community and species level contrasting tree densities versus maximum heights. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-76843-3
21. Behrendt, I., Fasshauer, M., & Eichner, G. (2020). Gluten Intake and All-Cause and Cause-Specific Mortality: Prospective Findings from the UK Biobank. The Journal of Nutrition, 151(3), 591–597. https://doi.org/10.1093/jn/nxaa387

Work with GitHub gists from R (e.g., https://en.wikipedia.org/wiki/GitHub#Gist, https://docs.github.com/en/github/writing-on-github/creating-gists/). A gist is simply one or more files with code/text/images/etc. This package allows the user to create new gists, update gists with new files, rename files, delete files, get and delete gists, star and un-star gists, fork gists, open a gist in your default browser, get embed code for a gist, list gist commits, and get rate limit information when authenticated. Some requests require authentication and some do not. Gists website: https://gist.github.com/.

It provides templates for roweb2 blogging and help for a GitHub forking workflow.

Provides a flexible container to transport and manipulate complex sets of data. These data may consist of multiple data files and associated meta data and ancillary files. Individual data objects have associated system level meta data, and data files are linked together using the OAI-ORE standard resource map which describes the relationships between the files. The OAI- ORE standard is described at https://www.openarchives.org/ore/. Data packages can be serialized and transported as structured files that have been created following the BagIt specification. The BagIt specification is described at https://tools.ietf.org/html/draft-kunze-bagit-08.

What the package does (one paragraph).

Tools for interacting with data from experiments done in microtiter plates. Easily read in plate-shaped data and convert it to tidy format, combine plate-shaped data with tidy data, and view tidy data in plate shape.

As different antipsychotic medications have different potencies, the doses of different medications cannot be directly compared. Various strategies are used to convert doses into a common reference so that comparison is meaningful. Chlorpromazine (CPZ) has historically been used as a reference medication into which other antipsychotic doses can be converted, as “chlorpromazine-equivalent doses”. Using conversion keys generated from widely-cited scientific papers, e.g. Gardner et. al 2010 doi:10.1176/appi.ajp.2009.09060802 and Leucht et al. 2016 doi:10.1093/schbul/sbv167, antipsychotic doses are converted to CPZ (or any specified antipsychotic) equivalents. The use of the package is described in the included vignette. Not for clinical use.

1. Kim, J., Plitman, E., Iwata, Y., Nakajima, S., Mar, W., Patel, R., … Graff-Guerrero, A. (2020). Neuroanatomical profiles of treatment-resistance in patients with schizophrenia spectrum disorders. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 99, 109839. https://doi.org/10.1016/j.pnpbp.2019.109839

Standardized survey outcome rate functions, including the response rate, contact rate, cooperation rate, and refusal rate. These outcome rates allow survey researchers to measure the quality of survey data using definitions published by the American Association of Public Opinion Research (AAPOR). For details on these standards, see AAPOR (2016) https://www.aapor.org/Standards-Ethics/Standard-Definitions-(1).aspx.