A User-Friendly Interface for Accessing the Prolific API
person(given="Simon", family="Lenau" , email="lenau@cispa.de", role = c("aut", "cre"))
A user-friendly interface for creating and managing empirical crowd-sourcing studies via API access to <https://www.prolific.co>.

Patient-Reported Outcome Data Analysis with Stan
Algorithms and subroutines for patient-reported outcome data analysis.

c(person("Miguel", "Angelo Lellis Moreira", role=c("aut", "cre"), email="miguellellis@hotmail.com"),
person("Marcos", "dos Santos", role="aut", email="marcosdossantos_doutorado_uff@yahoo.com.br"),
person("Carlos", "Francisco Simoes Gomes", role="aut", email="cfsg1@bol.com.br"))
Miguel Angelo Lellis Moreira [aut, cre],
Marcos dos Santos [aut],
Carlos Francisco Simoes Gomes [aut]
The PROMETHEE method is a multi-criteria decision-making method addressing with outranking problems.
The method establishes a preference structure between the alternatives, having a preference function for
each criterion. IN this context, three variants of the method is carried out: PROMETHEE I (Partial Outranking),
PROMETHEE II (Total Outranking), and PROMETHEE III (Outranking by Intervals).

Abstractions for Promise-Based Asynchronous Programming
c(
person("Joe", "Cheng", email = "joe@rstudio.com", role = c("aut", "cre")),
person("RStudio", role = c("cph", "fnd"))
)
Provides fundamental abstractions for doing asynchronous programming
in R using promises. Asynchronous programming is useful for allowing a single
R process to orchestrate multiple tasks in the background while also attending
to something else. Semantics are similar to 'JavaScript' promises, but with a
syntax that is idiomatic R.
Joe Cheng [aut, cre],
RStudio [cph, fnd]

testthat, future (>= 1.21.0), fastmap (>= 1.1.0), purrr,
knitr, rmarkdown, vembedr, spelling

Proteomics Data Analysis and Modeling Tools
person(given = "Chathurani",
family = "Ranathunge",
role = c("aut", "cre", "cph"),
email = "caranathunge86@gmail.com",
comment = c(ORCID = "0000-0003-1901-2119"))
A comprehensive, user-friendly package for label-free proteomics data analysis and machine learning-based modeling. Data generated from 'MaxQuant' can be easily used to conduct differential expression analysis, build predictive models with top protein candidates, and assess model performance. promor includes a suite of tools for quality control, visualization, missing data imputation (Lazar et. al. (2016) <doi:10.1021/acs.jproteome.5b00981>), differential expression analysis (Ritchie et. al. (2015) <doi:10.1093/nar/gkv007>), and machine learning-based modeling (Kuhn (2008) <doi:10.18637/jss.v028.i05>).
reshape2, ggplot2, ggrepel, gridExtra, limma, statmod,
pcaMethods, VIM, missForest, caret, kernlab, xgboost, viridis,
pROC
Chathurani Ranathunge [aut, cre, cph]
(<https://orcid.org/0000-0003-1901-2119>)