Rmpi (>= 0.6.0), parallel, knitr, testthat, withr, mlr (>=
2.15.0), ParamHelpers, devtools, covr

Iteratively Reweighted Boosting for Robust Analysis
Fit a predictive model with the iteratively reweighted boosting (IRBoost) that minimizes the robust loss functions in the CC-family (concave-convex). The convex optimization is conducted by functional descent boosting algorithm in the R package xgboost. The IRBoost reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include the robust generalized linear models and extensions, where the mean is related to the predictors by boosting, and robust accelerated failure time models. The package supersedes the R package ccboost. Wang (2021) <arXiv:2101.07718>.

Correlation Coefficients for Information Retrieval
c(person("Julián", "Urbano", email = "urbano.julian@gmail.com", role = c("aut", "cre")),
person("Mónica", "Marrero", email = "marrero.monica@gmail.com", role = c("aut")))
Provides implementation of various correlation coefficients of common use in
Information Retrieval. In particular, it includes Kendall (1970, isbn:0852641990) tau coefficient
as well as tau_a and tau_b for the treatment of ties. It also includes Yilmaz et al. (2008)
<doi:10.1145/1390334.1390435> tauAP correlation coefficient, and versions tauAP_a and tauAP_b
developed by Urbano and Marrero (2017) <doi:10.1145/3121050.3121106> to cope with ties.
Julián Urbano [aut, cre],
Mónica Marrero [aut]

Fits a double logistic function to NDVI time series and calculates
instantaneous rate of green (IRG) according to methods described
in Bischoff et al. (2012) <doi:10.1086/667590>.
testthat, knitr, rmarkdown, DiagrammeR, ggplot2

knitr, rmarkdown, DiagrammeR, ggplot2, curl, tinytest

Client for the EU Industrial Research and Industry Scoreboard
person("Thierry", "Warin", email = "thierry.warin@hec.ca", role =c("aut", "cre"), comment = c(ORCID = "0000-0002-5921-3428") )
The Economics of Industrial Research & Innovation (IRI) is a scientific project carried out within the Directorate B: Growth and Innovation, one of the seven scientific institutes of the European Commission's Joint Research Centre (JRC) (<https://iri.jrc.ec.europa.eu/home/>). 'iriR' objective is to allows an easy connection with R to the European Commission's Economics of Industrial Research and Innovation data. Please cite: Warin, Th. (2020) "iriR: An R Package for the EU Industrial Research and Industry Scoreboard", <10.6084/m9.figshare.11774640>.
gsheet, dplyr, reshape2, ggplot2, ggsci, WDI, scales, stats
Thierry Warin [aut, cre] (<https://orcid.org/0000-0002-5921-3428>)