testthat, covr, sampling, magrittr, tibble, dplyr, data.table

person("Martin", "Chevalier", role = c("aut", "cre", "cph"), email = "mc@slmc.fr")

Download and Process Public Domain Works from Project Gutenberg
person("David", "Robinson", email = "admiral.david@gmail.com", role = c("aut", "cre"))
Download and process public domain works in the Project
Gutenberg collection. Includes metadata for all Project Gutenberg works,
so that they can be searched and retrieved.
dplyr, readr, purrr, urltools, rvest, xml2, stringr, lazyeval
knitr, rmarkdown, testthat, tidytext, ggplot2, tidyr

dplyr, readr, purrr, urltools, rvest, stringr, lazyeval

Download and process public domain works in the Project
Gutenberg collection <http://www.gutenberg.org/>. Includes metadata for
all Project Gutenberg works, so that they can be searched and retrieved.
dplyr, readr, purrr, urltools, stringr, lazyeval

knitr, rmarkdown, testthat, tidytext, ggplot2, tidyr, curl

dplyr, readr, purrr, urltools, rvest, xml2, stringr

Several tools for Global Value Chain (GVC) analysis are implemented.
c( person("Bastiaan", "Quast", email = "bquast@gmail.com", role =
c("aut", "cre") ), person("Victor",
"Kummritz", role = "aut") )
Bastiaan Quast [aut, cre],
Victor Kummritz [aut]

Several tools for Global Value Chain ('GVC') analysis are implemented.

Several tools for Global Value Chain ('GVC') analysis are
implemented.

c( person("Bastiaan", "Quast", email = "bquast@gmail.com", role =
c("aut", "cre"), comment=c(ORCID="0000-0002-2951-3577") ), person("Victor",
"Kummritz", role = "aut") )
Bastiaan Quast [aut, cre] (<https://orcid.org/0000-0002-2951-3577>),
Victor Kummritz [aut]

Functionalities to compute model based genetic components i.e. genotypic variance, phenotypic variance and heritability for given traits of different genotypes from replicated data using methodology explained by Burton, G. W. & Devane, E. H. (1953) (<doi:10.2134/agronj1953.00021962004500100005x>) and Allard, R.W. (2010, ISBN:8126524154).

Regularized Categorial Effects/Categorial Effect Modifiers in
GLMs
The function fits generalized linear models with
categorial effects and categorial effect modifiers. The model
is specified by giving a symbolic description of the linear
predictor and a description of the error distribution.
Estimation employs regularization and model selection
strategies to fuse and/or select a covariate's categories.
These strategies are either a penalty combining the fused and
the pure Lasso or a forward selection strategy employing
AIC/BIC.