htmlTable (>= 1.9), htmltools, tibble, scales, dplyr, lazyeval
(>= 0.2.0), knitr, rmarkdown (>= 0.9.6), gplots, utils

htmlTable (>= 1.9), htmltools, tibble, scales, dplyr, lazyeval
(>= 0.2.0), knitr, rmarkdown (>= 0.9.6), gplots, magrittr,
rlang (>= 0.1.2), tidyselect

person("Sergio", c("Oller", "Moreno"),
email = "sergioller@gmail.com", role = c("aut", "cph", "cre"),
comment = c(ORCID = "0000-0002-8994-1549"))

promises, shiny (>= 1.0.5), testthat (>= 1.0)

Computation of P Values and Bayes Factors for Conditioning Data
Set of functions for the easy analyses of conditioning data.

Implements specialized conditions, i.e., typed errors,
warnings and messages. Offers a set of standardized conditions (value error,
deprecated warning, io message, ...) in the fashion of Python's built-in
exceptions.

Control How Many Times Conditions are Thrown
Provides ability to control how many times in function
calls conditions are thrown (shown to the user). Includes control of
warnings and messages.

Conditional Density Estimation with Neural Network Conditional
Mixtures
Conditional density estimation with mixtures for
heavy-tailed distributions

Neural network conditional mixtures are mixture models whose parameters are predicted by a neural network. The mixture model can thus change its parameters in response to changes in predictive covariates. Mixtures included are gaussian, log-normal and hybrid Pareto mixtures. The latter relies on the generalized Pareto distribution to account for the presence of large extreme events. The unconditional mixtures are also available.

Create Tours in 'Shiny' Apps Using 'Shepherd.js'
c(
person(given = "Etienne",
family = "Bacher",
role = c("aut", "cre"),
email = "etienne.bacher@protonmail.com")
)
Enable the use of 'Shepherd.js' to create tours in 'Shiny'
applications.

Query-Driven Pipeline Execution and Query Templates
Runs a function iteratively over each row of either a dataframe
or the results of a query.  Use the 'BigQuery' and 'DBI' wrappers to
iteratively pass each row of query results to a function.  If a field
contains a 'JSON' string, it will be converted to an object.  This is
helpful for queries that return 'JSON' strings that represent objects.
These fields can then be treated as objects by the pipeline.
knitr, rmarkdown, whisker, testthat, RSQLite
R (>= 3.3.2), jsonlite, assertthat, bigrquery, DBI