Description: Tools expand vectors of short URLs into long URLs. No API services are used,
which may mean that this operates more slowly than API services do (since they usually
cache results of expansions every user of the service performs).

person("John", "Coene", role = "ctb", comment = "Issue #4")
Description: Tools are provided to expand vectors of short URLs into long 'URLs'.
No 'API' services are used, which may mean that this operates more slowly than
'API' services do (since they usually cache results of expansions that every
user of the service requests). You can setup your own caching layer with the
'memoise' package if you wish to have a speedup during single sessions or add
larger dependencies, such as 'Redis', to gain a longer-term performance boost

Title: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian
Author: Aki Vehtari, Andrew Gelman, Jonah Gabry
Description: We efficiently approximate leave-one-out cross-validation (LOO)
using very good importance sampling (VGIS), a new procedure for regularizing
importance weights. As a byproduct of our calculations, we also obtain
approximate standard errors for estimated predictive errors, and for the
comparison of predictive errors between two models. We also compute the
widely applicable information criterion (WAIC).
Imports: matrixStats (>= 0.14.1), parallel

Authors@R: c(person("Aki", "Vehtari", email = "Aki.Vehtari@aalto.fi", role = c("aut")),
person("Andrew", "Gelman", email = "gelman@stat.columbia.edu", role = c("aut")),
person("Jonah", "Gabry", email = "jsg2201@columbia.edu", role = c("cre", "aut")),
person("Juho", "Piironen", role = c("ctb")))
using Pareto smoothed importance sampling (PSIS), a new procedure for
regularizing importance weights. As a byproduct of our calculations, we also
obtain approximate standard errors for estimated predictive errors, and for
the comparison of predictive errors between two models. We also compute the
Imports: graphics, matrixStats (>= 0.14.1), parallel, stats

person("Juho", "Piironen", role = c("ctb")),