Gregory R. Warnes, Ben Bolker, Thomas Lumley and CRAN team

c(person("Ben","Bolker", role=c("aut", "cre"),
email = "bolker@mcmaster.ca",
comment=c(ORCID="0000-0002-2127-0443")),
person("Gregory R.", "Warnes", role = "aut"),
person("Thomas", "Lumley", role = "aut"))
Ben Bolker [aut, cre] (<https://orcid.org/0000-0002-2127-0443>),
Gregory R. Warnes [aut],
Thomas Lumley [aut]

Functions to assist in R programming, including:
- assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion',
'getDependencies', 'keywords', 'scat'),
- calculate the logit and inverse logit transformations ('logit', 'inv.logit'),
- test if a value is missing, empty or contains only NA and NULL values ('invalid'),
- manipulate R's .Last function ('addLast'),
- define macros ('defmacro'),
- detect odd and even integers ('odd', 'even'),
- convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'),
- perform a binary search ('binsearch'),
- sort strings containing both numeric and character components ('mixedsort'),
- create a factor variable from the quantiles of a continuous variable ('quantcut'),
- enumerate permutations and combinations ('combinations', 'permutation'),
- calculate and convert between fold-change and log-ratio ('foldchange',
'logratio2foldchange', 'foldchange2logratio'),
- calculate probabilities and generate random numbers from Dirichlet distributions
('rdirichlet', 'ddirichlet'),
- apply a function over adjacent subsets of a vector ('running'),
- modify the TCP_NODELAY ('de-Nagle') flag for socket objects,
- efficient 'rbind' of data frames, even if the column names don't match ('smartbind'),
- generate significance stars from p-values ('stars.pval'),
- convert characters to/from ASCII codes ('asc', 'chr'),
- convert character vector to ASCII representation ('ASCIIfy'),
- apply title capitalization rules to a character vector ('capwords').

Game-Theoretically OPtimal (GTOP) Reconciliation Method
In hierarchical time series (HTS) forecasting, the hierarchical relation between multiple time series is exploited to make better forecasts. This hierarchical relation implies one or more aggregate consistency constraints that the series are known to satisfy. Many existing approaches, like for example bottom-up or top-down forecasting, therefore attempt to achieve this goal in a way that guarantees that the forecasts will also be aggregate consistent. This package provides with an implementation of the Game-Theoretically OPtimal (GTOP) reconciliation method proposed in van Erven and Cugliari (2015), which is guaranteed to only improve any given set of forecasts. This opens up new possibilities  for constructing the forecasts. For example, it is not necessary to assume that bottom-level forecasts are unbiased, and aggregate forecasts may be constructed by regressing both on bottom-level forecasts and on other covariates that may only be available at the aggregate level.