A C/C++ based package for advanced data transformation and
statistical computing in R that is extremely fast, flexible and
parsimonious to code with, class-agnostic and programmer friendly.
It is well integrated with base R, 'dplyr' / (grouped) 'tibble',
'data.table', 'plm' (panel-series and data frames), 'sf' data frames, and
non-destructively handles other matrix or data frame based classes (such as
'ts', 'xts' / 'zoo', 'timeSeries', 'tsibble', 'tibbletime' etc.)
--- Key Features: ---
(1) Advanced statistical programming: A full set of fast statistical functions
supporting grouped and weighted computations on vectors, matrices and
data frames. Fast and programmable grouping, ordering, unique values / rows,
factor generation and interactions. Fast and flexible functions for data
manipulation and data object conversions.
(2) Advanced aggregation: Fast and easy multi-data-type, multi-function,
weighted, parallelized and fully customized data aggregation.
(3) Advanced transformations: Fast row / column arithmetic, (grouped) replacing
and sweeping out of statistics, (grouped, weighted) scaling / standardizing,
between (averaging) and (quasi-)within (centering / demeaning) transformations,
higher-dimensional centering (i.e. multiple fixed effects transformations),
linear prediction / partialling-out, linear model fitting and testing.
(4) Advanced time-computations: Fast (sequences of) lags / leads, and
(lagged / leaded, iterated, quasi-, log-) differences, (compounded)
growth rates, and cumulative sums on (unordered, irregular) time series and
panel data. Multivariate auto-, partial- and cross-correlation functions for panel data.
Panel data to (ts-)array conversions.
(5) List processing: (Recursive) list search / identification, splitting,
extraction / subsetting, data-apply, and generalized recursive
row-binding / unlisting in 2D.
(6) Advanced data exploration: Fast (grouped, weighted, panel-decomposed)
summary statistics for complex multilevel / panel data.
Tests Vignettes
Available Snapshots
This version of collapse can be found in the following snapshots:
