dplyr, plm, data.table, matrixStats, magrittr, ggplot2,
scales, vars, knitr, rmarkdown, testthat, microbenchmark, covr

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' and 'plm' (panel-series and data frames), and non-
destructively handles other matrix or data frame based classes (such as
'ts', 'xts' / 'zoo', 'timeSeries', 'tsibble', 'sf' data frames 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 (grouped) replacing and sweeping out of
statistics, and (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 and (compounded)
growth rates on (unordered) 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.
lfe, dplyr, plm, data.table, matrixStats, magrittr,
RcppArmadillo, RcppEigen, ggplot2, scales, vars, knitr,
rmarkdown, testthat, microbenchmark, covr