dplyr, plm, data.table, matrixStats, magrittr, fixest, lfe,
vars, weights, kit, RcppArmadillo, RcppEigen, ggplot2, scales,
microbenchmark, testthat, covr

dplyr, plm, data.table, matrixStats, magrittr, fixest, lfe,
vars, weights, kit, RcppArmadillo, RcppEigen, ggplot2, scales,
microbenchmark, testthat, covr, knitr, rmarkdown

dplyr, plm, data.table, matrixStats, magrittr, fixest, lfe,
vars, weights, kit, RcppArmadillo, RcppEigen, ggplot2, scales,
microbenchmark, knitr, rmarkdown

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.
plm, data.table, sf, matrixStats, magrittr, fixest, lfe,
vars, weights, kit, RcppArmadillo, RcppEigen, dplyr, ggplot2,
scales, microbenchmark, testthat, covr, knitr, rmarkdown