Package: r-cran-collapse (2.0.16-1)

[ Source: r-cran-collapse  ]

GNU R advanced and fast data transformation

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.

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