Package: r-cran-collapse
Version: 2.1.6-1
Architecture: amd64
Maintainer: Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
Original-Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Installed-Size: 5373
Depends: r-api-4.0, r-cran-rcpp (>= 1.0.1), libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14)
Recommends: r-cran-data.table, r-cran-magrittr, r-cran-xts, r-cran-zoo, r-cran-plm, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-microbenchmark, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-bit64
Section: gnu-r
Priority: optional
Homepage: https://cran.r-project.org/package=collapse
Description: 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.
