软件包:r-cran-collapse(2.0.16-1)
r-cran-collapse 的相关链接
Debian 的资源:
下载源码包 r-cran-collapse:
- [r-cran-collapse_2.0.16-1.dsc]
- [r-cran-collapse_2.0.16.orig.tar.gz]
- [r-cran-collapse_2.0.16-1.debian.tar.xz]
维护小组:
外部的资源:
- 主页 [cran.r-project.org]
相似软件包:
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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