R-collapse
Port variant std
Summary Advanced and Fast Data Transformation
Package version 2.1.6
Homepage https://fastverse.org/collapse/
Keywords cran
Maintainer CRAN Automaton
License Not yet specified
Other variants There are no other variants.
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Last modified 18 JAN 2026, 19:34:33 UTC
Port created 23 FEB 2022, 02:10:24 UTC
Subpackage Descriptions
single collapse: Advanced and Fast Data Transformation A large C/C++-based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust, and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R, fast functions for data transformation and common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data. It seamlessly supports base R objects/classes as well as 'units', 'integer64', 'xts'/ 'zoo', 'tibble', 'grouped_df', 'data.table', 'sf', and 'pseries'/'pdata.frame'.
Configuration Switches (platform-specific settings discarded)
This port has no build options.
Package Dependencies by Type
Build (only) gmake:primary:std
R:primary:std
icu:dev:std
Build and Runtime R-Rcpp:single:std
Runtime (only) R:primary:std
R:nls:std
Download groups
main mirror://CRAN/src/contrib
https://loki.dragonflybsd.org/cranfiles/
Distribution File Information
94db70206397e3858c7f2009f9fce83e660b9ab5330e64f14582e5be55105ad9 1474879 CRAN/collapse_2.1.6.tar.gz
Ports that require R-collapse:std
R-plm:std Linear Models for Panel Data