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sebkrantz
collapse:Advanced and Fast Data Transformation
A 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. It further includes fast functions for 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 is well integrated with base R classes, 'dplyr'/'tibble', 'data.table', 'sf', 'units', 'plm' (panel-series and data frames), and 'xts'/'zoo'.
Maintained by Sebastian Krantz. Last updated 8 days ago.
data-aggregationdata-analysisdata-manipulationdata-processingdata-sciencedata-transformationeconometricshigh-performancepanel-datascientific-computingstatisticstime-seriesweightedweightscppopenmp
672 stars 16.68 score 708 scripts 99 dependents
mlr-org
mlr3verse:Easily Install and Load the 'mlr3' Package Family
The 'mlr3' package family is a set of packages for machine-learning purposes built in a modular fashion. This wrapper package is aimed to simplify the installation and loading of the core 'mlr3' packages. Get more information about the 'mlr3' project at <https://mlr3book.mlr-org.com/>.
Maintained by Marc Becker. Last updated 3 months ago.
55 stars 8.32 score 720 scripts 1 dependentsmlr-org
mlr3fselect:Feature Selection for 'mlr3'
Feature selection package of the 'mlr3' ecosystem. It selects the optimal feature set for any 'mlr3' learner. The package works with several optimization algorithms e.g. Random Search, Recursive Feature Elimination, and Genetic Search. Moreover, it can automatically optimize learners and estimate the performance of optimized feature sets with nested resampling.
Maintained by Marc Becker. Last updated 2 months ago.
evolutionary-algorithmsexhaustive-searchfeature-selectionmachine-learningmlr3optimizationrandom-searchrecursive-feature-eliminationsequential-feature-selection
23 stars 8.12 score 70 scripts 2 dependents