bigReg: Generalized Linear Models (GLM) for Large Data Sets

Allows the user to carry out GLM on very large data sets. Data can be created using the data_frame() function and appended to the object with object$append(data); data_frame and data_matrix objects are available that allow the user to store large data on disk. The data is stored as doubles in binary format and any character columns are transformed to factors and then stored as numeric (binary) data while a look-up table is stored in a separate .meta_data file in the same folder. The data is stored in blocks and GLM regression algorithm is modified and carries out a MapReduce- like algorithm to fit the model. The functions bglm(), and summary() and bglm_predict() are available for creating and post-processing of models. The library requires Armadillo installed on your system. It may not function on windows since multi-core processing is done using mclapply() which forks R on Unix/Linux type operating systems.

Version: 0.1.5
Depends: R (≥ 3.2.0), Rcpp (≥ 1.0.11), parallel, methods, stats, uuid (≥ 0.1-2), MASS (≥ 7.3-39)
LinkingTo: Rcpp, RcppArmadillo (≥
OS_type: unix
Published: 2023-12-11
DOI: 10.32614/CRAN.package.bigReg
Author: Chibisi Chima-Okereke
Maintainer: Chibisi Chima-Okereke <chibisi at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: bigReg results


Reference manual: bigReg.pdf


Package source: bigReg_0.1.5.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): bigReg_0.1.5.tgz, r-oldrel (arm64): bigReg_0.1.5.tgz, r-release (x86_64): bigReg_0.1.5.tgz, r-oldrel (x86_64): bigReg_0.1.5.tgz
Old sources: bigReg archive


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