Imports: googleAuthR (>= 0.5.1), googleCloudStorageR (>= 0.2.0),
jsonlite (>= 1.0), httr (>= 1.2.1), assertthat
Suggests: shiny (>= 0.12.1), knitr, rmarkdown, testthat

Imports: googleAuthR (>= 0.6.0), googleCloudStorageR (>= 0.2.0),

Imports: googleAuthR (>= 0.6.2), googleCloudStorageR (>= 0.2.0),
Suggests: shiny (>= 0.12.1), knitr, rmarkdown, testthat, data.table,

Imports: googleAuthR (>= 1.1.1), googleCloudStorageR (>= 0.2.0),

Title: Generalized Ridge Regression (with special advantage for p >> n
Author: Xia Shen, Moudud Alam and Lars Ronnegard
Description: The package fits large-scale (generalized) ridge regression for various distributions of response. The shrinkage parameters (lambdas) can be pre-specified or estimated using an internal update routine (fitting a heteroscedastic effects model, or HEM). It gives possibility to shrink any subset of parameters in the model. It has special computational advantage for the cases when the number of shrinkage parameters exceeds the number of observations. For example, the package is very useful for fitting large-scale omics data, such as high-throughput genotype data (genomics), gene expression data (transcriptomics), metabolomics data, etc.
Depends: R (>= 2.10), utils, hglm, DatABEL

Description: The package fits large-scale (generalized) ridge
regression for various distributions of response. The shrinkage
parameters (lambdas) can be pre-specified or estimated using an
internal update routine (fitting a heteroscedastic effects
model, or HEM). It gives possibility to shrink any subset of
parameters in the model. It has special computational advantage
for the cases when the number of shrinkage parameters exceeds
the number of observations. For example, the package is very
useful for fitting large-scale omics data, such as
high-throughput genotype data (genomics), gene expression data

Package: bigReg
Type: Package
Title: Generalized Linear Models (GLM) for Large Data Sets
Version: 0.1.2
Date: 2016-07-23
Author: Chibisi Chima-Okereke <chibisi@active-analytics.com>
Maintainer: Chibisi Chima-Okereke <chibisi@active-analytics.com>
Description: 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 probably won't
function on windows since multi-core processing is done using mclapply()
which forks R on Unix/Linux type operating systems.
License: GPL (>= 2)
Depends: R (>= 3.2.0), Rcpp (>= 0.12.3), parallel, methods, stats, uuid
(>= 0.1-2), MASS (>= 7.3-39)
LinkingTo: Rcpp, RcppArmadillo (>= 0.5.200.1.0)
OS_type: unix
Collate: 'RcppExports.R' 'data_frame.r' 'data_matrix.r' 'family.r'
'map_reg.r'
RoxygenNote: 5.0.1
NeedsCompilation: yes
Packaged: 2016-07-25 09:31:12 UTC; chib
Repository: CRAN
Date/Publication: 2016-07-25 19:16:58