A computationally-efficient leading-eigenvalue approximation to tail probabilities and quantiles of large quadratic forms, in particular for the Sequence Kernel Association Test (SKAT) used in genomics <doi:10.1002/gepi.22136>. Also provides stochastic singular value decomposition for dense or sparse matrices.

Interface with Google BigQuery with Shiny Compatibility
c(person("Mark", "Edmondson",email = "r@sunholo.com",
role = c("aut", "cre")),
person("Hadley", "Wickham", , "hadley@rstudio.com", role = "ctb")
)
Interface with Google BigQuery,
see <https://cloud.google.com/bigquery/> for more information.
This package uses 'googleAuthR' so is compatible with similar packages,
including Google Cloud Storage (<https://cloud.google.com/storage/>) for result extracts.
Mark Edmondson [aut, cre],
Hadley Wickham [ctb]

Interface with 'Google BigQuery',
see <https://cloud.google.com/bigquery/> for more information.
This package uses 'googleAuthR' so is compatible with similar packages,
including 'Google Cloud Storage' (<https://cloud.google.com/storage/>) for result extracts.
shiny (>= 0.12.1), jsonlite (>= 1.0), httr (>= 1.2.1), knitr,
rmarkdown

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

googleAuthR (>= 0.6.0), googleCloudStorageR (>= 0.2.0),
jsonlite (>= 1.0), httr (>= 1.2.1), assertthat

googleAuthR (>= 0.6.2), googleCloudStorageR (>= 0.2.0),
jsonlite (>= 1.0), httr (>= 1.2.1), assertthat
shiny (>= 0.12.1), knitr, rmarkdown, testthat, data.table,
purrr

googleAuthR (>= 1.1.1), googleCloudStorageR (>= 0.2.0),
jsonlite (>= 1.0), httr (>= 1.2.1), assertthat

Generalized Ridge Regression (with special advantage for p >> n
cases)
Xia Shen, Moudud Alam and Lars Ronnegard
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.