Package: dbreg 0.0.3.99

Grant McDermott

dbreg: Fast Regressions on Database Backends

Leverages database backends to run regressions on very large datasets, which may not fit into R's memory. Various acceleration strategies---e.g., Wong et al. (2021) <doi:10.48550/arXiv.2102.11297> and Arkhangelsky & Imbens (2024) <https://doi.org/10.1093/restud/rdad089>---allow for highly efficient computation, while robust standard errors are computed from sufficient statistics.

Authors:Grant McDermott [aut, cre], James Brand [aut], Apoorva Lal [ctb], Laurent Berge [ctb], Wojtek Klimowicz [ctb]

dbreg_0.0.3.99.tar.gz
dbreg_0.0.3.99.zip(r-4.7-any)dbreg_0.0.3.99.zip(r-4.6-any)dbreg_0.0.3.99.zip(r-4.5-any)
dbreg_0.0.3.99.tgz(r-4.6-any)dbreg_0.0.3.99.tgz(r-4.5-any)
dbreg_0.0.3.99.tar.gz(r-4.7-any)dbreg_0.0.3.99.tar.gz(r-4.6-any)
dbreg_0.0.3.99.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
dbreg/json (API)

# Install 'dbreg' in R:
install.packages('dbreg', repos = c('https://grantmcdermott.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/grantmcdermott/dbreg/issues

Pkgdown/docs site:https://grantmcdermott.com

On CRAN:

Conda:

5.74 score 82 stars 30 scripts 7 exports 9 dependencies

Last updated from:ef061a3cc1. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK218
source / vignettesOK191
linux-releaseOK165
macos-releaseOK144
macos-oldrelOK1938
windows-develOK164
windows-releaseOK106
windows-oldrelOK141
wasm-releaseOK100

Exports:dbbinsregdbivregdbregglancegofsql_model_matrixtidy

Dependencies:DBIduckdbFormulagenericsgluelatticeMASSMatrixtinyplot