Ben Sherwood [aut, cre], Adam Maidman [ctb]

Ben Sherwood [aut, cre], Adam Maidman [ctb], Shaobo Li [ctb]
methods, quantreg, hqreg, hrqglas, data.table, Rdpack,
lifecycle, plyr
Performs penalized quantile regression with LASSO, elastic net, SCAD and MCP penalty functions including group penalties. Provides a function that automatically generates lambdas and evaluates different models with cross validation or BIC, including a large p version of BIC.

c(
person("John", "Mount", email = "jmount@win-vector.com", role = c("aut", "cre")),
person(family = "Win-Vector LLC", role = c("cph"))
)
Implement the 'rquery' piped query algebra using 'data.table'.  This allows
for a high-speed in memory implementation of Codd-style data manipulation tools.
knitr, rmarkdown, DBI, RSQLite, parallel, testthat
John Mount [aut, cre],
Win-Vector LLC [cph]

Implements the 'rquery' piped query algebra using 'data.table'.  This allows
for a high-speed in memory implementation of Codd-style data manipulation tools.

Implements the 'rquery' piped Codd-style query algebra using 'data.table'.  This allows
for a high-speed in memory implementation of Codd-style data manipulation tools.

knitr, rmarkdown, DBI, RSQLite, parallel, RUnit

knitr, rmarkdown, DBI, RSQLite, parallel, tinytest

Markov Chain Marginal Bootstrap for Quantile Regression
Maria Kocherginsky <mkocherg@uchicago.edu>, Xuming He <x-he@uiuc.edu>
Markov Chain Marginal Bootstrap for Quantile Regression. A resampling
method for inference in quantile regression.  Suitable for modest to large data sets.

Maria Kocherginsky <mkocherg@uchicago.edu>, Xuming He
<x-he@uiuc.edu>
Markov Chain Marginal Bootstrap for Quantile Regression. A
resampling method for inference in quantile regression.
Suitable for modest to large data sets.

Relational Query Generator for Data Manipulation
A query generator based on Edgar F. Codd's relational
algebra and operator names (plus experience using 'SQL' at big data
scale).  The design represents an attempt to make 'SQL' more teachable by
denoting composition a sequential pipeline notation instead of nested
queries or functions.  Package features include: data processing trees
or pipelines as observable objects (able to report both columns
produced and columns used), optimized 'SQL' generation as an explicit
user visible modeling step, and convenience methods for applying query
trees to in-memory data.frames.
sparklyr, RPostgreSQL, RPostgres, knitr, rmarkdown, testthat