Description: A 'dplyr' back end for databases that allows you to
work with remote database tables as if they are in-memory data frames.
Basic features works with any database that has a 'DBI' back end; more
advanced features require 'SQL' translation to be provided by the
Suggests: bit64, covr, knitr, Lahman, nycflights13, RMariaDB (>=

Collate: 'utils.R' 'sql.R' 'escape.R' 'translate-sql-quantile.R'
'remote.R' 'schema.R' 'simulate.R' 'sql-build.R' 'sql-expr.R'
'src-sql.R' 'src_dbi.R' 'tbl-lazy.R' 'tbl-sql.R' 'test-frame.R'

'explain.R' 'ident.R' 'lazy-ops.R' 'memdb.R' 'partial-eval.R'
'schema.R' 'simulate.R' 'sql-build.R' 'sql-expr.R' 'src-sql.R'

(>= 1.2.0), lifecycle, magrittr, methods, purrr (>= 0.2.5), R6
Suggests: bit64, covr, knitr, Lahman, nycflights13, odbc, RMariaDB (>=

1.0.2), rmarkdown, RPostgres (>= 1.1.3), RPostgreSQL, RSQLite
'db-escape.R' 'db-io.R' 'db-sql.R' 'db.R' 'dbplyr.R'
'remote.R' 'schema.R' 'simulate.R' 'sql-build.R' 'sql-clause.R'
'sql-expr.R' 'src-sql.R' 'src_dbi.R' 'tbl-lazy.R' 'tbl-sql.R'

(>= 1.0.3), ellipsis, glue (>= 1.2.0), lifecycle, magrittr,
tibble (>= 1.4.2), tidyselect (>= 0.2.4), utils, vctrs, withr

magrittr, methods, purrr (>= 0.2.5), R6 (>= 2.2.2), rlang (>=
'data-nycflights13.R' 'db-escape.R' 'db-io.R' 'db-sql.R' 'db.R'
'dbplyr.R' 'explain.R' 'ident.R' 'lazy-ops.R' 'memdb.R'

person("Maximilian", "Girlich", role = "aut"),
remote database tables as if they are in-memory data frames.  Basic
features works with any database that has a 'DBI' back end; more
Collate: 'utils.R' 'sql.R' 'escape.R' 'translate-sql-cut.R'
'query-set-op.R' 'query.R' 'reexport.R' 'remote.R' 'rows.R'
Packaged: 2022-06-03 20:04:02 UTC; hadleywickham

Packaged: 2022-06-27 14:43:06 UTC; hadleywickham

Title: Density Based Clustering of Applications with Noise (DBSCAN)
person("Sunil", "Arya", role = c("ctb", "cph")),
person("David", "Mount", role = c("ctb", "cph")))
Description: A fast reimplementation of the DBSCAN clustering algorithm using the kd-tree data structure for speedup.

Copyright: ANN library is copyright University of Maryland, Sunil Arya
Description: A fast reimplementation of the density-based DBSCAN clustering algorithm for spatial data introduced by Ester et al. 'A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,' 1996. This implementation uses the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. The implementation is many times faster than the R-based implementation in package fpc.