Title: Penalized Linear Unbiased Selection

Author: Cun-Hui Zhang and Ofer Melnik, with contributions from Yi Yu
Packaged: 2012-04-06 22:47:10 UTC; stephaniezhang

Packaged: 2012-05-28 08:53:26 UTC; stephaniezhang

Description: Retrieves a 'pluscode' by inputting latitude and longitude. Includes additional functions to retrieve neighbouring 'pluscodes'.
Packaged: 2018-11-05 14:01:37 UTC; michael.doyle@paddypowerbetfair.com

Description: plusser provides an API interface to Google+ so that posts,
profiles and pages can be automatically retrieved
Authors@R: person("Christoph", "Waldhauser", email="chw@kdss.at", role=c("aut",
Imports: RCurl, RJSONIO, lubridate, plyr

profiles and pages can be automatically retrieved.

Title: Tools for splitting, applying and combining data
Description: plyr is a set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each pieces and then put all the pieces back together.
Packaged: Wed Oct  8 08:29:09 2008; hadley

Packaged: Mon Nov 17 18:44:16 2008; hadley

Packaged: Wed Nov 19 09:19:31 2008; hadley

Packaged: Fri Dec 12 08:38:57 2008; hadley

Packaged: Mon Feb 23 18:48:34 2009; hadley

Description: plyr is a set of tools that solves a common set of
problems: you need to break a big problem down into manageable
pieces, operate on each pieces and then put all the pieces back
together.  For example, you might want to fit a model to each
spatial location or time point in your study, summarise data by
panels or collapse high-dimensional arrays to simpler summary
Packaged: Wed Apr 15 07:40:19 2009; hadley

Packaged: Wed Apr 15 09:41:54 2009; hadley

Packaged: Mon Apr 20 19:44:14 2009; hadley

Title: Tools for splitting, applying and combining data.
Packaged: Mon Sep 29 17:40:41 2008; hadley

Suggests: RUnit, abind, tcltk, testthat (>= 0.2)

statistics. The development of plyr has been generously
Suggests: abind, testthat (>= 0.2), tcltk, foreach

Description: plyr is a set of tools that solves a common
set of problems: you need to break a big problem down
into manageable pieces, operate on each pieces and then
put all the pieces back together.  For example, you
might want to fit a model to each spatial location or
time point in your study, summarise data by panels or
collapse high-dimensional arrays to simpler summary
Suggests: abind, testthat (>= 0.2), tcltk, foreach, itertools,