person("Ernest", "Guevarra", email = "ernestgmd@gmail.com", role = c("aut", "cre")),
person("Laura", "Bramley", email = "laura@validinternational.org", role = "aut"),
person("Valid", "International", email = "office@validinternational", role = "cph"))
Description: Utility functions for working with datasets gathered using
'Open Data Kit' ('ODK') <https://opendatakit.org/>. These include an API to interface with 'ODK Briefcase',
a 'Java' application for fetching and pushing 'ODK' forms and their contents,
that allows pulling of data from either a remote 'ODK Aggregate Server' or a
local 'ODK' folder, a rename function to give more human readable variable
names for 'ODK' datasets, a merge function to create a single dataframe from a
nested 'ODK' dataset and an expand function to disaggregate multiple choice
answers that have been collapsed into single code by 'ODK'.

email = "ernestgmd@gmail.com", role = c("aut", "cre", "cph")),
person("Laura", "Bramley", email = "laura@validinternational.org", role = c("aut", "cph")),
'Open Data Kit' ('ODK') <https://opendatakit.org/>. These include an API to
interface with 'ODK Briefcase', a 'Java' application for fetching and pushing
'ODK' forms and their contents, that allows pulling of data from either a remote
'ODK Aggregate Server' or a local 'ODK' folder, a rename function to give more
human readable variable names for 'ODK' datasets, a merge function to create
a single dataframe from a nested 'ODK' dataset and an expand function to
disaggregate multiple choice answers that have been collapsed into single code

Title: Access Scottish Health and Social Care Open Data
Description: Allows potential users of Scottish Health and Social Care Open Data
(<https://www.opendata.nhs.scot/>) to easily explore and extract the
Imports: glue, httr, utils, data.table, methods, jsonlite
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), mockery, digest

Imports: glue, httr, utils, data.table, jsonlite

Title: One-Sided Dynamic Principal Components
Description: Functions to compute the one-sided dynamic
principal components ('odpc') introduced in Smucler, Peña and Yohai (2017)
<arXiv:1708.04705>. 'odpc' is a novel dimension
reduction technique for multivariate time series, that is useful
for forecasting. These dynamic principal components are defined as
the linear combinations of the present and past values of the series
that minimize the reconstruction mean squared error.
Imports: methods, Rcpp (>= 0.12.7), gdpc, forecast