(>= 0.1.9), patchwork (>= 1.1.2), PhysicalActivity, reactable
shinycssloaders (>= 1.0.0), shinydashboard (>= 0.7.2),
shinydashboardPlus (>= 2.0.3), shinyFeedback (>= 0.4.0),
Packaged: 2022-10-12 14:22:30 UTC; pydemullenheim

Title: Advanced Processing and Chart Generation from activPAL Events
person("Craig", "Speirs", email = "craig@paltechnologies.com", role = c("aut", "cre")),
person("Kate", "Lyden", email = "kate@paltechnologies.com", role = c("ctb")),
person("Douglas", "Maxwell", email = "douglas@paltechnologies.com", role = c("ctb")),
person("David", "Loudon", email = "david@paltechnologies.com", role = c("ctb")))
Description: Contains functions to generate pre-defined summary statistics from activPAL
events files <http:www.palt.com>.  The package also contains functions to produce informative
graphics that visualise physical activity behaviour and trends.  This includes
generating graphs that align physical activity behaviour with additional time based
observations described by other data sets, such as sleep diaries and continuous glucose
Imports: dplyr, ggplot2, tidyr, lubridate, magrittr, methods, devtools

Title: Utilities for Parsing and Plotting Activities
Authors@R: person("Daniel", "Schafer", email = "dan.schafer@gmail.com", role = c("aut", "cre"))
Description: This contains helpful functions for parsing, managing, plotting, and
visualizing activities, most often from GPX (GPS Exchange Format) files
recorded by GPS devices. It allows easy parsing of the source files into
standard R data formats, along with functions to compute derived data for
the activity, and to plot the activity in a variety of ways.
Imports: dplyr (>= 1.0.0), geosphere (>= 1.5), ggmap (>= 3.0.0), glue

Title: Gaussian Process Based Design and Analysis for the Active
Description: The active subspace method is a sensitivity analysis technique that finds important linear combinations of input variables for a simulator. This package provides functions allowing estimation of the active subspace without gradient information using Gaussian processes as well as sequential experimental design tools to minimize the amount of data required to do so. Implements Wycoff et al. (2019) <arXiv:1907.11572>.
Imports: Rcpp (>= 0.12.18), hetGP (>= 1.1.1), lhs, numDeriv, methods