Description: Generate all necessary R/Rmd/shell files for data processing after running 'GGIR' (v2.4.0) for accelerometer data. In part 1, all csv files in the GGIR output directory were read, transformed and then merged. In part 2, the GGIR output files were checked and summarized in one excel sheet. In part 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In part 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data.

Title: Tools for Interacting with 'PostgreSQL' / 'PostGIS' Databases
Description: Functions to convert geometry and 'hstore' data types from
'PostgreSQL' into standard R objects, as well as to simplify
the import of R data frames (including spatial data frames) into 'PostgreSQL'.
Authors@R: person('Philippe', 'Marchand', email = 'pmarchand@sesync.org',
Imports: DBI (>= 0.3), jsonlite (>= 0.9), methods, rgdal (>= 0.8),
rgeos (>= 0.3), RPostgreSQL (>= 0.4), stringr (>= 1.0)
Packaged: 2016-04-20 19:21:56 UTC; pmarchand

Authors@R: c(person('Philippe', 'Marchand', email = 'pmarchand@sesync.org',
person('Richard', 'Ellison', role = 'aut'))
Imports: DBI (>= 0.5), jsonlite (>= 0.9), methods, rgdal (>= 0.8),
Packaged: 2016-08-29 13:24:59 UTC; pmarchand

Packaged: 2016-10-09 23:29:36 UTC; pmarchand

Note: This package is deprecated. For new projects, we recommend
using the 'sf' package to interface with geodatabases.
Authors@R: c(person('Philippe', 'Marchand', email = 'marchand.philippe@gmail.com',
Packaged: 2018-03-24 14:50:59 UTC; phmarchand

Packaged: 2019-05-03 19:41:00 UTC; phmarchand

Packaged: 2019-09-20 13:01:14 UTC; phmarchand

Description: Implements a range of facilities for post-hoc analysis and
summarizing linear models, generalized linear models and
generalized linear mixed models, including grouping and clustering
via pairwise comparisons using graph representations and efficient
algorithms for finding maximal cliques of a graph.
It has S3 methods for printing summarizing, and producing plots,
line and barplots suitable for post-hoc analyses.
Author: Rodrigo Labouriau [aut, cre] (<https://orcid.org/0000-0001-8713-6864>)