c(person(given = "Leena",
family = "Choi",
role = c("aut", "cre"),
email = "leena.choi@Vanderbilt.Edu",
comment = c(ORCID = "0000-0002-2544-7090")),
person(given = "Cole",
family = "Beck",
role = "aut",
email = "cole.beck@vumc.org",
comment = c(ORCID = "0000-0002-6849-6255")),
person(given = "Zhouwen",
family = "Liu",
role = "aut"),
person(given = "Ryan",
family = "Moore",
role = "aut",
email = "ryan.moore@vumc.org"),
person(given = "Charles E.",
family = "Matthews",
role = "aut"),
person(given = "Maciej S.",
family = "Buchowski",
role = "aut"))
It provides a function "wearingMarking" for classification of monitor
wear and nonwear time intervals in accelerometer data collected to assess
physical activity. The package also contains functions for making plot for
accelerometer data and obtaining the summary of various information including
daily monitor wear time and the mean monitor wear time during valid days.
"deliveryPred" and "markDelivery" can classify days for ActiGraph delivery by mail;
"deliveryPreprocess" can process accelerometry data for analysis by zeropadding incomplete
days and removing low activity days; "markPAI" can categorize physical activity
intensity level based on user-defined cut-points of accelerometer counts. It also
supports importing ActiGraph AGD files with "readActigraph" and "queryActigraph" functions.
RSQLite, DBI, data.table, e1071, keras, randomForest,
reticulate, rms
Leena Choi [aut, cre] (<https://orcid.org/0000-0002-2544-7090>),
Cole Beck [aut] (<https://orcid.org/0000-0002-6849-6255>),
Zhouwen Liu [aut],
Ryan Moore [aut],
Charles E. Matthews [aut],
Maciej S. Buchowski [aut]

Bayesian Model to Find Changepoints Based on Rates and Count
Data
This function fits a reversible jump Bayesian piecewise exponential model that also includes the intensity of each event considered along with the rate of events.

c(person("Gleb", "Beliakov", role = c("aut", "cre"),
email = "gleb@deakin.edu.au"),
person("Gita", "Das", role = "aut"),
person("Jonathan", "Wells", role = "ctb"),
person("Hewlett-Packard", "Company", role = "ctb"),
person("Silicon Graphics Computer Systems", "Inc.", role = "ctb"))
Gleb Beliakov [aut, cre],
Gita Das [aut],
Jonathan Wells [ctb],
Hewlett-Packard Company [ctb],
Silicon Graphics Computer Systems Inc. [ctb]
Global univariate minimization of Lipschitz functions is performed by using Pijavski method, which was published in Pijavski (1972) <DOI:10.1016/0041-5553(72)90115-2>.
Implementation of the binary heap is by Hewlett-Packard
Company (1994), Silicon Graphics Computer Systems,
Inc.(1996-1999). Modifications to the code by Johnathan Wells
(2002)