Automatic Shift Detection using a Phylogenetic EM
c(
person("Paul", "Bastide", email = "paul.bastide@m4x.org", role = c("aut", "cre")),
person("Mahendra", "Mariadassou", role = "ctb"))
Implementation of the automatic shift detection method for
Brownian Motion (BM) or Ornstein–Uhlenbeck (OU) models of trait evolution on
phylogenies. Some tools to handle equivalent shifts configurations are also
available.
combinat (>= 0.0.8), doParallel (>= 1.0.10), phytools (>=
0.5.38), Rphylopars (>= 0.2.9), testthat (>= 1.0.2), TreeSim
(>= 2.2), knitr, rmarkdown
Paul Bastide [aut, cre],
Mahendra Mariadassou [ctb]

Implementation of the automatic shift detection method for
Brownian Motion (BM) or Ornstein–Uhlenbeck (OU) models of trait evolution on
phylogenies. Some tools to handle equivalent shifts configurations are also
available. See Bastide et al. (2017) <doi:10.1111/rssb.12206> and
Bastide et al. (2018) <doi:10.1093/sysbio/syy005>.

combinat (>= 0.0.8), doParallel (>= 1.0.10), phytools (>=
0.5.38), testthat (>= 1.0.2), TreeSim (>= 2.2), knitr,
rmarkdown

Marks Periods of 'Bedrest' in Actigraph Accelerometer Data
J. Dustin Tracy, Zhiyi Xu, Sari Acra, Kong Y. Chen, Maciej S. Buchowski
Contains a function to categorize accelerometer readings collected in free-living (e.g., for 24 hours/day for 7 days), preprocessed and compressed as counts (unit-less value) in a specified time period termed epoch (e.g., 1 minute) as either bedrest (sleep) or active.  The input is a matrix with a timestamp column and a column with number of counts per epoch. The output is the same dataframe with an additional column termed bedrest. In the bedrest column each line (epoch) contains a function-generated classification 'br' or 'a' denoting bedrest/sleep and activity, respectively.  The package is designed to be used after wear/nonwear marking function in the 'PhysicalActivity' package.

J. Dustin Tracy, Zhiyi Xu, Sari Acra, Kong Y. Chen, Maciej S. Buchowski, Thomas Donnelly
Contains a function to categorize accelerometer readings collected in free-living (e.g., for 24 hours/day for 7 days), preprocessed and compressed as counts (unit-less value) in a specified time period termed epoch (e.g., 1 minute) as either bedrest (sleep) or active.  The input is a matrix with a timestamp column and a column with number of counts per epoch. The output is the same dataframe with an additional column termed bedrest. In the bedrest column each line (epoch) contains a function-generated classification 'br' or 'a' denoting bedrest/sleep and activity, respectively.  The package is designed to be used after wear/nonwear marking function in the 'PhysicalActivity' package.  Version 1.1 adds preschool thresholds and corrects for possible errors in algorithm implementation.