Processes accelerometer data, generates summaries and plots data.

A Package to Processes Accelerometer Data
Processes accelerometer data from uni-axial and tri-axial devices and generates data summaries. Also includes functions to plot and simulate accelerometer data.
R (>= 2.10), zoo, PhysicalActivity, mhsmm, timeDate

R (>= 2.10), zoo, mhsmm, PhysicalActivity

Functions for Processing and Analyzing Accelerometer Data
Processes accelerometer data from uni-axial and tri-axial devices, and generates data summaries. Also includes functions to plot, analyze, and simulate accelerometer data.
R (>= 2.10), zoo, mhsmm, PhysicalActivity, nleqslv, plyr,
methods

Processes accelerometer data from uni-axial and tri-axial devices,
and generates data summaries. Also includes functions to plot, analyze, and
simulate accelerometer data.
zoo, PhysicalActivity, nleqslv, plyr, methods, DBI, RSQLite,
circlize, ggplot2, R.utils, iterators, Rcpp

c(
person("Gudmundur", "Einarsson", , "gumeo140688@gmail.com", role = c("aut", "cre", "trl")),
person("Line", "Clemmensen", , "lkhc@dtu.dk", role = c("aut","ths")),
person("Brendan", "Ames", , "bpames@ua.edu", role = c("aut")),
person("Summer", "Atkins", , "sratkins@crimson.ua.edu", role = c("aut"))
)
Implementation of sparse linear discriminant analysis, which is a supervised
classification method for multiple classes. Various novel optimization approaches to
this problem are implemented including alternating direction method of multipliers (ADMM),
proximal gradient (PG) and accelerated proximal gradient (APG) (See Atkins et al. <arXiv:1705.07194>).
Functions for performing cross validation are also supplied along with basic prediction
and plotting functions.
Sparse zero variance discriminant analysis (SZVD) is also included in the package
(See Ames and Hong, <arXiv:1401.5492>). See the github wiki for a more extended description.
Gudmundur Einarsson [aut, cre, trl],
Line Clemmensen [aut, ths],
Brendan Ames [aut],
Summer Atkins [aut]

Implementation of sparse linear discriminant analysis, which is a supervised
classification method for multiple classes. Various novel optimization approaches to
this problem are implemented including alternating direction method of multipliers ('ADMM'),
proximal gradient (PG) and accelerated proximal gradient ('APG') (See Atkins 'et al'. <arXiv:1705.07194>).
Functions for performing cross validation are also supplied along with basic prediction
and plotting functions.
Sparse zero variance discriminant analysis ('SZVD') is also included in the package
(See Ames and Hong, <arXiv:1401.5492>). See the 'github' wiki for a more extended description.