Process Data from Wearable Research Devices Using Two-Regression
Algorithms
Application of two-regression algorithms for wearable
research devices. It provides an easy way for users to read in
device data files and apply an appropriate two-regression
algorithm. More information is available from
Hibbing PR, LaMunion SR, Kaplan AS, & Crouter SE (2017)
<doi:10.1249/MSS.0000000000001532>.

Develop and Apply Two-Regression Algorithms
Facilitates development and application of two-regression
algorithms for research-grade wearable devices. It provides an easy
way for users to access previously-developed algorithms, and also to
develop their own. Initial motivation came from Hibbing PR, LaMunion SR,
Kaplan AS, & Crouter SE (2018) <doi:10.1249/MSS.0000000000001532>.
However, other algorithms are now supported. Please see the associated
references in the package documentation for full details of the algorithms
that are supported.
dplyr (>= 0.5.0), ggplot2 (>= 2.2.0), magrittr (>= 1.5),
gridExtra (>= 2.3), PAutilities (>= 1.1.0), pROC (>= 1.16.0),
RcppRoll, stats, rlang, lubridate, tidyr

A Two-Sample Test for the Equality of Distributions for
High-Dimensional Data
c(
person("Marta", "Cousido Rocha", email = "martacousido@uvigo.es", role = c("aut", "cre")),
person("José Carlos", "Soage González", email = "jsoage@uvigo.es", role = "ctr"),
person("Jacobo", "de Uña Álvarez", email = "jacobo@uvigo.es", role = c("aut", "ths")),
person("Jeffrey", "D. Hart", email = "hart@stat.tamu.edu", role = "aut"))
For high-dimensional data whose main feature is a large number, p, of variables but a small sample size, the null hypothesis that the marginal distributions of p variables are the same for two groups is tested. We propose a test statistic motivated by the simple idea of comparing, for each of the p variables, the empirical characteristic functions computed from the two samples. If one rejects this global null hypothesis of no differences in distributions between the two groups, a set of permutation p-values is reported to identify which variables are not equally distributed in both groups.
Marta Cousido Rocha [aut, cre],
José Carlos Soage González [ctr],
Jacobo de Uña Álvarez [aut, ths],
Jeffrey D. Hart [aut]