c(person("Xiongtao", "Dai", email="xdai@iastate.edu",
role=c("aut", "cre", "cph")),
person("Hao", "Wang", role="aut"),
person("Jan", "Dvorak", role="ctb"),
person("Jeffrey", "Bennetzen", role="ctb"),
person("Hans-Georg", "Mueller", role="ctb"))

Multiple Hypothesis Testing on an Aggregation Tree Method
An implementation of the TEAM algorithm to identify local differences
between two (e.g. case and control) independent, univariate distributions, as
described in J Pura, C Chan, and J Xie (2019) <arXiv:1906.07757>. The algorithm
is based on embedding a multiple-testing procedure on a hierarchical structure
to identify high-resolution differences between two distributions. The
hierarchical structure is designed to identify strong, short-range differences
at lower layers and weaker, but long-range differences at increasing layers.
TEAM yields consistent layer-specific and overall false discovery rate control.

Turbulence time series Event Detection and classification
Yanfei Kang, Danijel Belusic and Kate Smith-Miles
TED performs event detection and classification in turbulence time series.
foreach, zoo, fields, animation, geoR, tcltk, utils,
RcppArmadillo

Turbulence Time Series Event Detection and Classification
TED performs Turbulence time series Event Detection and classification.

foreach, zoo, fields, animation, geoR, tcltk, utils, doMC,
RcppArmadillo

Trimmed Elemental Estimation for Linear Models
For fitting multiple linear regressions, the ordinary least squares approach is sensitive to outliers and/or violations of model assumptions. The trimmed elemental estimators are more robust to such situations. This package contains functions for computing the trimmed elemental estimates, as well as for creating the bias-corrected and accelerated bootstrap confidence intervals in the context of trimmed elemental regressions.

For fitting multiple linear regressions, the ordinary least squares approach is sensitive to outliers and/or violations of model assumptions. The trimmed elemental estimators are more robust to such situations. This package contains functions for computing the trimmed elemental estimates, as well as for creating the bias-corrected and accelerated bootstrap confidence intervals based on elemental regressions.