Title: A Non-Parametric Statistical Test to Compare Clustering
Author: Maciel C. Vidal [aut, cre], Andre Fujita [aut]
Maintainer: Maciel C. Vidal <calebe@ime.usp.br>
Description: Provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test
to compare clustering structures with applications in functional magnetic resonance imaging
data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups
simultaneously and also to identify features that contribute to the differential clustering.

Author: Ravi Varadhan <rvaradhan@jhmi.edu> and Stephanie Kovalchik
Description: Tools for assessing multiple treatment-covariate
interactions with data from a parallel-group randomized
Depends: survival, grid, methods, glmnet, MASS
Packaged: 2012-04-24 00:30:42 UTC; kovalchiksa

Packaged: 2013-01-10 00:35:47 UTC; kovalchiksa

Packaged: 2013-03-06 23:28:38 UTC; kovalchiksa

Description: The tools in this package are intended to help researchers assess multiple treatment-covariate interactions with data from a parallel-group randomized controlled clinical trial.
Depends: R (>= 2.13.0), stats, survival, MASS, graphics, grid

The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data.
The main functions are time_decompose(), anomalize(), and time_recompose().
When combined, it's quite simple to decompose time series, detect anomalies,
and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series).
Time series decomposition is used to remove trend and seasonal components via the time_decompose() function
and methods include seasonal decomposition of time series by Loess ("stl") and
seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements
two methods for anomaly detection of residuals including using an inner quartile range ("iqr")
and generalized extreme studentized deviation ("gesd"). These methods are based on
those used in the 'forecast' package and the Twitter 'AnomalyDetection' package.
Refer to the associated functions for specific references for these methods.
Imports: dplyr, glue, timetk, sweep, tibbletime, purrr, rlang, tibble,
Suggests: tidyverse, tidyquant, testthat, covr, knitr, rmarkdown,