Author: Marek Walesiak <marek.walesiak@ue.wroc.pl>, Andrzej Dudek <andrzej.dudek@ue.wroc.pl>
Description: Distance measures (GDM1, GDM2,	Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap,	Davies-Bouldin),	data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method,	replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data).

Imports: ade4, e1071, rgl, R2HTML, grDevices, graphics, stats, utils

Imports: ade4, e1071, R2HTML, grDevices, graphics, stats, utils

Title: Performs Tests for Cluster Tendency of a Data Set
Authors@R: c(person("Zachariah", "Neville", email = "z.neville@stat.fsu.edu", role = c("aut", "cre")), person("Naomi", "Brownstein", email = "Naomi.Brownstein@moffitt.org", role = "aut"))
Description: Test for cluster tendency (clusterability) of a data set.
The methods implemented - reducing the data set to a single dimension using principal component analysis or computing
pairwise distances, and performing a multimodality test like the Dip Test or Silverman's Critical Bandwidth Test -
are described in Adolfsson, Ackerman, and Brownstein (2019) <doi:10.1016/j.patcog.2018.10.026>. Such methods can inform whether clustering algorithms

Authors@R: c(person("Zachariah", "Neville", email = "z.neville@stat.fsu.edu", role = c("aut", "cre")), person("Naomi", "Brownstein", email = "Naomi.Brownstein@moffitt.org", role = "aut"), person("Maya", "Ackerman", role = "aut"), person("Andreas", "Adolfsson", role = "aut"))

Title: Causal Effects from Observational Studies with Clustered
Authors@R: c(person("Brian G.", "Barkley", email = "BarkleyBG@outlook.com",
person("Bradley", "Saul", role = c("ctb")))
Author: Brian G. Barkley [aut, cre] (<https://orcid.org/0000-0003-1787-4735>),
Maintainer: Brian G. Barkley <BarkleyBG@outlook.com>
Description: Estimating causal effects from observational studies assuming
clustered (or partial) interference. These inverse probability-weighted
estimators target new estimands arising from population-level treatment
policies. The estimands and estimators are introduced in Barkley et al.
Suggests: testthat, rprojroot, knitr, rmarkdown, covr
Packaged: 2018-02-12 20:07:21 UTC; Brian G. Barkley