Depends: R (>= 2.15.1), coda (>= 0.13), parallel, Matrix
Suggests: MASS, lattice, R2WinBUGS, R2OpenBUGS, BRugs, rlecuyer
Imports: methods, stats, rjags (>= 3-5), snow

Imports: methods, stats, rjags (>= 4-4), snow

as described in Solymos 2010 (R Journal 2(2):29--37).

for 'JAGS', 'WinBUGS', 'OpenBUGS', and 'Stan'.
Imports: methods, stats, rjags (>= 4-4), rstan

Package: dclong.spt
Type: Package
Title: Sequential Permutation Test
Version: 0.1.1
Date: 2013-05-06
Author: Chuanlong (Ben) Du
Maintainer: Chuanlong (Ben) Du <dclong@iastate.edu>
Description: Permutation tests are commonly used. Usually it doesn't
take much time to do a permutation test, but it can be
time-consuming if one have to do many permutation tests (e.g.,
in gene data analysis) at the same time.  This package allows
users to do sequential permutation tests which can save lots of
time. This package has implemented (both in R and C++ version)
the two sequential permutation tests discussed in the paper
Bancroft, Du and Nettleton (2013).  It also allows users to do
general sequential permutation test by offering user-defined
functions for calculating observed and permuted test
statistics. For people who are confident in writing C++ code
and willing to use C++ code to speed up computation, this
package tries all efforts to make this easy (thanks to Rcpp and
C++ variadic template). Basically a user only has to offer C++
code for calculating observed and permuted test statistics
(similar to what he/she would do using pure R code).
Depends: R (>= 2.10)
Imports: Rcpp, RcppArmadillo, inline
License: GPL (>= 2)
Collate: 'fdr.r' 'plot.spt.r' 'test_statistics.r' 'spt.r' 'est_m0.r'
'cxxwrapper.r' 'read_code.r' 'rbunif.r'
Packaged: 2013-05-12 14:29:29 UTC; dclong
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-05-12 21:00:09

Authors@R: c(person("Shaun", "Wilkinson", role = c("aut", "cre"),
person("Paolo", "Giordani", role = "aut",
Description: Contains a single function dclust() for divisive hierarchical clustering based on
recursive k-means partitioning (k = 2). Useful for clustering large datasets
where computation of a n x n distance matrix is not feasible (e.g. n > 10,000 records).
For further information see Steinbach, Karypis and Kumar (2000) <http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf>.
Author: Shaun Wilkinson [aut, cre] (<https://orcid.org/0000-0002-7332-7931>),