Imports: dplyr, DT, easypower, EnvStats, epiR, ggplot2,
ICC.Sample.Size, kappaSize, longpower, plotly, powerMediation,
powerSurvEpi, presize, pROC, pwr, pwr2, shiny, shinycssloaders,
Suggests: rmarkdown, knitr, MESS, WebPower

Title: Test of Independence & Image Structural Similarity Measure PSSIM
Author: Luann C. Jung, Haiyan Wang, Diego Maldonado, Sharad Silwal, Siti Tolos, Suojin Wang
Description: Test-based Image structural similarity measure and test of independence.
This package implements the key functions of two tasks: (1) computing
image structural similarity measure PSSIM of Wang, Maldonado and
and (2) test of independence between a response and
a covariate in presence of heteroscedastic treatment effects
proposed by Wang, Tolos, and Wang (2010) <DOI:10.1002/cjs.10068>.

Title: Features Extracted from Position Specific Scoring Matrix (PSSM)
Imports: utils, entropy, infotheo, phonTools, dtt
Description: Returns almost all features that has been extracted from Position Specific
Scoring Matrix (PSSM) so far, which is a matrix of L rows (L is protein length)
and 20 columns produced by 'PSI-BLAST' which is a program to produce
PSSM Matrix from multiple sequence alignment of proteins
see <https://www.ncbi.nlm.nih.gov/books/NBK2590/> for mor details. some
of these features are described in Zahiri, J., et al.(2013)

Imports: utils, gtools, infotheo, phonTools, dtt

person("Javad", "zahiri", email = "zahiri@modares.ac.ir", role = "aut"),
person("Alireza", "mohammadi", email = "alireza691111@gmail.com", role = c("aut","cre")))

person("Alireza", "mohammadi", email = "alireza691111@gmail.com", role = c("aut","cre")),
person("Saber", "mohammadi", email = "sabermohammdi.1994@gmail.com", role = "aut"))
Suggests: testthat, spelling, waveslim, knitr, rmarkdown

Package: PST
Version: 0.81
Date: 2012-12-10
Title: Probabilistic Suffix Trees
Author: Alexis Gabadinho <alexis.gabadinho@unige.ch>
Maintainer: Alexis Gabadinho <alexis.gabadinho@unige.ch>
Depends: R (>= 2.8.1), methods, stats4, RColorBrewer, TraMineR
Description: This package provides functions to analyse state sequences
with probabilistic suffix trees (PST), the construction that
stores variable length Markov chains (VLMC). The PST library
allows to learn VLMC models and includes many additional tools
and features to analyse sequence data with these models:
visualization tools, functions for model optimization, for
sequence prediction and artificial sequences generation,
context and pattern mining. The package is specifically adapted
to the field of social sciences by allowing to learn VLMC
models from sets of individual sequences possibly containing
missing values and to account for case weights. The library
also allows to fit segmented VLMC, where conditional transition
probabilities are estimated and stored for each value of a
covariate. This software results from research work executed
within the framework of the Swiss National Centre of Competence
in Research LIVES, which is financed by the Swiss National
Science Foundation. The authors are grateful to the Swiss
National Science Foundation for its financial support.
License: GPL (>= 2)
Encoding: latin1
URL: http://r-forge.r-project.org/projects/pst
Packaged: 2012-12-10 09:32:08 UTC; alex
Repository: CRAN
Date/Publication: 2012-12-10 10:48:32