Mon, 16 Feb 2015

New package IBDLabels with initial version 1.1
Package: IBDLabels
Type: Package
Title: Convert Between Different IBD-State Labelling Schemes
Version: 1.1
Date: 2014-12-15
Author: Fiona Grimson
Maintainer: Fiona Grimson
Description: Convert "label", "lexicographic", "jacquard" and "vec", full state description vector. All conversions are done to and from "label", as used in IBD_Haplo. More information regarding IBD_Haplo can be found at http://www.stat.washington.edu/thompson/Genepi/pangaea.shtml.
License: GPL-3
LazyLoad: yes
LazyData: yes
Repository: CRAN
Repository/R-Forge/Project: morgan-rtools
Repository/R-Forge/Revision: 36
Repository/R-Forge/DateTimeStamp: 2015-02-14 01:16:14
Date/Publication: 2015-02-17 01:07:25
Packaged: 2015-02-14 01:25:41 UTC; rforge
NeedsCompilation: no

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Package IBDhaploRtools updated to version 1.8 with previous version 1.7 dated 2014-07-23

Title: Functions for the Analysis of IBD Haplo Output
Description: Functions to analyze, plot, and store the output of running IBD_Haplo software package. More information regarding IBD_Haplo can be found at http://www.stat.washington.edu/thompson/Genepi/pangaea.shtml.
Author: Marshall Brown, Fiona Grimson
Maintainer: Fiona Grimson

Diff between IBDhaploRtools versions 1.7 dated 2014-07-23 and 1.8 dated 2015-02-16

 DESCRIPTION                           |   16 +++++++--------
 MD5                                   |   30 ++++++++++++++---------------
 R/ibdhap.make.calls.R                 |    4 +--
 R/ibdhap.make.true.R                  |   25 ++++++++++++++++++++----
 build/vignette.rds                    |binary
 data/ids_phased.RData                 |binary
 data/ids_unphased.RData               |binary
 data/posvec.RData                     |binary
 data/qibd_phased.RData                |binary
 data/qibd_unphased.RData              |binary
 data/trueibd_phased.RData             |binary
 inst/doc/IBDhaploRtools_tutorial.R    |    2 -
 inst/doc/IBDhaploRtools_tutorial.Rnw  |   35 +++++++++++++++++++---------------
 inst/doc/IBDhaploRtools_tutorial.pdf  |binary
 man/IBDhaploRtools-package.Rd         |    4 +--
 vignettes/IBDhaploRtools_tutorial.Rnw |   35 +++++++++++++++++++---------------
 16 files changed, 89 insertions(+), 62 deletions(-)

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Package randomUniformForest updated to version 1.1.5 with previous version 1.1.2 dated 2015-01-06

Title: Random Uniform Forests for Classification, Regression and Unsupervised Learning
Description: Ensemble model, for classification, regression and unsupervised learning, based on a forest of unpruned and randomized binary decision trees. Each tree is grown by sampling, with replacement, a set of variables at each node. Each cut-point is generated randomly, according to the continuous Uniform distribution. For each tree, data are either bootstrapped or subsampled. The unsupervised mode introduces clustering, dimension reduction and variable importance, using a three-layer engine. Random Uniform Forests are mainly aimed to lower correlation between trees (or trees residuals), to provide a deep analysis of variable importance and to allow native distributed and incremental learning.
Author: Saip Ciss
Maintainer: Saip Ciss

Diff between randomUniformForest versions 1.1.2 dated 2015-01-06 and 1.1.5 dated 2015-02-16

 randomUniformForest-1.1.2/randomUniformForest/man/combine.unsupervised.Rd                           |only
 randomUniformForest-1.1.5/randomUniformForest/DESCRIPTION                                           |    8 
 randomUniformForest-1.1.5/randomUniformForest/MD5                                                   |   50 
 randomUniformForest-1.1.5/randomUniformForest/NAMESPACE                                             |    6 
 randomUniformForest-1.1.5/randomUniformForest/NEWS                                                  |   57 
 randomUniformForest-1.1.5/randomUniformForest/R/DecisionTreesCPP.R                                  |   23 
 randomUniformForest-1.1.5/randomUniformForest/R/OnliningRandomUniformForests.R                      |   44 
 randomUniformForest-1.1.5/randomUniformForest/R/RandomUniformForestsCPP.R                           | 1142 +++++++---
 randomUniformForest-1.1.5/randomUniformForest/R/genericFunctions.R                                  |   68 
 randomUniformForest-1.1.5/randomUniformForest/inst/doc/VariableImportanceInRandomUniformForests.pdf |binary
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 28 files changed, 1932 insertions(+), 762 deletions(-)

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Package wbs updated to version 1.3 with previous version 1.2 dated 2014-08-08

Title: Wild Binary Segmentation for Multiple Change-Point Detection
Description: Provides efficient implementation of the Wild Binary Segmentation and Binary Segmentation algorithms for estimation of the number and locations of multiple change-points in the piecewise constant function plus Gaussian noise model.
Author: Rafal Baranowski and Piotr Fryzlewicz
Maintainer: Rafal Baranowski

Diff between wbs versions 1.2 dated 2014-08-08 and 1.3 dated 2015-02-16

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 26 files changed, 196 insertions(+), 215 deletions(-)

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New package stepR with initial version 1.0
Package: stepR
Title: Fitting Step-Functions
Version: 1.0
Date: 2014-08-04
Authors@R: c(person("Thomas", "Hotz", role = c("aut", "cre"), email = "thomas.hotz@tu-ilmenau.de"), person("Hannes", "Sieling", role = "aut", email = "hsielin@uni-goettingen.de"))
Depends: R (>= 2.15.3), stats
Description: Allows to fit step-functions to univariate serial data where neither the number of jumps nor their positions is known.
License: GPL-3
Classification/MSC: 62G08, 92C40, 92D20
LazyData: yes
Packaged: 2015-02-16 18:03:33 UTC; thomas
Author: Thomas Hotz [aut, cre], Hannes Sieling [aut]
Maintainer: Thomas Hotz
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2015-02-16 19:28:55

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Package R4CouchDB updated to version 0.7.1 with previous version 0.6 dated 2014-08-12

Title: An R Convenience Layer for CouchDB
Description: Provides a collection of functions for basic database and document management operations such as add, get, list or delete. Every cdbFunction() gets and returns a list() containing the connection setup. Such a list (in the documentation mostly called cdb) can be generated by cdb <- cdbIni(). Then, the list cdb provides some functions respectively functionality e.g cdb$baseUrl() or cdb$getDocRev().
Author: Thomas Bock
Maintainer: Thomas Bock

Diff between R4CouchDB versions 0.6 dated 2014-08-12 and 0.7.1 dated 2015-02-16

 DESCRIPTION             |   17 ++--
 MD5                     |   58 ++++++++--------
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 R/cdbDeleteDoc.R        |    2 
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 R/cdbListDB.R           |    6 -
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 31 files changed, 167 insertions(+), 205 deletions(-)

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New package qrNLMM with initial version 1.0
Package: qrNLMM
Type: Package
Title: Quantile Regression for Nonlinear Mixed-Effects Models
Version: 1.0
Date: 2015-01-30
Author: Christian E. Galarza and Victor H. Lachos
Maintainer: Christian E. Galarza
Imports: matrixcalc, mvtnorm, ghyp, quantreg, psych, tcltk, ald
Description: Quantile regression (QR) for Nonlinear Mixed-Effects Models via the asymmetric Laplace distribution (ALD). It uses the Stochastic Approximation of the EM (SAEM) algorithm for deriving exact maximum likelihood estimates and full inference results for the fixed-effects and variance components. It also provides graphical summaries for assessing the algorithm convergence and fitting results.
License: GPL (>= 2)
Packaged: 2015-02-16 18:07:07 UTC; ra143711
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-02-16 19:28:54

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New package DNMF with initial version 1.0
Package: DNMF
Version: 1.0
Date: 2014-02-16
Title: Discriminant Non-Negative Matrix Factorization
Author: Zhilong Jia
Maintainer: Zhilong Jia
Description: Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. This algorithm refers to an article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695.
License: GPL (>= 2)
LazyData: true
URL: https://github.com/zhilongjia/DNMF
BugReports: https://github.com/zhilongjia/DNMF/issues
Packaged: 2015-02-16 17:18:13 UTC; zjia
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-02-16 19:22:58

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New package cds with initial version 1.0
Package: cds
Type: Package
Title: Constrained Dual Scaling for Detecting Response Styles
Version: 1.0
Date: 2015-02-16
Author: Pieter Schoonees [aut, cre]
Authors@R: c(person(given = "Pieter", family = "Schoonees", role = c("aut", "cre"), email = "schoonees@gmail.com"))
Maintainer: Pieter Schoonees
Description: This is an implementation of constrained dual scaling for detecting response styles in categorical data, including utility functions. The procedure involves adding additional columns to the data matrix representing the boundaries between the rating categories. The resulting matrix is then doubled and analyzed by dual scaling. One-dimensional solutions are sought which provide optimal scores for the rating categories. These optimal scores are constrained to follow monotone quadratic splines. Clusters are introduced within which the response styles can vary. The type of response style present in a cluster can be diagnosed from the optimal scores for said cluster, and this can be used to construct an imputed version of the data set which adjusts for response styles.
Depends: R(>= 3.1.2), parallel
Imports: MASS, limSolve, clue, colorspace, copula
LazyLoad: yes
LazyData: yes
ByteCompile: yes
License: GPL (>= 2)
Packaged: 2015-02-16 17:24:39 UTC; pieter
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-02-16 19:23:01

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Package SIMMS updated to version 1.0.0 with previous version 0.0.1 dated 2013-06-11

Title: Subnetwork Integration for Multi-Modal Signatures
Description: Algorithms to create prognostic biomarkers using biological networks.
Author: Syed Haider, Michal Grzadkowski, Paul C. Boutros
Maintainer: Syed Haider

Diff between SIMMS versions 0.0.1 dated 2013-06-11 and 1.0.0 dated 2015-02-16

 mRNA_abundance.txt                                                                                               |    6 
 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/DESCRIPTION                                                          |   15 
 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/MD5                                                                  |  116 ++-
 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/NAMESPACE                                                            |   26 
 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/R/calculate.meta.survival.R                                          |   93 +--
 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/R/calculate.network.coefficients.R                                   |  301 ++++++---
 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/R/create.KM.plot.R                                                   |   12 
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 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/R/create.survobj.R                                                   |    7 
 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/R/derive.network.features.R                                          |  307 +++++-----
 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/R/dichotomize.meta.dataset.R                                         |   11 
 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/R/fit.coxmodel.R                                                     |  135 +++-
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 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/R/get.adjacency.matrix.R                                             |    2 
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 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/man/SIMMS.Rd                                                         |   14 
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 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/man/create.KM.plot.Rd                                                |    4 
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 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/man/create.survobj.Rd                                                |    6 
 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/man/derive.network.features.Rd                                       |    9 
 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/man/dichotomize.meta.dataset.Rd                                      |    4 
 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/man/fit.coxmodel.Rd                                                  |    6 
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 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/man/fit.survivalmodel.Rd                                             |    5 
 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/man/get.program.defaults.Rd                                          |    2 
 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/man/load.cancer.datasets.Rd                                          |    7 
 tmp/cranberry712862096ace/SIMMS-1.0.0/SIMMS/man/pred.survivalmodel.Rd                                            |   10 
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New package sgt with initial version 1.0
Package: sgt
Version: 1.0
Type: Package
Title: Skewed Generalized T
Date: 2015-02-12
Author: Carter Davis
Maintainer: Carter Davis
Depends: R (>= 2.4.0), maxLik (>= 1.2-4)
Description: Density, distribution function, quantile function and random generation for the skewed generalized t distribution. This package also provides a function that can fit data to the skewed generalized t distribution using maximum likelihood estimation.
License: GPL (>= 3)
Repository: CRAN
Packaged: 2015-02-16 16:35:38 UTC; carterdavis
NeedsCompilation: yes
Date/Publication: 2015-02-16 17:58:53

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Package SCGLR updated to version 2.0.2 with previous version 2.0.1 dated 2014-10-14

Title: Supervised Component Generalized Linear Regression
Description: The Fisher Scoring Algorithm is extended so as to combine Partial Least Squares regression with Generalized Linear Model estimation in the multivariate context.
Author: Guillaume Cornu [cre, aut], Frederic Mortier [aut], Catherine Trottier [aut], Xavier Bry [aut]
Maintainer: Guillaume Cornu

Diff between SCGLR versions 2.0.1 dated 2014-10-14 and 2.0.2 dated 2015-02-16

 DESCRIPTION                   |   30 ++++++++-------
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 43 files changed, 178 insertions(+), 155 deletions(-)

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Package PKfit updated to version 1.2.5 with previous version 1.2.4 dated 2014-10-21

Title: Data Analysis Tool for Pharmacokinetics
Description: A nonlinear regression program which is designed to perform model/curve fitting and model simulations for pharmacokinetics.
Author: Chun-ying Lee, Yung-jin Lee
Maintainer: Yung-jin Lee

Diff between PKfit versions 1.2.4 dated 2014-10-21 and 1.2.5 dated 2015-02-16

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Package PCIT updated to version 1.5-3 with previous version 1.04-5 dated 2014-01-29

Title: Partial Correlation Coefficient with Information Theory
Description: Apply Partial Correlation coefficient with Information Theory (PCIT) to a correlation matrix. The PCIT algorithm identifies meaningful correlations to define edges in a weighted network. The algorithm can be applied to any correlation-based network including but not limited to gene co-expression networks. To reduce compute time by making use of multiple compute cores, simply run PCIT on a computer with has multiple cores and also has the Rmpi package installed. PCIT will then auto-detect the multicore environment and run in parallel mode without the need to rewrite your scripts. This makes scripts, using PCIT, portable across single core (or no Rmpi package installed) computers which will run in serial mode and multicore (with Rmpi package installed) computers which will run in parallel mode.
Author: Nathan S. Watson-Haigh
Maintainer: Nathan S. Watson-Haigh

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New package glba with initial version 0.2
Package: glba
Version: 0.2
Title: General Linear Ballistic Accumulator Models
Date: 2015-02-15
Author: Ingmar Visser
Maintainer: Ingmar Visser
Depends: R (>= 3.1.2)
Description: Analyses response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.
License: GPL
Packaged: 2015-02-16 13:34:50 UTC; ingmar
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-02-16 17:45:52

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Package FunChisq updated to version 2.0.0 with previous version 1.0 dated 2014-03-07

Title: Chi-Square Tests for Non-Parametric Functional Dependencies
Description: Statistical hypothesis testing methods for non-parametric functional dependencies based on chi-square statistics. These tests were designed to reveal causality based on evidence from non-parametric functional dependencies. It implements an asymptotic non-constant functional chi-square test, an exact functional test, and a comparative functional chi-square test. The normalized functional chi-square test was used by the Best Performer NMSUSongLab in DREAM8 Breast Cancer Network Inference Challenges. These tests offer an advantage over regression analysis when a parametric functional form cannot be assumed.
Author: Yang Zhang, Hua Zhong, and Joe Song
Maintainer: Joe Song

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Package FDboost updated to version 0.0-8 with previous version 0.0-5 dated 2014-04-22

Title: Boosting Functional Regression Models
Description: Regression models for functional data, i.e. scalar-on-function, function-on-scalar and function-on-function regression models are fitted using a component-wise gradient boosting algorithm.
Author: Sarah Brockhaus
Maintainer: Sarah Brockhaus

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Package BANFF updated to version 0.3 with previous version 0.2 dated 2015-01-08

Title: Bayesian Network Feature Finder
Description: Provides a full package of posterior inference, model comparison, and graphical illustration of model fitting. A parallel computing algorithm for the Markov chain Monte Carlo (MCMC) based posterior inference and an Expectation-Maximization (EM) based algorithm for posterior approximation are are developed, both of which greatly reduce the computational time for model inference.
Author: Zhou Lan, Yize Zhao, Jian Kang, Tianwei Yu
Maintainer: Zhou Lan

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Package saeSim updated to version 0.7.0 with previous version 0.6.0 dated 2014-10-22

Title: Simulation Tools for Small Area Estimation
Description: Tools for the simulation of data in the context of small area estimation. Combine all steps of your simulation - from data generation over drawing samples to model fitting - in one object. This enables easy modification and combination of different scenarios. You can store your results in a folder or start the simulation in parallel.
Author: Sebastian Warnholz [aut, cre], Timo Schmid [aut]
Maintainer: Sebastian Warnholz

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Package Rlibeemd updated to version 1.3.2 with previous version 1.3.1 dated 2015-02-09

Title: Ensemble Empirical Mode Decomposition (EEMD) and Its Complete Variant (CEEMDAN)
Description: An R interface for C library libeemd for performing the ensemble empirical mode decomposition (EEMD), its complete variant (CEEMDAN) or the regular empirical mode decomposition (EMD).
Author: Jouni Helske [aut, cre] (R interface), Perttu Luukko [aut] (Original libeemd C library)
Maintainer: Jouni Helske

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Package retistruct updated to version 0.5.10 with previous version 0.5.9 dated 2014-12-02

Title: Retinal Reconstruction Program
Description: Reconstructs retinae by morphing a flat surface with cuts (a dissected flat-mount retina) onto a curvilinear surface (the a standard retinal shape). It can estimate the position of a point on the intact adult retina to within 8 degrees of arc (3.6% of nasotemporal axis). The coordinates in reconstructed retinae can be transformed to visuotopic coordinates.
Author: David C. Sterratt [aut, cre, cph], Daniel Lyngholm [aut, cph]
Maintainer: David C. Sterratt

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Package lsbclust updated to version 1.0.1 with previous version 1.0 dated 2015-02-10

Title: Least-Squares Bilinear Clustering for Three-Way Data
Description: Functions for performing least-squares bilinear clustering of three-way data. The method uses the bilinear decomposition (or biadditive model) to model two-way matrix slices while clustering over the third way. Up to four different types of clusters are included, one for each term of the bilinear decomposition. In this way, matrices are clustered simultaneously on (a subset of) their overall means, row margins, column margins and row-column interactions. The orthogonality of the bilinear model results in separability of the joint clustering problem into four separate ones. Three of these subproblems are specific k-means problems, while a special algorithm is implemented for the interactions. Plotting methods are provided, including biplots for the low-rank approximations of the interactions.
Author: Pieter Schoonees [aut, cre], Patrick Groenen [ctb]
Maintainer: Pieter Schoonees

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Package HDPenReg updated to version 0.91 with previous version 0.90 dated 2014-11-03

Title: High-Dimensional Penalized Regression
Description: Algorithms for lasso and fused-lasso problems: implementation of the lars algorithm [1], for the lasso and fusion penalization and EM-based algorithms for (logistic) lasso and fused-lasso.
Author: Quentin Grimonprez [aut, cre], Serge Iovleff [ctb]
Maintainer: Quentin Grimonprez

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New package fdrDiscreteNull with initial version 1.0
Package: fdrDiscreteNull
Type: Package
Title: False Discovery Rate Procedure Under Discrete Null Distributions
Version: 1.0
Date: 2015-02-06
Author: Xiongzhi Chen and R.W. Doerge
Maintainer: Xiongzhi Chen
Description: It is known that current false discovery rate (FDR) procedures can be very conservative when applied to p-values (and test statistics) with discrete (and heterogeneous) null distributions. This package implements the more powerful weighted generalized FDR procedure that adapts to these two features of the discrete paradigm for multiple testing. The package takes in the original data set rather than the p-values in order to carry out the adjustments needed for multiple testing in this paradigm. The methodology applies also to multiple testing where the null p-values are uniformly distributed. The package implements the method for three types of test statistics and their p-values: (a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions, (c) the exact negative binomial test on if two independent negative binomial distributions with the same size parameter have the same means. It depends on the R packages ``MCMCpack'' to use its function ``dnoncenhypergeom'' for hypergeometric distributions, and edgeR to uses its normalization techniques for data that follow negative binomial distributions.
Imports: MCMCpack, edgeR
Depends: R(>= 3.1.0)
URL: http://www.princeton.edu/~xiongzhi/fdrDiscreteNull
License: LGPL
Encoding: UTF-8
Packaged: 2015-02-07 02:21:30 UTC; Admin
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-02-16 14:10:01

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Package bde updated to version 1.0.1 with previous version 1.0 dated 2014-11-13

Title: Bounded Density Estimation
Description: A collection of S4 classes which implements different methods to estimate and deal with densities in bounded domains. That is, densities defined within the interval [lower.limit, upper.limit], where lower.limit and upper.limit are values that can be set by the user.
Author: Guzman Santafe, Borja Calvo, Aritz Perez and Jose A. Lozano
Maintainer: Guzman Santafe

Diff between bde versions 1.0 dated 2014-11-13 and 1.0.1 dated 2015-02-16

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Package sybilEFBA updated to version 1.0.2 with previous version 1.0.1 dated 2013-10-24

Title: Using Gene Expression Data to Improve Flux Balance Analysis Predictions
Description: Three different approaches to use gene expression data (or protein measurements) for improving FBA predictions.
Author: Abdelmoneim Amer Desouki [aut, cre]
Maintainer: Abdelmoneim Amer Desouki

Diff between sybilEFBA versions 1.0.1 dated 2013-10-24 and 1.0.2 dated 2015-02-16

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Package rms updated to version 4.3-0 with previous version 4.2-1 dated 2014-09-18

Title: Regression Modeling Strategies
Description: Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. rms is a collection of functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models, ordinal models for continuous Y with a variety of distribution families, and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. rms works with almost any regression model, but it was especially written to work with binary or ordinal regression models, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression.
Author: Frank E Harrell Jr
Maintainer: Frank E Harrell Jr

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Package ramps updated to version 0.6-13 with previous version 0.6-12 dated 2013-11-12

Title: Bayesian Geostatistical Modeling with RAMPS
Description: Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples. Package performance is tuned for large spatial datasets.
Author: Brian J Smith [aut, cre], Jun Yan [aut], Mary Kathryn Cowles [aut]
Maintainer: Brian J Smith

Diff between ramps versions 0.6-12 dated 2013-11-12 and 0.6-13 dated 2015-02-16

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Package phia updated to version 0.2-0 with previous version 0.1-5 dated 2013-10-12

Title: Post-Hoc Interaction Analysis
Description: Analysis of terms in linear, generalized and mixed linear models, on the basis of multiple comparisons of factor contrasts. Specially suited for the analysis of interaction terms.
Author: Helios De Rosario-Martinez [aut, cre], John Fox [ctb], R Core Team [ctb]
Maintainer: Helios De Rosario-Martinez

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Package mets updated to version 1.1.0 with previous version 1.0 dated 2014-11-18

Title: Analysis of Multivariate Event Times
Description: Implementation of various statistical models for multivariate event history data. Including multivariate cumulative incidence models, and bivariate random effects probit models (Liability models). Also contains two-stage binomial modelling that can do pairwise odds-ratio dependence modelling based marginal logistic regression models. This is an alternative to the alternating logistic regression approach (ALR).
Author: Klaus K. Holst and Thomas Scheike
Maintainer: Klaus K. Holst

Diff between mets versions 1.0 dated 2014-11-18 and 1.1.0 dated 2015-02-16

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Package LPS updated to version 1.0.10 with previous version 1.0.4 dated 2013-12-20

Title: Linear Predictor Score, for Binary Inference from Multiple Continuous Variables
Description: An implementation of the Linear Predictor Score approach, as initiated by Radmacher et al. (J Comput Biol 2001) and enhanced by Wright et al. (PNAS 2003) for gene expression signatures. Several tools for unsupervised clustering of gene expression data are also provided.
Author: Sylvain Mareschal
Maintainer: Sylvain Mareschal

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Package Hmisc updated to version 3.15-0 with previous version 3.14-6 dated 2014-11-21

Title: Harrell Miscellaneous
Description: Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX code, and recoding variables.
Author: Frank E Harrell Jr , with contributions from Charles Dupont and many others.
Maintainer: Frank E Harrell Jr

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Package HH updated to version 3.1-15 with previous version 3.1-14 dated 2015-01-09

Title: Statistical Analysis and Data Display: Heiberger and Holland
Description: Support software for Statistical Analysis and Data Display (First Edition, Springer, ISBN 0-387-40270-5, 2004) and (Second Edition, Springer, ISBN 978-1-4939-2121-8, anticipated 2015) by Richard M. Heiberger and Burt Holland. This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print. All chapters have exercises. All functions introduced in the book are in the package. R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.
Author: Richard M. Heiberger
Maintainer: Richard M. Heiberger

Diff between HH versions 3.1-14 dated 2015-01-09 and 3.1-15 dated 2015-02-16

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More information about HH at CRAN
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Package HDclassif updated to version 1.2.3 with previous version 1.2.2 dated 2013-01-08

Title: High Dimensional Supervised Classification and Clustering
Description: Discriminant analysis and data clustering methods for high dimensional data, based on the assumption that high-dimensional data live in different subspaces with low dimensionality proposing a new parametrization of the Gaussian mixture model which combines the ideas of dimension reduction and constraints on the model.
Author: Laurent Berge, Charles Bouveyron and Stephane Girard
Maintainer: Laurent Berge

Diff between HDclassif versions 1.2.2 dated 2013-01-08 and 1.2.3 dated 2015-02-16

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 31 files changed, 196 insertions(+), 158 deletions(-)

More information about HDclassif at CRAN
Permanent link


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