Fri, 24 Oct 2014

Package vecsets updated to version 1.1 with previous version 1.0 dated 2014-02-11

Title: like base::sets tools but keeps duplicate elements.
Description: The base 'sets' tools follow the algebraic definition that each element of a set must be unique. Since it's often helpful to compare all elements of two vectors, this toolset treats every element as unique for counting purposes. For ease of use, all functions in vecsets have an argument 'multiple' which, when set to FALSE, reverts them to the base::set tools functionality.
Author: Carl Witthoft
Maintainer: Carl Witthoft

Diff between vecsets versions 1.0 dated 2014-02-11 and 1.1 dated 2014-10-24

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More information about vecsets at CRAN
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Package TwoStepCLogit updated to version 1.2.3 with previous version 1.2.2 dated 2013-06-11

Title: Conditional logistic regression: A two-step estimation method
Description: Conditional logistic regression with longitudinal follow up and individual-level random coefficients: A stable and efficient two-step estimation method
Author: Radu V. Craiu, Thierry Duchesne, Daniel Fortin and Sophie Baillargeon
Maintainer: Thierry Duchesne

Diff between TwoStepCLogit versions 1.2.2 dated 2013-06-11 and 1.2.3 dated 2014-10-24

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Package Surrogate updated to version 0.1-3 with previous version 0.1-2 dated 2014-07-30

Title: Evaluation of surrogate endpoints in clinical trials
Description: In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a biomarker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks (for normally distributed endpoints). Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
Author: Wim Van der Elst, Ariel Alonso & Geert Molenberghs
Maintainer: Wim Van der Elst

Diff between Surrogate versions 0.1-2 dated 2014-07-30 and 0.1-3 dated 2014-10-24

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Package sparr updated to version 0.3-6 with previous version 0.3-5 dated 2014-05-30

Title: The sparr package: SPAtial Relative Risk
Description: Provides functions to estimate kernel-smoothed relative risk functions and perform subsequent inference.
Author: T.M. Davies, M.L. Hazelton and J.C. Marshall
Maintainer: Tilman M. Davies

Diff between sparr versions 0.3-5 dated 2014-05-30 and 0.3-6 dated 2014-10-24

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Package RSQLite updated to version 1.0.0 with previous version 0.11.4 dated 2013-05-26

Title: SQLite Interface for R
Description: This package embeds the SQLite database engine in R and provides an interface compliant with the DBI package. The source for the SQLite engine (version 3.8.6) is included.
Author: Hadley Wickham [aut, cre], David A. James [aut], Seth Falcon [aut], SQLite Authors [ctb] (for the included SQLite sources), Liam Healy [ctb] (for the include SQLite extensions), RStudio [cph]
Maintainer: Hadley Wickham

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Package replicationInterval updated to version 0.2 with previous version 0.1 dated 2014-08-27

Title: Replication Interval Functions
Description: A common problem faced by journal reviewers and authors is the question of whether the results of a replication study are consistent with the original published study. One solution to this problem is to examine the effect size from the published study and generate the range of effect sizes that could reasonably be obtained (due to random sampling) in a replication attempt (i.e., calculate a replication interval). If a replication effect size falls outside the replication interval then that value could not have occurred to due the effects of sampling error alone. Alternatively, if a replication effect size falls within the replication interval then the replication results could have reasonably occurred due to the effects of sampling error alone. This package has functions that calculate the replication interval for two different types of effect sizes, namely correlation (i.e., r) and the standardized mean difference (i.e.,d-value).
Author: David Stanley
Maintainer: David Stanley

Diff between replicationInterval versions 0.1 dated 2014-08-27 and 0.2 dated 2014-10-24

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Package qmethod updated to version 1.2 with previous version 1.1 dated 2014-06-23

Title: Analysis of subjective perspectives using Q methodology
Description: Analysis of Q methodology data to identify the distinct perspectives existing within a group. This methodology is used across social, health and environmental sciences to understand diversity of attitudes, discourses, or decision-making styles (for more information, see http://qmethod.org). The function qmethod() runs the full analysis. Each step can be run separately using the corresponding functions: qflag() for automatic flagging of Q-sorts (manual flagging is optional), qzscores() for scores for statements, qda() for distinguishing and consensus statements, and qfcharact() for general characteristics of the factors. Additional functions import data from PQMethod software, export plain text data for interpretation in two flavours, print and plot Q method results in a syntethic manner. The package uses principal components and it offers the following choices: manual or automatic flagging, a number of mathematical methods for rotation, and a number of correlation coefficients for the initial correlation matrix.
Author: Aiora Zabala [aut, cre]
Maintainer: Aiora Zabala

Diff between qmethod versions 1.1 dated 2014-06-23 and 1.2 dated 2014-10-24

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Package MAINT.Data updated to version 0.3 with previous version 0.2 dated 2011-11-07

Title: Model and Analyse Interval Data
Description: MAINT.Data implements methodologies for modelling interval data, considering appropriate parameterizations of the variance-covariance matrix that take into account the intrinsic nature of interval data, and lead to five different possible configuration structures.
Author: Pedro Duarte Silva Paula Brito
Maintainer: Pedro Duarte Silva

Diff between MAINT.Data versions 0.2 dated 2011-11-07 and 0.3 dated 2014-10-24

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New package highD2pop with initial version 1.0
Package: highD2pop
Type: Package
Title: Two-Sample Tests for Equality of Means in High Dimension
Version: 1.0
Date: 2012-11-02
Author: Karl Gregory
Maintainer: Karl Gregory
Description: Performs the generalized component test from Gregory et al (2015), as well as the tests from Chen and Qin (2010), Srivastava and Kubokawa (2013), and Cai, Liu, and Xia (2014) for equality of two population mean vectors when the length of the vectors exceeds the sample size.
License: GPL (>= 2)
LazyLoad: true
LazyData: true
ZipData: no
Depends: fastclime
Packaged: 2014-10-24 14:25:33 UTC; karlgregory
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-10-25 01:28:48

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New package ruv with initial version 0.9.4
Package: ruv
Title: Detect and Remove Unwanted Variation using Negative Controls
Description: The algorithms in this package attempt to adjust for systematic errors of unknown origin in high-dimensional data. The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well. The algorithms included in this package are RUV-2, RUV-4, RUV-inv, and RUV-rinv, along with various supporting algorithms. These algorithms were proposed by Gagnon-Bartsch and Speed (2012), and by Gagnon-Bartsch, Jacob and Speed (2013). The algorithms require the user to specifiy a set of negative control variables, as described in the references.
Version: 0.9.4
Date: 2014-10-24
Author: Johann Gagnon-Bartsch
Maintainer: Johann Gagnon-Bartsch
License: GPL
URL: http://www.stat.berkeley.edu/~johann/ruv
LazyLoad: yes
Packaged: 2014-10-24 09:52:13 UTC; johann
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-10-24 13:55:18

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Package polyCub updated to version 0.5-1 with previous version 0.5-0 dated 2014-05-07

Title: Cubature over Polygonal Domains
Description: A package providing methods for cubature (numerical integration) over polygonal domains. Currently, four cubature methods are implemented: the two-dimensional midpoint rule as a simple wrapper around as.im.function() from package spatstat (Baddeley and Turner, 2005), the product Gauss cubature by Sommariva and Vianello (2007), an adaptive cubature for isotropic functions via line integrate() along the boundary (Meyer and Held, 2014), and quasi-exact methods specific to the integration of the bivariate Gaussian density over polygonal and circular domains (based on formulae from the Abramowitz and Stegun (1972) handbook). For cubature over simple hypercubes, the packages "cubature" and "R2Cuba" are more appropriate.
Author: Sebastian Meyer [aut, cre, trl], Michael Hoehle [ths]
Maintainer: Sebastian Meyer

Diff between polyCub versions 0.5-0 dated 2014-05-07 and 0.5-1 dated 2014-10-24

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More information about polyCub at CRAN
Permanent link

Package spatstat updated to version 1.39-1 with previous version 1.39-0 dated 2014-10-23

Title: Spatial Point Pattern analysis, model-fitting, simulation, tests
Description: A package for analysing spatial data, mainly Spatial Point Patterns, including multitype/marked points and spatial covariates, in any two-dimensional spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, and point patterns on a linear network. Contains about 2000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Diggle-Cressie-Loosmore-Ford, Dao-Genton) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov) are also supported. Parametric models can be fitted to point pattern data using the functions ppm, kppm, slrm similar to glm. Types of models include Poisson, Gibbs, Cox and cluster point processes. Models may involve dependence on covariates, interpoint interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise, AIC). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
Author: Adrian Baddeley , Rolf Turner and Ege Rubak , with substantial contributions of code by Kasper Klitgaard Berthelsen; Abdollah Jalilian; Marie-Colette van Lieshout; Dominic Schuhmacher; and Rasmus Waagepetersen. Additional contributions by Q.W. Ang; S. Azaele; M. Baddeley; C. Beale; M. Bell; R. Bernhardt; T. Bendtsen; A. Bevan; B. Biggerstaff; L. Bischof; R. Bivand; J.M. Blanco Moreno; F. Bonneu; J. Burgos; S. Byers; Y.M. Chang; J.B. Chen; I. Chernayavsky; Y.C. Chin; B. Christensen; J.-F. Coeurjolly; R. Corria Ainslie; M. de la Cruz; P. Dalgaard; S. Das; P.J. Diggle; P. Donnelly; I. Dryden; S. Eglen; O. Flores; E.D. Ford; P. Forbes; S. Frank; J. Franklin; N. Funwi-Gabga; O. Garcia; A. Gault; M. Genton; S. Ghalandarayeshi; J. Gilbey; J. Goldstick; P. Grabarnik; C. Graf; U. Hahn; A. Hardegen; M. Hering; M.B. Hansen; M. Hazelton; J. Heikkinen; K. Hornik; P. Hunziker; R. Ihaka; A. Jammalamadaka; R. John-Chandran; D. Johnson; M. Khanmohammadi; M. Kuhn; J. Laake; F. Lavancier; T. Lawrence; R.A. Lamb; J. Lee; G.P. Leser; H.T. Li; G. Limitsios; B. Madin; J. Marcus; K. Marchikanti; R. Mark; J. Mateu; P. McCullagh; U. Mehlig; S. Meyer; X.C. Mi; L. De Middeleer; R.K. Milne; J. Moller; E. Mudrak; G.M. Nair; N. Nava; L.S. Nielsen; F. Nunes; J.R. Nyengaard; J. Oehlschlaegel; T. Onkelinx; S. O'Riordan; E. Parilov; J. Picka; N. Picard; M. Porter; S. Protsiv; A. Raftery; S. Rakshit; P. Ramon; X. Raynaud, M. Reiter; T.O. Richardson; B.D. Ripley; E. Rosenbaum; B. Rowlingson; J. Rudokas; J. Rudge; C. Ryan; F. Safavimanesh; A. Sarkka; K. Schladitz; S. Schutte; B.T. Scott; G.C. Shen; V. Shcherbakov; I.-M. Sintorn; Y. Song; M. Spiess; M. Stevenson; K. Stucki; M. Sumner; P. Surovy; B. Taylor; T. Thorarinsdottir; B. Turlach; K. Ummer; M. Uppala; A. van Burgel; T. Verbeke; M. Vihtakari; A. Villers; F. Vinatier; H. Wang; H. Wendrock; J. Wild; C. Witthoft; S. Wong; M.E. Zamboni and A. Zeileis.
Maintainer: Adrian Baddeley

Diff between spatstat versions 1.39-0 dated 2014-10-23 and 1.39-1 dated 2014-10-24

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More information about spatstat at CRAN
Permanent link

New package PANICr with initial version 0.0.0.2
Package: PANICr
Title: PANIC Tests of Nonstationarity
Description: This package contains a methodology that makes use of the factor structure of large dimensional panels to understand the nature of nonstationarity inherent in data. This is referred to as PANIC - Panel Analysis of Nonstationarity in Idiosyncratic and Common Components. PANIC (2004) includes valid pooling methods that allow panel tests to be constructed. PANIC (2004) can detect whether the nonstationarity in a series is pervasive, or variable specific, or both. PANIC (2010) includes two new tests on the idiosyncratic component that estimates the pooled autoregressive coefficient and sample moment, respectively. The PANIC model approximates the number of factors based on Bai and Ng (2002)
Depends: R(>= 2.10.0)
Version: 0.0.0.2
License: GPL-3
LazyData: no
Author: Steve Bronder
Maintainer: Steve Bronder
URL: stevebronder.com
Suggests: knitr
VignetteBuilder: knitr
Imports: MCMCpack
Packaged: 2014-10-24 06:46:57 UTC; brond_000
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-10-24 10:55:46

More information about PANICr at CRAN
Permanent link

Package matrixpls updated to version 0.4.0 with previous version 0.3.0 dated 2014-04-30

Title: Matrix-based Partial Least Squares estimation
Description: matrixpls is implements Partial Least Squares Path Modeling algorithm and related algorithms. The algorithm implementations aim for computational efficiency using matrix algebra and covariance data. The package is designed toward Monte Carlo simulations and includes functions to perform simple Monte Carlo simulations.
Author: Mikko Rönkkö [aut, cre]
Maintainer: Mikko Rönkkö

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More information about matrixpls at CRAN
Permanent link

Package bio3d (with last version 2.1-2) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2014-10-21 2.1-2
2014-10-15 2.1-1

Permanent link
Package WRS2 updated to version 0.2-0 with previous version 0.1-0 dated 2014-10-03

Title: Wilcox Robust Estimation and Testing.
Description: A user-friendly version of Wilcox' robust statistics functions (WRS package on GitHub). It implements robust tests for ANOVA and ANCOVA as described in R. Wilcox (2012) 'Introduction to Robust Estimation and Hypothesis Testing'.
Author: Patrick Mair [cre, aut], Felix Schoenbrodt [aut], Rand Wilcox [aut]
Maintainer: Patrick Mair

Diff between WRS2 versions 0.1-0 dated 2014-10-03 and 0.2-0 dated 2014-10-24

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Package ppmlasso (with last version 1.0) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2013-10-15 1.0

Permanent link
Package spocc updated to version 0.2.2 with previous version 0.2.0 dated 2014-08-06

Title: R Interface to Many Species Occurrence Data Sources.
Description: A programmatic interface to many species occurrence data sources, including GBIF, USGS's BISON, iNaturalist, Berkeley Ecoinformatics Engine eBird, AntWeb, and more as they sources become easily available.
Author: Scott Chamberlain [aut, cre], Karthik Ram [aut], Ted Hart [aut]
Maintainer: Scott Chamberlain

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 spocc-0.2.2/spocc/man/spocc_get_credentials.Rd     |    2 
 spocc-0.2.2/spocc/man/spocc_gist.Rd                |    2 
 spocc-0.2.2/spocc/man/spocc_objects.Rd             |only
 spocc-0.2.2/spocc/man/spocc_rcharts_togeojson.Rd   |    2 
 spocc-0.2.2/spocc/man/spocc_stylegeojson.Rd        |    2 
 spocc-0.2.2/spocc/man/spocc_togeojson.Rd           |    2 
 spocc-0.2.2/spocc/man/spocc_type_sum.Rd            |    2 
 spocc-0.2.2/spocc/man/wkt_vis.Rd                   |    2 
 spocc-0.2.2/spocc/tests/testthat/test-ggmap.R      |    4 
 spocc-0.2.2/spocc/tests/testthat/test-mapleaflet.R |    4 
 spocc-0.2.2/spocc/tests/testthat/test-occ.R        |   57 +--
 spocc-0.2.2/spocc/vignettes/spocc_vignette.Rmd     |  354 ++++++++++++---------
 67 files changed, 1206 insertions(+), 679 deletions(-)

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New package PHENIX with initial version 1.0
Package: PHENIX
Type: Package
Title: Phenotypic Integration Index
Version: 1.0
Date: 2014-10-15
Author: R. Torices, A. J. Muñoz-Pajares
Maintainer: A. J. Muñoz-Pajares
Imports: ppcor
Encoding: UTF-8
Description: Provides functions to estimate the size-controlled phenotypic integration index, a novel method by Torices & Méndez (2014) to solve problems due to individual size when estimating integration (namely, larger individuals have larger components, which will drive a correlation between components only due to resource availability that might overestimate the observed measures of integration). In addition, the package also provides the classical estimation by Wagner (1984) and a bootstrapping method to test the significance of both integration indices.
License: GPL (>= 2)
Packaged: 2014-10-23 20:33:24 UTC; ajesusmp
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-10-24 07:51:38

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New package P2C2M with initial version 0.5
Package: P2C2M
Type: Package
Title: Posterior Predictive Checks of Coalescent Models
Version: 0.5
Date: 2014-10-23
Author: Michael Gruenstaeudl, Noah Reid
Maintainer: Michael Gruenstaeudl
Depends: R (>= 3.0.0)
Imports: ape (>= 3.1-4), apTreeshape (>= 1.4-5), ggplot2 (>= 1.0.0), rPython (>= 0.0-5), stringr (>= 0.6.2)
Suggests: genealogicalSorting (>= 0.92), phybase (>= 1.3.1), Rmpi (>= 0.6-5), xtermStyle (>= 2.2-4)
Description: P2C2M is an R package to conduct posterior predictive checks of coalescent models using gene and species trees generated by BEAST and *BEAST, respectively. The functionality of P2C2M can be extended via two third-party R packages that are available from the author websites only: genealogicalSorting (http://www.genealogicalsorting.org) and phybase (http://odyssey.bioinformatics.uga.edu/~lliu/phybase/). To use these optional packages, installation of the Python libraries NumPy (>= 1.9.0) and DendroPy (= 3.12.0) is necessary.
License: GPL (>= 2)
OS_type: unix
NeedsCompilation: yes (automatic)
SystemRequirements: Python (= 2.7)
Packaged: 2014-10-23 06:55:27 UTC; michael
Repository: CRAN
Date/Publication: 2014-10-24 07:51:37

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