Thu, 23 Mar 2017

Package monmlp updated to version 1.1.4 with previous version 1.1.3 dated 2015-07-23

Title: Monotone Multi-Layer Perceptron Neural Network
Description: Train and make predictions from a multi-layer perceptron neural network with optional partial monotonicity constraints.
Author: Alex J. Cannon
Maintainer: Alex J. Cannon <alex.cannon@canada.ca>

Diff between monmlp versions 1.1.3 dated 2015-07-23 and 1.1.4 dated 2017-03-23

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Package GEVcdn updated to version 1.1.5 with previous version 1.1.4 dated 2015-07-23

Title: GEV Conditional Density Estimation Network
Description: Implements a flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology.
Author: Alex J. Cannon
Maintainer: Alex J. Cannon <alex.cannon@canada.ca>

Diff between GEVcdn versions 1.1.4 dated 2015-07-23 and 1.1.5 dated 2017-03-23

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Package CaDENCE updated to version 1.2.4 with previous version 1.2.3 dated 2015-07-23

Title: Conditional Density Estimation Network Construction and Evaluation
Description: Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, and the like.
Author: Alex J. Cannon
Maintainer: Alex J. Cannon <alex.cannon@canada.ca>

Diff between CaDENCE versions 1.2.3 dated 2015-07-23 and 1.2.4 dated 2017-03-23

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Package NormPsy updated to version 1.0.5 with previous version 1.0.4 dated 2016-12-13

Title: Normalisation of Psychometric Tests
Description: Functions for normalizing psychometric test scores. The normalization aims at correcting the metrological properties of the psychometric tests such as the ceiling and floor effects and the curvilinearity (unequal interval scaling). Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided.
Author: Cecile Proust-Lima, Viviane Philipps
Maintainer: Cecile Proust-Lima <cecile.proust-lima@inserm.fr>

Diff between NormPsy versions 1.0.4 dated 2016-12-13 and 1.0.5 dated 2017-03-23

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Package lcmm updated to version 1.7.7 with previous version 1.7.6 dated 2016-12-13

Title: Extended Mixed Models Using Latent Classes and Latent Processes
Description: Estimation of various extensions of the mixed models including latent class mixed models, joint latent latent class mixed models and mixed models for curvilinear univariate or multivariate longitudinal outcomes using a maximum likelihood estimation method.
Author: Cecile Proust-Lima, Viviane Philipps, Amadou Diakite and Benoit Liquet
Maintainer: Cecile Proust-Lima <cecile.proust-lima@inserm.fr>

Diff between lcmm versions 1.7.6 dated 2016-12-13 and 1.7.7 dated 2017-03-23

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Package rfUtilities updated to version 2.0-1 with previous version 2.0-0 dated 2016-04-27

Title: Random Forests Model Selection and Performance Evaluation
Description: Utilities for Random Forest model selection, class balance correction, significance test, cross validation and partial dependency plots.
Author: Jeffrey S. Evans [cre, aut], Melanie A. Murphy [ctb]
Maintainer: Jeffrey S. Evans <jeffrey_evans@tnc.org>

Diff between rfUtilities versions 2.0-0 dated 2016-04-27 and 2.0-1 dated 2017-03-23

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New package multdyn with initial version 1.5.1
Package: multdyn
Version: 1.5.1
Date: 2017-03-21
Title: Multiregression Dynamic Models
Author: Simon Schwab <schw4b@gmail.com>, Ruth Harbord <r.harbord@warwick.ac.uk>, Lilia Costa <liliacosta@ufba.br>, Thomas Nichols <t.e.nichols@warwick.ac.uk>
Maintainer: Simon Schwab <schw4b@gmail.com>
Imports: Rcpp (>= 0.12.4), data.table (>= 1.10.0), reshape2 (>= 1.4.2), ggplot2 (>= 2.2.1)
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat
Description: The Multiregression Dynamic Models (MDM) are a multivariate graphical model for a multidimensional time series that allows the estimation of time-varying effective connectivity.
Depends: R (>= 3.2.0)
License: GPL-3
URL: https://github.com/schw4b/multdyn
BugReports: https://github.com/schw4b/multdyn/issues
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2017-03-23 17:00:11 UTC; simon
Repository: CRAN
Date/Publication: 2017-03-23 17:14:28 UTC

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New package lero.lero with initial version 0.1
Package: lero.lero
Type: Package
Title: Generate 'Lero Lero' Quotes
Version: 0.1
Date: 2017-03-22
Author: Lucas Processi and Luiz Aizemberg
Maintainer: Lucas Processi <lucasprocessi@gmail.com>
Description: Generates quotes from 'Lero Lero', a database for meaningless sentences filled with corporate buzzwords, intended to be used as corporate lorem ipsum (see <http://www.lerolero.com/> for more information). Unfortunately, quotes are currently portuguese-only.
License: GPL
Depends: stringr
LazyData: TRUE
Packaged: 2017-03-23 17:32:21 UTC; ldpro
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2017-03-23 17:49:36 UTC

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Package diverse updated to version 0.1.5 with previous version 0.1.4 dated 2016-12-09

Title: Diversity Measures for Complex Systems
Description: Computes the most common diversity measures used in social and other sciences, and includes new measures from interdisciplinary research.
Author: Miguel R. Guevara <miguel.guevara@upla.cl>, Dominik Hartmann <d.hartmann@uni-hohenheim.de>, Marcelo Mendoza <marcelo.mendoza@usm.cl>
Maintainer: Miguel R. Guevara <miguel.guevara@upla.cl>

Diff between diverse versions 0.1.4 dated 2016-12-09 and 0.1.5 dated 2017-03-23

 DESCRIPTION   |   10 ++++++----
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New package RApiDatetime with initial version 0.0.1
Package: RApiDatetime
Type: Package
Title: R API Datetime
Version: 0.0.1
Date: 2017-03-23
Author: Dirk Eddelbuettel and R Core (original code)
Maintainer: Dirk Eddelbuettel <edd@debian.org>
Description: Access to the C-level R date and datetime code is provided for C-level API use by other packages via registration of native functions. Client packages simply include a single header 'RApiDatetime.h' provided by this package, and also 'import' it. The R Core group is the original author of the code made available with slight modifications by this package.
OS_type: unix
License: GPL (>= 2)
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2017-03-23 15:50:20.506292 UTC; edd
Repository: CRAN
Date/Publication: 2017-03-23 17:00:24

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Package phylopath updated to version 0.2.3 with previous version 0.2.2 dated 2017-01-04

Title: Perform Phylogenetic Path Analysis
Description: A comprehensive and easy to use R implementation of confirmatory phylogenetic path analysis as described by Von Hardenberg and Gonzalez-Voyer (2012) <doi:10.1111/j.1558-5646.2012.01790.x>.
Author: Wouter van der Bijl
Maintainer: Wouter van der Bijl <wouter.van.der.bijl@zoologi.su.se>

Diff between phylopath versions 0.2.2 dated 2017-01-04 and 0.2.3 dated 2017-03-23

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Package gimme updated to version 0.2-1 with previous version 0.2-0 dated 2016-11-20

Title: Group Iterative Multiple Model Estimation
Description: Automated identification and estimation of group- and individual-level relations in time series data from within a structural equation modeling framework.
Author: Stephanie Lane [aut, cre, trl], Kathleen Gates [aut], Peter Molenaar [aut], Michael Hallquist [ctb], Hallie Pike [ctb]
Maintainer: Stephanie Lane <slane@unc.edu>

Diff between gimme versions 0.2-0 dated 2016-11-20 and 0.2-1 dated 2017-03-23

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Package aLFQ updated to version 1.3.4 with previous version 1.3.3 dated 2016-01-05

Title: Estimating Absolute Protein Quantities from Label-Free LC-MS/MS Proteomics Data
Description: Determination of absolute protein quantities is necessary for multiple applications, such as mechanistic modeling of biological systems. Quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics can measure relative protein abundance on a system-wide scale. To estimate absolute quantitative information using these relative abundance measurements requires additional information such as heavy-labeled references of known concentration. Multiple methods have been using different references and strategies; some are easily available whereas others require more effort on the users end. Hence, we believe the field might benefit from making some of these methods available under an automated framework, which also facilitates validation of the chosen strategy. We have implemented the most commonly used absolute label-free protein abundance estimation methods for LC-MS/MS modes quantifying on either MS1-, MS2-levels or spectral counts together with validation algorithms to enable automated data analysis and error estimation. Specifically, we used Monte-carlo cross-validation and bootstrapping for model selection and imputation of proteome-wide absolute protein quantity estimation. Our open-source software is written in the statistical programming language R and validated and demonstrated on a synthetic sample.
Author: George Rosenberger, Hannes Roest, Christina Ludwig, Ruedi Aebersold, Lars Malmstroem
Maintainer: George Rosenberger <rosenberger@imsb.biol.ethz.ch>

Diff between aLFQ versions 1.3.3 dated 2016-01-05 and 1.3.4 dated 2017-03-23

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Package optiSel updated to version 0.5 with previous version 0.3 dated 2017-02-23

Title: Optimum Contribution Selection and Population Genetics
Description: A framework for the optimization of breeding programs via optimum contribution selection. An easy to use set of function for computation of optimum contributions of selection candidates, and of the population genetic parameters to be optimized. These parameters can be estimated using pedigree or genotype information, and include kinships, kinships at native haplotype segments, and breed composition of crossbred individuals. They are suitable for managing genetic diversity, removing introgressed genetic material, and accelerating genetic gain. Additionally, functions are provided for computing genetic contributions from ancestors, inbreeding coefficients, the native effective size, the native genome equivalent, pedigree completeness, and for preparing and plotting pedigrees.
Author: Robin Wellmann
Maintainer: Robin Wellmann <r.wellmann@uni-hohenheim.de>

Diff between optiSel versions 0.3 dated 2017-02-23 and 0.5 dated 2017-03-23

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Package mokken updated to version 2.8.5 with previous version 2.8.4 dated 2016-11-19

Title: Perform Mokken Scale Analysis in R
Description: Contains functions for performing Mokken scale analysis on test and questionnaire data. It includes an automated item selection algorithm, and various checks of model assumptions.
Author: L. Andries van der Ark <L.A.vanderArk@uva.nl>
Maintainer: L. Andries van der Ark <L.A.vanderArk@uva.nl>

Diff between mokken versions 2.8.4 dated 2016-11-19 and 2.8.5 dated 2017-03-23

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Package wicket updated to version 0.3.0 with previous version 0.2.0 dated 2017-01-21

Title: Utilities to Handle WKT Spatial Data
Description: Utilities to generate bounding boxes from 'WKT' (Well-Known Text) objects and R data types, validate 'WKT' objects and convert object types from the 'sp' package into 'WKT' representations.
Author: Oliver Keyes [aut, cre]
Maintainer: Oliver Keyes <ironholds@gmail.com>

Diff between wicket versions 0.2.0 dated 2017-01-21 and 0.3.0 dated 2017-03-23

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Package tsxtreme updated to version 0.3.1 with previous version 0.3 dated 2017-03-14

Title: Bayesian Modelling of Extremal Dependence in Time Series
Description: Characterisation of the extremal dependence structure of time series, avoiding pre-processing and filtering as done typically with peaks-over-threshold methods. It uses the conditional approach of Heffernan and Tawn (2004) <DOI:10.1111/j.1467-9868.2004.02050.x> which is very flexible in terms of extremal and asymptotic dependence structures, and Bayesian methods improve efficiency and allow for deriving measures of uncertainty. For example, the extremal index, related to the size of clusters in time, can be estimated and samples from its posterior distribution obtained.
Author: Thomas Lugrin
Maintainer: Thomas Lugrin <thomas.lugrin@epfl.ch>

Diff between tsxtreme versions 0.3 dated 2017-03-14 and 0.3.1 dated 2017-03-23

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New package summariser with initial version 0.1.0
Package: summariser
Type: Package
Title: Easy Calculation and Visualisation of Confidence Intervals
Version: 0.1.0
Authors@R: person("Conor", "Neilson", email="condwanaland@gmail.com", role= c("aut", "cre"))
Maintainer: Conor Neilson <condwanaland@gmail.com>
Description: Functions to speed up the exploratory analysis of simple datasets using 'dplyr' and 'ggplot2'. Functions are provided to do the common tasks of calculating confidence intervals and visualising the results.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: dplyr, ggplot2, lazyeval, plotrix
Suggests: testthat
RoxygenNote: 6.0.1
URL: https://github.com/condwanaland/summariser
NeedsCompilation: no
Packaged: 2017-03-23 07:25:21 UTC; apple
Author: Conor Neilson [aut, cre]
Repository: CRAN
Date/Publication: 2017-03-23 13:10:18 UTC

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Package spatsurv updated to version 1.1 with previous version 1.0 dated 2017-03-07

Title: Bayesian Spatial Survival Analysis with Parametric Proportional Hazards Models
Description: Bayesian inference for parametric proportional hazards spatial survival models; flexible spatial survival models.
Author: Benjamin M. Taylor and Barry S. Rowlingson Additional contributions Ziyu Zheng
Maintainer: Benjamin M. Taylor <b.taylor1@lancaster.ac.uk>

Diff between spatsurv versions 1.0 dated 2017-03-07 and 1.1 dated 2017-03-23

 DESCRIPTION                                   |    8 
 MD5                                           |   54 ++--
 R/GMRF.R                                      |   55 ++--
 R/log_posteriors_and_derivatives.R            |  289 +++++++++++-----------
 R/log_posteriors_and_derivatives_SPDE.R       |  327 ++++++++++++--------------
 R/log_posteriors_and_derivatives_gridded.R    |  321 ++++++++++++-------------
 R/log_posteriors_and_derivatives_polygonal.R  |  319 ++++++++++++-------------
 R/nonspatial_log_likelihood_and_derivatives.R |    4 
 R/spatsurv.R                                  |    8 
 R/survspat.R                                  |    2 
 R/survspatNS.R                                |   86 +++---
 inst/CITATION                                 |   36 +-
 man/GammaFromY_SPDE.Rd                        |    2 
 man/NonSpatialLogLikelihood_or_gradient.Rd    |    2 
 man/SPDEprec.Rd                               |    2 
 man/YFromGamma_SPDE.Rd                        |    2 
 man/getgrd.Rd                                 |    2 
 man/logPosterior.Rd                           |    2 
 man/logPosterior_SPDE.Rd                      |    2 
 man/logPosterior_gridded.Rd                   |    2 
 man/logPosterior_polygonal.Rd                 |    2 
 man/maxlikparamPHsurv.Rd                      |    2 
 man/neighLocs.Rd                              |    2 
 man/neighOrder.Rd                             |    2 
 man/setupPrecMatStruct.Rd                     |    2 
 man/spatsurv-package.Rd                       |    6 
 man/survspat.Rd                               |    2 
 man/survspatNS.Rd                             |    4 
 28 files changed, 770 insertions(+), 777 deletions(-)

More information about spatsurv at CRAN
Permanent link

Package spatstat updated to version 1.50-0 with previous version 1.49-0 dated 2017-02-08

Title: Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests
Description: Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 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(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. 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 <Adrian.Baddeley@curtin.edu.au>, Rolf Turner <r.turner@auckland.ac.nz> and Ege Rubak <rubak@math.aau.dk>, with substantial contributions of code by Kasper Klitgaard Berthelsen; Ottmar Cronie; Yongtao Guan; Ute Hahn; Abdollah Jalilian; Marie-Colette van Lieshout; Tuomas Rajala; Suman Rakshit; Dominic Schuhmacher; Rasmus Waagepetersen; and Hangsheng Wang. Additional contributions by M. Adepeju; C. Anderson; Q.W. Ang; M. Austenfeld; S. Azaele; M. Baddeley; C. Beale; M. Bell; R. Bernhardt; T. Bendtsen; A. Bevan; B. Biggerstaff; A. Bilgrau; L. Bischof; C. Biscio; 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; K. Colyvas; R. Corria Ainslie; R. Cotton; M. de la Cruz; P. Dalgaard; M. D'Antuono; S. Das; T. Davies; P.J. Diggle; P. Donnelly; I. Dryden; S. Eglen; A. El-Gabbas; B. Fandohan; O. Flores; E.D. Ford; P. Forbes; S. Frank; J. Franklin; N. Funwi-Gabga; O. Garcia; A. Gault; J. Geldmann; M. Genton; S. Ghalandarayeshi; J. Gilbey; J. Goldstick; P. Grabarnik; C. Graf; U. Hahn; A. Hardegen; M.B. Hansen; M. Hazelton; J. Heikkinen; M. Hering; M. Herrmann; P. Hewson; K. Hingee; K. Hornik; P. Hunziker; J. Hywood; R. Ihaka; C. Icos; A. Jammalamadaka; R. John-Chandran; D. Johnson; M. Khanmohammadi; R. Klaver; P. Kovesi; M. Kuhn; J. Laake; F. Lavancier; T. Lawrence; R.A. Lamb; J. Lee; G.P. Leser; H.T. Li; G. Limitsios; A. Lister; B. Madin; M. Maechler; J. Marcus; K. Marchikanti; R. Mark; J. Mateu; P. McCullagh; U. Mehlig; F. Mestre; S. Meyer; X.C. Mi; L. De Middeleer; R.K. Milne; E. Miranda; J. Moller; M. Moradi; V. Morera Pujol; E. Mudrak; G.M. Nair; N. Najari; 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; B. Ramage; P. Ramon; X. Raynaud, M. Reiter; I. Renner; T.O. Richardson; B.D. Ripley; E. Rosenbaum; B. Rowlingson; J. Rudokas; J. Rudge; C. Ryan; F. Safavimanesh; A. Sarkka; C. Schank; K. Schladitz; S. Schutte; B.T. Scott; O. Semboli; F. Semecurbe; V. Shcherbakov; G.C. Shen; P. Shi; H.-J. Ship; I.-M. Sintorn; Y. Song; M. Spiess; M. Stevenson; K. Stucki; M. Sumner; P. Surovy; B. Taylor; T. Thorarinsdottir; B. Turlach; T. Tvedebrink; K. Ummer; M. Uppala; A. van Burgel; T. Verbeke; M. Vihtakari; A. Villers; F. Vinatier; S. Voss; S. Wagner; H. Wang; H. Wendrock; J. Wild; C. Witthoft; S. Wong; M. Woringer; M.E. Zamboni and A. Zeileis.
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>

Diff between spatstat versions 1.49-0 dated 2017-02-08 and 1.50-0 dated 2017-03-23

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Package simctest updated to version 2.5 with previous version 2.4.1 dated 2015-03-04

Title: Safe Implementation of Monte Carlo Tests
Description: Algorithms for the implementation and evaluation of Monte Carlo tests, as well as for their use in multiple testing procedures.
Author: Axel Gandy <a.gandy@imperial.ac.uk> with contributions from Patrick Rubin-Delanchy <patrick.rubin-delanchy@imperial.ac.uk>, Georg Hahn <g.hahn11@imperial.ac.uk>, Dong Ding <dong.ding10@imperial.ac.uk>
Maintainer: Axel Gandy <a.gandy@imperial.ac.uk>

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Package oXim updated to version 1.2.1 with previous version 1.1.2 dated 2016-12-16

Title: Oxycline Index from Matrix Echograms
Description: Tools for oxycline depth calculation from echogram matrices.
Author: Wencheng Lau-Medrano [aut, cre], Ricardo Oliveros-Ramos [ctb]
Maintainer: Wencheng Lau-Medrano <luis.laum@gmail.com>

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Package openxlsx updated to version 4.0.17 with previous version 4.0.0 dated 2017-01-09

Title: Read, Write and Edit XLSX Files
Description: Simplifies the creation of Excel .xlsx files by providing a high level interface to writing, styling and editing worksheets. Through the use of 'Rcpp', read/write times are comparable to the 'xlsx' and 'XLConnect' packages with the added benefit of removing the dependency on Java.
Author: Alexander Walker [aut, cre], Luca Braglia [ctb]
Maintainer: Alexander Walker <Alexander.Walker1989@gmail.com>

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Package imagine updated to version 1.1.1 with previous version 1.1.0 dated 2016-12-01

Title: Imaging Engine, Tools for Application of Image Filters to Data Matrices
Description: Provides fast application of image filters to data matrices, using R and C++ algorithms.
Author: Wencheng Lau-Medrano [aut, cre]
Maintainer: Wencheng Lau-Medrano <luis.laum@gmail.com>

Diff between imagine versions 1.1.0 dated 2016-12-01 and 1.1.1 dated 2017-03-23

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Package DynNom updated to version 4.1 with previous version 4.0 dated 2016-12-14

Title: Dynamic Nomograms for Linear, Generalized Linear and Proportional Hazard Models
Description: Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. Also, the generic DNbuilder() function in this package provides a simple and straightforward way to build and publish a dynamic nomogram on the web to use the app independent of R. 'DynNom' supports a variety of model objects; lm(), glm(), coxph() models and also ols(), Glm(), lrm(), cph() models in the 'rms' package.
Author: Amirhossein Jalali, Davood Roshan, Alberto Alvarez-Iglesias, John Newell
Maintainer: Amirhossein Jalali <a.jalali2@nuigalway.ie>

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Package chinese.misc updated to version 0.1.4 with previous version 0.1.3 dated 2017-03-13

Title: Miscellaneous Tools for Chinese Text Mining and More
Description: Efforts are made to make Chinese text mining easier, faster, and robust to errors. Document term matrix can be generated by only one line of code; detecting encoding, segmenting and removing stop words are done automatically. Some convenient tools are also supplied.
Author: Jiang Wu [aut, cre] (from Tsinghua University)
Maintainer: Jiang Wu <textidea@sina.com>

Diff between chinese.misc versions 0.1.3 dated 2017-03-13 and 0.1.4 dated 2017-03-23

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Permanent link

Package aroma.core updated to version 3.1.0 with previous version 3.0.0 dated 2016-01-06

Title: Core Methods and Classes Used by 'aroma.*' Packages Part of the Aroma Framework
Description: Core methods and classes used by higher-level aroma.* packages part of the Aroma Project, e.g. aroma.affymetrix and aroma.cn.
Author: Henrik Bengtsson [aut, cre, cph], Mark Robinson [ctb], Ken Simpson [ctb]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

Diff between aroma.core versions 3.0.0 dated 2016-01-06 and 3.1.0 dated 2017-03-23

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 R/999.DEPRECATED.R                                                       |   43 
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 R/AromaTransform.R                                                       |    6 
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 R/ChromosomalModel.R                                                     |    1 
 R/ChromosomeExplorer.R                                                   |    2 
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 R/CopyNumberOutliers.R                                                   |    2 
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 R/CopyNumberRegions.R                                                    |    2 
 R/CopyNumberSegmentationModel.R                                          |   18 
 R/CopyNumberSegmentationModel.plot.R                                     |    6 
 R/Explorer.R                                                             |    2 
 R/PairedPSCNData.PLOT.R                                                  |   31 
 R/ProbePositionEffects.R                                                 |    6 
 R/RichDataFrame.R                                                        |    3 
 R/norm2d.matrix.R                                                        |    2 
 R/shakyText.R                                                            |    4 
 R/smoothWRMA.matrix.R                                                    |    2 
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 man/AromaPlatform.Rd                                                     |    2 
 man/AromaRepository.Rd                                                   |    2 
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 man/AromaTransform.Rd                                                    |    2 
 man/AromaUnitCallFile.Rd                                                 |    2 
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 man/AromaUnitTypesFile.Rd                                                |    2 
 man/BinnedScatter.Rd                                                     |    4 
 man/CbsModel.Rd                                                          |    2 
 man/ChromosomalModel.Rd                                                  |    2 
 man/ChromosomeExplorer.Rd                                                |    2 
 man/CopyNumberChromosomalModel.Rd                                        |    2 
 man/CopyNumberSegmentationModel.Rd                                       |    2 
 man/Explorer.Rd                                                          |    2 
 man/GladModel.Rd                                                         |    2 
 man/HaarSegModel.Rd                                                      |    2 
 man/Non-documented_objects.Rd                                            |    3 
 man/RawCopyNumberModel.Rd                                                |    2 
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 tests/RawGenomicSignals.SEG,MP.R                                         |    2 
 tests/RawGenomicSignals.SEG.R                                            |    2 
 tests/binScatter.R                                                       |    4 
 tests/fitGenotypeCone.R                                                  |    2 
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New package affluenceIndex with initial version 1.0
Package: affluenceIndex
Type: Package
Title: Affluence Indices
Version: 1.0
Date: 2017-02-26
Author: Alicja Wolny-Dominiak and Anna Saczewska-Piotrowska
Maintainer: Alicja Wolny-Dominiak <alicja.wolny-dominiak@ue.katowice.pl>
Description: Computes the statistical indices of affluence (richness) and constructs bootstrap confidence intervals for these indices. Also computes the Wolfson polarization index.
Depends: stats
License: GPL-2
Encoding: latin2
NeedsCompilation: no
Packaged: 2017-03-23 08:19:06 UTC; Woali
Repository: CRAN
Date/Publication: 2017-03-23 13:02:42 UTC

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Package adegraphics updated to version 1.0-7 with previous version 1.0-6 dated 2016-12-13

Title: An S4 Lattice-Based Package for the Representation of Multivariate Data
Description: Graphical functionalities for the representation of multivariate data. It is a complete re-implementation of the functions available in the 'ade4' package.
Author: Stéphane Dray <stephane.dray@univ-lyon1.fr> and Aurélie Siberchicot <aurelie.siberchicot@univ-lyon1.fr>, with contributions from Jean Thioulouse. Based on earlier work by Alice Julien-Laferrière.
Maintainer: Aurélie Siberchicot <aurelie.siberchicot@univ-lyon1.fr>

Diff between adegraphics versions 1.0-6 dated 2016-12-13 and 1.0-7 dated 2017-03-23

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Package ade4 updated to version 1.7-6 with previous version 1.7-5 dated 2016-12-13

Title: Analysis of Ecological Data : Exploratory and Euclidean Methods in Environmental Sciences
Description: Multivariate data analysis and graphical display.
Author: Stéphane Dray <stephane.dray@univ-lyon1.fr>, Anne-Béatrice Dufour <anne-beatrice.dufour@univ-lyon1.fr>, and Jean Thioulouse <jean.thioulouse@univ-lyon1.fr>, with contributions from Thibaut Jombart, Sandrine Pavoine, Jean R. Lobry, Sébastien Ollier, and Aurélie Siberchicot. Based on earlier work by Daniel Chessel.
Maintainer: Aurélie Siberchicot <aurelie.siberchicot@univ-lyon1.fr>

Diff between ade4 versions 1.7-5 dated 2016-12-13 and 1.7-6 dated 2017-03-23

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New package modeval with initial version 0.1.1
Package: modeval
Type: Package
Title: Evaluation of Classification Model Options
Version: 0.1.1
Authors@R: c( person("Younggun", "You", email = "younggun@stanford.edu", role = "cre"), person("Eric", "Stickney", email = "eric13@stanford.edu", role = "aut") )
Maintainer: Younggun You <younggun@stanford.edu>
Description: Designed to assist novice to intermediate analysts in choosing an optimal classification model, particularly for working with relatively small data sets. It provides cross-validated results comparing several different models at once using a consistent set of performance metrics, so users can hone in on the most promising approach rather than attempting single model fittings at a time. The package predefined 12 most common classification models, although users are free to select from the 200+ other options available in caret package.
License: CC0
Encoding: UTF-8
LazyData: true
Imports: mlbench, e1071, purrr, magrittr, ggplot2, ModelMetrics, dplyr, stringr, gridExtra, tibble, psych, utils, knitr, caret
VignetteBuilder: knitr
RoxygenNote: 6.0.1
Suggests: testthat
NeedsCompilation: no
Packaged: 2017-03-23 06:56:55 UTC; i0206158
Author: Younggun You [cre], Eric Stickney [aut]
Repository: CRAN
Date/Publication: 2017-03-23 07:13:08 UTC

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Package weightr updated to version 1.1.1 with previous version 1.1.0 dated 2017-03-12

Title: Estimating Weight-Function Models for Publication Bias
Description: Estimates the Vevea and Hedges (1995) <doi:10.1007/BF02294384> weight-function model. By specifying arguments, users can also estimate the modified model described in Vevea and Woods (2005) <doi:10.1037/1082-989X.10.4.428>, which may be more practical with small datasets. Users can also specify moderators to estimate a linear model. The package functionality allows users to easily extract the results of these analyses as R objects for other uses. In addition, the package includes a function to launch both models as a Shiny application. Although the Shiny application is also available online, this function allows users to launch it locally if they choose.
Author: Kathleen M. Coburn [aut, cre], Jack L. Vevea [aut]
Maintainer: Kathleen M. Coburn <kcoburn@ucmerced.edu>

Diff between weightr versions 1.1.0 dated 2017-03-12 and 1.1.1 dated 2017-03-23

 DESCRIPTION |    6 +++---
 MD5         |    4 ++--
 NEWS        |   24 ++----------------------
 3 files changed, 7 insertions(+), 27 deletions(-)

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Package timedelay updated to version 1.0.6 with previous version 1.0.5 dated 2016-12-13

Title: Time Delay Estimation for Stochastic Time Series of Gravitationally Lensed Quasars
Description: We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement.
Author: Hyungsuk Tak, Kaisey Mandel, David A. van Dyk, Vinay L. Kashyap, Xiao-Li Meng, and Aneta Siemiginowska
Maintainer: Hyungsuk Tak <hyungsuk.tak@gmail.com>

Diff between timedelay versions 1.0.5 dated 2016-12-13 and 1.0.6 dated 2017-03-23

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New package shinyWidgets with initial version 0.2.0
Package: shinyWidgets
Title: Custom Inputs Widgets for Shiny
Version: 0.2.0
Authors@R: c( person("Victor", "Perrier", email = "victor.perrier@dreamrs.fr", role = c("aut", "cre")), person("Fanny", "Meyer", email = "fanny.meyer@dreamrs.fr", role = c("aut")), person("Silvio", "Moreto", role = c("ctb", "cph"), comment = "bootstrap-select"), person("Ana", "Carolina", role = c("ctb", "cph"), comment = "bootstrap-select"), person("caseyjhol", role = c("ctb", "cph"), comment = "bootstrap-select"), person("Matt", "Bryson", role = c("ctb", "cph"), comment = "bootstrap-select"), person("t0xicCode", role = c("ctb", "cph"), comment = "bootstrap-select"), person("Mattia", "Larentis", role = c("ctb", "cph"), comment = "Bootstrap Switch"), person("Emanuele", "Marchi", role = c("ctb", "cph"), comment = "Bootstrap Switch"), person("Mark", "Otto", role = "ctb", comment = "Bootstrap library"), person("Jacob", "Thornton", role = "ctb", comment = "Bootstrap library"), person(family = "Bootstrap contributors", role = "ctb", comment = "Bootstrap library"), person(family = "Twitter, Inc", role = "cph", comment = "Bootstrap library"), person(family = "Flatlogic", role = "cph", comment = "Awesome Bootstrap Checkbox"), person("mouse0270", role = c("ctb", "cph"), comment = "Material Design Switch"), person("Tristan", "Edwards", role = c("ctb", "cph"), comment = "SweetAlert") )
Description: Custom inputs widgets to use in Shiny applications.
Depends: R (>= 3.2.0)
Imports: shiny (>= 0.13), htmltools, jsonlite
License: GPL-3 | file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: shinydashboard, formatR
NeedsCompilation: no
Packaged: 2017-03-22 21:10:28 UTC; Victor
Author: Victor Perrier [aut, cre], Fanny Meyer [aut], Silvio Moreto [ctb, cph] (bootstrap-select), Ana Carolina [ctb, cph] (bootstrap-select), caseyjhol [ctb, cph] (bootstrap-select), Matt Bryson [ctb, cph] (bootstrap-select), t0xicCode [ctb, cph] (bootstrap-select), Mattia Larentis [ctb, cph] (Bootstrap Switch), Emanuele Marchi [ctb, cph] (Bootstrap Switch), Mark Otto [ctb] (Bootstrap library), Jacob Thornton [ctb] (Bootstrap library), Bootstrap contributors [ctb] (Bootstrap library), Twitter, Inc [cph] (Bootstrap library), Flatlogic [cph] (Awesome Bootstrap Checkbox), mouse0270 [ctb, cph] (Material Design Switch), Tristan Edwards [ctb, cph] (SweetAlert)
Maintainer: Victor Perrier <victor.perrier@dreamrs.fr>
Repository: CRAN
Date/Publication: 2017-03-23 06:18:22 UTC

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New package sbart with initial version 0.1.0
Package: sbart
Type: Package
Title: Sequential BART for Imputation of Missing Covariates
Version: 0.1.0
Author: Michael Daniels
Maintainer: Aarti Singh <mdstat2016@gmail.com>
Description: Implements the sequential BART (Bayesian Additive Regression Trees) approach to impute the missing covariates. The algorithm applies a Bayesian nonparametric approach on factored sets of sequential conditionals of the joint distribution of the covariates and the missingness and applying the Bayesian additive regression trees to model each of these univariate conditionals. Each conditional distribution is then sampled using MCMC algorithm. The published journal can be found at <https://doi.org/10.1093/biostatistics/kxw009> Package provides a function, seqBART(), which computes and returns the imputed values.
License: MIT + file LICENSE
LazyData: TRUE
RoxygenNote: 6.0.1
Depends: R (>= 2.10)
Imports: LaplacesDemon, msm, Rcpp
LinkingTo: Rcpp
Suggests: testthat
NeedsCompilation: yes
Packaged: 2017-03-22 19:38:15 UTC; as82986
Repository: CRAN
Date/Publication: 2017-03-23 06:18:20 UTC

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Package rzmq updated to version 0.9.1 with previous version 0.9.0 dated 2017-01-06

Title: R Bindings for ZeroMQ
Description: Interface to the 'ZeroMQ' lightweight messaging kernel (see <http://www.zeromq.org/> for more information).
Author: Whit Armstrong [aut], Jeroen Ooms [aut, cre]
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between rzmq versions 0.9.0 dated 2017-01-06 and 0.9.1 dated 2017-03-23

 DESCRIPTION    |   20 ++++++++++----------
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 src/register.c |only
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New package phase1RMD with initial version 1.0.5
Package: phase1RMD
Version: 1.0.5
Date: 2017-03-22
Title: Repeated Measurement Design for Phase I Clinical Trial
Author: Jun Yin
Maintainer: Jun Yin <vivien.jyin@gmail.com>
Depends: R (>= 3.0.0), coda, boot, rjags, mvtnorm
Suggests: R2WinBUGS
Description: Implements our Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints from multiple treatment cycles. The package also provides a novel design to account for both multidimensional toxicity endpoints and early-stage efficacy endpoints in the phase I design. For both designs, functions are provided to recommend the next dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.
License: GPL (>= 2)
Packaged: 2017-03-22 20:29:49 UTC; shihao
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2017-03-23 06:18:12 UTC

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Package multitaper updated to version 1.0-13 with previous version 1.0-12 dated 2016-04-26

Title: Spectral Analysis Tools using Multitaper Method
Description: Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates.
Author: Karim Rahim <karim.rahim@queensu.ca>, Wesley S. Burr <wesley.burr@gmail.com>
Maintainer: Karim Rahim <karim.rahim@queensu.ca>

Diff between multitaper versions 1.0-12 dated 2016-04-26 and 1.0-13 dated 2017-03-23

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Package epitools updated to version 0.5-8 with previous version 0.5-7 dated 2012-09-30

Title: Epidemiology Tools
Description: Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables.
Author: Tomas J. Aragon [aut], Michael P. Fay [ctb], Daniel Wollschlaeger [ctb], Adam Omidpanah [cre, ctb]
Maintainer: Adam Omidpanah <adam.omidpanah@wsu.edu>

Diff between epitools versions 0.5-7 dated 2012-09-30 and 0.5-8 dated 2017-03-23

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Package crunch updated to version 1.15.2 with previous version 1.15.0 dated 2017-02-23

Title: Crunch.io Data Tools
Description: The Crunch.io service <http://crunch.io/> provides a cloud-based data store and analytic engine, as well as an intuitive web interface. Using this package, analysts can interact with and manipulate Crunch datasets from within R. Importantly, this allows technical researchers to collaborate naturally with team members, managers, and clients who prefer a point-and-click interface.
Author: Neal Richardson [aut, cre]
Maintainer: Neal Richardson <neal@crunch.io>

Diff between crunch versions 1.15.0 dated 2017-02-23 and 1.15.2 dated 2017-03-23

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 R/copy.R                                            |    2 
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Package assertr updated to version 2.0.2 with previous version 2.0.0 dated 2017-03-17

Title: Assertive Programming for R Analysis Pipelines
Description: Provides functionality to assert conditions that have to be met so that errors in data used in analysis pipelines can fail quickly. Similar to 'stopifnot()' but more powerful, friendly, and easier for use in pipelines.
Author: Tony Fischetti [aut, cre]
Maintainer: Tony Fischetti <tony.fischetti@gmail.com>

Diff between assertr versions 2.0.0 dated 2017-03-17 and 2.0.2 dated 2017-03-23

 DESCRIPTION                      |    6 
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 R/predicates.R                   |    1 
 inst/doc/assertr.html            |    4 
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 tests/testthat/test-row-redux.R  |   26 +
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Package alakazam updated to version 0.2.6 with previous version 0.2.5 dated 2016-08-05

Title: Immunoglobulin Clonal Lineage and Diversity Analysis
Description: Provides immunoglobulin (Ig) sequence lineage reconstruction, diversity profiling, and amino acid property analysis.
Author: Jason Vander Heiden [aut, cre], Namita Gupta [aut], Susanna Marquez [ctb], Daniel Gadala-Maria [ctb], Roy Jiang [ctb], Steven Kleinstein [aut, cph]
Maintainer: Jason Vander Heiden <jason.vanderheiden@yale.edu>

Diff between alakazam versions 0.2.5 dated 2016-08-05 and 0.2.6 dated 2017-03-23

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 NAMESPACE                        |   10 ++
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