Wed, 03 Jul 2019

Package foieGras updated to version 0.2.2 with previous version 0.2.1 dated 2019-04-04

Title: Fit Continuous-Time State-Space Models for Filtering Argos Satellite (and Other) Telemetry Data
Description: Fits continuous-time random walk and correlated random walk state-space models to filter Argos satellite location data. Template Model Builder ('TMB') is used for fast estimation. The Argos data can be: (older) least squares-based locations; (newer) Kalman filter-based locations with error ellipse information; or a mixture of both. Separate measurement models are used for these two data types. The models estimate two sets of location states corresponding to: 1) each observation, which are (usually) irregularly timed; and 2) user-specified time intervals (regular or irregular). Jonsen I, McMahon CR, Patterson TA, Auger-Methe M, Harcourt R, Hindell MA, Bestley S (2019) Movement responses to environment: fast inference of variation among southern elephant seals with a mixed effects model. Ecology 100:e02566 <doi:10.1002/ecy.2566>.
Author: Ian Jonsen [aut, cre], Toby Patterson [aut, ctb]
Maintainer: Ian Jonsen <ian.jonsen@mq.edu.au>

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New package factorMerger with initial version 0.4.0
Package: factorMerger
Title: The Merging Path Plot
Version: 0.4.0
Authors@R: c( person("Agnieszka", "Sitko", email = "ag.agnieszka.sitko@gmail.com", role = c("aut")), person("Aleksandra", "Grudziąż", email = "oll.dabrowska@gmail.com", role = c("aut")), person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut","ths")), person("Tomasz", "Mikołajczyk", email = "t.mikolajczyk@gmail.com", role = c("cre")) )
Description: The Merging Path Plot is a methodology for adaptive fusing of k-groups with likelihood-based model selection. This package contains tools for exploration and visualization of k-group dissimilarities. Comparison of k-groups is one of the most important issues in exploratory analyses and it has zillions of applications. The traditional approach is to use pairwise post hoc tests in order to verify which groups differ significantly. However, this approach fails with a large number of groups in both interpretation and visualization layer. The Merging Path Plot solves this problem by using an easy-to-understand description of dissimilarity among groups based on Likelihood Ratio Test (LRT) statistic (Sitko, Biecek 2017) <arXiv:1709.04412>. 'factorMerger' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>. Work on this package was financially supported by the 'NCN Opus grant 2016/21/B/ST6/02176'.
Depends: R (>= 3.0)
License: GPL
Encoding: UTF-8
LazyData: true
Imports: ggplot2, dplyr, reshape2, colorRamps, proxy, MASS, ggpubr, scales, mvtnorm, knitr, magrittr, survival, agricolae, forcats, formula.tools
RoxygenNote: 6.1.1
URL: https://github.com/MI2DataLab/factorMerger
Suggests: survminer, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-06-27 11:00:06 UTC; pbiecek
Author: Agnieszka Sitko [aut], Aleksandra Grudziąż [aut], Przemyslaw Biecek [aut, ths], Tomasz Mikołajczyk [cre]
Maintainer: Tomasz Mikołajczyk <t.mikolajczyk@gmail.com>
Repository: CRAN
Date/Publication: 2019-07-03 22:50:26 UTC

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Package expoRkit updated to version 0.9.4 with previous version 0.9.2 dated 2018-05-06

Title: Expokit in R
Description: An R-interface to the Fortran package Expokit.
Author: Roger B. Sidje [aut, cph], Niels Richard Hansen [aut, cre, cph]
Maintainer: Niels Richard Hansen <Niels.R.Hansen@math.ku.dk>

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Package RSGHB updated to version 1.2.2 with previous version 1.2.1 dated 2019-01-06

Title: Functions for Hierarchical Bayesian Estimation: A Flexible Approach
Description: Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: <http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html> See Train's chapter on HB in Discrete Choice with Simulation here: <http://elsa.berkeley.edu/books/choice2.html>; and his paper on using HB with non-normal distributions here: <http://eml.berkeley.edu//~train/trainsonnier.pdf>. The authors would also like to thank the invaluable contributions of Stephane Hess and the Choice Modelling Centre: <https://cmc.leeds.ac.uk/>.
Author: Jeff Dumont [aut, cre], Jeff Keller [aut], Chase Carpenter [ctb]
Maintainer: Jeff Dumont <Jeff.Dumont@rsginc.com>

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Package WebGestaltR updated to version 0.4.1 with previous version 0.4.0 dated 2019-04-27

Title: Gene Set Analysis Toolkit WebGestaltR
Description: The web version WebGestalt <http://www.webgestalt.org> supports 12 organisms, 354 gene identifiers and 321,251 function categories. Users can upload the data and functional categories with their own gene identifiers. In addition to the Over-Representation Analysis, WebGestalt also supports Gene Set Enrichment Analysis and Network Topology Analysis. The user-friendly output report allows interactive and efficient exploration of enrichment results. The WebGestaltR package not only supports all above functions but also can be integrated into other pipeline or simultaneously analyze multiple gene lists.
Author: Jing Wang [aut], Yuxing Liao [aut, cre], Eric Jaehnig [ctb], Zhiao Shi [ctb], Quanhu Sheng [ctb]
Maintainer: Yuxing Liao <yuxingliao@gmail.com>

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Package KScorrect updated to version 1.4.0 with previous version 1.2.4 dated 2018-08-14

Title: Lilliefors-Corrected Kolmogorov-Smirnov Goodness-of-Fit Tests
Description: Implements the Lilliefors-corrected Kolmogorov-Smirnov test for use in goodness-of-fit tests, suitable when population parameters are unknown and must be estimated by sample statistics. P-values are estimated by simulation. Can be used with a variety of continuous distributions, including normal, lognormal, univariate mixtures of normals, uniform, loguniform, exponential, gamma, and Weibull distributions. Functions to generate random numbers and calculate density, distribution, and quantile functions are provided for use with the log uniform and mixture distributions.
Author: Phil Novack-Gottshall, Steve C. Wang
Maintainer: Phil Novack-Gottshall <pnovack-gottshall@ben.edu>

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Package learningCurve (with last version 1.1.2) was removed from CRAN

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

2018-03-30 1.1.2
2017-03-03 1.1.1
2016-08-10 1.0

Permanent link
Package factorMerger (with last version 0.3.6) was removed from CRAN

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

2018-04-04 0.3.6
2018-02-21 0.3.5
2017-10-04 0.3.2
2017-06-30 0.3.1
2017-06-25 0.3

Permanent link
Package tricolore (with last version 1.2.0) was removed from CRAN

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

2018-09-13 1.2.0
2018-05-27 1.0.3

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Package ggpval (with last version 0.2.1) was removed from CRAN

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

2018-07-21 0.2.1
2017-07-26 0.2.0
2017-06-12 0.1.0

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Package simmr updated to version 0.4.1 with previous version 0.4.0 dated 2019-06-14

Title: A Stable Isotope Mixing Model
Description: Fits Stable Isotope Mixing Models (SIMMs) and is meant as a longer term replacement to the previous widely-used package SIAR. SIMMs are used to infer dietary proportions of organisms consuming various food sources from observations on the stable isotope values taken from the organisms' tissue samples. However SIMMs can also be used in other scenarios, such as in sediment mixing or the composition of fatty acids. The main functions are simmr_load and simmr_mcmc. The two vignettes contain a quick start and a full listing of all the features. The methods used are detailed in the papers Parnell et al 2010 <doi:10.1371/journal.pone.0009672>, and Parnell et al 2013 <doi:10.1002/env.2221>.
Author: Andrew Parnell
Maintainer: Andrew Parnell <andrew.parnell@mu.ie>

Diff between simmr versions 0.4.0 dated 2019-06-14 and 0.4.1 dated 2019-07-03

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Package Omisc updated to version 0.1.2 with previous version 0.1.1 dated 2019-04-19

Title: Univariate Bootstrapping and Other Things
Description: Primarily devoted to implementing the Univariate Bootstrap (as well as the Traditional Bootstrap). In addition there are multiple functions for DeFries-Fulker behavioral genetics models. The univariate bootstrapping functions, DeFries-Fulker functions, regression and traditional bootstrapping functions form the original core. Additional features may come online later, however this software is a work in progress. For more information about univariate bootstrapping see: Lee and Rodgers (1998) and Beasley et al (2007) <doi.org/10.1037/1082-989X.12.4.414>.
Author: Patrick O'Keefe
Maintainer: Patrick O'Keefe <patrick.okeefe@vanderbilt.edu>

Diff between Omisc versions 0.1.1 dated 2019-04-19 and 0.1.2 dated 2019-07-03

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Package MarketMatching updated to version 1.1.2 with previous version 1.1.1 dated 2018-11-14

Title: Market Matching and Causal Impact Inference
Description: For a given test market find the best control markets using time series matching and analyze the impact of an intervention. The intervention could be be a marketing event or some other local business tactic that is being tested. The workflow implemented in the Market Matching package utilizes dynamic time warping (the 'dtw' package) to do the matching and the 'CausalImpact' package to analyze the causal impact. In fact, this package can be considered a "workflow wrapper" for those two packages.
Author: Larsen Kim [aut, cre]
Maintainer: Larsen Kim <kblarsen4@gmail.com>

Diff between MarketMatching versions 1.1.1 dated 2018-11-14 and 1.1.2 dated 2019-07-03

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New package RPEIF with initial version 1.0
Package: RPEIF
Type: Package
Title: Computation and Plots of Influence Functions for Risk and Performance Measures
Version: 1.0
Date: 2019-06-30
Author: Anthony Christidis <anthony.christidis@stat.ubc.ca>, Shengyu Zhang <syzhang@uw.edu>, Douglas Martin <doug@amath.washington.edu>
Maintainer: Anthony Christidis <anthony.christidis@stat.ubc.ca>
Description: Computes the influence functions time series of the returns for the risk and performance measures as mentioned in Zhang and Martin (2017) <https://ssrn.com/abstract=2747179> as well as Chen and Martin (2018) <https://ssrn.com/abstract=3085672>. Also evaluates estimators influence functions at a set of parameter values and plots them to display the shapes of the influence functions.
License: GPL (>= 2)
Biarch: true
Imports: Rcpp (>= 0.12.17), ggplot2, PerformanceAnalytics, xts, zoo, RobStatTM, stats
Depends:
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 6.1.1
Suggests: R.rsp, testthat
VignetteBuilder: R.rsp
NeedsCompilation: yes
LazyData: true
Packaged: 2019-07-02 23:38:56 UTC; antho
Repository: CRAN
Date/Publication: 2019-07-03 16:20:07 UTC

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Package rbacon updated to version 2.3.9.1 with previous version 2.3.8 dated 2019-05-25

Title: Age-Depth Modelling using Bayesian Statistics
Description: Bacon is an approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for deposits, through combining radiocarbon and other dates with prior information. See Blaauw & Christen (2011) <doi:10.1214/11-BA618>.
Author: Maarten Blaauw [aut, cre], J. Andres Christen [aut], Judith Esquivel Vazquez [ctb], Ted Belding [cph], James Theiler [cph], Brian Gough [cph], Charles Karney [cph]
Maintainer: Maarten Blaauw <maarten.blaauw@qub.ac.uk>

Diff between rbacon versions 2.3.8 dated 2019-05-25 and 2.3.9.1 dated 2019-07-03

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New package kernelTDA with initial version 0.1.1
Package: kernelTDA
Type: Package
Title: Statistical Learning with Kernel for Persistence Diagrams
License: GPL-3
Version: 0.1.1
Date: 2019-07-01
Authors@R: c( person("Tullia", "Padellini", email = "tullia.padellini@uniroma1.it", role = c("aut", "cre")), person("Francesco", "Palini", role = "aut"), person("Pierpaolo", "Brutti", role = "ctb"), person("David", "Meyer", role = c("ctb","cph"), comment = "libsvm to R code (e1071 package)"), person("Chih-Chung", "Chang", role = c("ctb","cph"), comment = "LIBSVM C++ code"), person("Chih-Chen", "Lin", role = c("ctb","cph"), comment = "LIBSVM C++ code"), person("Michael","Kerber", role = c("ctb", "cph"), comment = "HERA C++ code"), person("Dmitriy","Morozov", role = c("ctb", "cph"), comment = "HERA C++ code"), person("Arnur","Nigmetov", role = c("ctb", "cph"), comment = "HERA C++ code"))
Maintainer: Tullia Padellini <tullia.padellini@uniroma1.it>
Description: Provides tools for exploiting topological information into standard statistical learning algorithms. To this aim, this package contains the most popular kernels defined on the space of persistence diagrams, and persistence images. Moreover, it provides a solver for kernel Support Vector Machines problems, whose kernels are not necessarily positive semidefinite, based on the C++ library 'LIBSVM' <https://www.csie.ntu.edu.tw/~cjlin/libsvm/>, and on its R implementation 'e1071'. Additionally, it allows to compute Wasserstein distance between persistence diagrams with an arbitrary ground metric, building an R interface for the C++ library 'HERA' <https://bitbucket.org/grey_narn/hera/src/master/>.
Imports: Rcpp (>= 1.0.1), mvtnorm, Rdpack, methods, stats
Suggests: TDA, knitr, rmarkdown, SparseM, Matrix, kernlab, viridis
LinkingTo: Rcpp, RcppEigen, BH
RoxygenNote: 6.1.1
VignetteBuilder: knitr
RdMacros: Rdpack
NeedsCompilation: yes
Packaged: 2019-07-02 15:54:03 UTC; tulliapadellini
Author: Tullia Padellini [aut, cre], Francesco Palini [aut], Pierpaolo Brutti [ctb], David Meyer [ctb, cph] (libsvm to R code (e1071 package)), Chih-Chung Chang [ctb, cph] (LIBSVM C++ code), Chih-Chen Lin [ctb, cph] (LIBSVM C++ code), Michael Kerber [ctb, cph] (HERA C++ code), Dmitriy Morozov [ctb, cph] (HERA C++ code), Arnur Nigmetov [ctb, cph] (HERA C++ code)
Repository: CRAN
Date/Publication: 2019-07-03 16:20:02 UTC

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Package blockForest updated to version 0.2.3 with previous version 0.2.0 dated 2019-04-10

Title: Block Forests: Random Forests for Blocks of Clinical and Omics Covariate Data
Description: A random forest variant 'block forest' ('BlockForest') tailored to the prediction of binary, survival and continuous outcomes using block-structured covariate data, for example, clinical covariates plus measurements of a certain omics data type or multi-omics data, that is, data for which measurements of different types of omics data and/or clinical data for each patient exist. Examples of different omics data types include gene expression measurements, mutation data and copy number variation measurements. Block forest are presented in Hornung & Wright (2019). The package includes four other random forest variants for multi-omics data: 'RandomBlock', 'BlockVarSel', 'VarProb', and 'SplitWeights'. These were also considered in Hornung & Wright (2019), but performed worse than block forest in their comparison study based on 20 real multi-omics data sets. Therefore, we recommend to use block forest ('BlockForest') in applications. The other random forest variants can, however, be consulted for academic purposes, for example, in the context of further methodological developments. Reference: Hornung, R. & Wright, M. N. (2019) Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. <doi:10.1186/s12859-019-2942-y>.
Author: Roman Hornung, Marvin N. Wright
Maintainer: Marvin N. Wright <cran@wrig.de>

Diff between blockForest versions 0.2.0 dated 2019-04-10 and 0.2.3 dated 2019-07-03

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New package xlink with initial version 1.0.0
Package: xlink
Title: Genetic Association Models for X-Chromosome SNPS on Continuous, Binary and Survival Outcomes
Version: 1.0.0
Authors@R: c( person(given = "Wei", family = "Xu", role = c("aut"), email = "Wei.Xu@uhnresearch.ca"), person(given = "Meiling", family = "Hao", role = c("aut"), email = "Meiling.Hao@uhnresearch.ca"), person(given = "Yi", family = "Zhu", role = c("cre"), email = "yizhu87@gmail.com"))
Maintainer: Yi Zhu <yizhu87@gmail.com>
URL: https://github.com/qiuanzhu/xlink
BugReports: https://github.com/qiuanzhu/xlink/issues
Description: The expression of X-chromosome undergoes three possible biological processes: X-chromosome inactivation (XCI), escape of the X-chromosome inactivation (XCI-E),and skewed X-chromosome inactivation (XCI-S). To analyze the X-linked genetic association for phenotype such as continuous, binary, and time-to-event outcomes with the actual process unknown, we propose a unified approach of maximizing the likelihood or partial likelihood over all of the potential biological processes. The methods are described in Wei Xu, Meiling Hao (2017) <doi:10.1002/gepi.22097>.
License: GPL-2
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.1.0)
Imports: survival (>= 2.41.3)
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-07-02 16:56:52 UTC; Yi
Author: Wei Xu [aut], Meiling Hao [aut], Yi Zhu [cre]
Repository: CRAN
Date/Publication: 2019-07-03 15:10:03 UTC

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New package snahelper with initial version 0.3.0
Package: snahelper
Type: Package
Title: 'RStudio' Addin for Network Analysis and Visualization
Version: 0.3.0
Authors@R: person("David", "Schoch", email = "david.schoch@manchester.ac.uk", role = c("aut", "cre"))
Description: 'RStudio' addin which provides a GUI to visualize and analyse networks. After finishing a session, the code to produce the plot is inserted in the current script. Alternatively, the function SNAhelperGadget() can be used directly from the console.
URL: https://github.com/schochastics/snahelper
BugReports: https://github.com/schochastics/snahelper/issues
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: ggraph, graphlayouts, igraph, formatR, miniUI, rstudioapi, ggplot2, shiny, DT, colourpicker
Depends: R (>= 3.0.0)
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-07-02 16:33:21 UTC; david
Author: David Schoch [aut, cre]
Maintainer: David Schoch <david.schoch@manchester.ac.uk>
Repository: CRAN
Date/Publication: 2019-07-03 15:10:07 UTC

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New package sdcSpatial with initial version 0.1.0
Package: sdcSpatial
Title: Statistical Disclosure Control for Spatial Data
Version: 0.1.0
Authors@R: c( person("Edwin", "de Jonge", email = "edwindjonge@gmail.com", role = c("aut", "cre"), comment=c(ORCID="0000-0002-6580-4718")), person("Peter-Paul", "de Wolf", role = c("aut")), person("Sapphire", "Han", role = c("ctb")) )
Description: Privacy protected raster maps can be created from spatial point data. Protection methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) <doi:10.1007/978-3-319-45381-1_9>, continuous variables by de Wolf and de Jonge (2018) <doi:10.1007/978-3-319-99771-1_23>, suppressing revealing values and a generalization of the quad tree method by Suñé, Rovira, Ibáñez and Farré (2017) <doi:10.2901/EUROSTAT.C2017.001>.
License: GPL-2
Encoding: UTF-8
LazyData: true
URL: https://github.com/edwindj/sdcSpatial
BugReports: https://github.com/edwindj/sdcSpatial/issues
RoxygenNote: 6.1.1
Suggests: testthat, knitr, rmarkdown, sp, sf
Imports: raster, methods
Depends: R (>= 2.10)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-07-02 20:15:10 UTC; edwin
Author: Edwin de Jonge [aut, cre] (<https://orcid.org/0000-0002-6580-4718>), Peter-Paul de Wolf [aut], Sapphire Han [ctb]
Maintainer: Edwin de Jonge <edwindjonge@gmail.com>
Repository: CRAN
Date/Publication: 2019-07-03 15:50:03 UTC

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New package RKHSMetaMod with initial version 1.0
Package: RKHSMetaMod
Type: Package
Title: Ridge Group Sparse Optimization Problem for Estimation of a Meta Model Based on Reproducing Kernel Hilbert Spaces
Version: 1.0
Date: 2019-07-02
Author: Halaleh Kamari
Maintainer: Halaleh Kamari <halaleh.kamari@univ-evry.fr>
Description: Estimates the Hoeffding decomposition of an unknown function by solving ridge group sparse optimization problem based on reproducing kernel Hilbert spaces, and approximates its sensitivity indices (see Kamari, H., Huet, S. and Taupin, M.-L. (2019) <arXiv:1905.13695>).
License: GPL (>= 2)
Imports: Rcpp (>= 1.0.0)
Suggests: lhs
LinkingTo: Rcpp, RcppEigen, RcppGSL
NeedsCompilation: yes
Packaged: 2019-07-02 15:43:19 UTC; hkamari
Repository: CRAN
Date/Publication: 2019-07-03 15:10:19 UTC

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New package psychNET with initial version 0.0.1
Package: psychNET
Type: Package
Title: Psychometric Networks for Intensive Longitudinal Data
Version: 0.0.1
Date: 2019-6-21
Authors@R: c(person("Spyros", "Balafas", email = "s.balafas@rug.nl", role = c("aut", "cre")),person("Ernst", "Wit", role = "aut"),person("Marco", "Grzegorczyk", role = "aut"), person("Sanne", "Booij", role = "ctb"), person("Hanneke", "Wardenaar-Wigman", role = "ctb") )
Description: In the past decade, mental processes have been conceptualized as complex networks of interacting psychiatric symptoms. These networks that can be visualized by means of conditional independence graphs. For estimating the underlying directed graph from intensive longitudinal data, vector autoregression (VAR) is the most commonly used tool. This package wraps several methods in the VAR family that can be used to estimate conditional independence graphs (networks) from multivariate time-series data. The package can fit the simple VAR and VARX model Lutkepohl, H. (2005) <doi:10.1007/978-3-540-27752-1> that are currently available from the R package 'vars', and its sparse alternative by Basu S. and Michailidis, G.(2015) <doi:10.1214/15-AOS1315> and sparse VECM implemented in the R package 'sparsevar'. The sparse graphical VAR with covariance estimation by Wild, B., Eichler, M., Friederich, H. C., Hartmann, M., Zipfel, S., & Herzog, W. (2010) <doi:10.1186/1471-2288-10-28> from the R package 'graphicalVAR' and the dynamic factor model by Doz, Gianone & Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> from the R package 'dynfactoR' are also available. Sparse estimation of high dimensional VAR, VARMA and VARX models using hierarchical lag structures Nicholson, W. B., Bien, J., Matteson, D. S. (2017) <arXiv:1412.5250v3> implemented in the R package 'bigtime' and mixed VAR for symptom time series with marginal distributions in the exponential family Haslbeck, J., Waldorp, L. J. (2015) <arXiv:1510.06871> from the package 'mgm' can also be used with this package. For the inference of symptom networks from multivariate time series of multiple individuals the 'psychNET' package adopts the multi-level VAR by Epskamp, S., Waldorp, L. J., Mottus, R., & Borsboom, D. (2017) <arXiv:1609.04156v6> implemented in the R package 'mlVAR' and for the high-dimensional setting the sparse time series chain graphical (group graphical VAR) model by Abegaz, F., Wit, E. (2013) <doi:10.1093/biostatistics/kxt005> available from the R package 'sparseTSCGM'.
Depends: R (>= 3.5)
Imports: vars, igraph, imputeTS, Hmisc, SparseTSCGM, mlVAR, qgraph, graphicalVAR, sparsevar, bigtime, mgm, crayon, longitudinal, networktools, gtools, car, stats, Matrix, methods, MASS, ordinalNet, glmnet, fastDummies
License: GPL (>= 2)
URL: http://www.math.rug.nl/stat/Main/HomePage
Maintainer: Spyros Balafas <s.balafas@rug.nl>
Repository: CRAN
NeedsCompilation: no
Packaged: 2019-07-02 16:08:57 UTC; balaf
Author: Spyros Balafas [aut, cre], Ernst Wit [aut], Marco Grzegorczyk [aut], Sanne Booij [ctb], Hanneke Wardenaar-Wigman [ctb]
Date/Publication: 2019-07-03 15:20:03 UTC

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Package nopaco updated to version 1.0.5 with previous version 1.0.4 dated 2019-06-26

Title: Non-Parametric Concordance Coefficient
Description: A non-parametric test for multi-observer concordance and differences between concordances in (un)balanced data.
Author: Rowan Kuiper [cre, aut] (<https://orcid.org/0000-0002-3703-1762>), Remco Hoogenboezem [aut], Sjoerd Huisman [ctb] (<https://orcid.org/0000-0002-4322-8289>), Pieter Sonneveld [ths], Mark van Duin [ths]
Maintainer: Rowan Kuiper <r.kuiper.emc@gmail.com>

Diff between nopaco versions 1.0.4 dated 2019-06-26 and 1.0.5 dated 2019-07-03

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New package modeLLtest with initial version 1.0.0
Package: modeLLtest
Type: Package
Title: Compare Models with Cross-Validated Log-Likelihood
Version: 1.0.0
Date: 2019-06-25
Author: Shana Scogin <shanarscogin@gmail.com>, Sarah Petersen <sarahllpetersen@gmail.com>, Jeff Harden <jeff.harden@nd.edu>, Bruce A. Desmarais <bdesmarais@psu.edu>
Maintainer: Shana Scogin <shanarscogin@gmail.com>
Description: An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014) <doi:10.1007/s11135-013-9884-7> (see also Harden and Desmarais, 2011 <doi:10.1177/1532440011408929>) and the cross-validated median fit (CVMF) test by Desmarais and Harden (2012) <doi:10.1093/pan/mpr042>. These tests use leave-one-out cross-validated log-likelihoods to assist in selecting among model estimations. You can also utilize data from Golder (2010) <doi:10.1177/0010414009341714> and Joshi & Mason (2008) <doi:10.1177/0022343308096155> that are included to facilitate examples from real-world analysis.
URL: https://github.com/ShanaScogin/modeLLtest
License: GPL-3
NeedsCompilation: yes
Imports: stats, quantreg, survival, coxrobust, methods, MASS, Rcpp
Encoding: UTF-8
LazyData: TRUE
LazyLoad: TRUE
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
SystemRequirements: GNU make
RoxygenNote: 6.1.1
LinkingTo: Rcpp, RcppArmadillo
Packaged: 2019-07-02 14:31:13 UTC; shanascogin
Repository: CRAN
Date/Publication: 2019-07-03 15:10:10 UTC

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Package marmap updated to version 1.0.3 with previous version 1.0.2 dated 2018-10-15

Title: Import, Plot and Analyze Bathymetric and Topographic Data
Description: Import xyz data from the NOAA (National Oceanic and Atmospheric Administration, <http://www.noaa.gov>), GEBCO (General Bathymetric Chart of the Oceans, <http://www.gebco.net>) and other sources, plot xyz data to prepare publication-ready figures, analyze xyz data to extract transects, get depth / altitude based on geographical coordinates, or calculate z-constrained least-cost paths.
Author: Eric Pante, Benoit Simon-Bouhet, and Jean-Olivier Irisson
Maintainer: Benoit Simon-Bouhet <besibo@gmail.com>

Diff between marmap versions 1.0.2 dated 2018-10-15 and 1.0.3 dated 2019-07-03

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Package lavaan updated to version 0.6-4 with previous version 0.6-3 dated 2018-09-22

Title: Latent Variable Analysis
Description: Fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models.
Author: Yves Rosseel [aut, cre] (<https://orcid.org/0000-0002-4129-4477>), Terrence D. Jorgensen [aut] (<https://orcid.org/0000-0001-5111-6773>), Daniel Oberski [ctb], Jarrett Byrnes [ctb], Leonard Vanbrabant [ctb], Victoria Savalei [ctb], Ed Merkle [ctb], Michael Hallquist [ctb], Mijke Rhemtulla [ctb], Myrsini Katsikatsou [ctb], Mariska Barendse [ctb], Florian Scharf [ctb]
Maintainer: Yves Rosseel <Yves.Rosseel@UGent.be>

Diff between lavaan versions 0.6-3 dated 2018-09-22 and 0.6-4 dated 2019-07-03

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Package jstable updated to version 0.8.4 with previous version 0.8.3 dated 2019-06-19

Title: Create Tables from Different Types of Regression
Description: Create regression tables from generalized linear model(GLM), generalized estimating equation(GEE), generalized linear mixed-effects model(GLMM), Cox proportional hazards model, survey-weighted generalized linear model(svyglm) and survey-weighted Cox model results for publication.
Author: Jinseob Kim [aut, cre] (<https://orcid.org/0000-0002-9403-605X>), Zarathu [cph, fnd]
Maintainer: Jinseob Kim <jinseob2kim@gmail.com>

Diff between jstable versions 0.8.3 dated 2019-06-19 and 0.8.4 dated 2019-07-03

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Package fxtract updated to version 0.9.2 with previous version 0.9.1 dated 2019-02-12

Title: Feature Extraction from Grouped Data
Description: An R6 implementation for calculating features from grouped data. The output will be one row for each group. This functionality is often needed in the feature extraction process of machine learning problems. Very large datasets are supported, since data is only read into RAM when needed. Calculation can be done in parallel and the process can be monitored. Global error handling is supported. Results are available in one final dataframe.
Author: Quay Au [aut, cre], Clemens Stachl [ctb], Ramona Schoedel [ctb], Theresa Ullmann [ctb], Andreas Hofheinz [ctb]
Maintainer: Quay Au <quayau@gmail.com>

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New package cassandRa with initial version 0.1.0
Package: cassandRa
Type: Package
Title: Finds Missing Links and Metric Confidence Intervals in Ecological Bipartite Networks
Version: 0.1.0
Authors@R: person("Chris", "Terry", email = "christerry3@btinternet.com", role = c("aut", "cre"))
Description: Provides methods to deal with under sampling in ecological bipartite networks. Includes tools to fit a variety of statistical network models and sample coverage estimators to highlight most likely missing links. Also includes simple functions to resample from observed networks to generate confidence intervals for common ecological network metrics.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: bipartite (>= 2.11), reshape2 (>= 1.4.3), magrittr (>= 1.5), vegan (>= 2.5-3), purrr (>= 0.2.5), dplyr, tidyr(>= 0.8), ggplot2 (>= 3.1.0), boot
Suggests: knitr (>= 1.20), rmarkdown (>= 1.10), pROC (>= 1.13.0), lattice
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-07-02 15:42:24 UTC; pemb4504
Author: Chris Terry [aut, cre]
Maintainer: Chris Terry <christerry3@btinternet.com>
Repository: CRAN
Date/Publication: 2019-07-03 15:10:15 UTC

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Package vip updated to version 0.1.3 with previous version 0.1.2 dated 2018-09-30

Title: Variable Importance Plots
Description: A general framework for constructing variable importance plots from various types of machine learning models in R. Aside from some standard model- specific variable importance measures, this package also provides model- agnostic approaches that can be applied to any supervised learning algorithm. These include an efficient permutation-based variable importance measure as well as novel approaches based on partial dependence plots (PDPs) and individual conditional expectation (ICE) curves which are described in Greenwell et al. (2018) <arXiv:1805.04755>. An experimental method for quantifying the relative strength of interaction effects is also included (see the previous reference for details).
Author: Brandon Greenwell [aut, cre] (<https://orcid.org/0000-0002-8120-0084>), Brad Boehmke [aut] (<https://orcid.org/0000-0002-3611-8516>), Bernie Gray [aut] (<https://orcid.org/0000-0001-9190-6032>)
Maintainer: Brandon Greenwell <greenwell.brandon@gmail.com>

Diff between vip versions 0.1.2 dated 2018-09-30 and 0.1.3 dated 2019-07-03

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Package sf updated to version 0.7-5 with previous version 0.7-4 dated 2019-04-25

Title: Simple Features for R
Description: Support for simple features, a standardized way to encode spatial vector data. Binds to 'GDAL' for reading and writing data, to 'GEOS' for geometrical operations, and to 'PROJ' for projection conversions and datum transformations.
Author: Edzer Pebesma [aut, cre] (<https://orcid.org/0000-0001-8049-7069>), Roger Bivand [ctb] (<https://orcid.org/0000-0003-2392-6140>), Etienne Racine [ctb], Michael Sumner [ctb], Ian Cook [ctb], Tim Keitt [ctb], Robin Lovelace [ctb], Hadley Wickham [ctb], Jeroen Ooms [ctb] (<https://orcid.org/0000-0002-4035-0289>), Kirill Müller [ctb], Thomas Lin Pedersen [ctb]
Maintainer: Edzer Pebesma <edzer.pebesma@uni-muenster.de>

Diff between sf versions 0.7-4 dated 2019-04-25 and 0.7-5 dated 2019-07-03

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New package semnar with initial version 0.7.1
Package: semnar
Title: Constructing and Interacting with Databases of Presentations
Version: 0.7.1
Authors@R: c(person(given = "Ioannis", family = "Kosmidis", role = c("aut", "cre"), email = "ioannis.kosmidis@warwick.ac.uk", comment = c(ORCID = "0000-0003-1556-0302")))
Description: Provides methods for constructing and maintaining a database of presentations in R. The presentations are either ones that the user gives or gave or presentations at a particular event or event series. The package also provides a plot method for the interactive mapping of the presentations using 'leaflet' by grouping them according to country, city, year and other presentation attributes. The markers on the map come with popups providing presentation details (title, institution, event, links to materials and events, and so on).
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.6.0)
Imports: jsonlite, lubridate, leaflet, magrittr, urlshorteneR
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-07-02 12:38:19 UTC; yiannis
Author: Ioannis Kosmidis [aut, cre] (<https://orcid.org/0000-0003-1556-0302>)
Maintainer: Ioannis Kosmidis <ioannis.kosmidis@warwick.ac.uk>
Repository: CRAN
Date/Publication: 2019-07-03 15:00:03 UTC

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New package retractcheck with initial version 1.0.0
Package: retractcheck
Version: 1.0.0
Title: Retraction Scanner
Description: Using 'Digital Object Identifiers', check for retracted (or otherwise updated) articles using 'Open Retractions' <http://openretractions.com>.
Authors@R: c( person("Chris", "Hartgerink", email = "chris@libscie.org", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1050-6809")), person("Frederik", "Aust", email = "frederik.aust@uni-koeln.de", role = c("aut"), comment = c(ORCID = "0000-0003-4900-788X")) )
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
ByteCompile: true
Imports: plyr, textreadr, httr
RoxygenNote: 6.1.1
Suggests: covr
NeedsCompilation: no
Packaged: 2019-07-02 12:49:38 UTC; chjh
Author: Chris Hartgerink [aut, cre] (<https://orcid.org/0000-0003-1050-6809>), Frederik Aust [aut] (<https://orcid.org/0000-0003-4900-788X>)
Maintainer: Chris Hartgerink <chris@libscie.org>
Repository: CRAN
Date/Publication: 2019-07-03 15:00:07 UTC

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Package heemod updated to version 0.10.0 with previous version 0.9.4 dated 2019-02-24

Title: Markov Models for Health Economic Evaluations
Description: An implementation of the modelling and reporting features described in reference textbook and guidelines (Briggs, Andrew, et al. Decision Modelling for Health Economic Evaluation. Oxford Univ. Press, 2011; Siebert, U. et al. State-Transition Modeling. Medical Decision Making 32, 690-700 (2012).): deterministic and probabilistic sensitivity analysis, heterogeneity analysis, time dependency on state-time and model-time (semi-Markov and non-homogeneous Markov models), etc.
Author: Kevin Zarca [aut, cre], Antoine Filipovic-Pierucci [aut], Matthew Wiener [ctb], Zdenek Kabat [ctb], Vojtech Filipec [ctb], Jordan Amdahl [ctb], Yonatan Carranza Alarcon [ctb], Vince Daniels [ctb]
Maintainer: Kevin Zarca <kevin.zarca@gmail.com>

Diff between heemod versions 0.9.4 dated 2019-02-24 and 0.10.0 dated 2019-07-03

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Package GGIR updated to version 1.9-2 with previous version 1.9-1 dated 2019-05-08

Title: Raw Accelerometer Data Analysis
Description: A tool to process and analyse data collected with wearable raw acceleration sensors as described in van Hees and colleagues (2014) <doi: 10.1152/japplphysiol.00421.2014> and (2015) <doi: 10.1371/journal.pone.0142533>. The package has been developed and tested for binary data from 'GENEActiv' <https://www.activinsights.com/> and GENEA devices (not for sale), .csv-export data from 'Actigraph' <http://actigraphcorp.com> devices, and .cwa and .wav-format data from 'Axivity' <https://axivity.com/product/ax3>. These devices are currently widely used in research on human daily physical activity.
Author: Vincent T van Hees [aut, cre], Zhou Fang [ctb], Jing Hua Zhao [ctb], Joe Heywood [ctb], Evgeny Mirkes [ctb], Severine Sabia [ctb], Joan Capdevila Pujol [ctb], Jairo H Migueles [ctb]
Maintainer: Vincent T van Hees <vincentvanhees@gmail.com>

Diff between GGIR versions 1.9-1 dated 2019-05-08 and 1.9-2 dated 2019-07-03

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New package alfr with initial version 1.1.0
Package: alfr
Type: Package
Title: Connectivity to 'Alfresco' Content Management Repositories
Version: 1.1.0
Author: Roy Wetherall <rwetherall@gmail.com>
Maintainer: Roy Wetherall <rwetherall@gmail.com>
Description: Allows you to connect to an 'Alfresco' content management repository and interact with its contents using simple and intuitive functions. You will be able to establish a connection session to the 'Alfresco' repository, read and upload content and manage folder hierarchies. For more details on the 'Alfresco' content management repository see <https://www.alfresco.com/ecm-software/document-management>.
Depends: R (>= 3.5.0)
License: GPL-3 | file LICENSE
URL: https://github.com/rwetherall/alfr
BugReports: https://github.com/rwetherall/alfr/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: httr, jsonlite, magrittr
Suggests: devtools, httptest, roxygen2, testthat, knitr, rmarkdown, covr, remotes
VignetteBuilder: knitr
SystemRequirements: Alfresco Content Repository (Community or Enterprise)
NeedsCompilation: no
Packaged: 2019-07-02 12:06:32 UTC; rwetherall
Repository: CRAN
Date/Publication: 2019-07-03 15:00:11 UTC

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Package FactoInvestigate updated to version 1.4 with previous version 1.3 dated 2018-05-06

Title: Automatic Description of Factorial Analysis
Description: Brings a set of tools to help and automatically realise the description of principal component analyses (from 'FactoMineR' functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).
Author: Simon Thuleau, Francois Husson
Maintainer: Simon Thuleau <factoinvestigate@gmail.com>

Diff between FactoInvestigate versions 1.3 dated 2018-05-06 and 1.4 dated 2019-07-03

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Package BayesianFROC updated to version 0.1.4 with previous version 0.1.3 dated 2019-06-11

Title: FROC Analysis by Bayesian Approaches
Description: Before reading this, execute BayesianFROC::fit_GUI(), then reader will understand this package without any explanation. Provides new methods for the so-called Free-response Receiver Operating Characteristic (FROC) analysis. The ultimate aim of FROC analysis is to compare observer performances, which means comparing characteristics, such as area under the curve (AUC) or figure of merit (FOM). In this package, we only use the notion of AUC for modality comparison, where by "modality", we mean imaging methods such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), ..., etc. So there is a problem that which imaging method is better to detect lesions from shadows in radiographs. To solve modality comparison issues, this package provides new methods using hierarchical Bayesian models proposed by the author of this package. Using this package, one can obtain at least one conclusion that which imaging methods are better for finding lesions in radiographs with the case of your data. Fitting FROC statistical models is sometimes not so good, it can easily confirm by drawing FROC curves and comparing these curves and the points constructed by False Positive fractions (FPFs) and True Positive Fractions (TPFs), we can validate the goodness of fit intuitively. Such validation is also implemented by the Chi square goodness of fit statistics in the Bayesian context which means that the parameter is not deterministic, thus by integrating it with the posterior predictive measure, we get a desired value. To compare modalities (imaging methods: MRI, CT, PET, ... , etc), we evaluate AUCs for each modality. FROC is developed by Dev Chakraborty, his FROC model in his 1989 paper relies on the maximal likelihood methodology. The author modified and provided the alternative Bayesian FROC model. Strictly speaking, his model does not coincide with models in this package. In FROC context, we means by multiple reader and multiple case (MRMC) the case of the number of reader or modality is two or more. The MRMC data is available for functions of this package. I hope that medical researchers use not only the frequentist method but also alternative Bayesian methods. In medical research, many problems are considered under only frequentist methods, such as the notion of p-values. But p-value is sometimes misunderstood. Bayesian methods provide very simple, direct, intuitive answer for research questions. Combining frequentist methods with Bayesian methods, we can obtain more reliable answer for research questions. Please execute the following R scripts from the R (R studio) console, demo(demo_MRMC, package = "BayesianFROC"); demo(demo_srsc, package = "BayesianFROC"); demo(demo_stan, package = "BayesianFROC"); demo(demo_drawcurves_srsc, package = "BayesianFROC"); demo_Bayesian_FROC(); demo_Bayesian_FROC_without_pause(). References: Dev Chakraborty (1989) <doi:10.1118/1.596358> Maximum likelihood analysis of free - response receiver operating characteristic (FROC) data. Pre-print: Issei Tsunoda; Bayesian Models for free-response receiver operating characteristic analysis. See the vignettes for more details.
Author: Issei Tsunoda [aut, cre]
Maintainer: Issei Tsunoda <tsunoda.issei1111@gmail.com>

Diff between BayesianFROC versions 0.1.3 dated 2019-06-11 and 0.1.4 dated 2019-07-03

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Package dad updated to version 3.3.0 with previous version 3.2.0 dated 2019-01-17

Title: Three-Way / Multigroup Data Analysis Through Densities
Description: The data consist of a set of variables measured on several groups of individuals. To each group is associated an estimated probability density function. The package provides tools to create or manage such data and functional methods (principal component analysis, multidimensional scaling, cluster analysis, discriminant analysis...) for such probability densities.
Author: Rachid Boumaza[aut, cre], Pierre Santagostini [aut], Smail Yousfi [aut], Gilles Hunault [ctb], Julie Bourbeillon [ctb], Besnik Pumo [ctb], Sabine Demotes-Mainard [aut]
Maintainer: Rachid Boumaza <rachid.boumaza@agrocampus-ouest.fr>

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Package afex updated to version 0.24-1 with previous version 0.23-0 dated 2019-02-19

Title: Analysis of Factorial Experiments
Description: Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), automatically aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex_plot() provides a high-level interface for interaction or one-way plots using ggplot2, combining raw data and model estimates. afex uses type 3 sums of squares as default (imitating commercial statistical software).
Author: Henrik Singmann [aut, cre] (<https://orcid.org/0000-0002-4842-3657>), Ben Bolker [aut], Jake Westfall [aut], Frederik Aust [aut] (<https://orcid.org/0000-0003-4900-788X>), Mattan S. Ben-Shachar [aut], Søren Højsgaard [ctb], John Fox [ctb], Michael A. Lawrence [ctb], Ulf Mertens [ctb], Jonathon Love [ctb], Russell Lenth [ctb], Rune Haubo Bojesen Christensen [ctb]
Maintainer: Henrik Singmann <singmann+afex@gmail.com>

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New package socceR with initial version 0.1.1
Package: socceR
Type: Package
Title: Evaluating Sport Tournament Predictions
Version: 0.1.1
Date: 2019-07-01
Authors@R: c(person(given="Claus Thorn", family="Ekstrøm", email="ekstrom@sund.ku.dk", role=c("aut", "cre")))
Maintainer: Claus Thorn Ekstrøm <ekstrom@sund.ku.dk>
Description: Functions for evaluating tournament predictions, simulating results from individual soccer matches and tournaments. See <http://sandsynligvis.dk/2018/08/03/world-cup-prediction-winners/> for more information.
License: GPL (>= 2)
Depends: R (>= 3.1.0)
Imports: Rcpp (>= 1.0.0)
LinkingTo: Rcpp
LazyData: true
RoxygenNote: 6.1.1
Encoding: UTF-8
URL: https://github.com/ekstroem/socceR
BugReports: https://github.com/ekstroem/socceR/issues
NeedsCompilation: yes
Packaged: 2019-07-02 09:09:01 UTC; cld189
Author: Claus Thorn Ekstrøm [aut, cre]
Repository: CRAN
Date/Publication: 2019-07-03 11:50:03 UTC

More information about socceR at CRAN
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Package SAFD updated to version 2.1 with previous version 2.0 dated 2018-09-12

Title: Statistical Analysis of Fuzzy Data
Description: The aim of the package is to provide some basic functions for doing statistics with one dimensional Fuzzy Data (in the form of polygonal fuzzy numbers). In particular, the package contains functions for the basic operations on the class of fuzzy numbers (sum, scalar product, mean, median, Hukuhara difference) as well as for calculating (Bertoluzza) distance and sample variance. Moreover a function to simulate fuzzy random variables and bootstrap tests for the equality of means is included. Version 2.1 fixes some bugs of previous versions.
Author: Wolfgang Trutschnig <wolfgang@trutschnig.net>, Asun Lubiano <lubiano@uniovi.es>
Maintainer: Asun Lubiano <lubiano@uniovi.es>

Diff between SAFD versions 2.0 dated 2018-09-12 and 2.1 dated 2019-07-03

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Package zip updated to version 2.0.3 with previous version 2.0.2 dated 2019-05-13

Title: Cross-Platform 'zip' Compression
Description: Cross-Platform 'zip' Compression Library. A replacement for the 'zip' function, that does not require any additional external tools on any platform.
Author: Gábor Csárdi, Kuba Podgórski, Rich Geldreich
Maintainer: Gábor Csárdi <csardi.gabor@gmail.com>

Diff between zip versions 2.0.2 dated 2019-05-13 and 2.0.3 dated 2019-07-03

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Package FactoMineR updated to version 1.42 with previous version 1.41 dated 2018-05-06

Title: Multivariate Exploratory Data Analysis and Data Mining
Description: Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).
Author: Francois Husson, Julie Josse, Sebastien Le, Jeremy Mazet
Maintainer: Francois Husson <francois.husson@agrocampus-ouest.fr>

Diff between FactoMineR versions 1.41 dated 2018-05-06 and 1.42 dated 2019-07-03

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Package PRSim updated to version 1.1 with previous version 1.0 dated 2019-03-29

Title: Stochastic Simulation of Streamflow Time Series using Phase Randomization
Description: Provides a simulation framework to simulate streamflow time series with similar main characteristics as observed data. These characteristics include the distribution of daily streamflow values and their temporal correlation as expressed by short- and long-range dependence. The approach is based on the randomization of the phases of the Fourier transform. We further use the flexible four-parameter Kappa distribution, which allows for the extrapolation to yet unobserved low and high flows. Alternatively, the empirical or any other distribution can be used. A detailed description of the simulation approach and an application example can be found in <https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-142/>.
Author: Manuela Brunner [aut, cre] (<https://orcid.org/0000-0001-8824-877X>), Reinhard Furrer [aut] (<https://orcid.org/0000-0002-6319-2332>)
Maintainer: Manuela Brunner <manuela.brunner@wsl.ch>

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Package processx updated to version 3.4.0 with previous version 3.3.1 dated 2019-05-08

Title: Execute and Control System Processes
Description: Tools to run system processes in the background. It can check if a background process is running; wait on a background process to finish; get the exit status of finished processes; kill background processes. It can read the standard output and error of the processes, using non-blocking connections. 'processx' can poll a process for standard output or error, with a timeout. It can also poll several processes at once.
Author: Gábor Csárdi [aut, cre, cph] (<https://orcid.org/0000-0001-7098-9676>), Winston Chang [aut], RStudio [cph, fnd], Mango Solutions [cph, fnd]
Maintainer: Gábor Csárdi <csardi.gabor@gmail.com>

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Package LeafArea updated to version 0.1.8 with previous version 0.1.7 dated 2017-03-12

Title: Rapid Digital Image Analysis of Leaf Area
Description: An interface for the image processing program 'ImageJ', which allows a rapid digital image analysis for particle sizes. This package includes function to write an 'ImageJ' macro which is optimized for a leaf area analysis by default.
Author: Masatoshi Katabuchi <mattocci27@gmail.com>
Maintainer: Masatoshi Katabuchi <mattocci27@gmail.com>

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Package GWmodel updated to version 2.1-1 with previous version 2.0-9 dated 2019-04-29

Title: Geographically-Weighted Models
Description: In GWmodel, we introduce techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. GWmodel includes functions to calibrate: GW summary statistics, GW principal components analysis, GW discriminant analysis and various forms of GW regression; some of which are provided in basic and robust (outlier resistant) forms.
Author: Binbin Lu[aut], Paul Harris[aut], Martin Charlton[aut], Chris Brunsdon[aut], Tomoki Nakaya[aut], Daisuke Murakami[aut],Isabella Gollini[ctb]
Maintainer: Binbin Lu <binbinlu@whu.edu.cn>

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Package ggmcmc updated to version 1.3 with previous version 1.2 dated 2019-02-15

Title: Tools for Analyzing MCMC Simulations from Bayesian Inference
Description: Tools for assessing and diagnosing convergence of Markov Chain Monte Carlo simulations, as well as for graphically display results from full MCMC analysis. The package also facilitates the graphical interpretation of models by providing flexible functions to plot the results against observed variables.
Author: Xavier Fernández i Marín <xavier.fim@gmail.com>
Maintainer: Xavier Fernández i Marín <xavier.fim@gmail.com>

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Package lazytrade (with last version 0.3.1) was removed from CRAN

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

2019-07-01 0.3.1

Permanent link
Package gam updated to version 1.16.1 with previous version 1.16 dated 2018-07-20

Title: Generalized Additive Models
Description: Functions for fitting and working with generalized additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990).
Author: Trevor Hastie
Maintainer: Trevor Hastie <hastie@stanford.edu>

Diff between gam versions 1.16 dated 2018-07-20 and 1.16.1 dated 2019-07-03

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Package SmartEDA updated to version 0.3.2 with previous version 0.3.1 dated 2019-05-04

Title: Summarize and Explore the Data
Description: Exploratory analysis on any input data describing the structure and the relationships present in the data. The package automatically select the variable and does related descriptive statistics. Analyzing information value, weight of evidence, custom tables, summary statistics, graphical techniques will be performed for both numeric and categorical predictors.
Author: Dayanand Ubrangala [aut, cre], Kiran R [aut, ctb], Ravi Prasad Kondapalli [aut, ctb], Sayan Putatunda [aut, ctb]
Maintainer: Dayanand Ubrangala <daya6489@gmail.com>

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Package shallot updated to version 0.4.6 with previous version 0.4.5 dated 2018-10-30

Title: Random Partition Distribution Indexed by Pairwise Information
Description: Implementations are provided for the models described in the paper D. B. Dahl, R. Day, J. Tsai (2017) <DOI:10.1080/01621459.2016.1165103>. The Ewens, Ewens-Pitman, Ewens attraction, Ewens-Pitman attraction, and ddCRP distributions are available for prior and posterior simulation. Posterior simulation is based on a user-supplied likelihood. Supporting functions for partition estimation and plotting are also provided.
Author: David B. Dahl [aut, cre]
Maintainer: David B. Dahl <dahl@stat.byu.edu>

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Package RcppAlgos updated to version 2.3.4 with previous version 2.3.3 dated 2019-06-30

Title: High Performance Tools for Combinatorics and Computational Mathematics
Description: Provides optimized functions implemented in C++ with 'Rcpp' for solving problems in combinatorics and computational mathematics. Utilizes parallel programming via 'RcppThread' for maximal performance. Also makes use of the RMatrix class from the 'RcppParallel' library. There are combination/permutation functions with constraint parameters that allow for generation of all combinations/permutations of a vector meeting specific criteria (e.g. finding all combinations such that the sum is between two bounds). Capable of generating specific combinations/permutations (e.g. retrieve only the nth lexicographical result) which sets up nicely for parallelization as well as random sampling. Gmp support permits exploration where the total number of results is large (e.g. comboSample(10000, 500, n = 4)). Additionally, there are several high performance number theoretic functions that are useful for problems common in computational mathematics. Some of these functions make use of the fast integer division library 'libdivide' by <http://ridiculousfish.com>. The primeSieve function is based on the segmented sieve of Eratosthenes implementation by Kim Walisch. It is also efficient for large numbers by using the cache friendly improvements originally developed by Tomás Oliveira. Finally, there is a prime counting function that implements Legendre's formula based on the algorithm by Kim Walisch.
Author: Joseph Wood
Maintainer: Joseph Wood <jwood000@gmail.com>

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Package radir updated to version 1.0.4 with previous version 1.0.3 dated 2018-06-03

Title: Inverse-Regression Estimation of Radioactive Doses
Description: Radioactive doses estimation using individual chromosomal aberrations information. See Higueras M, Puig P, Ainsbury E, Rothkamm K. (2015) <doi:10.1088/0952-4746/35/3/557>.
Author: David Moriña (Barcelona Graduate School of Mathematics), Manuel Higueras (Basque Center for Applied Mathematics) and Pedro Puig (Universitat Autònoma de Barcelona)
Maintainer: David Moriña Soler <david.morina@uab.cat>

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Package plot.matrix updated to version 1.2 with previous version 1.1 dated 2019-05-13

Title: Visualizes a Matrix as Heatmap
Description: Visualizes a matrix object plainly as heatmap. It provides S3 functions to plot simple matrices and loading matrices.
Author: Sigbert Klinke
Maintainer: Sigbert Klinke <sigbert@hu-berlin.de>

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Package hesim updated to version 0.2.1 with previous version 0.2.0 dated 2019-03-28

Title: Health-Economic Simulation Modeling and Decision Analysis
Description: Parameterize, simulate, and analyze health-economic simulation models. Supports N-state partitioned survival models (Glasziou et al. 1990) <doi:10.1002/sim.4780091106> and continuous time state transition models (Siebert et al. 2012) <doi:10.1016/j.jval.2012.06.014> parameterized using survival or multi-state modeling (de Wreede et al. 2011, Jackson 2015) <doi:10.18637/jss.v038.i07>, <doi:10.18637/jss.v070.i08>. Decision uncertainty from a cost-effectiveness analysis is quantified with standard graphical and tabular summaries of a probabilistic sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) <doi:10.1002/hec.985>, <doi:10.1111/j.1524-4733.2008.00358.x>. Simulation code written in C++ to boost performance.
Author: Devin Incerti [aut, cre], Jeroen P. Jansen [aut]
Maintainer: Devin Incerti <devin.incerti@gmail.com>

Diff between hesim versions 0.2.0 dated 2019-03-28 and 0.2.1 dated 2019-07-03

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Package mrgsolve (with last version 0.9.1) was removed from CRAN

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

2019-06-14 0.9.1
2019-01-23 0.9.0
2018-04-27 0.8.12
2018-04-26 0.8.11
2017-11-01 0.8.10
2017-08-28 0.8.9
2017-03-16 0.8.6
2017-03-12 0.8.2

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Package bujar (with last version 0.2-5) was removed from CRAN

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

2019-02-27 0.2-5
2019-01-15 0.2-4
2017-04-27 0.2-3
2015-12-19 0.2-1
2015-10-27 0.1-5
2014-06-24 0.1-4
2014-05-01 0.1-3

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Package mpath (with last version 0.3-15) was removed from CRAN

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

2019-06-05 0.3-15
2019-05-19 0.3-13
2019-04-15 0.3-12
2019-02-27 0.3-7
2019-01-14 0.3-6
2018-07-21 0.3-5
2017-10-23 0.3-4
2017-08-18 0.3-3
2016-08-30 0.2-4
2016-08-29 0.2-3
2016-02-04 0.2-1
2015-11-22 0.1-20
2015-07-23 0.1-19
2015-06-10 0.1-18
2014-11-10 0.1-17
2014-11-09 0.1-16
2014-09-23 0.1-15
2014-09-22 0.1-14

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Package boot updated to version 1.3-23 with previous version 1.3-22 dated 2019-04-02

Title: Bootstrap Functions (Originally by Angelo Canty for S)
Description: Functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Application" by A. C. Davison and D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.
Author: Angelo Canty [aut], Brian Ripley [aut, trl, cre] (author of parallel support)
Maintainer: Brian Ripley <ripley@stats.ox.ac.uk>

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Package zCompositions updated to version 1.3.2-1 with previous version 1.3.2 dated 2019-06-02

Title: Treatment of Zeros, Left-Censored and Missing Values in Compositional Data Sets
Description: Principled methods for the imputation of zeros, left-censored and missing data in compositional data sets.
Author: Javier Palarea-Albaladejo and Josep Antoni Martin-Fernandez
Maintainer: Javier Palarea-Albaladejo <javier.palarea@bioss.ac.uk>

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Package sdols updated to version 1.7.5 with previous version 1.7 dated 2018-10-30

Title: Summarizing Distributions of Latent Structures
Description: Summaries of distributions on clusterings and feature allocations are provided. Specifically, point estimates are obtained by the sequentially-allocated latent structure optimization (SALSO) algorithm to minimize squared error loss, absolute error loss, Binder loss, or the lower bound of the variation of information loss. Clustering uncertainty can be assessed with the confidence calculations and the associated plot.
Author: David B. Dahl [aut, cre], Peter Müller [aut]
Maintainer: David B. Dahl <dahl@stat.byu.edu>

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Package glinternet updated to version 1.0.10 with previous version 1.0.9 dated 2019-06-12

Title: Learning Interactions via Hierarchical Group-Lasso Regularization
Description: Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) <DOI:10.1080/10618600.2014.938812>.
Author: Michael Lim, Trevor Hastie
Maintainer: Michael Lim <michael626@gmail.com>

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Package dslabs updated to version 0.7.0 with previous version 0.6.0 dated 2019-05-17

Title: Data Science Labs
Description: Datasets and functions that can be used for data analysis practice, homework and projects in data science courses and workshops. 26 datasets are available for case studies in data visualization, statistical inference, modeling, linear regression, data wrangling and machine learning.
Author: Rafael A. Irizarry, Amy Gill
Maintainer: Rafael A. Irizarry <rafa@jimmy.harvard.edu>

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Package CrossVA updated to version 0.9.9 with previous version 0.9.8 dated 2019-05-17

Title: Verbal Autopsy Data Transformation for InSilicoVA and InterVA5 Algorithms
Description: Enables transformation of Verbal Autopsy data collected with the WHO 2016 questionnaire (versions 1.4.1 & 1.5.1) or the WHO 2014 questionnaire for automated coding of Cause of Death using the InSilicoVA (data.type = "WHO2016") and InterVA5 algorithms. Previous versions of this package supported user-supplied mappings (via the map_records function), but this functionality has been removed. This package is made available by WHO and the Bloomberg Data for Health Initiative.
Author: Peter Byass [aut], Eungang Choi [aut], Sam Clark [aut], Zehang Li [aut], Nicolas Maire [aut], Tyler McCormick [aut], Jason Thomas [aut, cre]
Maintainer: Jason Thomas <jarathomas@gmail.com>

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