Thu, 07 Mar 2019

Package MODIS (with last version 1.1.4) was removed from CRAN

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

2018-10-25 1.1.4
2018-09-11 1.1.3
2018-01-25 1.1.2

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Package svd updated to version 0.4.2 with previous version 0.4.1 dated 2017-09-25

Title: Interfaces to Various State-of-Art SVD and Eigensolvers
Description: R bindings to SVD and eigensolvers (PROPACK, nuTRLan).
Author: Anton Korobeynikov [aut, cre], Rasmus Munk Larsen [ctb, cph], Lawrence Berkeley National Laboratory [ctb, cph]
Maintainer: Anton Korobeynikov <anton@korobeynikov.info>

Diff between svd versions 0.4.1 dated 2017-09-25 and 0.4.2 dated 2019-03-07

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Package varbvs updated to version 2.5-16 with previous version 2.4-0 dated 2017-09-08

Title: Large-Scale Bayesian Variable Selection Using Variational Methods
Description: Fast algorithms for fitting Bayesian variable selection models and computing Bayes factors, in which the outcome (or response variable) is modeled using a linear regression or a logistic regression. The algorithms are based on the variational approximations described in "Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies" (P. Carbonetto & M. Stephens, 2012, <DOI:10.1214/12-BA703>). This software has been applied to large data sets with over a million variables and thousands of samples.
Author: Peter Carbonetto [aut, cre], Matthew Stephens [aut], David Gerard [ctb]
Maintainer: Peter Carbonetto <peter.carbonetto@gmail.com>

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Package STV updated to version 1.0.1 with previous version 1.0.0 dated 2018-03-23

Title: Single Transferable Vote Counting
Description: Implementations of the Single Transferable Vote counting system. By default, it uses the Cambridge method for surplus allocation and Droop method for quota calculation. Fractional surplus allocation and the Hare quota are available as options.
Author: John Emerson [cre, aut], Saksham Chandra [aut], Lilla Orr [aut], Efim Abrikosov [ctb], Shadie Khubba [ctb], William Meyerson [ctb], Shylee Ezroni [ctb], Junchao Ma [ctb]
Maintainer: John Emerson <john.emerson@yale.edu>

Diff between STV versions 1.0.0 dated 2018-03-23 and 1.0.1 dated 2019-03-07

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Package SIMMS updated to version 1.1.2 with previous version 1.1.1 dated 2017-10-11

Title: Subnetwork Integration for Multi-Modal Signatures
Description: Algorithms to create prognostic biomarkers using biological genesets or networks.
Author: Syed Haider [aut, cre], Paul C. Boutros [aut], Michal Grzadkowski [ctb]
Maintainer: Syed Haider <Syed.Haider@icr.ac.uk>

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Package SDraw updated to version 2.1.8 with previous version 2.1.3 dated 2016-06-13

Title: Spatially Balanced Samples of Spatial Objects
Description: Routines for drawing samples from spatial objects, focused on spatially balanced algorithms. Draws Halton Iterative Partition (HIP) (Robertson et al., 2018; <doi:10.1007/s10651-018-0406-6>), Balanced Acceptance Samples (BAS) (Robertson et al., 2013; <doi:10.1111/biom.12059>), Generalized Random Tessellation Stratified (GRTS) (Stevens and Olsen, 2004; <doi:10.1198/016214504000000250>), Simple Systematic Samples (SSS) and Simple Random Samples (SRS) from point, line, and polygon resources. Frames are 'SpatialPoints', 'SpatialLines', or 'SpatialPolygons' objects from package 'sp'.
Author: Trent McDonald [cre, aut], Aidan McDonald [aut] (HIP sampling methods), Michael Kleinsausser [ctb]
Maintainer: Trent McDonald <tmcdonald@west-inc.com>

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Package EMCluster updated to version 0.2-11 with previous version 0.2-10 dated 2018-01-31

Title: EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution
Description: EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning.
Author: Wei-Chen Chen [aut, cre], Ranjan Maitra [aut], Volodymyr Melnykov [ctb], Dan Nettleton [ctb], David Faden [ctb], Rouben Rostamian [ctb], R Core team [ctb] (some functions are modified from the R source code)
Maintainer: Wei-Chen Chen <wccsnow@gmail.com>

Diff between EMCluster versions 0.2-10 dated 2018-01-31 and 0.2-11 dated 2019-03-07

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New package cricketr with initial version 0.0.17
Package: cricketr
Type: Package
Title: Analyze Cricketers Based on ESPN Cricinfo Statsguru
Description: Tools for analyzing performances of cricketers based on stats in ESPN Cricinfo Statsguru. The toolset can be used for analysis of Tests,ODIs and Twenty20 matches of both batsmen and bowlers.
Version: 0.0.17
Date: 2019-03-07
Author: Tinniam V Ganesh
Maintainer: Tinniam V Ganesh <tvganesh.85@gmail.com>
License: MIT + file LICENSE
Depends: R (>= 3.1.2)
Imports: dplyr, plotrix, ggplot2, scatterplot3d, forecast, lubridate, XML, graphics, grDevices, stats, utils
URL: https://github.com/tvganesh/cricketr
BugReports: https://github.com/tvganesh/cricketr/issues
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2019-03-07 17:08:25 UTC; Ganesh
Repository: CRAN
Date/Publication: 2019-03-07 21:43:02 UTC

More information about cricketr at CRAN
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Package CARRoT updated to version 2.0.0 with previous version 1.5.0 dated 2018-11-27

Title: Predicting Categorical and Continuous Outcomes Using One in Ten Rule
Description: Predicts categorical or continuous outcomes while concentrating on four key points. These are Cross-validation, Accuracy, Regression and Rule of Ten or "one in ten rule" (CARRoT). It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying a rule of ten events per variable are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Receiver Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) <doi:10.1016/S0895-4356(96)00236-3> and Rhemtulla et al. (2012) <doi:10.1037/a0029315>.
Author: Alina Bazarova [aut, cre], Marko Raseta [aut]
Maintainer: Alina Bazarova <a.bazarova@bham.ac.uk>

Diff between CARRoT versions 1.5.0 dated 2018-11-27 and 2.0.0 dated 2019-03-07

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Package chngpt updated to version 2019.3-6 with previous version 2018.10-17 dated 2018-10-30

Title: Estimation and Hypothesis Testing for Threshold Regression
Description: Threshold regression models are also called two-phase regression, broken-stick regression, split-point regression, structural change models, and regression kink models. Methods for both continuous and discontinuous threshold models are included, but the support for the former is much greater. This package is described in Fong, Huang, Gilbert and Permar (2017) chngpt: threshold regression model estimation and inference, BMC Bioinformatics, in press, <DOI:10.1186/s12859-017-1863-x>.
Author: Youyi Fong [cre], Tao Yang [aut], Zonglin He [aut], Adam Elder [aut]
Maintainer: Youyi Fong <youyifong@gmail.com>

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Package spBayes updated to version 0.4-2 with previous version 0.4-1 dated 2017-07-19

Title: Univariate and Multivariate Spatial-Temporal Modeling
Description: Fits univariate and multivariate spatio-temporal random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) <doi:10.18637/jss.v063.i13> and Finley, Banerjee, and Cook (2014) <doi:10.1111/2041-210X.12189>.
Author: Andrew Finley [aut, cre], Sudipto Banerjee [aut]
Maintainer: Andrew Finley <finleya@msu.edu>

Diff between spBayes versions 0.4-1 dated 2017-07-19 and 0.4-2 dated 2019-03-07

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New package TSDFGS with initial version 1.0
Package: TSDFGS
Type: Package
Title: Training Set Determination for Genomic Selection
Version: 1.0
Date: 2019-03-06
Author: Jen-Hsiang Ou and Chen-Tuo Liao
Maintainer: Jen-Hsiang Ou<oumark.me@outlook.com>
Description: Determining training set for genomic selection using a genetic algorithm (Holland J.H. (1975) <DOI:10.1145/1216504.1216510>) or simple exchange algorithm (change an individual every iteration). Three different criteria are used in both algorithms, which are r-score (Ou J.H., Liao C.T. (2018) <DOI:10.6342/NTU201802290>), PEV-score (Akdemir D. et al. (2015) <DOI:10.1186/s12711-015-0116-6>) and CD-score (Laloe D. (1993) <DOI:10.1186/1297-9686-25-6-557>). Phenotypic data for candidate set is not necessary for all these methods. By using it, one may readily determine a training set that can be expected to provide a better training set comparing to random sampling.
URL: https://tsdfgs.oumark.me
BugReports: https://gitlab.com/oumark/TSDFGS/issues
License: GPL (>= 3)
Imports: Rcpp (>= 1.0.0)
LinkingTo: Rcpp, RcppEigen
NeedsCompilation: yes
Packaged: 2019-03-06 10:09:26 UTC; mark
Repository: CRAN
Date/Publication: 2019-03-07 17:42:53 UTC

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New package SurvBoost with initial version 0.1.0
Package: SurvBoost
Type: Package
Title: Gradient Boosting for Survival Data
Version: 0.1.0
Author: Emily Morris
Maintainer: Emily Morris <emorrisl@umich.edu>
Description: Gradient boosting for optimizing loss functions of different types of survival data, including stratified data. Emily Morris, Kevin He, Yanming Li, Yi Li, Jian Kang (2018) <arXiv:1803.07715> see for description of methods.
License: GPL-3
LazyData: TRUE
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
RoxygenNote: 6.1.1
Imports: Rcpp, mvtnorm, ggplot2, plyr, reshape2, directlabels
NeedsCompilation: yes
Packaged: 2019-03-06 21:19:20 UTC; kdm
Depends: R (>= 2.10), survival
Repository: CRAN
Date/Publication: 2019-03-07 17:32:47 UTC

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Package SOMbrero updated to version 1.2-4 with previous version 1.2-3 dated 2018-02-05

Title: SOM Bound to Realize Euclidean and Relational Outputs
Description: The stochastic (also called on-line) version of the Self-Organising Map (SOM) algorithm is provided. Different versions of the algorithm are implemented, for numeric and relational data and for contingency tables as described, respectively, in Kohonen (2001) <isbn:3-540-67921-9>, Olteanu & Villa-Vialaneix (2005) <doi:10.1016/j.neucom.2013.11.047> and Cottrell et al (2004) <doi:10.1016/j.neunet.2004.07.010>. The package also contains many plotting features (to help the user interpret the results) and a graphical user interface based on 'shiny'.
Author: Nathalie Vialaneix [aut, cre], Jerome Mariette [aut], Madalina Olteanu [aut], Fabrice Rossi [aut], Laura Bendhaiba [ctb], Julien Boelaert [ctb]
Maintainer: Nathalie Vialaneix <nathalie.vialaneix@inra.fr>

Diff between SOMbrero versions 1.2-3 dated 2018-02-05 and 1.2-4 dated 2019-03-07

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New package SCRABBLE with initial version 0.0.1
Package: SCRABBLE
Type: Package
Title: Impute Single-Cell RNA-Seq Data Method
Version: 0.0.1
Author: Tao Peng; Kai Tan
Maintainer: Tao Peng <software.github@gmail.com>
Description: It aims to impute drop-out data by optimizing an objective function that consists of three terms. The first term ensures that imputed values for genes with nonzero expression remain as close to their original values as possible, thus minimizing unwanted bias towards expressed genes. The second term ensures the rank of the imputed data matrix to be as small as possible. The rationale is that we only expect a limited number of distinct cell types in the samples. The third term operates on the bulk RNA-Seq data. It ensures consistency between the average gene expression of the aggregated imputed data and the average gene expression of the bulk RNA-Seq data. We developed a convex optimization algorithm to minimize the objective function.
License: GPL-3
Encoding: UTF-8
Depends: R(>= 3.3)
LazyData: true
Imports: Rcpp (>= 0.12.13), rARPACK, pracma, ggplot2, RColorBrewer, reshape2, gridExtra, methods, grDevices
LinkingTo: Rcpp, RcppEigen
RoxygenNote: 6.1.1
Suggests: knitr,rmarkdown,BiocStyle
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2019-03-06 17:37:33 UTC; taopeng
Repository: CRAN
Date/Publication: 2019-03-07 17:22:50 UTC

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New package processR with initial version 0.1.0
Package: processR
Type: Package
Title: Implementation of the 'PROCESS' Macro
Version: 0.1.0
URL: https://github.com/cardiomoon/processR
BugReports: https://github.com/cardiomoon/processR/issues
Authors@R: c(person("Keon-Woong","Moon", role=c("aut", "cre"), email="cardiomoon@gmail.com"), person("Sokyoung","Hong",role=c("ctb"), email="hsychild@hanmail.net"))
Description: Perform moderation, mediation, moderated mediation and moderated moderation. Inspired from famous 'PROCESS' macro for 'SPSS' and 'SAS' created by Andrew Hayes.
Depends: R (>= 2.10)
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: lavaan(>= 0.6.3), diagram, magrittr, dplyr, flextable, ggiraphExtra, ggplot2, jtools, mycor, officer, psych, purrr, rrtable, semTools, stringr, tidyselect, shiny, modelr, prediction, rlang, tidyr, TH.data, shinyWidgets
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-03-06 15:30:52 UTC; cardiomoon
Author: Keon-Woong Moon [aut, cre], Sokyoung Hong [ctb]
Maintainer: Keon-Woong Moon <cardiomoon@gmail.com>
Repository: CRAN
Date/Publication: 2019-03-07 17:40:03 UTC

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Package probably updated to version 0.0.2 with previous version 0.0.1 dated 2018-12-18

Title: Tools for Post-Processing Class Probability Estimates
Description: Models can be improved by post-processing class probabilities, by: recalibration, conversion to hard probabilities, assessment of equivocal zones, and other activities. 'probably' contains tools for conducting these operations.
Author: Max Kuhn [aut, cre], Davis Vaughan [aut], RStudio [cph]
Maintainer: Max Kuhn <max@rstudio.com>

Diff between probably versions 0.0.1 dated 2018-12-18 and 0.0.2 dated 2019-03-07

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New package nvctr with initial version 0.1.1
Package: nvctr
Type: Package
Title: The n-vector Approach to Geographical Position Calculations using an Ellipsoidal Model of Earth
Version: 0.1.1
Authors@R: c( person("Enrico", "Spinielli", email = "enrico.spinielli@eurocontrol.int", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8584-9131")), person("EUROCONTROL", role = c("cph", "fnd")) )
Maintainer: Enrico Spinielli <enrico.spinielli@eurocontrol.int>
Description: The n-vector framework uses the normal vector to the Earth ellipsoid (called n-vector) as a non-singular position representation that turns out to be very convenient for practical position calculations. The n-vector is simple to use and gives exact answers for all global positions, and all distances, for both ellipsoidal and spherical Earth models. This package is a translation of the 'Matlab' library from FFI, the Norwegian Defence Research Establishment, as described in Gade (2010) <doi:10.1017/S0373463309990415>.
URL: https://github.com/euctrl-pru/nvctr
BugReports: https://github.com/euctrl-pru/nvctr/issues
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Suggests: testthat, knitr, rmarkdown, geosphere, png, knitcitations, covr, spelling
Imports: pracma, magrittr
RoxygenNote: 6.1.1
VignetteBuilder: knitr
Language: en-US
NeedsCompilation: no
Packaged: 2019-03-06 14:44:01 UTC; spi
Author: Enrico Spinielli [aut, cre] (<https://orcid.org/0000-0001-8584-9131>), EUROCONTROL [cph, fnd]
Repository: CRAN
Date/Publication: 2019-03-07 17:30:03 UTC

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New package moc.gapbk with initial version 0.1.0
Package: moc.gapbk
Type: Package
Title: Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge
Version: 0.1.0
Date: 2019-03-03
Authors@R: c(person("Jorge Parraga-Alava", email = "jorge.parraga@usach.cl", comment = c(ORCID = "0000-0001-8558-9122"), role = c("aut", "cre")), person("Marcio Dorn", email = "mdorn@inf.ufrgs.br", role=c("aut")), person("Mario Inostroza-Ponta", email = "mario.inostroza@usach.cl", role = c("aut")))
Author: Jorge Parraga-Alava [aut, cre] (<https://orcid.org/0000-0001-8558-9122>), Marcio Dorn [aut], Mario Inostroza-Ponta [aut]
Maintainer: Jorge Parraga-Alava <jorge.parraga@usach.cl>
Description: Implements the Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge (MOC-GaPBK) which was proposed by Parraga-Alava, J. et. al. (2018) <doi:10.1186/s13040-018-0178-4>.
Depends: R (>= 3.2.5)
License: GPL-2
Encoding: UTF-8
Imports: stats, amap, mco, nsga2R, reshape2, miscTools, matrixStats, fields, Rmisc, plyr, foreach, parallel, doParallel, utils, doSNOW, doMPI
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown
LazyData: true
NeedsCompilation: no
Packaged: 2019-03-06 16:18:55 UTC; jorgeklz
Repository: CRAN
Date/Publication: 2019-03-07 17:20:03 UTC

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New package longpower with initial version 1.0-19
Package: longpower
Type: Package
Title: Sample Size Calculations for Longitudinal Data
Version: 1.0-19
Date: 2019-03-06
Author: Michael C. Donohue
Maintainer: Michael C. Donohue <mdonohue@usc.edu>
Description: Compute power and sample size for linear models of longitudinal data. Supported models include mixed-effects models and models fit by generalized least squares and generalized estimating equations. Relevant formulas are derived by Liu and Liang (1997) <DOI:10.2307/2533554>, Diggle et al (2002) <ISBN:9780199676750>, and Lu, Luo, and Chen (2008) <DOI:10.2202/1557-4679.1098>.
License: GPL (>= 2)
Depends: R (>= 3.0.0), lme4 (>= 1.0), nlme
Suggests: knitr, gee, testthat, methods
LazyLoad: yes
VignetteBuilder: knitr
URL: https://github.com/mcdonohue/longpower
Collate: 'longpower-package.R' 'diggle.linear.power.R' 'edland.linear.power.R' 'liu.liang.linear.power.R' 'lmmpower.R' 'power_mmrm.R' 'print.power.longtest.R'
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-03-07 16:40:27 UTC; mdonohue
Repository: CRAN
Date/Publication: 2019-03-07 17:10:04 UTC

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New package invctr with initial version 0.1.0
Package: invctr
Title: Infix Functions For Vector Operations
Version: 0.1.0
Authors@R: c(person(given = "Fred", family = "Hasselman", email = "f.hasselman@bsi.ru.nl", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1384-8361")))
Maintainer: Fred Hasselman <f.hasselman@bsi.ru.nl>
Description: Vector operations between grapes: An infix-only package! The 'invctr' functions perform common and less common operations on vectors, data frames matrices and list objects: - Extracting a value (range), or, finding the indices of a value (range). - Trimming, or padding a vector with a value of your choice. - Simple polynomial regression. - Set and membership operations. - General check & replace function for NAs, Inf and other values.
Imports: rlang (>= 0.1.2), plyr
ByteCompile: true
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
License: GPL-3
Suggests: spelling, knitr, rmarkdown, testthat
Language: en-US
VignetteBuilder: knitr
URL: https://github.com/FredHasselman/invctr
BugReports: https://github.com/FredHasselman/invctr/issues
NeedsCompilation: no
Packaged: 2019-03-06 16:42:10 UTC; fred
Author: Fred Hasselman [aut, cre] (<https://orcid.org/0000-0003-1384-8361>)
Repository: CRAN
Date/Publication: 2019-03-07 17:20:06 UTC

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New package GMDHreg with initial version 0.1.0
Package: GMDHreg
Type: Package
Title: Regression using GMDH Algorithms
Version: 0.1.0
Author: Manuel Villacorta Tilve
Maintainer: Manuel Villacorta Tilve <mvt.oviedo@gmail.com>
Description: Regression using GMDH algorithms from Prof. Alexey G. Ivakhnenko. Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms that performs gradually complicated polynomial models and selecting the best solution by an external criterion. In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and selecting an optimal structure of model or network. The package includes GMDH Combinatorial, and GMDH MIA (Multilayered Iterative Algorithm) using PRESS (Predicted Residual Error Sum of Squares Statistic) criteria. It is calculated as the sums of squares of the prediction residuals for those observations. An introduction of GMDH algorithms: Farlow, S.J. (1981): "The GMDH algorithm of Ivakhnenko", The American Statistician, 35(4), pp. 210-215. <doi:10.2307/2683292> Ivakhnenko A.G. (1968): "The Group Method of Data Handling - A Rival of the Method of Stochastic Approximation", Soviet Automatic Control, 13(3), pp. 43-55.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 2.15)
Imports: stats, utils
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-03-07 15:17:43 UTC; manuel
Repository: CRAN
Date/Publication: 2019-03-07 17:32:56 UTC

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New package GaussianHMM1d with initial version 1.0.1
Package: GaussianHMM1d
Title: Inference, Goodness-of-Fit and Forecast for Univariate Gaussian Hidden Markov Models
Version: 1.0.1
Author: Bouchra R. Nasri and Bruno N. Remillard
Maintainer: Bouchra Nasri <bouchra.nasri@gmail.com>
Description: Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) <doi:10.1201/b14285>.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Depends: foreach, doParallel, parallel
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-03-06 18:12:51 UTC; Utilisateur
Repository: CRAN
Date/Publication: 2019-03-07 17:22:53 UTC

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New package DTAXG with initial version 0.1.0
Package: DTAXG
Type: Package
Title: Diagnostic Test Assessment in the Absence of Gold Standard
Version: 0.1.0
Author: Zhicheng Du, Yuantao Hao
Maintainer: Zhicheng Du <dgdzc@hotmail.com>
Description: To calculate the sensitivity and specificity in the absence of gold standard using the Bayesian method. The Bayesian method can be referenced at Haiyan Gu and Qiguang Chen (1999) <doi:10.3969/j.issn.1002-3674.1999.04.004>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: stats
NeedsCompilation: no
Packaged: 2019-03-06 23:59:13 UTC; dgdzc
Repository: CRAN
Date/Publication: 2019-03-07 17:32:59 UTC

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New package ChillModels with initial version 1.0.0
Package: ChillModels
Title: Processing Chill and Heat Models for Temperate Fruit Trees
Version: 1.0.0
Authors@R: c(person("Rafael Henrique", "Pertille", email = "henriquepertille@gmail.com", role = c("aut","cre"), comment = c(ORCID = 0000-0002-4888-2001)), person("Marcos Robson", "Sachet", email = "marcos.sachet@gmail.com", role = c("aut","ctb"), comment = c(ORCID = 0000-0002-0395-2475)), person("Idemir", "Citadin", email = "idemir@utfpr.edu.br", role = c("aut","ctb"), comment = c(ORCID = 0000-0001-9416-2761)), person("Marieli Teresinha", "Guerrezi", email = "marieliguerrezi@hotmail.com ", role = c("aut","ctb"), comment = c(ORCID = 0000-0003-3314-4046)))
Description: Calculates the chilling and heat accumulation for studies of the temperate fruit trees. The models in this package are: Utah (Richardson et al., 1974, ISSN:0018-5345), Positive Chill Units - PCU (Linsley-Noaks et al., 1995, ISSN:1017-0316), GDH-A - Growing Degree Hours by Anderson et al.(1986, ISSN:0567-7572), GDH-R - Growing Degree Hours by Richardson et al.(1975, ISSN:0018-5345), North Carolina (Shaltout e Unrath, 1983, ISSN:0003-1062), Landsberg Model (Landsberg, 1974, ISSN:0305-7364), Q10 Model (Bidabe, 1967, ISSN:0031-9368), Jones Model (Jones et al., 2013 <DOI:10.1111/j.1438-8677.2012.00590.x>), Low-Chill Model (Gilreath and Buchanan, 1981, ISSN:0003-1062), Model for Cherry "Sweetheart" (Guak and Nielsen, 2013 <DOI:10.1007/s13580-013-0140-9>), Model for apple "Gala" (Guak and Nielsen, 2013 <DOI:10.1007/s13580-013-0140-9>), Taiwan Model (Lu et al., 2012 <DOI:10.17660/ActaHortic.2012.962.35>), Dynamic Model (Fishman et al., 1987, ISSN:0022-5193) adapted from the function Dynamic_Model() of the 'chillR' package (Luedeling, 2018), Unified Model (Chuine et al., 2016 <DOI:10.1111/gcb.13383>) and Heat Restriction model.
Depends: R (>= 3.4.0)
License: GPL-3
Encoding: UTF-8
LazyData: TRUE
RoxygenNote: 6.1.1
Suggests: chillR, dplyr, lubridate, ggplot2
NeedsCompilation: no
Packaged: 2019-03-06 22:14:11 UTC; Rafael
Author: Rafael Henrique Pertille [aut, cre] (<https://orcid.org/-6891>), Marcos Robson Sachet [aut, ctb] (<https://orcid.org/-2872>), Idemir Citadin [aut, ctb] (<https://orcid.org/-12178>), Marieli Teresinha Guerrezi [aut, ctb] (<https://orcid.org/-7363>)
Maintainer: Rafael Henrique Pertille <henriquepertille@gmail.com>
Repository: CRAN
Date/Publication: 2019-03-07 17:33:02 UTC

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Package randomNames updated to version 1.4-0.0 with previous version 1.0-0.0 dated 2017-06-06

Title: Generate Random Given and Surnames
Description: Function for generating random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.
Author: Damian W. Betebenner [aut, cre]
Maintainer: Damian W. Betebenner <dbetebenner@nciea.org>

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Package Thresher updated to version 1.1.0 with previous version 1.0.2 dated 2018-10-17

Title: Threshing and Reaping for Principal Components
Description: Defines the classes used to identify outliers (threshing) and compute the number of significant principal components and number of clusters (reaping) in a joint application of PCA and hierarchical clustering. See Wang et al., 2018, <doi:10.1186/s12859-017-1998-9>.
Author: Kevin R. Coombes
Maintainer: Kevin R. Coombes <krc@silicovore.com>

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Package ML.MSBD updated to version 1.1.1 with previous version 1.1.0 dated 2018-07-09

Title: Maximum Likelihood Inference on Multi-State Trees
Description: Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani and T. Stadler (2017) <doi:10.1101/215491>.
Author: Joelle Barido-Sottani [aut, cre]
Maintainer: Joelle Barido-Sottani <joelle.barido-sottani@m4x.org>

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Package GJRM updated to version 0.2 with previous version 0.1-5 dated 2018-11-14

Title: Generalised Joint Regression Modelling
Description: Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.
Author: Giampiero Marra <giampiero.marra@ucl.ac.uk> and Rosalba Radice <rosalba.radice@city.ac.uk>
Maintainer: Giampiero Marra <giampiero.marra@ucl.ac.uk>

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Package sybil updated to version 2.1.5 with previous version 2.1.2 dated 2018-08-15

Title: Efficient Constrained Based Modelling
Description: This Systems Biology Package (Gelius-Dietrich et. al. (2012) <doi:10.1186/1752-0509-7-125>) implements algorithms for constraint based analyses of metabolic networks, e.g. flux-balance analysis (FBA), minimization of metabolic adjustment (MOMA), regulatory on/off minimization (ROOM), robustness analysis and flux variability analysis. The package is easily extendable for additional algorithms. Most of the current LP/MILP solvers are supported via additional packages.
Author: Mayo Roettger [cre], Gabriel Gelius-Dietrich [aut], C. Jonathan Fritzemeier [ctb], Rajen Piernikarczyk [ctb], Marc Andre Daxer [ctb], Benjamin Braasch [ctb], Abdelmoneim Desouki [ctb], Martin J. Lercher [ctb]
Maintainer: Mayo Roettger <mayo.roettger@hhu.de>

Diff between sybil versions 2.1.2 dated 2018-08-15 and 2.1.5 dated 2019-03-07

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Package ranger updated to version 0.11.2 with previous version 0.11.1 dated 2019-01-24

Title: A Fast Implementation of Random Forests
Description: A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported. Data from genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') can be directly analyzed.
Author: Marvin N. Wright [aut, cre], Stefan Wager [ctb], Philipp Probst [ctb]
Maintainer: Marvin N. Wright <cran@wrig.de>

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Package lmSubsets updated to version 0.4 with previous version 0.3 dated 2018-05-01

Title: Exact Variable-Subset Selection in Linear Regression
Description: Exact and approximation algorithms for variable-subset selection in ordinary linear regression models. Either compute all submodels with the lowest residual sum of squares, or determine the single-best submodel according to a pre-determined statistical criterion. Hofmann, Gatu, Kontoghiorghes, Colubi, Zeileis (2018, submitted).
Author: Marc Hofmann [aut, cre], Cristian Gatu [aut], Erricos J. Kontoghiorghes [aut], Ana Colubi [aut], Achim Zeileis [aut] (<https://orcid.org/0000-0003-0918-3766>), Martin Moene [cph] (for the GSL Lite library), Microsoft Corporation [cph] (for the GSL Lite library), Free Software Foundation, Inc. [cph] (for snippets from the GNU ISO C++ Library)
Maintainer: Marc Hofmann <marc.hofmann@gmail.com>

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Package gower updated to version 0.2.0 with previous version 0.1.2 dated 2017-02-23

Title: Gower's Distance
Description: Compute Gower's distance (or similarity) coefficient between records. Compute the top-n matches between records. Core algorithms are executed in parallel on systems supporting OpenMP.
Author: Mark van der Loo [aut, cre], David Turner [ctb]
Maintainer: Mark van der Loo <mark.vanderloo@gmail.com>

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Package ggforce updated to version 0.2.0 with previous version 0.1.3 dated 2018-07-07

Title: Accelerating 'ggplot2'
Description: The aim of 'ggplot2' is to aid in visual data investigations. This focus has led to a lack of facilities for composing specialised plots. 'ggforce' aims to be a collection of mainly new stats and geoms that fills this gap. All additional functionality is aimed to come through the official extension system so using 'ggforce' should be a stable experience.
Author: Thomas Lin Pedersen [cre, aut] (<https://orcid.org/0000-0002-5147-4711>), RStudio [cph]
Maintainer: Thomas Lin Pedersen <thomasp85@gmail.com>

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Package partitionComparison updated to version 0.2.5 with previous version 0.2.4 dated 2018-10-04

Title: Implements Measures for the Comparison of Two Partitions
Description: Provides several measures ((dis)similarity, distance/metric, correlation, entropy) for comparing two partitions of the same set of objects. The different measures can be assigned to three different classes: Pair comparison (containing the famous Jaccard and Rand indices), set based, and information theory based. Many of the implemented measures can be found in Albatineh AN, Niewiadomska-Bugaj M and Mihalko D (2006) <doi:10.1007/s00357-006-0017-z> and Meila M (2007) <doi:10.1016/j.jmva.2006.11.013>. Partitions are represented by vectors of class labels which allow a straightforward integration with existing clustering algorithms (e.g. kmeans()). The package is mostly based on the S4 object system.
Author: Fabian Ball [aut, cre, cph, ctb], Andreas Geyer-Schulz [cph]
Maintainer: Fabian Ball <mail@fabian-ball.de>

Diff between partitionComparison versions 0.2.4 dated 2018-10-04 and 0.2.5 dated 2019-03-07

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Package jstable updated to version 0.8.1 with previous version 0.7.10 dated 2019-02-24

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>

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New package FishResp with initial version 1.0.3
Package: FishResp
Version: 1.0.3
Title: Analytical Tool for Aquatic Respirometry
Description: Calculates metabolic rate of fish and other aquatic organisms measured using an intermittent-flow respirometry approach. The tool is used to run a set of graphical QC tests of raw respirometry data, correct it for background respiration and chamber effect, filter and extract target values of absolute and mass-specific metabolic rate. Experimental design should include background respiration tests and measuring of one or more metabolic rate traits. The package allows a user to import raw respirometry data obtained from 'AquaResp' (free software), 'AutoResp' ('LoligoSystems'), 'OxyView' ('PreSens'), 'Pyro Oxygen Logger' ('PyroScience') and 'Q-box Aqua' ('QubitSystems').
URL: https://fishresp.org
Authors@R: c( person("Sergey", "Morozov", role = c("aut", "cre"), email = "sergey.morozov@helsinki.fi") , person("R.J. Scott", "McCairns", role = c("aut", "ths"), email = "scott.mccairns@inra.fr"), person("Federico", "Calboli", role = "ctb"), person("Juha", "Merila", role = "ths"), person("Stephen", "White", role = "dtc"), person("Daniel", "Montgomery", role = "dtc"), person("Laura", "Almodovar-Acevedo", role = "dtc"), person("Niclas", "Kolm", role = "dtc"), person("Alexander", "Kotrschal", role = "dtc"))
Maintainer: Sergey Morozov <sergey.morozov@helsinki.fi>
License: GPL-3
SystemRequirements: Display resolution >= 1280x800; RAM >= 4GB
Depends: R (>= 3.0.0)
Imports: chron, lattice, mclust, rMR, respirometry
LazyData: FALSE
RoxygenNote: 6.1.1
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2019-03-06 15:46:50 UTC; samoedd
Author: Sergey Morozov [aut, cre], R.J. Scott McCairns [aut, ths], Federico Calboli [ctb], Juha Merila [ths], Stephen White [dtc], Daniel Montgomery [dtc], Laura Almodovar-Acevedo [dtc], Niclas Kolm [dtc], Alexander Kotrschal [dtc]
Repository: CRAN
Date/Publication: 2019-03-07 13:42:45 UTC

More information about FishResp at CRAN
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Package Anthropometry updated to version 1.11 with previous version 1.10 dated 2018-04-06

Title: Statistical Methods for Anthropometric Data
Description: Statistical methodologies especially developed to analyze anthropometric data. These methods are aimed at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the statistical concept of data depth, statistical shape analysis and archetypal analysis. Please see Vinue (2017) <doi:10.18637/jss.v077.i06>.
Author: Guillermo Vinue, Irene Epifanio, Amelia Simo, M. Victoria Ibanez, Juan Domingo, Guillermo Ayala
Maintainer: Guillermo Vinue <Guillermo.Vinue@uv.es>

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Package rmytarget updated to version 2.1.4 with previous version 2.1.3 dated 2019-02-14

Title: Load Data from 'MyTarget API v2'
Description: Allows work with 'MyTarget API v2' <https://target.my.com/doc/apiv2/ru/detailed.html> and load data by ads, campaigns, agency clients and statistic from your ads account.
Author: Alexey Seleznev
Maintainer: Alexey Seleznev <selesnow@gmail.com>

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Package mvSLOUCH updated to version 2.0.2 with previous version 2.0.1 dated 2019-01-29

Title: Multivariate Stochastic Linear Ornstein-Uhlenbeck Models for Phylogenetic Comparative Hypotheses
Description: Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data from species related by a common evolutionary history. See K. Bartoszek, J, Pienaar, P. Mostad, S. Andersson, T. F.Hansen (2012) <doi:10.1016/j.jtbi.2012.08.005>.
Author: Krzysztof Bartoszek <krzbar@protonmail.ch>
Maintainer: Krzysztof Bartoszek <krzbar@protonmail.ch>

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Package mri updated to version 1.0.1 with previous version 1.0.0 dated 2019-02-13

Title: Modified Rand and Wallace Indices
Description: It provides functions to compute the values of different modifications of the Rand and Wallace indices. The indices are used to measure the stability or similarity of two partitions obtained on two different sets of units with a non-empty intercept. Splitting and merging of clusters can (depends on the selected index) have a different effect on the value of the indices. The indices are proposed in Cugmas and Ferligoj (2018) <http://ibmi.mf.uni-lj.si/mz/2018/no-1/Cugmas2018.pdf>.
Author: Marjan Cugmas [aut, cre]
Maintainer: Marjan Cugmas <marjan.cugmas@fdv.uni-lj.si>

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Package MendelianRandomization updated to version 0.4.0 with previous version 0.3.0 dated 2018-03-03

Title: Mendelian Randomization Package
Description: Encodes several methods for performing Mendelian randomization analyses with summarized data. Summarized data on genetic associations with the exposure and with the outcome can be obtained from large consortia. These data can be used for obtaining causal estimates using instrumental variable methods.
Author: Olena Yavorska <olena.yavorska@ludwig.ox.ac.uk> James Staley <james.staley@bristol.ac.uk>
Maintainer: Stephen Burgess <sb452@medschl.cam.ac.uk>

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Package cptcity updated to version 1.0.4 with previous version 1.0.3 dated 2018-04-18

Title: 'cpt-city' Colour Gradients
Description: Incorporates colour gradients from the 'cpt-city' web archive available at <http://soliton.vm.bytemark.co.uk/pub/cpt-city/>.
Author: Sergio Ibarra-Espinosa [aut, cre] (<https://orcid.org/0000-0002-3162-1905>)
Maintainer: Sergio Ibarra-Espinosa <sergio.ibarra@usp.br>

Diff between cptcity versions 1.0.3 dated 2018-04-18 and 1.0.4 dated 2019-03-07

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Package sivipm updated to version 1.1-4.2 with previous version 1.1-4.1 dated 2019-01-14

Title: Sensitivity Indices with Dependent Inputs
Description: Sensitivity indices with dependent correlated inputs, using a method based on PLS regression.
Author: A. Bouvier [aut], J.-P. Gauchi [aut, cre], E. Volatier [ctb]
Maintainer: ORPHANED

Diff between sivipm versions 1.1-4.1 dated 2019-01-14 and 1.1-4.2 dated 2019-03-07

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Package LINselect updated to version 1.1.1 with previous version 1.1 dated 2017-04-20

Title: Selection of Linear Estimators
Description: Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators. In particular it solves the problem of variable selection by choosing the best predictor among predictors emanating from different methods as lasso, elastic-net, adaptive lasso, pls, randomForest. Moreover, it can be applied for choosing the tuning parameter in a Gauss-lasso procedure.
Author: Yannick Baraud, Christophe Giraud, Sylvie Huet
Maintainer: ORPHANED

Diff between LINselect versions 1.1 dated 2017-04-20 and 1.1.1 dated 2019-03-07

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Package vistributions updated to version 0.1.1 with previous version 0.1.0 dated 2018-11-30

Title: Visualize Probability Distributions
Description: Visualize and compute percentiles/probabilities of normal, t, f, chi square and binomial distributions.
Author: Aravind Hebbali [aut, cre]
Maintainer: Aravind Hebbali <hebbali.aravind@gmail.com>

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Package qpdf updated to version 1.1 with previous version 1.0 dated 2019-02-28

Title: Split, Combine and Compress PDF Files
Description: Content-preserving transformations transformations of PDF files such as split, combine, and compress. This package interfaces directly to the 'qpdf' C++ API and does not require any command line utilities. Note that 'qpdf' does not read actual content from PDF files: to extract text and data you need the 'pdftools' package.
Author: Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>), Jay Berkenbilt [cph] (Author of libqpdf)
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between qpdf versions 1.0 dated 2019-02-28 and 1.1 dated 2019-03-07

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Package multinet updated to version 3.0.0 with previous version 2.0.1 dated 2018-12-14

Title: Analysis and Mining of Multilayer Social Networks
Description: Functions for the creation/generation and analysis of multilayer social networks.
Author: Matteo Magnani, Davide Vega, Mikael Dubik (glouvain). The package uses functions from eclat (www.borgelt.net/eclat.html), for association rule mining, Eigen (eigen.tuxfamily.org) and spectra (https://spectralib.org), for matrix manipulation, Infomap (www.mapequation.org), for the Infomap community detection method, and Howard Hinnant's date and time library (https://github.com/HowardHinnant/date). The code from this libraries has been included in our source package.
Maintainer: Matteo Magnani <matteo.magnani@it.uu.se>

Diff between multinet versions 2.0.1 dated 2018-12-14 and 3.0.0 dated 2019-03-07

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 multinet-3.0.0/multinet/src/include/core/datastructures/observers/Subject.h                          |    2 
 multinet-3.0.0/multinet/src/include/mnet/community/glouvain.h                                        |    2 
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Package RSuite updated to version 0.36-252 with previous version 0.35-250 dated 2019-01-07

Title: Supports Developing, Building and Deploying R Solution
Description: Supports safe and reproducible solutions development in R. It will help you with environment separation per project, dependency management, local packages creation and preparing deployment packs for your solutions.
Author: Walerian Sokolowski [aut, cre], Wit Jakuczun [aut], Yulia Yakimechko [aut], Mateusz Kalinowski [aut], Ryszard Szymanski [aut], WLOG Solutions [cph], R Consortium [ctb] (Definitions of system requirements are taken from sysreqsdb project.)
Maintainer: Walerian Sokolowski <rsuite@wlogsolutions.com>

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Package mice updated to version 3.4.0 with previous version 3.3.0 dated 2018-07-27

Title: Multivariate Imputation by Chained Equations
Description: Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.
Author: Stef van Buuren [aut, cre], Karin Groothuis-Oudshoorn [aut], Alexander Robitzsch [ctb], Gerko Vink [ctb], Lisa Doove [ctb], Shahab Jolani [ctb], Rianne Schouten [ctb], Philipp Gaffert [ctb], Florian Meinfelder [ctb], Bernie Gray [ctb]
Maintainer: Stef van Buuren <stef.vanbuuren@tno.nl>

Diff between mice versions 3.3.0 dated 2018-07-27 and 3.4.0 dated 2019-03-07

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Package Matching updated to version 4.9-5 with previous version 4.9-3 dated 2018-05-06

Title: Multivariate and Propensity Score Matching with Balance Optimization
Description: Provides functions for multivariate and propensity score matching and for finding optimal balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance has been obtained are also provided.
Author: Jasjeet Singh Sekhon <sekhon@berkeley.edu>
Maintainer: Jasjeet Singh Sekhon <sekhon@berkeley.edu>

Diff between Matching versions 4.9-3 dated 2018-05-06 and 4.9-5 dated 2019-03-07

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New package ggfan with initial version 0.1.3
Package: ggfan
Type: Package
Title: Summarise a Distribution Through Coloured Intervals
Description: Implements the functionality of the 'fanplot' package as 'geoms' for 'ggplot2'. Designed for summarising MCMC samples from a posterior distribution, where a visualisation is desired for several values of a continuous covariate. Increasing posterior intervals of the sampled quantity are mapped to a continuous colour scale.
Version: 0.1.3
Authors@R: person("Jason", "Hilton", email = "jason_hilton@yahoo.com", role = c("aut", "cre"))
License: GPL-2 | file LICENSE
LazyData: TRUE
Depends: R (>= 3.1)
Imports: ggplot2, colorspace, dplyr, stats, grid, rstan
Suggests: testthat, knitr, rmarkdown, tidyr, magrittr, tibble
URL: https://github.com/jasonhilton/ggfan
BugReports: https://github.com/jasonhilton/ggfan/issues
VignetteBuilder: knitr
RoxygenNote: 6.1.1
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2019-03-06 23:43:59 UTC; jason
Author: Jason Hilton [aut, cre]
Maintainer: Jason Hilton <jason_hilton@yahoo.com>
Repository: CRAN
Date/Publication: 2019-03-07 09:02:42 UTC

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Package fingertipsR updated to version 0.2.1 with previous version 0.2.0 dated 2018-11-12

Title: Fingertips Data for Public Health
Description: Fingertips (<http://fingertips.phe.org.uk/>) contains data for many indicators of public health in England. The underlying data is now more easily accessible by making use of the API.
Author: Sebastian Fox [aut, cre], Julian Flowers [aut, ctb], Simon Thelwall [ctb] (<https://orcid.org/0000-0002-0434-2724>), Duncan Gormansway [ctb], Carl Ganz [ctb], David Whiting [ctb], Crown Copyright 2018 [cph]
Maintainer: Sebastian Fox <sebastian.fox@phe.gov.uk>

Diff between fingertipsR versions 0.2.0 dated 2018-11-12 and 0.2.1 dated 2019-03-07

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New package cdfquantreg with initial version 1.2.2
Package: cdfquantreg
Type: Package
Title: Quantile Regression for Random Variables on the Unit Interval
Version: 1.2.2
Date: 2019-03-08
Authors@R: c(person(given = "Yiyun", family = "Shou", email = "yiyun.shou@anu.edu.au", role = c("aut", "cre")), person(given = "Michael", family = "Smithson", email = "Michael.Smithson@anu.edu.au", role = "aut"))
Description: Employs a two-parameter family of distributions for modelling random variables on the (0, 1) interval by applying the cumulative distribution function (cdf) of one parent distribution to the quantile function of another.
BugReports: https://cloudyshou.wordpress.com/cdfquantreg-bugs-report
Depends: R (>= 3.1.0)
License: GPL-3
Imports: pracma (>= 1.8), Formula (>= 1.2), stats, MASS
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2019-03-07 02:56:54 UTC; cici_blue
Author: Yiyun Shou [aut, cre], Michael Smithson [aut]
Maintainer: Yiyun Shou <yiyun.shou@anu.edu.au>
Repository: CRAN
Date/Publication: 2019-03-07 09:02:47 UTC

More information about cdfquantreg at CRAN
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Package arules updated to version 1.6-3 with previous version 1.6-2 dated 2018-12-03

Title: Mining Association Rules and Frequent Itemsets
Description: Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides C implementations of the association mining algorithms Apriori and Eclat.
Author: Michael Hahsler [aut, cre, cph], Christian Buchta [aut, cph], Bettina Gruen [aut, cph], Kurt Hornik [aut, cph], Ian Johnson [ctb, cph], Christian Borgelt [ctb, cph]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>

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Package pulsar updated to version 0.3.5 with previous version 0.3.4 dated 2018-09-02

Title: Parallel Utilities for Lambda Selection along a Regularization Path
Description: Model selection for penalized graphical models using the Stability Approach to Regularization Selection ('StARS'), with options for speed-ups including Bounded StARS (B-StARS), batch computing, and other stability metrics (e.g., graphlet stability G-StARS). Christian L. Müller, Richard Bonneau, Zachary Kurtz (2016) <arXiv:1605.07072>.
Author: Zachary Kurtz [aut, cre], Christian Müller [aut, ctb]
Maintainer: Zachary Kurtz <zdkurtz@gmail.com>

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Package monomvn updated to version 1.9-9 with previous version 1.9-8 dated 2018-09-14

Title: Estimation for Multivariate Normal and Student-t Data with Monotone Missingness
Description: Estimation of multivariate normal and student-t data of arbitrary dimension where the pattern of missing data is monotone. Through the use of parsimonious/shrinkage regressions (plsr, pcr, lasso, ridge, etc.), where standard regressions fail, the package can handle a nearly arbitrary amount of missing data. The current version supports maximum likelihood inference and a full Bayesian approach employing scale-mixtures for Gibbs sampling. Monotone data augmentation extends this Bayesian approach to arbitrary missingness patterns. A fully functional standalone interface to the Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown), Horseshoe (from Carvalho, Polson, & Scott), and ridge regression with model selection via Reversible Jump, and student-t errors (from Geweke) is also provided.
Author: Robert B. Gramacy <rbg@vt.edu>
Maintainer: Robert B. Gramacy <rbg@vt.edu>

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Package languageserver updated to version 0.2.7 with previous version 0.2.6 dated 2019-02-24

Title: Language Server Protocol
Description: An implementation of the Language Server Protocol for R. The Language Server protocol is used by an editor client to integrate features like auto completion. See <https://microsoft.github.io/language-server-protocol> for details.
Author: Randy Lai
Maintainer: Randy Lai <randy.cs.lai@gmail.com>

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Package collections updated to version 0.1.5 with previous version 0.1.4 dated 2019-02-27

Title: High Performance Container Data Types
Description: Provides high performance container data types such as Queue, Stack, Deque, Dict and OrderedDict. Benchmarks <https://randy3k.github.io/collections/articles/benchmark.html> have shown that these containers are asymptotically more efficient than those offered by other packages.
Author: Randy Lai [aut, cre]
Maintainer: Randy Lai <randy.cs.lai@gmail.com>

Diff between collections versions 0.1.4 dated 2019-02-27 and 0.1.5 dated 2019-03-07

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Package sdcHierarchies updated to version 0.17 with previous version 0.16 dated 2019-02-28

Title: Create and (Interactively) Modify Nested Hierarchies
Description: Provides functionality to generate, (interactively) modify (by adding, removing and renaming nodes) and convert nested hierarchies between different formats. These tree like structures can be used to define for example complex hierarchical tables used for statistical disclosure control.
Author: Bernhard Meindl [aut, cre]
Maintainer: Bernhard Meindl <Bernhard.Meindl@statistik.gv.at>

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Package reproducible updated to version 0.2.7 with previous version 0.2.6 dated 2019-02-01

Title: A Set of Tools that Enhance Reproducibility Beyond Package Management
Description: Collection of high-level, robust, machine- and OS-independent tools for making deeply reproducible and reusable content in R. This includes light weight package management (similar to 'packrat' and 'checkpoint', but more flexible, lightweight and simpler than both), tools for caching, downloading and verifying or writing checksums, post-processing of common spatial datasets, and accessing GitHub repositories. Some features are still under active development.
Author: Eliot J B McIntire [aut, cre] (<https://orcid.org/0000-0002-6914-8316>), Alex M Chubaty [aut] (<https://orcid.org/0000-0001-7146-8135>), Tati Micheletti [ctb] (<https://orcid.org/0000-0003-4838-8342>), Ceres Barros [ctb] (<https://orcid.org/0000-0003-4036-977X>), Ian Eddy [ctb] (<https://orcid.org/0000-0001-7397-2116>), Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources Canada [cph]
Maintainer: Eliot J B McIntire <eliot.mcintire@canada.ca>

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Package mimi updated to version 0.2.0 with previous version 0.1.0 dated 2019-01-11

Title: Main Effects and Interactions in Mixed and Incomplete Data
Description: Generalized low-rank models for mixed and incomplete data frames. The main function may be used for dimensionality reduction of imputation of numeric, binary and count data (simultaneously). Main effects such as column means, group effects, or effects of row-column side information (e.g. user/item attributes in recommendation system) may also be modelled in addition to the low-rank model. Geneviève Robin, Olga Klopp, Julie Josse, Éric Moulines, Robert Tibshirani (2018) <arXiv:1806.09734>.
Author: Geneviève Robin
Maintainer: Genevieve Robin <genevieve.robin@polytechnique.edu>

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Package lori updated to version 2.1.2 with previous version 2.1.1 dated 2019-02-26

Title: Low-Rank Interactions in Count Data with Covariates
Description: Analysis, imputation, and multiple imputation of count data using covariates. LORI uses a log-linear model where main row and column effects are decomposed as regression terms on known covariates. A residual low-rank interaction term is also fitted. LORI returns estimates of covariate effects and interactions, as well as an imputed count table. The package also contains a multiple imputation procedure.
Author: Genevieve Robin [aut, cre]
Maintainer: Genevieve Robin <genevieve.robin@polytechnique.edu>

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Package hdme updated to version 0.2.2 with previous version 0.2.1 dated 2018-10-15

Title: High-Dimensional Regression with Measurement Error
Description: Penalized regression for generalized linear models for measurement error problems (aka. errors-in-variables). The package contains a version of the lasso (L1-penalization) which corrects for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>). It also contains an implementation of the Generalized Matrix Uncertainty Selector, which is a version the (Generalized) Dantzig Selector for the case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).
Author: Oystein Sorensen
Maintainer: Oystein Sorensen <oystein.sorensen.1985@gmail.com>

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Package future.apply updated to version 1.2.0 with previous version 1.1.0 dated 2019-01-17

Title: Apply Function to Elements in Parallel using Futures
Description: Implementations of apply(), by(), eapply(), lapply(), Map(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster. These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework.
Author: Henrik Bengtsson [aut, cre, cph], R Core Team [cph, ctb]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

Diff between future.apply versions 1.1.0 dated 2019-01-17 and 1.2.0 dated 2019-03-07

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Package calibrator updated to version 1.2-8 with previous version 1.2-6 dated 2013-12-09

Title: Bayesian Calibration of Complex Computer Codes
Description: Performs Bayesian calibration of computer models as per Kennedy and O'Hagan 2001. The package includes routines to find the hyperparameters and parameters; see the help page for stage1() for a worked example using the toy dataset. A tutorial is provided in the calex.Rnw vignette; and a suite of especially simple one dimensional examples appears in inst/doc/one.dim/.
Author: Robin K. S. Hankin [aut, cre] (<https://orcid.org/0000-0001-5982-0415>)
Maintainer: Robin K. S. Hankin <hankin.robin@gmail.com>

Diff between calibrator versions 1.2-6 dated 2013-12-09 and 1.2-8 dated 2019-03-07

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 48 files changed, 214 insertions(+), 141 deletions(-)

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Package WMWssp updated to version 0.3.6 with previous version 0.3.4 dated 2018-07-08

Title: Wilcoxon-Mann-Whitney Sample Size Planning
Description: Calculates the minimal sample size for the Wilcoxon-Mann-Whitney test that is needed for a given power and two sided type I error rate. The method works for metric data with and without ties, count data, ordered categorical data, and even dichotomous data. But data is needed for the reference group to generate synthetic data for the treatment group based on a relevant effect. For details, see Brunner, E., Bathke A. C. and Konietschke, F: Rank- and Pseudo-Rank Procedures in Factorial Designs - Using R and SAS, Springer Verlag, to appear.
Author: Arne C. Bathke [aut], Edgar Brunner [aut], Martin Happ [aut, cre] (<https://orcid.org/0000-0003-0009-2665>), Frank Konietschke [aut]
Maintainer: Martin Happ <martin.happ@aon.at>

Diff between WMWssp versions 0.3.4 dated 2018-07-08 and 0.3.6 dated 2019-03-07

 DESCRIPTION                  |   10 +++++-----
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 tests/testthat/test_WMWssp.R |   10 ++++++----
 10 files changed, 44 insertions(+), 39 deletions(-)

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Package UCSCXenaTools updated to version 1.0.1 with previous version 1.0.0 dated 2019-02-26

Title: Download and Explore Datasets from UCSC Xena Data Hubs
Description: Download and explore datasets from UCSC Xena data hubs, which are a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. Databases are normalized so they can be combined, linked, filtered, explored and downloaded. This package is based on 'XenaR' package.
Author: Shixiang Wang [aut, cre] (<https://orcid.org/0000-0001-9855-7357>), Martin Morgan [aut]
Maintainer: Shixiang Wang <w_shixiang@163.com>

Diff between UCSCXenaTools versions 1.0.0 dated 2019-02-26 and 1.0.1 dated 2019-03-07

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More information about UCSCXenaTools at CRAN
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