Fri, 30 Nov 2018

New package snfa with initial version 0.0.1
Package: snfa
Title: Smooth Non-Parametric Frontier Analysis
Version: 0.0.1
Authors@R: person("Taylor", "McKenzie", email = "tkmckenzie@gmail.com", role = c("aut", "cre"))
Description: Fitting of non-parametric production frontiers for use in efficiency analysis. Methods are provided for both a smooth analogue of Data Envelopment Analysis (DEA) and a non-parametric analogue of Stochastic Frontier Analysis (SFA). Frontiers are constructed for multiple inputs and a single output using constrained kernel smoothing as in Racine et al. (2009), which allow for the imposition of monotonicity and concavity constraints on the estimated frontier.
Maintainer: Taylor McKenzie <tkmckenzie@gmail.com>
Depends: R (>= 3.5.0)
Imports: abind (>= 1.4.5), ggplot2 (>= 3.1.0), prodlim (>= 2018.4.18), quadprog (>= 1.5.5), Rdpack (>= 0.10.1), rootSolve (>= 1.7)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
RdMacros: Rdpack
NeedsCompilation: no
Packaged: 2018-11-20 16:08:34 UTC; tmcken
Author: Taylor McKenzie [aut, cre]
Repository: CRAN
Date/Publication: 2018-12-01 00:00:03 UTC

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New package smartR with initial version 0.62.0
Package: smartR
Type: Package
Title: Spatial Management and Assessment of Demersal Resources for Trawl Fisheries
Version: 0.62.0
Date: 2018-11-20
Author: Lorenzo D'Andrea, Tommaso Russo, Antonio Parisi, Stefano Cataudella
Maintainer: Lorenzo D'Andrea <dandrea.lorenz@gmail.com>
Description: A tool for assessing bio-economic feedback in different management scenarios. 'smartR' (Spatial Management and Assessment of demersal Resources for Trawl fisheries) combines information from different tasks gathered within the European Data Collection Framework for the fishery sector. The 'smartR' package implements the SMART model (Russo et al., 2014 <doi:10.1371/journal.pone.0086222>), through the object-oriented programming paradigm, and within this package it is possible to achieve the complete set of analyses required by the SMART approach: from the editing and formatting of the raw data; the construction and maintenance of coherent datasets; the numerical and visual inspection of the generated metadata; to the final simulation of management scenarios and the forecast of their effects. The interaction between the user and the application could take place through invocation of methods via the command line or could be entirely committed to the graphical user interfaces (GUI).
License: GPL (>= 2)
Depends: R6, rjags, maptools, mapdata
Imports: PBSmapping, gWidgets2, gWidgets2RGtk2, gstat, lattice, plyr, sp, maps, clues, cluster, spdep, marmap, shape, jpeg, grid, ggplot2, sqldf, gsubfn, chron, reshape2, foreign, ROCR, caret, nnls, mtsdi, gridExtra, rgeos, scales, grDevices, ggthemes, vegan, rpart, ggrepel, rgdal, RColorBrewer, jsonlite, igraph, mapproj
LazyData: true
RoxygenNote: 6.1.0
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2018-11-20 13:51:26 UTC; Lomo
Repository: CRAN
Date/Publication: 2018-11-30 23:40:03 UTC

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New package SeedCalc with initial version 1.0.0
Package: SeedCalc
Type: Package
Title: Seed Germination and Seedling Growth Indexes
Version: 1.0.0
Author: Laercio Junio da Silva, Andre Danta de Medeiros e Ariadne Morbeck Santos Oliveira
Maintainer: Laercio Junio da Silva <laerciojdsilva@gmail.com>
Description: Functions to calculate seed germination and seedling emergence and growth indexes. The main indexes for germination and seedling emergence, considering the time for seed germinate are: T10, T50 and T90, in Farooq et al. (2005) <10.1111/j.1744-7909.2005.00031.x>; and MGT, in Labouriau (1983). Considering the germination speed are: Germination Speed Index, in Maguire (1962), Mean Germination Rate, in Labouriau (1983); considering the homogeneity of germination are: Coefficient of Variation of the Germination Time, in Carvalho et al. (2005) <10.1590/S0100-84042005000300018>, and Variance of Germination, in Labouriau (1983); Uncertainty, in Labouriau and Valadares (1976) <ISSN:0001-3765>; and Synchrony, in Primack (1980). The main seedling indexes are Growth, in Sako (2001), Uniformity, in Sako (2001) and Castan et al. (2018) <doi:10.1590/1678-992x-2016-0401>; and Vigour, in Medeiros and Pereira (2018) <doi:10.1590/1983-40632018v4852340>.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2018-11-20 13:19:23 UTC; laerc
Repository: CRAN
Date/Publication: 2018-12-01 00:00:16 UTC

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New package PCMBase with initial version 1.2.7
Package: PCMBase
Type: Package
Title: Simulation and Likelihood Calculation of Phylogenetic Comparative Models
Version: 1.2.7
Authors@R: c(person("Venelin", "Mitov", email = "vmitov@gmail.com", role = c("aut", "cre", "cph")), person("Krzysztof", "Bartoszek", role = c("ctb")), person("Georgios", "Asimomitis", role = c("ctb")), person("Tanja", "Stadler", role = c("ctb")))
Maintainer: Venelin Mitov <vmitov@gmail.com>
Description: Phylogenetic comparative methods represent models of continuous trait data associated with the tips of a phylogenetic tree. Examples of such models are Gaussian continuous time branching stochastic processes such as Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the tips of the tree as an observed (final) state of a Markov process starting from an initial state at the root and evolving along the branches of the tree. The PCMBase R package provides a general framework for manipulating such models. This framework consists of an application programming interface for specifying data and model parameters, and efficient algorithms for simulating trait evolution under a model and calculating the likelihood of model parameters for an assumed model and trait data. The package implements a growing collection of models, which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed Gaussian models, in which different types of the above models can be associated with different branches of the tree. The PCMBase package is limited to trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic models. The PCMFit package provides functionality for ML and Bayesian fit of these models to tree and trait data. The package web-site <https://venelin.github.io/PCMBase/> provides access to the documentation and other resources.
Encoding: UTF-8
License: GPL (>= 3.0)
LazyData: true
Depends: R (>= 3.1.0)
Imports: ape, expm, mvtnorm, data.table, ggplot2
Suggests: testthat, knitr, rmarkdown, abind, ggtree, covr
RoxygenNote: 6.1.1
ByteCompile: yes
VignetteBuilder: knitr, rmarkdown
URL: https://venelin.github.io/PCMBase/, https://github.com/venelin/PCMBase
BugReports: https://github.com/venelin/PCMBase/issues
NeedsCompilation: no
Packaged: 2018-11-20 15:10:33 UTC; vmitov
Author: Venelin Mitov [aut, cre, cph], Krzysztof Bartoszek [ctb], Georgios Asimomitis [ctb], Tanja Stadler [ctb]
Repository: CRAN
Date/Publication: 2018-11-30 23:50:04 UTC

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Package JGL updated to version 2.3.1 with previous version 2.3 dated 2013-04-16

Title: Performs the Joint Graphical Lasso for Sparse Inverse Covariance Estimation on Multiple Classes
Description: The Joint Graphical Lasso is a generalized method for estimating Gaussian graphical models/ sparse inverse covariance matrices/ biological networks on multiple classes of data. We solve JGL under two penalty functions: The Fused Graphical Lasso (FGL), which employs a fused penalty to encourage inverse covariance matrices to be similar across classes, and the Group Graphical Lasso (GGL), which encourages similar network structure between classes. FGL is recommended over GGL for most applications. Reference: Danaher P, Wang P, Witten DM. (2013) <doi:10.1111/rssb.12033>.
Author: Patrick Danaher
Maintainer: Patrick Danaher <pdanaher@uw.edu>

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New package ggnormalviolin with initial version 0.1.1
Package: ggnormalviolin
Title: A 'ggplot2' Extension to Make Normal Violin Plots
Version: 0.1.1
Authors@R: person(given = "W. Joel", family = "Schneider", role = c("aut", "cre"), email = "w.joel.schneider@gmail.com", comment = c(ORCID = "0000-0002-8393-5316"))
Description: Uses 'ggplot2' to create normally distributed violin plots with specified means and standard deviations. This function can be useful in showing hypothetically normal distributions and confidence intervals.
License: CC0
URL: https://github.com/wjschne/ggnormalviolin
BugReports: https://github.com/wjschne/ggnormalviolin/issues
Imports: dplyr, ggplot2, grid, magrittr, scales
Suggests: spelling
Encoding: UTF-8
Language: en-US
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2018-11-20 12:49:56 UTC; renee
Author: W. Joel Schneider [aut, cre] (<https://orcid.org/0000-0002-8393-5316>)
Maintainer: W. Joel Schneider <w.joel.schneider@gmail.com>
Repository: CRAN
Date/Publication: 2018-11-30 23:40:06 UTC

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Package effects updated to version 4.1-0 with previous version 4.0-3 dated 2018-08-19

Title: Effect Displays for Linear, Generalized Linear, and Other Models
Description: Graphical and tabular effect displays, e.g., of interactions, for various statistical models with linear predictors.
Author: John Fox [aut, cre], Sanford Weisberg [aut], Michael Friendly [aut], Jangman Hong [aut], Robert Andersen [ctb], David Firth [ctb], Steve Taylor [ctb], R Core Team [ctb]
Maintainer: John Fox <jfox@mcmaster.ca>

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New package bamp with initial version 2.0.4
Package: bamp
Type: Package
Title: Bayesian Age-Period-Cohort Modeling and Prediction
Version: 2.0.4
Date: 2018-11-20
Author: Volker Schmid [aut, cre], Florian Geressen [ctb], Leonhard Held [ctb], Evi Rainer [ctb]
Authors@R: c(person("Volker","Schmid",email="volker.schmid@lmu.de", role=c("aut","cre")),person("Florian","Geressen",role="ctb"), person("Leonhard","Held",role="ctb"),person("Evi","Rainer",role="ctb"))
Maintainer: Volker Schmid <volker.schmid@lmu.de>
Description: Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) <DOI:10.18637/jss.v021.i08> Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman's R.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: coda, graphics, parallel, stats, abind
LazyData: true
RoxygenNote: 6.1.1
VignetteBuilder: knitr, R.rsp
Suggests: knitr, rmarkdown, R.rsp
URL: https://volkerschmid.github.io/bamp/
BugReports: https://github.com/volkerschmid/bamp/issues
NeedsCompilation: yes
Packaged: 2018-11-20 15:50:56 UTC; schmid
Repository: CRAN
Date/Publication: 2018-11-30 23:40:11 UTC

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Package Rpolyhedra updated to version 0.3.0 with previous version 0.2.8 dated 2018-10-05

Title: Polyhedra Database
Description: A polyhedra database scraped from various sources as R6 objects and 'rgl' visualizing capabilities.
Author: Alejandro Baranek [aut, com, cre, cph], Leonardo Belen [aut, com, cph]
Maintainer: Alejandro Baranek <abaranek@dc.uba.ar>

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Package BART updated to version 2.1 with previous version 2.0 dated 2018-11-18

Title: Bayesian Additive Regression Trees
Description: Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information on BART, see Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285> and Sparapani, Logan, McCulloch and Laud (2016) <doi:10.1002/sim.6893>.
Author: Robert McCulloch [aut], Rodney Sparapani [aut, cre], Robert Gramacy [aut], Charles Spanbauer [aut], Matthew Pratola [aut], Bill Venables [ctb], Brian Ripley [ctb]
Maintainer: Rodney Sparapani <rsparapa@mcw.edu>

Diff between BART versions 2.0 dated 2018-11-18 and 2.1 dated 2018-11-30

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Package argparse updated to version 2.0.0 with previous version 1.1.1 dated 2018-01-24

Title: Command Line Optional and Positional Argument Parser
Description: A command line parser to be used with Rscript to write "#!" shebang scripts that gracefully accept positional and optional arguments and automatically generate usage.
Author: Trevor L Davis [aut, cre], Allen Day [ctb] (Some documentation and examples ported from the getopt package.), Python Software Foundation [ctb] (Some documentation from the optparse Python module.), Paul Newell [ctb]
Maintainer: Trevor L Davis <trevor.l.davis@gmail.com>

Diff between argparse versions 1.1.1 dated 2018-01-24 and 2.0.0 dated 2018-11-30

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Package ARTP2 updated to version 0.9.45 with previous version 0.9.44 dated 2018-09-11

Title: Pathway and Gene-Level Association Test
Description: Pathway and gene level association test using raw data or summary statistics.
Author: Han Zhang, Bill Wheeler, Kai Yu, Yifan Yang, R Core Team
Maintainer: Bill Wheeler <wheelerb@imsweb.com>

Diff between ARTP2 versions 0.9.44 dated 2018-09-11 and 0.9.45 dated 2018-11-30

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Package interactionTest updated to version 1.1 with previous version 1.0.1 dated 2017-03-21

Title: Calculates Critical Test Statistics to Control False Discovery Rates in Marginal Effects Plots
Description: Implements the procedures suggested in Esarey and Sumner (2017) <http://justinesarey.com/interaction-overconfidence.pdf> for controlling the false discovery rate when constructing marginal effects plots for models with interaction terms.
Author: Justin Esarey and Jane Lawrence Sumner
Maintainer: Justin Esarey <justin@justinesarey.com>

Diff between interactionTest versions 1.0.1 dated 2017-03-21 and 1.1 dated 2018-11-30

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New package vistributions with initial version 0.1.0
Package: vistributions
Type: Package
Title: Visualize Probability Distributions
Version: 0.1.0
Authors@R: person("Aravind", "Hebbali", email = "hebbali.aravind@gmail.com", role = c("aut", "cre"))
Description: Visualize and compute percentiles/probabilities of normal, t, f, chi square and binomial distributions.
Depends: R(>= 3.1)
Imports: ggplot2, magrittr, shiny, stats, tibble
Suggests: testthat, knitr, rmarkdown, shinyBS, shinycssloaders, shinythemes
License: MIT + file LICENSE
URL: https://github.com/rsquaredacademy/vistributions, https://vistributions.rsquaredacademy.com
BugReports: https://github.com/rsquaredacademy/vistributions/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-11-20 10:38:13 UTC; HP
Author: Aravind Hebbali [aut, cre]
Maintainer: Aravind Hebbali <hebbali.aravind@gmail.com>
Repository: CRAN
Date/Publication: 2018-11-30 16:50:03 UTC

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New package rSEA with initial version 1.0
Package: rSEA
Type: Package
Title: Simultaneous Enrichment Analysis
Version: 1.0
Author: Mitra Ebrahimpoor
Maintainer: Mitra Ebrahimpoor<m.ebrahimpoor@lumc.nl>
Description: SEA performs simultaneous feature-set testing for (gen)omics data. It tests the unified null hypothesis and controls the family-wise error rate for all possible pathways. The unified null hypothesis is defined as: "The proportion of true features in the set is less than or equal to a threshold." Family-wise error rate control is provided through use of closed testing with Simes test. There are some practical functions to make use of three databases for genomics, GO, Reactome and Wikipathways.
Depends: R (>= 2.10), hommel (>= 1.2)
License: GPL (>= 2)
Date: 2018-11-16
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2018-11-20 09:11:50 UTC; mebrahimpoor
Repository: CRAN
Date/Publication: 2018-11-30 16:50:10 UTC

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New package rdbnomics with initial version 0.4.3
Package: rdbnomics
Type: Package
Title: Download DBnomics Data
Version: 0.4.3
Authors@R: c(person("Thomas", "Brand", role = c("aut"), email = "thomas.brand@cepremap.org"), person("Sebastien", "Galais", role = c("cre", "ctb"), email = "Sebastien.GALAIS@banque-france.fr"))
Description: R access to hundreds of millions data series from DBnomics API (<https://db.nomics.world/>).
Depends: R (>= 3.1.0)
License: AGPL-3
URL: https://github.com/dbnomics/rdbnomics
BugReports: https://github.com/dbnomics/rdbnomics/issues
Encoding: UTF-8
LazyData: true
Imports: jsonlite, data.table
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown, dplyr, magrittr, ggplot2, DT, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-11-19 22:48:28 UTC; sebas
Author: Thomas Brand [aut], Sebastien Galais [cre, ctb]
Maintainer: Sebastien Galais <Sebastien.GALAIS@banque-france.fr>
Repository: CRAN
Date/Publication: 2018-11-30 16:50:06 UTC

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New package rCRM with initial version 0.1
Package: rCRM
Type: Package
Title: Regularized Continual Reassessment Method
Version: 0.1
Author: Xiang Li, Hong Tian, Kevin Liu, Pilar Lim
Maintainer: Xiang Li <xli256@its.jnj.com>
Description: Fit a 2-parameter continual reassessment method (CRM) model (O'Quigley and Shen (1996), <doi: 10.2307/2532905>) regularized with L2 norm (Friedman et al. (2010), <doi: 10.18637/jss.v033.i01>) adjusted by the distance with the target dose limiting toxicity (DLT) rate.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Imports: Rcpp (>= 0.12.19)
LinkingTo: Rcpp, RcppEigen
NeedsCompilation: yes
Packaged: 2018-11-19 19:48:02 UTC; xli256
Repository: CRAN
Date/Publication: 2018-11-30 16:40:03 UTC

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Package RcppEnsmallen updated to version 0.1.11.1.1 with previous version 0.1.10.0.1 dated 2018-10-30

Title: Header-Only C++ Mathematical Optimization Library for 'Armadillo'
Description: 'Ensmallen' is a templated C++ mathematical optimization library (by the 'MLPACK' team) that provides a simple set of abstractions for writing an objective function to optimize. Provided within are various standard and cutting-edge optimizers that include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization. The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library and pairs the appropriate header files from 'armadillo' through the 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2, 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under similar terms. Note that 'Ensmallen' requires a compiler that supports 'C++11' and 'Armadillo' 6.500 or later.
Author: James Joseph Balamuta [aut, cre, cph] (<https://orcid.org/0000-0003-2826-8458>), Dirk Eddelbuettel [aut, cph] (<https://orcid.org/0000-0001-6419-907X>)
Maintainer: James Joseph Balamuta <balamut2@illinois.edu>

Diff between RcppEnsmallen versions 0.1.10.0.1 dated 2018-10-30 and 0.1.11.1.1 dated 2018-11-30

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New package MetaboLouise with initial version 1.0.0
Package: MetaboLouise
Version: 1.0.0
Date: 2018-11-20
Title: METABOlomics LOngitudinal SimUlation Engine
Author: Charlie Beirnaert
Maintainer: Charlie Beirnaert <charlie_beirnaert@icloud.com>
Description: Simulating dynamic (longitudinal, time-resolved) metabolomics data based on an underlying biological network. The network is initiating with certain concentrations and evolves over a simulated time period. Optionally external influxes (concentration drivers) can be added.
Depends: R (>= 3.1.0),
Imports: igraph, graphics, stats
Suggests: knitr, rmarkdown
LazyData: true
VignetteBuilder: knitr
License: GPL-3
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2018-11-20 10:54:21 UTC; Charlie
Repository: CRAN
Date/Publication: 2018-11-30 16:50:13 UTC

More information about MetaboLouise at CRAN
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Package BayesMallows updated to version 0.2.0 with previous version 0.1.1 dated 2018-10-15

Title: Bayesian Preference Learning with the Mallows Rank Model
Description: An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 <http://jmlr.org/papers/v18/15-481.html>). Both Cayley, footrule, Kendall, and Spearman distances are supported in the models. The rank data to be analyzed can be the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).
Author: Oystein Sorensen, Valeria Vitelli, Marta Crispino, Qinghua Liu
Maintainer: Oystein Sorensen <oystein.sorensen.1985@gmail.com>

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Package strex updated to version 0.1.3 with previous version 0.1.2 dated 2018-10-20

Title: Extra String Manipulation Functions
Description: There are some things that I wish were easier with the 'stringr' or 'stringi' packages. The foremost of these is the extraction of numbers from strings. 'stringr' and 'stringi' make you figure out the regular expression for yourself; 'strex' takes care of this for you. There are many other handy functionalities in 'strex'. Contributions to this package are encouraged: it is intended as a miscellany of string manipulation functions that cannot be found in 'stringi' or 'stringr'.
Author: Rory Nolan [aut, cre] (<https://orcid.org/0000-0002-5239-4043>)
Maintainer: Rory Nolan <rorynoolan@gmail.com>

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Package lg updated to version 0.3.0 with previous version 0.2.0 dated 2018-10-05

Title: Locally Gaussian Distributions: Estimation and Methods
Description: An implementation of locally Gaussian distributions. It provides methods for implementing the locally Gaussian density estimator (LGDE) by Otneim and Tjøstheim (2017) <doi:10.1007/s11222-016-9706-6>, as well as the corresponding estimator for conditional density functions by Otneim and Tjøstheim (2018) <doi:10.1007/s11222-017-9732-z>.
Author: Håkon Otneim [aut, cre]
Maintainer: Håkon Otneim <hakon.otneim@nhh.no>

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Package SeqFeatR updated to version 0.3 with previous version 0.2.7 dated 2018-05-18

Title: A Tool to Associate FASTA Sequences and Features
Description: Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) <doi:10.1371/journal.pone.0146409>.
Author: Bettina Budeus
Maintainer: Bettina Budeus <bettina.budeus@stud.uni-due.de>

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Package AMR updated to version 0.5.0 with previous version 0.4.0 dated 2018-10-01

Title: Antimicrobial Resistance Analysis
Description: Functions to simplify the analysis and prediction of Antimicrobial Resistance (AMR) to work with microbial and antimicrobial properties by using evidence-based methods.
Author: Matthijs S. Berends [aut, cre] (<https://orcid.org/0000-0001-7620-1800>), Christian F. Luz [aut, rev] (<https://orcid.org/0000-0001-5809-5995>), Erwin E.A. Hassing [ctb], Corinna Glasner [ths] (<https://orcid.org/0000-0003-1241-1328>), Alex W. Friedrich [ths] (<https://orcid.org/0000-0003-4881-038X>), Bhanu Sinha [ths] (<https://orcid.org/0000-0003-1634-0010>)
Maintainer: Matthijs S. Berends <m.s.berends@umcg.nl>

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Package relsurv updated to version 2.2-3 with previous version 2.2-2 dated 2018-10-18

Title: Relative Survival
Description: Contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables.
Author: Maja Pohar Perme [aut, cre]
Maintainer: Maja Pohar Perme <maja.pohar@mf.uni-lj.si>

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New package rdtLite with initial version 1.0.2
Package: rdtLite
Title: Provenance Collector
Version: 1.0.2
Date: 2018-11-08
Authors@R: c( person("Barbara", "Lerner", role = c("aut", "cre"), email = "blerner@mtholyoke.edu"), person("Emery", "Boose", role = "aut", email = "boose@fas.harvard.edu"), person("Elizabeth", "Fong", role = "aut", email = "fong22e@mtholyoke.edu"), person("Luis", "Perez", role = "aut", email = "luis.perez.live@gmail.com"), person("Thomas", "Pasquier", role = "ctb", email = "thomas.francis.pasquier@gmail.com"), person("Matthew", "Lau", role = "ctb", email = "matthewklau@fas.harvard.edu"), person("Yada", "Pruksachatkun", role = "ctb", email = "pruks22y@mtholyoke.edu"), person("Alex", "Liu", role = "ctb", email = "uilxela7@gmail.com"), person("Moe Pwint Phyu", role = "ctb", email = "phyu22m@mtholyoke.edu"), person("Connor", "Gregorich-Trevor", role = "ctb", email = "gregoric@grinnell.edu"), person("Aaron", "Ellison", role = "res", email = "aellison@fas.harvard.edu"), person("Margo", "Seltzer", role = "res", email = "margo@eecs.harvard.edu"), person("Joe", "Wonsil", role = "res", email = "jwonsil@carthage.edu"), person("Orenna", "Brand", role = "res", email = "o.brand@columbia.edu") )
Copyright: President and Fellows of Harvard College, Trustees of Mount Holyoke College
Depends: R (>= 3.5.0)
Description: Defines functions that can be used to collect provenance as an R script executes or during a console session. The output is a text file in PROV-JSON format.
License: GPL-3 | file LICENSE
URL: https://github.com/End-to-end-provenance/rdtLite
BugReports: https://github.com/End-to-end-provenance/rdtLite/issues
Imports: curl, digest, ggplot2, grDevices, gtools, jsonlite, knitr, methods, sessioninfo, tools, utils, XML
Suggests: roxygen2, testthat
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2018-11-19 18:43:49 UTC; blerner
Author: Barbara Lerner [aut, cre], Emery Boose [aut], Elizabeth Fong [aut], Luis Perez [aut], Thomas Pasquier [ctb], Matthew Lau [ctb], Yada Pruksachatkun [ctb], Alex Liu [ctb], Moe Pwint Phyu [ctb], Connor Gregorich-Trevor [ctb], Aaron Ellison [res], Margo Seltzer [res], Joe Wonsil [res], Orenna Brand [res]
Maintainer: Barbara Lerner <blerner@mtholyoke.edu>
Repository: CRAN
Date/Publication: 2018-11-30 12:50:02 UTC

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New package nCopula with initial version 0.1.1
Package: nCopula
Type: Package
Title: Hierarchical Archimedean Copulas Constructed with Multivariate Compound Distributions
Version: 0.1.1
Authors@R: c(person("Simon-Pierre", "Gadoury", role=c("cre", "aut"), email="spgadou@me.com"), person("David", "Beauchemin", role="ctb", email="david.beauchemin.5@ulaval.ca"))
Maintainer: Simon-Pierre Gadoury <spgadou@me.com>
Description: Construct and manipulate hierarchical Archimedean copulas with multivariate compound distributions. The model used is the one of Cossette et al. (2017) <doi:10.1016/j.insmatheco.2017.06.001>.
Depends: R (>= 3.4.0), copula
Imports: Deriv, stringr, stringi, compiler, methods,
License: GPL (>= 2)
LazyData: FALSE
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown
NeedsCompilation: no
Packaged: 2018-11-19 20:48:52 UTC; Simon-Pierre
Author: Simon-Pierre Gadoury [cre, aut], David Beauchemin [ctb]
Repository: CRAN
Date/Publication: 2018-11-30 12:40:07 UTC

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Package ImputeRobust updated to version 1.3-1 with previous version 1.3 dated 2018-07-01

Title: Robust Multiple Imputation with Generalized Additive Models for Location Scale and Shape
Description: Provides new imputation methods for the 'mice' package based on generalized additive models for location, scale, and shape (GAMLSS) as described in de Jong, van Buuren and Spiess <doi:10.1080/03610918.2014.911894>.
Author: Daniel Salfran [aut, cre], Martin Spieß [aut, ths]
Maintainer: Daniel Salfran <danielsalfran@gmail.com>

Diff between ImputeRobust versions 1.3 dated 2018-07-01 and 1.3-1 dated 2018-11-30

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New package HMB with initial version 1.0
Package: HMB
Type: Package
Title: Hierarchical Model-Based Estimation Approach
Version: 1.0
Date: 2018-11-20
Authors@R: c( person("Svetlana", "Saarela", role = c("cre", "aut"), email = "admin@svetlanasaarela.com"), person("Sören", "Holm", role = "aut"), person("Zhiqiang", "Yang", role = "aut", email='zhiqiangyang@fs.fed.us'), person("Wilmer", "Prentius", role = "ctb") )
Description: For estimation of a variable of interest using two sources of auxiliary information available in a nested structure. For reference see Saarela et al. (2016)<doi:10.1007/s13595-016-0590-1> and Saarela et al. (2018) <doi:10.3390/rs10111832>.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: TRUE
Imports: Rcpp (>= 0.12.16)
Depends: methods, stats, R (>= 3.5)
LinkingTo: Rcpp, RcppArmadillo
Collate: RcppExports.R helper_functions.R HMB-class.R SummaryHMB-class.R ghmb.R gtsmb.R hmb.R tsmb.R
RoxygenNote: 6.1.0
NeedsCompilation: yes
Packaged: 2018-11-19 19:12:33 UTC; svla0001
Author: Svetlana Saarela [cre, aut], Sören Holm [aut], Zhiqiang Yang [aut], Wilmer Prentius [ctb]
Maintainer: Svetlana Saarela <admin@svetlanasaarela.com>
Repository: CRAN
Date/Publication: 2018-11-30 12:50:06 UTC

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New package fishtree with initial version 0.1.0
Package: fishtree
Type: Package
Title: Interface to the Fish Tree of Life API
Version: 0.1.0
Authors@R: c( person("Jonathan", "Chang", role = c("aut", "cre"), email = "jonathan.chang@ucla.edu", comment = c("ORCID" = "0000-0002-3811-1254")), person("Michael E", "Alfaro", role = "ctb"), person("Daniel L", "Rabosky", role = "ctb"), person("Stephen A", "Smith", role = "ctb") )
Description: An interface to the Fish Tree of Life API to download taxonomies, phylogenies, fossil calibrations, and diversification rate information.
License: BSD_2_clause + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
URL: https://fishtreeoflife.org/
BugReports: https://github.com/jonchang/fishtree/issues
Depends: R (>= 3.4)
Imports: ape (>= 5.1), rlang (>= 0.2.2), jsonlite (>= 1.5), utils
Suggests: rfishbase, hisse, geiger, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-11-19 15:52:32 UTC; jonchang
Author: Jonathan Chang [aut, cre] (<https://orcid.org/0000-0002-3811-1254>), Michael E Alfaro [ctb], Daniel L Rabosky [ctb], Stephen A Smith [ctb]
Maintainer: Jonathan Chang <jonathan.chang@ucla.edu>
Repository: CRAN
Date/Publication: 2018-11-30 12:40:10 UTC

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Package incidence updated to version 1.5.2 with previous version 1.5.0 dated 2018-11-01

Title: Compute, Handle, Plot and Model Incidence of Dated Events
Description: Provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class 'incidence' is used to store computed incidence and can be easily manipulated, subsetted, and plotted. In addition, log-linear models can be fitted to 'incidence' objects using 'fit'. This package is part of the RECON (<http://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
Author: Thibaut Jombart [aut], Zhian N. Kamvar [aut, cre], Rich FitzJohn [aut], Jun Cai [ctb], Sangeeta Bhatia [ctb], Jakob Schumacher [ctb]
Maintainer: Zhian N. Kamvar <zkamvar@gmail.com>

Diff between incidence versions 1.5.0 dated 2018-11-01 and 1.5.2 dated 2018-11-30

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Package jmvconnect updated to version 1.0.7 with previous version 1.0.5 dated 2018-11-17

Title: Connect to the 'jamovi' Statistical Spreadsheet
Description: Methods to access data sets from the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org> for more information) from R.
Author: Jonathon Love [aut, cre]
Maintainer: Jonathon Love <jon@thon.cc>

Diff between jmvconnect versions 1.0.5 dated 2018-11-17 and 1.0.7 dated 2018-11-30

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Package merDeriv updated to version 0.1-6 with previous version 0.1-5 dated 2018-09-12

Title: Case-Wise and Cluster-Wise Derivatives for Mixed Effects Models
Description: Compute analytic case-wise and cluster-wise derivative for mixed effects models with respect to fixed effects parameter, random effect (co)variances, and residual variance.
Author: Ting Wang [aut, cre], Edgar Merkle [aut]
Maintainer: Ting Wang <twb8d@mail.missouri.edu>

Diff between merDeriv versions 0.1-5 dated 2018-09-12 and 0.1-6 dated 2018-11-30

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Package logging updated to version 0.8-104 with previous version 0.7-103 dated 2013-04-12

Title: R Logging Package
Description: Pure R implementation of the ubiquitous log4j package. It offers hierarchic loggers, multiple handlers per logger, level based filtering, space handling in messages and custom formatting.
Author: Mario Frasca [aut], Walerian Sokolowski [cre]
Maintainer: Walerian Sokolowski <r-logging@wlogsolutions.com>

Diff between logging versions 0.7-103 dated 2013-04-12 and 0.8-104 dated 2018-11-30

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Package greybox updated to version 0.3.3 with previous version 0.3.2 dated 2018-10-28

Title: Toolbox for Model Building and Forecasting
Description: Implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.
Author: Ivan Svetunkov [aut, cre] (Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK)
Maintainer: Ivan Svetunkov <ivan@svetunkov.ru>

Diff between greybox versions 0.3.2 dated 2018-10-28 and 0.3.3 dated 2018-11-30

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Package ezpickr updated to version 0.1.2 with previous version 0.1.1 dated 2018-11-28

Title: Import Various Data File Types as a Rectangular Form Using a File Picker Dialogue Box
Description: Easy data importing for the most frequently used file formats in a 'tibble' form.
Author: JooYoung Seo [aut, cre], Soyoung Choi [aut]
Maintainer: JooYoung Seo <jooyoung@psu.edu>

Diff between ezpickr versions 0.1.1 dated 2018-11-28 and 0.1.2 dated 2018-11-30

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Package RATest updated to version 0.1.4 with previous version 0.1.3 dated 2018-10-31

Title: Randomization Tests
Description: A collection of randomization tests, data sets and examples. The current version focuses on three testing problems and their implementation in empirical work. First, it facilitates the empirical researcher to test for particular hypotheses, such as comparisons of means, medians, and variances from k populations using robust permutation tests, which asymptotic validity holds under very weak assumptions, while retaining the exact rejection probability in finite samples when the underlying distributions are identical. Second, the description and implementation of a permutation test for testing the continuity assumption of the baseline covariates in the sharp regression discontinuity design (RDD) as in Canay and Kamat (2017) <https://goo.gl/UZFqt7>. More specifically, it allows the user to select a set of covariates and test the aforementioned hypothesis using a permutation test based on the Cramer-von Miss test statistic. Graphical inspection of the empirical CDF and histograms for the variables of interest is also supported in the package. Third, it provides the practitioner with an effortless implementation of a permutation test based on the martingale decomposition of the empirical process for the goodness-of-fit testing problem with an estimated nuisance parameter. An application of this testing problem is the one of testing for heterogeneous treatment effects in a randomized control trial.
Author: Mauricio Olivares-Gonzalez [aut, cre], Ignacio Sarmiento-Barbieri [aut]
Maintainer: Mauricio Olivares-Gonzalez <maurice.olivares@gmail.com>

Diff between RATest versions 0.1.3 dated 2018-10-31 and 0.1.4 dated 2018-11-30

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