Fri, 21 Jun 2019

Package rtika updated to version 1.21 with previous version 1.20 dated 2019-02-27

Title: R Interface to 'Apache Tika'
Description: Extract text or metadata from over a thousand file types, using Apache Tika <https://tika.apache.org/>. Get either plain text or structured XHTML content.
Author: Sasha Goodman [aut, cre], The Apache Software Foundation [aut, cph], Julia Silge [rev] (Reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/191), David Gohel [rev] (Reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/191)
Maintainer: Sasha Goodman <goodmansasha@gmail.com>

Diff between rtika versions 1.20 dated 2019-02-27 and 1.21 dated 2019-06-21

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Package mlt updated to version 1.0-5 with previous version 1.0-4 dated 2018-12-03

Title: Most Likely Transformations
Description: Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>.
Author: Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>)
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between mlt versions 1.0-4 dated 2018-12-03 and 1.0-5 dated 2019-06-21

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Package ICRanks updated to version 3.1 with previous version 3.0 dated 2019-06-13

Title: Simultaneous Confidence Intervals for Ranks
Description: Algorithms to construct simultaneous confidence intervals for the ranks of means mu_1,...,mu_n based on an independent Gaussian sample using multiple testing techniques.
Author: Diaa Al Mohamad and Erik W. van Zwet and Jelle J. Goeman
Maintainer: Diaa Al Mohamad <diaa.almohamad@gmail.com>

Diff between ICRanks versions 3.0 dated 2019-06-13 and 3.1 dated 2019-06-21

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Package conflicted updated to version 1.0.4 with previous version 1.0.3 dated 2019-05-01

Title: An Alternative Conflict Resolution Strategy
Description: R's default conflict management system gives the most recently loaded package precedence. This can make it hard to detect conflicts, particularly when they arise because a package update creates ambiguity that did not previously exist. 'conflicted' takes a different approach, making every conflict an error and forcing you to choose which function to use.
Author: Hadley Wickham [aut, cre], RStudio [cph]
Maintainer: Hadley Wickham <hadley@rstudio.com>

Diff between conflicted versions 1.0.3 dated 2019-05-01 and 1.0.4 dated 2019-06-21

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Package revdbayes updated to version 1.3.4 with previous version 1.3.3 dated 2019-03-08

Title: Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis
Description: Provides functions for the Bayesian analysis of extreme value models. The 'rust' package <https://cran.r-project.org/package=rust> is used to simulate a random sample from the required posterior distribution. The functionality of 'revdbayes' is similar to the 'evdbayes' package <https://cran.r-project.org/package=evdbayes>, which uses Markov Chain Monte Carlo ('MCMC') methods for posterior simulation. Also provided are functions for making inferences about the extremal index, using the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292>. Also provided are d,p,q,r functions for the Generalised Extreme Value ('GEV') and Generalised Pareto ('GP') distributions that deal appropriately with cases where the shape parameter is very close to zero.
Author: Paul J. Northrop [aut, cre, cph]
Maintainer: Paul J. Northrop <p.northrop@ucl.ac.uk>

Diff between revdbayes versions 1.3.3 dated 2019-03-08 and 1.3.4 dated 2019-06-21

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Package phonics updated to version 1.3.2 with previous version 1.3.0 dated 2019-03-01

Title: Phonetic Spelling Algorithms
Description: Provides a collection of phonetic algorithms including Soundex, Metaphone, NYSIIS, Caverphone, and others.
Author: James Howard [aut, cre], Kyle Haynes [ctb], Amanda Hood [ctb], Os Keyes [ctb]
Maintainer: James Howard <jh@jameshoward.us>

Diff between phonics versions 1.3.0 dated 2019-03-01 and 1.3.2 dated 2019-06-21

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Package cplexAPI updated to version 1.3.6 with previous version 1.3.3 dated 2017-01-31

Title: R Interface to C API of IBM ILOG CPLEX
Description: This is the R Interface to the C API of IBM ILOG CPLEX. It necessarily depends on IBM ILOG CPLEX (>= 12.1).
Author: Mayo Roettger [cre], Gabriel Gelius-Dietrich [aut], C. Jonathan Fritzemeier [ctb]
Maintainer: Mayo Roettger <mayo.roettger@hhu.de>

Diff between cplexAPI versions 1.3.3 dated 2017-01-31 and 1.3.6 dated 2019-06-21

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Package SuperExactTest updated to version 1.0.7 with previous version 1.0.6 dated 2019-02-26

Title: Exact Test and Visualization of Multi-Set Intersections
Description: Identification of sets of objects with shared features is a common operation in all disciplines. Analysis of intersections among multiple sets is fundamental for in-depth understanding of their complex relationships. This package implements a theoretical framework for efficient computation of statistical distributions of multi-set intersections based upon combinatorial theory, and provides multiple scalable techniques for visualizing the intersection statistics. The statistical algorithm behind this package was published in Wang et al. (2015) <doi:10.1038/srep16923>.
Author: Minghui Wang, Yongzhong Zhao and Bin Zhang
Maintainer: Minghui Wang <minghui.wang@mssm.edu>

Diff between SuperExactTest versions 1.0.6 dated 2019-02-26 and 1.0.7 dated 2019-06-21

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Package alphahull updated to version 2.2 with previous version 2.1 dated 2016-02-15

Title: Generalization of the Convex Hull of a Sample of Points in the Plane
Description: Computation of the alpha-shape and alpha-convex hull of a given sample of points in the plane. The concepts of alpha-shape and alpha-convex hull generalize the definition of the convex hull of a finite set of points. The programming is based on the duality between the Voronoi diagram and Delaunay triangulation. The package also includes a function that returns the Delaunay mesh of a given sample of points and its dual Voronoi diagram in one single object.
Author: Beatriz Pateiro-Lopez [aut, cre], Alberto Rodriguez-Casal, [aut].
Maintainer: Beatriz Pateiro-Lopez <beatriz.pateiro@usc.es>

Diff between alphahull versions 2.1 dated 2016-02-15 and 2.2 dated 2019-06-21

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Package subgroup.discovery updated to version 0.2.1 with previous version 0.2.0 dated 2017-08-02

Title: Subgroup Discovery and Bump Hunting
Description: Developed to assist in discovering interesting subgroups in high-dimensional data. The PRIM implementation is based on the 1998 paper "Bump hunting in high-dimensional data" by Jerome H. Friedman and Nicholas I. Fisher. <doi:10.1023/A:1008894516817> PRIM involves finding a set of "rules" which combined imply unusually large (or small) values of some other target variable. Specifically one tries to find a set of sub regions in which the target variable is substantially larger than overall mean. The objective of bump hunting in general is to find regions in the input (attribute/feature) space with relatively high (low) values for the target variable. The regions are described by simple rules of the type if: condition-1 and ... and condition-n then: estimated target value. Given the data (or a subset of the data), the goal is to produce a box B within which the target mean is as large as possible. There are many problems where finding such regions is of considerable practical interest. Often these are problems where a decision maker can in a sense choose or select the values of the input variables so as to optimize the value of the target variable. In bump hunting it is customary to follow a so-called covering strategy. This means that the same box construction (rule induction) algorithm is applied sequentially to subsets of the data.
Author: Jurian Baas [aut, cre, cph], Ad Feelders [ctb]
Maintainer: Jurian Baas <j.baas@uu.nl>

Diff between subgroup.discovery versions 0.2.0 dated 2017-08-02 and 0.2.1 dated 2019-06-21

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New package shinyMolBio with initial version 0.1
Package: shinyMolBio
Type: Package
Title: Molecular Biology Visualization Tools for 'Shiny' Apps
Version: 0.1
Date: 2019-06-20
Authors@R: c( person("Konstantin A.", "Blagodatskikh", email = "k.blag@yandex.ru", role = c("cre", "aut")))
Description: Interactive visualization of 'RDML' files via 'shiny' apps. Package provides (1) PCR plate interface with ability to select individual tubes and (2) amplification/melting plots.
License: MIT + file LICENSE
URL: https://github.com/kablag/shinyMolBio
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.4.0)
Imports: dplyr, purrr, checkmate, RDML, shiny, stringr, whisker, plotly, lazyeval, RColorBrewer
Collate: 'global.R' 'pcrPlate-input.R' 'renderCurves.R' 'runExample.R'
Suggests: knitr, chipPCR
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-06-21 08:24:08 UTC; kablag
Author: Konstantin A. Blagodatskikh [cre, aut]
Maintainer: Konstantin A. Blagodatskikh <k.blag@yandex.ru>
Repository: CRAN
Date/Publication: 2019-06-21 16:20:03 UTC

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Package praznik updated to version 6.0.0 with previous version 5.0.0 dated 2018-05-08

Title: Collection of Information-Based Feature Selection Filters
Description: A collection of feature selection filters performing greedy optimisation of mutual information-based usefulness criteria, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) <http://www.jmlr.org/papers/v13/brown12a.html>. Implements, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) <doi:10.1109/TPAMI.2005.159>; joint mutual information ('JMI') method by Yang and Moody (1999) <http://papers.nips.cc/paper/1779-data-visualization-and-feature-selection-new-algorithms-for-nongaussian-data>; double input symmetrical relevance ('DISR') method by Meyer and Bontempi (2006) <doi:10.1007/11732242_9> as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) <doi:10.1016/j.eswa.2015.07.007>.
Author: Miron B. Kursa [aut, cre] (<https://orcid.org/0000-0001-7672-648X>)
Maintainer: Miron B. Kursa <M.Kursa@icm.edu.pl>

Diff between praznik versions 5.0.0 dated 2018-05-08 and 6.0.0 dated 2019-06-21

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Package plm updated to version 2.0-2 with previous version 2.0-1 dated 2019-05-23

Title: Linear Models for Panel Data
Description: A set of estimators and tests for panel data econometrics, as described in Baltagi (2013) Econometric Analysis of Panel Data, ISBN-13:978-1-118-67232-7, Hsiao (2014) Analysis of Panel Data <doi:10.1017/CBO9781139839327> and Croissant and Millo (2018), Panel Data Econometrics with R, ISBN:978-1-118-94918-4.
Author: Yves Croissant [aut, cre], Giovanni Millo [aut], Kevin Tappe [aut], Ott Toomet [ctb], Christian Kleiber [ctb], Achim Zeileis [ctb], Arne Henningsen [ctb], Liviu Andronic [ctb], Nina Schoenfelder [ctb]
Maintainer: Yves Croissant <yves.croissant@univ-reunion.fr>

Diff between plm versions 2.0-1 dated 2019-05-23 and 2.0-2 dated 2019-06-21

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New package ormPlot with initial version 0.3.2
Package: ormPlot
Type: Package
Title: Advanced Plotting of Ordinal Regression Models
Version: 0.3.2
Authors@R: c(person("Richard", "Meitern", , "richard.meitern@ut.ee", c("aut", "cre")))
Maintainer: Richard Meitern <richard.meitern@ut.ee>
Description: An extension to the Regression Modeling Strategies package that facilitates plotting ordinal regression model predictions together with confidence intervals for each dependent variable level. It also adds a functionality to plot the model summary as a modifiable object.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0)
Imports: ggplot2 (>= 3.1.0), rms (>= 5.1.3), gtable (>= 0.3.0), grid (>= 3.5.0)
Suggests: testthat (>= 2.1.0), vdiffr (>= 0.3.0), knitr (>= 1.22), rmarkdown (>= 1.13), pander (>= 0.6.3)
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-06-21 13:16:22 UTC; rix133
Author: Richard Meitern [aut, cre]
Repository: CRAN
Date/Publication: 2019-06-21 16:30:08 UTC

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Package DDD updated to version 4.1 with previous version 4.0 dated 2019-04-22

Title: Diversity-Dependent Diversification
Description: Implements maximum likelihood and bootstrap methods based on the diversity-dependent birth-death process to test whether speciation or extinction are diversity-dependent, under various models including various types of key innovations. See Etienne et al. 2012, Proc. Roy. Soc. B 279: 1300-1309, <DOI:10.1098/rspb.2011.1439>, Etienne & Haegeman 2012, Am. Nat. 180: E75-E89, <DOI:10.1086/667574> and Etienne et al. 2016. Meth. Ecol. Evol. 7: 1092-1099, <DOI:10.1111/2041-210X.12565>. Also contains functions to simulate the diversity-dependent process.
Author: Rampal S. Etienne & Bart Haegeman
Maintainer: Rampal S. Etienne <r.s.etienne@rug.nl>

Diff between DDD versions 4.0 dated 2019-04-22 and 4.1 dated 2019-06-21

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New package cppRouting with initial version 1.1
Package: cppRouting
Type: Package
Title: Fast Implementation of Dijkstra Algorithm
Version: 1.1
Date: 2019-06-15
Author: Vincent Larmet
Maintainer: Vincent Larmet <larmet.vincent@gmail.com>
Description: Calculation of distances, shortest paths and isochrones on weighted graphs using several variants of Dijkstra algorithm. Proposed algorithms are unidirectional Dijkstra (Dijkstra, E. W. (1959) <doi:10.1007/BF01386390>), bidirectional Dijkstra (Goldberg, Andrew & Fonseca F. Werneck, Renato (2005) <https://pdfs.semanticscholar.org/0761/18dfbe1d5a220f6ac59b4de4ad07b50283ac.pdf>), A* search (P. E. Hart, N. J. Nilsson et B. Raphael (1968) <doi:10.1109/TSSC.1968.300136>), new bidirectional A* (Pijls & Post (2009) <http://repub.eur.nl/pub/16100/ei2009-10.pdf>).
License: GPL (>= 2)
Imports: Rcpp (>= 1.0.1), parallel
LinkingTo: Rcpp
SystemRequirements: GNU make, C++11
RoxygenNote: 6.1.1
URL: https://github.com/vlarmet/cppRouting
Suggests: knitr, rmarkdown, igraph
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2019-06-21 13:53:49 UTC; vlarmet
Repository: CRAN
Date/Publication: 2019-06-21 16:30:12 UTC

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Package nlMS updated to version 1.1 with previous version 1.0 dated 2019-06-19

Title: Non-Linear Model Selection
Description: Package to select best model among several linear and nonlinear models. The main function uses the gnls() function from the 'nlme' package to fit the data to nine regression models, named: "linear", "quadratic", "cubic", "logistic", "exponential", "power", "monod", "haldane", "logit".
Author: Carme Font <carme.font.moragon@gmail.com>
Maintainer: Carme Font <carme.font.moragon@gmail.com>

Diff between nlMS versions 1.0 dated 2019-06-19 and 1.1 dated 2019-06-21

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Package mmmgee updated to version 1.20 with previous version 1.13 dated 2018-10-29

Title: Simultaneous Inference for Multiple Linear Contrasts in GEE Models
Description: Provides global hypothesis tests, multiple testing procedures and simultaneous confidence intervals for multiple linear contrasts of regression coefficients in a single generalized estimating equation (GEE) model or across multiple GEE models. GEE models are fit by a modified version of the 'geeM' package.
Author: Robin Ristl [aut, cre], Lee McDaniel [ctb] (Author of 'geeM' package), Nick Henderson [ctb] (Author of 'geeM' package), Melanie Prague [ctb] (Contributor to 'geeM' package)
Maintainer: Robin Ristl <robin.ristl@meduniwien.ac.at>

Diff between mmmgee versions 1.13 dated 2018-10-29 and 1.20 dated 2019-06-21

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Package CARBayes updated to version 5.1.2 with previous version 5.1.1 dated 2018-12-06

Title: Spatial Generalised Linear Mixed Models for Areal Unit Data
Description: Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al. (1991) <doi:10.1007/BF00116466>), the Leroux model (Leroux et al. (2000) <doi:10.1007/978-1-4612-1284-3_4>) and the localised model (Lee et al. (2015) <doi:10.1002/env.2348>). Additionally, a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance.
Author: Duncan Lee
Maintainer: Duncan Lee <Duncan.Lee@glasgow.ac.uk>

Diff between CARBayes versions 5.1.1 dated 2018-12-06 and 5.1.2 dated 2019-06-21

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Package BEDMatrix updated to version 1.6.1 with previous version 1.5.0 dated 2019-04-25

Title: Extract Genotypes from a PLINK .bed File
Description: A matrix-like data structure that allows for efficient, convenient, and scalable subsetting of binary genotype/phenotype files generated by PLINK (<https://www.cog-genomics.org/plink2>), the whole genome association analysis toolset, without loading the entire file into memory.
Author: Alexander Grueneberg [aut, cre], Gustavo de los Campos [ctb]
Maintainer: Alexander Grueneberg <alexander.grueneberg@googlemail.com>

Diff between BEDMatrix versions 1.5.0 dated 2019-04-25 and 1.6.1 dated 2019-06-21

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Package basefun updated to version 1.0-5 with previous version 1.0-4 dated 2019-02-27

Title: Infrastructure for Computing with Basis Functions
Description: Some very simple infrastructure for basis functions.
Author: Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>)
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between basefun versions 1.0-4 dated 2019-02-27 and 1.0-5 dated 2019-06-21

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New package rmdcev with initial version 0.9.0
Package: rmdcev
Type: Package
Title: Multiple Discrete-Continuous Extreme Value (MDCEV) Model
Version: 0.9.0
Authors@R: c( person(given = "Patrick", family = "Lloyd-Smith", role = c("aut", "cre"), email = "patrick.lloydsmith@usask.ca"), person("Trustees of", "Columbia University", role = "cph"))
Maintainer: Patrick Lloyd-Smith <patrick.lloydsmith@usask.ca>
Description: Estimates different multiple discrete-continuous extreme value (MDCEV) demand model specifications with observed and unobserved individual heterogeneity (Bhat (2008) <doi:10.1016/j.trb.2007.06.002>). Fixed parameter, latent class, and random parameter models can be estimated. These models are estimated using maximum likelihood or Bayesian estimation techniques and are implemented in 'Stan', which is a C++ package for performing full Bayesian inference (see Stan Development Team (2018) <http://mc-stan.org>). The 'rmdcev' package also includes functions for demand simulation (Pinjari and Bhat (2011) <https://repositories.lib.utexas.edu/handle/2152/23880>) and welfare simulation (Lloyd-Smith (2018) <doi:10.1016/j.jocm.2017.12.002>).
License: MIT + file LICENSE
Depends: R (>= 3.4.0), Rcpp (>= 0.12.0), methods
Imports: rstan (>= 2.18.2), rstantools (>= 1.5.1), dplyr (>= 0.7.8), tidyselect, parallel, stringr, purrr, tibble, tidyr, rlang, utils, stats, tmvtnorm
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.4.0), rstan (>= 2.18.2), StanHeaders (>= 2.18.0)
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
SystemRequirements: GNU make C++14
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown, testthat
Packaged: 2019-06-20 23:21:03 UTC; Pat
Author: Patrick Lloyd-Smith [aut, cre], Trustees of Columbia University [cph]
Repository: CRAN
Date/Publication: 2019-06-21 15:00:03 UTC

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New package pesel with initial version 0.7.2
Package: pesel
Type: Package
Title: Automatic Estimation of Number of Principal Components in PCA
Version: 0.7.2
Date: 2019-06-17
Author: Piotr Sobczyk, Julie Josse, Malgorzata Bogdan
Maintainer: Piotr Sobczyk <pj.sobczyk@gmail.com>
Description: Automatic estimation of number of principal components in PCA with PEnalized SEmi-integrated Likelihood (PESEL). See Piotr Sobczyk, Malgorzata Bogdan, Julie Josse 'Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood' (2017) <doi:10.1080/10618600.2017.1340302>.
License: GPL-3
Encoding: UTF-8
URL: https://github.com/psobczyk/pesel
BugReports: https://github.com/psobczyk/pesel/issues
Depends: R (>= 3.1.3),
Imports: stats, graphics
LazyData: TRUE
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-06-20 17:44:21 UTC; piotr
Repository: CRAN
Date/Publication: 2019-06-21 14:40:03 UTC

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Package morse updated to version 3.2.4 with previous version 3.2.2 dated 2019-02-21

Title: Modelling Tools for Reproduction and Survival Data in Ecotoxicology
Description: Tools for ecotoxicologists and regulators dedicated to the mathematical and statistical modelling of toxicity test data. They use advanced and innovative methods for a valuable quantitative environmental risk assessment. See also Delignette-Muller et al. (2017) <doi:10.1021/acs.est.6b05326>. and Baudrot et al. (2018) <doi:10.1021/acs.est.7b05464>.
Author: Virgile Baudrot [aut], Sandrine Charles [aut], Marie Laure Delignette-Muller [aut], Wandrille Duchemin [ctb], Benoit Goussen [ctb], Guillaume Kon-Kam-King [ctb], Christelle Lopes [ctb], Philippe Ruiz [aut], Alexander Singer [ctb], Philippe Veber [aut]
Maintainer: Philippe Veber <philippe.veber@univ-lyon1.fr>

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Package BIGL updated to version 1.4.1 with previous version 1.3.0 dated 2019-02-28

Title: Biochemically Intuitive Generalized Loewe Model
Description: Response surface methods for drug synergy analysis. Available methods include generalized and classical Loewe formulations as well as Highest Single Agent methodology. Response surfaces can be plotted in an interactive 3-D plot and formal statistical tests for presence of synergistic effects are available. Implemented methods and tests are described in the article "BIGL: Biochemically Intuitive Generalized Loewe null model for prediction of the expected combined effect compatible with partial agonism and antagonism" by Koen Van der Borght, Annelies Tourny, Rytis Bagdziunas, Olivier Thas, Maxim Nazarov, Heather Turner, Bie Verbist & Hugo Ceulemans (2017) <doi:10.1038/s41598-017-18068-5>.
Author: Heather Turner, Annelies Tourny, Olivier Thas, Maxim Nazarov, Rytis Bagdziunas
Maintainer: Maxim Nazarov <maxim.nazarov@openanalytics.eu>

Diff between BIGL versions 1.3.0 dated 2019-02-28 and 1.4.1 dated 2019-06-21

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Package TraMineR updated to version 2.0-12 with previous version 2.0-11.1 dated 2019-03-22

Title: Trajectory Miner: a Toolbox for Exploring and Rendering Sequences
Description: Toolbox for the manipulation, description and rendering of sequences, and more generally the mining of sequence data in the field of social sciences. Although the toolbox is primarily intended for analyzing state or event sequences that describe life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and simple functions for extracting the most frequent subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
Author: Alexis Gabadinho [aut, cph], Matthias Studer [aut, cph] (<https://orcid.org/0000-0002-6269-1412>), Nicolas Müller [aut], Reto Bürgin [aut], Pierre-Alexandre Fonta [aut], Gilbert Ritschard [aut, cre, cph] (<https://orcid.org/0000-0001-7776-0903>)
Maintainer: Gilbert Ritschard <gilbert.ritschard@unige.ch>

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Package MODIStsp updated to version 1.3.9 with previous version 1.3.8 dated 2019-03-08

Title: A Tool for Automating Download and Preprocessing of MODIS Land Products Data
Description: Allows automating the creation of time series of rasters derived from MODIS Satellite Land Products data. It performs several typical preprocessing steps such as download, mosaicking, reprojection and resize of data acquired on a specified time period. All processing parameters can be set using a user-friendly GUI. Users can select which layers of the original MODIS HDF files they want to process, which additional Quality Indicators should be extracted from aggregated MODIS Quality Assurance layers and, in the case of Surface Reflectance products , which Spectral Indexes should be computed from the original reflectance bands. For each output layer, outputs are saved as single-band raster files corresponding to each available acquisition date. Virtual files allowing access to the entire time series as a single file are also created. Command-line execution exploiting a previously saved processing options file is also possible, allowing to automatically update time series related to a MODIS product whenever a new image is available.
Author: Lorenzo Busetto [aut, cre] (<https://orcid.org/0000-0001-9634-6038>), Luigi Ranghetti [aut] (<https://orcid.org/0000-0001-6207-5188>), Leah Wasser [rev] (Leah Wasser reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/184), Jeff Hanson [rev] (Jeff Hanson reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/184)
Maintainer: Lorenzo Busetto <lbusett@gmail.com>

Diff between MODIStsp versions 1.3.8 dated 2019-03-08 and 1.3.9 dated 2019-06-21

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Permanent link

New package usdarnass with initial version 0.1.0
Package: usdarnass
Type: Package
Title: USDA NASS Quick Stats API
Version: 0.1.0
Description: An alternative for downloading various United States Department of Agriculture (USDA) data from <https://quickstats.nass.usda.gov/> through R. You must sign up for an API token from the mentioned website in order for this package to work.
URL: https://github.com/rdinter/usdarnass
BugReports: https://github.com/rdinter/usdarnass/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: httr, jsonlite, methods, utils, readr
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown, tidyverse, dplyr, tidyr
VignetteBuilder: knitr
Authors@R: c(person(given = "Robert", family = "Dinterman", role = c("cre", "aut"), email = "robert.dinterman@gmail.com", comment = c(ORCID = "0000-0002-9055-6082")), person(given = "Jonathan", family = "Eyer", role = "aut", email = "jeyer@usc.edu"))
NeedsCompilation: no
Packaged: 2019-06-20 15:22:05 UTC; robert
Author: Robert Dinterman [cre, aut] (<https://orcid.org/0000-0002-9055-6082>), Jonathan Eyer [aut]
Maintainer: Robert Dinterman <robert.dinterman@gmail.com>
Repository: CRAN
Date/Publication: 2019-06-21 08:50:03 UTC

More information about usdarnass at CRAN
Permanent link

Package UCSCXenaTools updated to version 1.2.3 with previous version 1.2.2 dated 2019-06-06

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.
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 1.2.2 dated 2019-06-06 and 1.2.3 dated 2019-06-21

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Package RxODE updated to version 0.9.0-8 with previous version 0.9.0-6 dated 2019-05-30

Title: Facilities for Simulating from ODE-Based Models
Description: Facilities for running simulations from ordinary differential equation (ODE) models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The VODE and LSODA are in the public domain. The information is available in the inst/COPYRIGHTS.
Author: Matthew L. Fidler [aut] (<https://orcid.org/0000-0001-8538-6691>), Melissa Hallow [aut], Wenping Wang [aut, cre], Zufar Mulyukov [ctb], Justin Wilkins [ctb] (<https://orcid.org/0000-0002-7099-9396>), Simon Frost [ctb], Heng Li [ctb], Yu Feng [ctb], Alan Hindmarsh [ctb], Linda Petzold [ctb], Ernst Hairer [ctb], Gerhard Wanner [ctb], J Colinge [ctb], Hadley Wickham [ctb], G Grothendieck [ctb], Robert Gentleman [ctb], Ross Ihaka [ctb], R core team [cph], odepack authors [cph]
Maintainer: Wenping Wang <wwang8198@gmail.com>

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Package plotrix updated to version 3.7-6 with previous version 3.7-5 dated 2019-04-07

Title: Various Plotting Functions
Description: Lots of plots, various labeling, axis and color scaling functions.
Author: Jim Lemon, Ben Bolker, Sander Oom, Eduardo Klein, Barry Rowlingson, Hadley Wickham, Anupam Tyagi, Olivier Eterradossi, Gabor Grothendieck, Michael Toews, John Kane, Rolf Turner, Carl Witthoft, Julian Stander, Thomas Petzoldt, Remko Duursma, Elisa Biancotto, Ofir Levy, Christophe Dutang, Peter Solymos, Robby Engelmann, Michael Hecker, Felix Steinbeck, Hans Borchers, Henrik Singmann, Ted Toal, Derek Ogle, Darshan Baral, Ulrike Groemping, Bill Venables
Maintainer: Jim Lemon <drjimlemon@gmail.com>

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Package parSim updated to version 0.1.1 with previous version 0.1 dated 2017-11-10

Title: Parallel Simulation Studies
Description: Perform flexible simulation studies using one or multiple computer cores. The package is set up to be usable on high-performance clusters in addition to being run locally, see examples on <https://github.com/SachaEpskamp/parSim>.
Author: Sacha Epskamp
Maintainer: Sacha Epskamp <mail@sachaepskamp.com>

Diff between parSim versions 0.1 dated 2017-11-10 and 0.1.1 dated 2019-06-21

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New package imgw with initial version 0.1.0
Package: imgw
Title: Polish Meteorological and Hydrological Data
Version: 0.1.0
Authors@R: c(person(given = "Bartosz", family = "Czernecki", role = c("aut", "cre"), email = "nwp@amu.edu.pl", comment = c(ORCID = "0000-0001-6496-1386")), person(given = "Arkadiusz", family = "Głogowski", role = "aut"), person(given = "Jakub", family = "Nowosad", role = "aut", email = "nowosad.jakub@gmail.com", comment = c(ORCID = "0000-0002-1057-3721")), person("IMGW-PIB", role = "ctb", comment = "source of the data"))
Description: Download Polish meteorological and hydrological data from the Institute of Meteorology and Water Management - National Research Institute (<https://dane.imgw.pl/>). This package also allows for adding geographical coordinates for each observation.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Depends: R (>= 3.1)
Imports: RCurl, XML
Suggests: testthat
URL: https://imgw.ml
BugReports: https://github.com/bczernecki/imgw/issues
NeedsCompilation: no
Packaged: 2019-06-20 09:48:45 UTC; bartosz
Author: Bartosz Czernecki [aut, cre] (<https://orcid.org/0000-0001-6496-1386>), Arkadiusz Głogowski [aut], Jakub Nowosad [aut] (<https://orcid.org/0000-0002-1057-3721>), IMGW-PIB [ctb] (source of the data)
Maintainer: Bartosz Czernecki <nwp@amu.edu.pl>
Repository: CRAN
Date/Publication: 2019-06-21 08:20:14 UTC

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Package h2o updated to version 3.24.0.5 with previous version 3.22.1.1 dated 2019-01-10

Title: R Interface for 'H2O'
Description: R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models, Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Cox Proportional Hazards, K-Means, PCA, Word2Vec, as well as a fully automatic machine learning algorithm (AutoML).
Author: Erin LeDell [aut, cre], Navdeep Gill [aut], Spencer Aiello [aut], Anqi Fu [aut], Arno Candel [aut], Cliff Click [aut], Tom Kraljevic [aut], Tomas Nykodym [aut], Patrick Aboyoun [aut], Michal Kurka [aut], Michal Malohlava [aut], Ludi Rehak [ctb], Eric Eckstrand [ctb], Brandon Hill [ctb], Sebastian Vidrio [ctb], Surekha Jadhawani [ctb], Amy Wang [ctb], Raymond Peck [ctb], Wendy Wong [ctb], Jan Gorecki [ctb], Matt Dowle [ctb], Yuan Tang [ctb], Lauren DiPerna [ctb], H2O.ai [cph, fnd]
Maintainer: Erin LeDell <erin@h2o.ai>

Diff between h2o versions 3.22.1.1 dated 2019-01-10 and 3.24.0.5 dated 2019-06-21

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More information about h2o at CRAN
Permanent link

Package airGR updated to version 1.3.2.23 with previous version 1.2.13.16 dated 2019-04-03

Title: Suite of GR Hydrological Models for Precipitation-Runoff Modelling
Description: Hydrological modelling tools developed at Irstea-Antony (HYCAR Research Unit, France). The package includes several conceptual rainfall-runoff models (GR4H, GR4J, GR5J, GR6J, GR2M, GR1A), a snow accumulation and melt model (CemaNeige) and the associated functions for their calibration and evaluation. Use help(airGR) for package description and references.
Author: Laurent Coron [aut, trl] (<https://orcid.org/0000-0002-1503-6204>), Olivier Delaigue [aut, cre] (<https://orcid.org/0000-0002-7668-8468>), Guillaume Thirel [aut] (<https://orcid.org/0000-0002-1444-1830>), Charles Perrin [aut, ths] (<https://orcid.org/0000-0001-8552-1881>), Claude Michel [aut, ths], Vazken Andréassian [ctb, ths] (<https://orcid.org/0000-0001-7124-9303>), François Bourgin [ctb] (<https://orcid.org/0000-0002-2820-7260>, 'Parameter estimation' vignettes), Pierre Brigode [ctb] (<https://orcid.org/0000-0001-8257-0741>), Nicolas Le Moine [ctb], Thibaut Mathevet [ctb], Safouane Mouelhi [ctb], Ludovic Oudin [ctb] (<https://orcid.org/0000-0002-3712-0933>), Raji Pushpalatha [ctb], Audrey Valéry [ctb]
Maintainer: Olivier Delaigue <airGR@irstea.fr>

Diff between airGR versions 1.2.13.16 dated 2019-04-03 and 1.3.2.23 dated 2019-06-21

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 airGR-1.3.2.23/airGR/R/DataAltiExtrapolation_Valery.R       | 1102 +++----
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More information about airGR at CRAN
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Package spaMM updated to version 3.0.0 with previous version 2.7.1 dated 2019-04-14

Title: Mixed-Effect Models, Particularly Spatial Models
Description: Inference based on mixed-effect models, including generalized linear mixed models with spatial correlations and models with non-Gaussian random effects (e.g., Beta). Both classical geostatistical models, and Markov random field models on irregular grids, can be fitted. Variation in residual variance (heteroscedasticity) can itself be represented by a generalized linear mixed model. Various approximations of likelihood or restricted likelihood are implemented, in particular h-likelihood (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) and Laplace approximation.
Author: François Rousset [aut, cre, cph] (<https://orcid.org/0000-0003-4670-0371>), Jean-Baptiste Ferdy [aut, cph], Alexandre Courtiol [aut] (<https://orcid.org/0000-0003-0637-2959>), GSL authors [ctb] (src/gsl_bessel.*)
Maintainer: François Rousset <francois.rousset@umontpellier.fr>

Diff between spaMM versions 2.7.1 dated 2019-04-14 and 3.0.0 dated 2019-06-21

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New package SDMtune with initial version 0.1.0
Package: SDMtune
Type: Package
Title: Species Distribution Model Selection
Version: 0.1.0
Date: 2019-06-20
Authors@R: c( person("Sergio", "Vignali", email = "sergio.vignali@iee.unibe.ch", role = c("aut", "cre"), comment = c(ORCID = "00000-0002-3390-5442")), person("Arnaud", "Barras", role = "aut", comment = c(ORCID = "0000-0003-0850-6965")), person("Veronika", "Braunisch", role = "aut", comment = c(ORCID = "00000-0001-7035-4662")), person("Conservation Biology - University of Bern", role = "fnd") )
Description: User-friendly framework that enables the training and the evaluation of species distribution models (SDMs). The package implements functions for data driven variable selection and model tuning and includes numerous utilities to display the results. All the functions used to select variables or to tune model hyperparameters have an interactive real-time chart displayed in the 'RStudio' viewer pane during their execution. At the moment only the maximum entropy method is available using the 'Java' implementation, Phillips et al. (2006) <doi:10.1016/j.ecolmodel.2005.03.026>, through the 'dismo' package and the 'R' implementation through the 'maxnet' package, Phillips et al. (2017) <doi:10.1111/ecog.03049>. 'SDMtune' uses its own script to predict maxent models, resulting in much faster predictions for large datasets compared to native predictions from the use of the 'Java' software. This reduces considerably the computation time when tuning the model using the AICc.
License: GPL-3
URL: https://consbiol-unibern.github.io/SDMtune/
BugReports: https://github.com/ConsBiol-unibern/SDMtune/issues
Depends: R (>= 3.2.0)
Imports: cli (>= 1.1.0), crayon (>= 1.3.4), dismo (>= 1.1-4), ggplot2 (>= 3.2.0), htmltools (>= 0.3.6), jsonlite (>= 1.6), kableExtra (>= 1.1.0), maxnet (>= 0.1.2), methods, progress (>= 1.2.2), raster (>= 2.9-5), rasterVis (>= 0.45), Rcpp (>= 1.0.1), reshape2 (>= 1.4.3), rgdal (>= 1.4-4), rJava (>= 0.9-11), rstudioapi (>= 0.10), scales (>= 1.0.0), stringr (>= 1.4.0), whisker (>= 0.3-2)
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp
RoxygenNote: 6.1.1
Suggests: covr, knitr (>= 1.23), maps (>= 3.3.0), pkgdown (>= 1.3.0), rmarkdown (>= 1.13), roxygen2 (>= 6.1.1), snow (>= 0.4-3), testthat (>= 2.1.1), zeallot (>= 0.1.0)
VignetteBuilder: knitr
Collate: 'Maxent_class.R' 'Maxnet_class.R' 'RcppExports.R' 'SDMmodel2MaxEnt.R' 'SWD_class.R' 'SDMmodelCV.R' 'SDMmodel_class.R' 'SDMtune.R' 'SDMtune_class.R' 'aicc.R' 'auc.R' 'chart_utils.R' 'condor.R' 'confMatrix.R' 'corVar.R' 'doJk.R' 'getSubsample.R' 'gridSearch.R' 'maxentTh.R' 'maxentVarImp.R' 'mergeSWD.R' 'modelReport.R' 'optimizeModel.R' 'plotCor.R' 'plotJk.R' 'plotPA.R' 'plotPred.R' 'plotROC.R' 'plotResponse.R' 'plotVarImp.R' 'predict_Maxent.R' 'predict_SDMmodel.R' 'prepareSWD.R' 'randomSearch.R' 'reduceVar.R' 'swd2csv.R' 'thinData.R' 'thresholds.R' 'train.R' 'trainMaxent.R' 'trainMaxnet.R' 'trainValTest.R' 'tss.R' 'utils.R' 'varImp.R' 'varSel.R' 'zzz.R'
NeedsCompilation: yes
Packaged: 2019-06-20 07:08:49 UTC; vignali
Author: Sergio Vignali [aut, cre] (<https://orcid.org/00000-0002-3390-5442>), Arnaud Barras [aut] (<https://orcid.org/0000-0003-0850-6965>), Veronika Braunisch [aut] (<https://orcid.org/00000-0001-7035-4662>), Conservation Biology - University of Bern [fnd]
Maintainer: Sergio Vignali <sergio.vignali@iee.unibe.ch>
Repository: CRAN
Date/Publication: 2019-06-21 08:00:20 UTC

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New package psSubpathway with initial version 0.1.0
Package: psSubpathway
Type: Package
Title: Flexible Identification of Phenotype-Specific Subpathways
Version: 0.1.0
Author: Xudong Han, Junwei Han, Qingfei Kong
Maintainer: Junwei Han <hanjunwei1981@163.com>
Description: A network-based systems biology tool for flexible identification of phenotype-specific subpathways in the cancer gene expression data with multiple categories (such as multiple subtype or developmental stages of cancer). Subtype Set Enrichment Analysis (SubSEA) and Dynamic Changed Subpathway Analysis (DCSA) are developed to flexible identify subtype specific and dynamic changed subpathways respectively. The operation modes include extraction of subpathways from biological pathways, inference of subpathway activities in the context of gene expression data, identification of subtype specific subpathways with SubSEA, identification of dynamic changed subpathways associated with the cancer developmental stage with DCSA, and visualization of the activities of resulting subpathways by using box plots and heat maps. Its capabilities render the tool could find the specific abnormal subpathways in the cancer dataset with multi-phenotype samples.
Depends: R (>= 3.5.0)
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: GSVA, igraph,mpmi, pheatmap
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2019-06-20 00:31:29 UTC; 12859
Repository: CRAN
Date/Publication: 2019-06-21 07:20:04 UTC

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Package lvnet updated to version 0.3.5 with previous version 0.3.4 dated 2018-11-18

Title: Latent Variable Network Modeling
Description: Estimate, fit and compare Structural Equation Models (SEM) and network models (Gaussian Graphical Models; GGM) using OpenMx. Allows for two possible generalizations to include GGMs in SEM: GGMs can be used between latent variables (latent network modeling; LNM) or between residuals (residual network modeling; RNM). For details, see Epskamp, Rhemtulla and Borsboom (2017) <doi:10.1007/s11336-017-9557-x>.
Author: Sacha Epskamp
Maintainer: Sacha Epskamp <mail@sachaepskamp.com>

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Package log4r updated to version 0.3.0 with previous version 0.2 dated 2014-09-29

Title: A Simple Logging System for R, Based on 'log4j'
Description: The log4r package is meant to provide a fast, lightweight, object-oriented approach to logging in R based on the widely-emulated 'log4j' system and etymology.
Author: John Myles White [aut, cph], Kenton White [ctb], Kirill Müller [ctb], Aaron Jacobs [aut, cre]
Maintainer: Aaron Jacobs <atheriel@gmail.com>

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New package frostr with initial version 0.1.0
Package: frostr
Type: Package
Title: R Client to MET Norway's 'Frost' API
Version: 0.1.0
Authors@R: person("Iman", "Ghayoornia", email = "ghayoornia.iman@gmail.com", role = c("aut", "cre"))
Description: An unofficial R client to MET Norway's 'Frost' API <https://frost.met.no/index2.html> to retrieve data as data frames. The 'Frost' API, and the underlying data, is made available by the Norwegian Meteorological Institute (MET Norway). The data and products are distributed under the Norwegian License for Open Data 2.0 (NLOD) <https://data.norge.no/nlod/en/2.0> and Creative Commons 4.0 <https://creativecommons.org/licenses/by/4.0/>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: httr, jsonlite, tibble, tidyr
Suggests: testthat
NeedsCompilation: no
Packaged: 2019-06-19 22:45:25 UTC; ImanGhayoornia
Author: Iman Ghayoornia [aut, cre]
Maintainer: Iman Ghayoornia <ghayoornia.iman@gmail.com>
Repository: CRAN
Date/Publication: 2019-06-21 07:10:03 UTC

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Package did updated to version 1.2.2 with previous version 1.2.1 dated 2019-06-14

Title: Treatment Effects with Multiple Periods and Groups
Description: The standard Difference-in-Differences (DID) setup involves two periods and two groups -- a treated group and untreated group. Many applications of DID methods involve more than two periods and have individuals that are treated at different points in time. This package contains tools for computing average treatment effect parameters in Difference in Differences models with more than two periods and with variation in treatment timing using the methods developed in Callaway and Sant'Anna (2018) <https://ssrn.com/abstract=3148250>. The main parameters are group-time average treatment effects which are the average treatment effect for a particular group at a a particular time. These can be aggregated into a fewer number of treatment effect parameters, and the package deals with the cases where there is selective treatment timing, dynamic treatment effects, calendar time effects, or combinations of these. There are also functions for testing the Difference in Differences assumption, and plotting group-time average treatment effects.
Author: Brantly Callaway [aut, cre], Pedro H.C. Sant'Anna [aut]
Maintainer: Brantly Callaway <brantly.callaway@temple.edu>

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Package antitrust updated to version 0.99.11 with previous version 0.99.10 dated 2018-06-10

Title: Tools for Antitrust Practitioners
Description: A collection of tools for antitrust practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of mergers under different competitive regimes.
Author: Charles Taragin and Michael Sandfort
Maintainer: Charles Taragin <ctaragin@ftc.gov>

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More information about antitrust at CRAN
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Package mefa4 updated to version 0.3-6 with previous version 0.3-5 dated 2018-03-25

Title: Multivariate Data Handling with S4 Classes and Sparse Matrices
Description: An S4 update of the 'mefa' package using sparse matrices for enhanced efficiency. Sparse array-like objects are supported via lists of sparse matrices.
Author: Peter Solymos
Maintainer: Peter Solymos <solymos@ualberta.ca>

Diff between mefa4 versions 0.3-5 dated 2018-03-25 and 0.3-6 dated 2019-06-21

 DESCRIPTION  |   10 +++++-----
 MD5          |    8 +++++---
 NAMESPACE    |    2 --
 R/find_max.R |   17 ++++++++++-------
 tests        |only
 5 files changed, 20 insertions(+), 17 deletions(-)

More information about mefa4 at CRAN
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