Sun, 25 Mar 2018

Package vennLasso updated to version 0.1.2 with previous version 0.1.1 dated 2017-11-16

Title: Variable Selection for Heterogeneous Populations
Description: Provides variable selection and estimation routines for models with main effects stratified on multiple binary factors. The 'vennLasso' package is an implementation of the method introduced in Huling, et al. (2017) <doi:10.1111/biom.12769>.
Author: Jared Huling [aut, cre], Muxuan Liang [ctb], Yixuan Qiu [cph], Gael Guennebaud [cph], Ray Gardner [cph], Jitse Niesen [cph]
Maintainer: Jared Huling <jaredhuling@gmail.com>

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More information about vennLasso at CRAN
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Package listviewer updated to version 2.0.0 with previous version 1.4.0 dated 2016-11-03

Title: 'htmlwidget' for Interactive Views of R Lists
Description: R lists, especially nested lists, can be very difficult to visualize or represent. Sometimes 'str()' is not enough, so this suite of htmlwidgets is designed to help see, understand, and maybe even modify your R lists. The function 'reactjson()' requires a package 'reactR' that can be installed from CRAN or <https://github.com/timelyportfolio/reactR>.
Author: Jos de Jong [aut, cph] (jsoneditor.js library in htmlwidgets/jsoneditor, http://github.com/josdejong/jsoneditor/), Mac Gainer [aut, cph] (react-json-view library in htmlwidgets/react-json, https://github.com/mac-s-g/react-json-view), Kent Russell [aut, cre] (R interface)
Maintainer: Kent Russell <kent.russell@timelyportfolio.com>

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Package tmle updated to version 1.3.0-1 with previous version 1.3.0 dated 2018-02-01

Title: Targeted Maximum Likelihood Estimation
Description: Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.
Author: Susan Gruber [aut, cre], Mark van der Laan [aut]
Maintainer: Susan Gruber <sgruber@cal.berkeley.edu>

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Package sjstats updated to version 0.14.2-2 with previous version 0.14.2 dated 2018-03-25

Title: Collection of Convenient Functions for Common Statistical Computations
Description: Collection of convenient functions for common statistical computations, which are not directly provided by R's base or stats packages. This package aims at providing, first, shortcuts for statistical measures, which otherwise could only be calculated with additional effort (like standard errors or root mean squared errors). Second, these shortcut functions are generic (if appropriate), and can be applied not only to vectors, but also to other objects as well (e.g., the Coefficient of Variation can be computed for vectors, linear models, or linear mixed models; the r2()-function returns the r-squared value for 'lm', 'glm', 'merMod' or 'lme' objects). The focus of most functions lies on summary statistics or fit measures for regression models, including generalized linear models and mixed effects models. However, some of the functions also deal with other statistical measures, like Cronbach's Alpha, Cramer's V, Phi etc.
Author: Daniel Lüdecke <d.luedecke@uke.de>
Maintainer: Daniel Lüdecke <d.luedecke@uke.de>

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Package orthoDr updated to version 0.5.1 with previous version 0.5 dated 2018-03-25

Title: An Orthogonality Constrained Optimization Approach for Semi-Parametric Dimension Reduction Problems
Description: Utilize an orthogonality constrained optimization algorithm of Wen & Yin (2013) <DOI:10.1007/s10107-012-0584-1> to solve a variety of dimension reduction problems in the semiparametric framework, such as Ma & Zhu (2012) <DOI:10.1080/01621459.2011.646925>, Ma & Zhu (2013) <DOI:10.1214/12-AOS1072>, and Sun, Zhu, Wang & Zeng (2017) <arXiv:1704.05046>. It also serves as a general purpose optimization solver for problems with orthogonality constraints. Parallel computing is enabled through `OpenMP'.
Author: Ruilin Zhao, Jiyang Zhang and Ruoqing Zhu
Maintainer: Ruoqing Zhu <teazrq@gmail.com>

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Package interp updated to version 1.0-31 with previous version 1.0-30 dated 2018-01-17

Title: Interpolation Methods
Description: Bivariate data interpolation on regular and irregular grids, either linear or using splines are the main part of this package. It is intended to provide FOSS replacement functions for the ACM licensed akima::interp and tripack::tri.mesh functions. Currently the piecewise linear interpolation part of akima::interp (and also akima::interpp) is implemented in interp::interp, this corresponds to the call akima::interp(..., linear=TRUE) which is the default setting and covers most of akima::interp use cases in depending packages. A re-implementation of Akimas spline interpolation (akima::interp(..., linear=FALSE)) is currently under development and will complete this package in a later version. Estimators for partial derivatives are already available, these are a prerequisite for the spline interpolation. The basic part is currently a GPLed triangulation algorithm (sweep hull algorithm by David Sinclair) providing the starting point for the piecewise linear interpolator. As side effect this algorithm is also used to provide replacements for the basic functions of the tripack package which also suffer from the ACM restrictions. All functions are designed to be backward compatible with their akima / tripack counterparts.
Author: Albrecht Gebhardt [aut, cre, cph] (...), Roger Bivand [aut], David Sinclair [aut, cph]
Maintainer: Albrecht Gebhardt <albrecht.gebhardt@aau.at>

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Package icr updated to version 0.5.3 with previous version 0.5.2 dated 2018-01-27

Title: Compute Krippendorff's Alpha
Description: Provides functions to compute and plot Krippendorff's inter-coder reliability coefficient alpha and bootstrapped uncertainty estimates (Krippendorff 2004, ISBN:0761915443). The bootstrap routines are set up to make use of parallel threads where supported.
Author: Alexander Staudt [aut, cre], Pierre L'Ecuyer [ctb] (author of the C++ RNG code)
Maintainer: Alexander Staudt <staudtlex@live.de>

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Package rclimateca updated to version 1.0.1 with previous version 1.0.0 dated 2018-01-09

Title: Fetch Climate Data from Environment Canada
Description: The Environment Canada climate archives <http://climate.weather.gc.ca/> are an important source of data for climate researchers in Canada and world wide. The repository contains temperature, precipitation, and wind data for more than 8,000 locations. The functions in this package simplify the process of downloading, subsetting, and manipulating these data for the purposes of more efficient workflows in climate research.
Author: Dewey Dunnington <dewey@fishandwhistle.net>
Maintainer: Dewey Dunnington <dewey@fishandwhistle.net>

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Package aire.zmvm updated to version 0.6.0 with previous version 0.5.0 dated 2017-11-12

Title: Download Mexico City Pollution, Wind, and Temperature Data
Description: Tools for downloading hourly averages, daily maximums and minimums from each of the pollution, wind, and temperature measuring stations or geographic zones in the Mexico City metro area. The package also includes the locations of each of the stations and zones. See <http://aire.cdmx.gob.mx/> for more information.
Author: Diego Valle-Jones [aut, cre]
Maintainer: Diego Valle-Jones <diego@diegovalle.net>

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Package autothresholdr updated to version 1.2.0 with previous version 1.1.2 dated 2018-02-13

Title: An R Port of the 'ImageJ' Plugin 'Auto Threshold'
Description: Provides the 'ImageJ' 'Auto Threshold' plugin functionality to R users. See <http://imagej.net/Auto_Threshold> and Landini et al. (2017) <DOI:10.1111/jmi.12474>.
Author: Rory Nolan [aut, cre, trl], Luis Alvarez [ctb], Sergi Padilla-Parra [ctb, ths], Gabriel Landini [ctb, cph]
Maintainer: Rory Nolan <rorynoolan@gmail.com>

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New package antaresProcessing with initial version 0.15
Package: antaresProcessing
Type: Package
Title: 'Antares' Results Processing
Version: 0.15
Date: 2018-03-08
Authors@R: c( person("Jalal-Edine", "ZAWAM", , "jalal-edine.zawam@rte-france.com", role = c("aut", "cre")), person("Francois", "Guillem", , "francois.guillem@rte-france.com", role = "aut"), person("Benoit", "Thieurmel", email = "benoit.thieurmel@datastorm.fr", role = "aut"), person(given = "Titouan", family = "Robert", email = "titouan.robert@datastorm.fr", role = "aut"), person("RTE", role = "cph") )
Maintainer: Jalal-Edine ZAWAM <jalal-edine.zawam@rte-france.com>
Description: Process results generated by 'Antares', a powerful software developed by RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems (more information about 'Antares' here: <https://antares.rte-france.com>). This package provides functions to create new columns like net load, load factors, upward and downward margins or to compute aggregated statistics like economic surpluses of consumers, producers and sectors.
URL: https://github.com/rte-antares-rpackage/antaresProcessing
BugReports: https://github.com/rte-antares-rpackage/antaresProcessing/issues
License: GPL (>= 2) | file LICENSE
LazyData: TRUE
Depends: antaresRead (>= 1.1.5)
Imports: data.table, methods, stats
Suggests: rhdf5 (>= 2.20.0), parallel, testthat, knitr, rmarkdown, covr
RoxygenNote: 6.0.1
VignetteBuilder: knitr
Encoding: UTF-8
biocViews: Infrastructure, DataImport
NeedsCompilation: no
Packaged: 2018-03-23 13:43:18 UTC; jalazawa
Author: Jalal-Edine ZAWAM [aut, cre], Francois Guillem [aut], Benoit Thieurmel [aut], Titouan Robert [aut], RTE [cph]
Repository: CRAN
Date/Publication: 2018-03-25 16:02:33 UTC

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Package tabuSearch updated to version 1.1.1 with previous version 1.1 dated 2012-03-07

Title: Tabu Search Algorithm for Binary Configurations
Description: Tabu search algorithm for binary configurations. A basic version of the algorithm as described by Fouskakis and Draper (2007) <doi:10.1111/j.1751-5823.2002.tb00174.x>.
Author: Katarina Domijan
Maintainer: Katarina Domijan <domijank@tcd.ie>

Diff between tabuSearch versions 1.1 dated 2012-03-07 and 1.1.1 dated 2018-03-25

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Package symbolicDA updated to version 0.5-3 with previous version 0.4-3 dated 2017-12-11

Title: Analysis of Symbolic Data
Description: Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation. (Milligan, G.W., Cooper, M.C. (1985) <doi:10.1007/BF02294245>, Breiman, L. (1996), <doi:10.1007/BF00058655>, Hubert, L., Arabie, P. (1985), <doi:10.1007%2FBF01908075>, Ichino, M., & Yaguchi, H. (1994), <doi:10.1109/21.286391>, Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, Carmone, F.J., Kara, A., Maxwell, S. (1999) <doi:10.2307/3152003>, Calinski, T., Harabasz, J. (1974) <doi:10.1080/03610927408827101>, Breckenridge, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>, Walesiak, M., Dudek, A. (2008) <doi:10.1007/978-3-540-78246-9_11>, Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).
Author: Andrzej Dudek, Marcin Pelka <marcin.pelka@ue.wroc.pl>, Justyna Wilk<justyna.wilk@ue.wroc.pl> (to 2017-09-20), Marek Walesiak <Marek.Walesiak@ue.wroc.pl> (from 2018-02-01)
Maintainer: Andrzej Dudek <andrzej.dudek@ue.wroc.pl>

Diff between symbolicDA versions 0.4-3 dated 2017-12-11 and 0.5-3 dated 2018-03-25

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Package spData updated to version 0.2.8.3 with previous version 0.2.8.1 dated 2018-03-10

Title: Datasets for Spatial Analysis
Description: Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis. It includes R data of class sf (defined by the package 'sf'), Spatial ('sp'), and nb ('spdep'). Unlike other spatial data packages such as 'rnaturalearth' and 'maps', it also contains data stored in a range of file formats including GeoJSON, ESRI Shapefile and GeoPackage. Some of the datasets are designed to illustrate specific analysis techniques. cycle_hire and cycle_hire_osm, for example, is designed to illustrate point pattern analysis techniques.
Author: Roger Bivand [aut] (<https://orcid.org/0000-0003-2392-6140>), Jakub Nowosad [aut, cre] (<https://orcid.org/0000-0002-1057-3721>), Robin Lovelace [aut] (<https://orcid.org/0000-0001-5679-6536>)
Maintainer: Jakub Nowosad <nowosad.jakub@gmail.com>

Diff between spData versions 0.2.8.1 dated 2018-03-10 and 0.2.8.3 dated 2018-03-25

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Package snakecase updated to version 0.9.1 with previous version 0.9.0 dated 2018-02-25

Title: Convert Strings into any Case
Description: A consistent, flexible and easy to use tool to parse and convert strings into cases like snake or camel among others.
Author: Malte Grosser [aut, cre]
Maintainer: Malte Grosser <malte.grosser@gmail.com>

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Package sjstats updated to version 0.14.2 with previous version 0.14.1 dated 2018-02-04

Title: Collection of Convenient Functions for Common Statistical Computations
Description: Collection of convenient functions for common statistical computations, which are not directly provided by R's base or stats packages. This package aims at providing, first, shortcuts for statistical measures, which otherwise could only be calculated with additional effort (like standard errors or root mean squared errors). Second, these shortcut functions are generic (if appropriate), and can be applied not only to vectors, but also to other objects as well (e.g., the Coefficient of Variation can be computed for vectors, linear models, or linear mixed models; the r2()-function returns the r-squared value for 'lm', 'glm', 'merMod' or 'lme' objects). The focus of most functions lies on summary statistics or fit measures for regression models, including generalized linear models and mixed effects models. However, some of the functions also deal with other statistical measures, like Cronbach's Alpha, Cramer's V, Phi etc.
Author: Daniel Lüdecke <d.luedecke@uke.de>
Maintainer: Daniel Lüdecke <d.luedecke@uke.de>

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Package sjmisc updated to version 2.7.1 with previous version 2.7.0 dated 2018-02-03

Title: Data and Variable Transformation Functions
Description: Collection of miscellaneous utility functions, supporting data transformation tasks like recoding, dichotomizing or grouping variables, setting and replacing missing values. The data transformation functions also support labelled data, and all integrate seamlessly into a 'tidyverse'-workflow.
Author: Daniel Lüdecke <d.luedecke@uke.de>
Maintainer: Daniel Lüdecke <d.luedecke@uke.de>

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Package servr updated to version 0.9 with previous version 0.8 dated 2017-11-06

Title: A Simple HTTP Server to Serve Static Files or Dynamic Documents
Description: Start an HTTP server in R to serve static files, or dynamic documents that can be converted to HTML files (e.g., R Markdown) under a given directory.
Author: Yihui Xie [aut, cre] (<https://orcid.org/0000-0003-0645-5666>), Carson Sievert [ctb], Jesse Anderson [ctb], Ramnath Vaidyanathan [ctb]
Maintainer: Yihui Xie <xie@yihui.name>

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Package randomForest updated to version 4.6-14 with previous version 4.6-12 dated 2015-10-07

Title: Breiman and Cutler's Random Forests for Classification and Regression
Description: Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.
Author: Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener.
Maintainer: Andy Liaw <andy_liaw@merck.com>

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Package prioritizr updated to version 3.0.4 with previous version 3.0.3 dated 2017-11-22

Title: Systematic Conservation Prioritization in R
Description: Conservation prioritization using integer programming techniques. To solve large-scale problems, users should install the 'gurobi' optimizer (available from <http://www.gurobi.com/>).
Author: Jeffrey O Hanson [aut], Richard Schuster [aut, cre], Nina Morrell [aut], Matthew Strimas-Mackey [aut], Matthew E Watts [aut], Peter Arcese [aut], Joeseph Bennett [aut], Hugh P Possingham [aut]
Maintainer: Richard Schuster <mail@richard-schuster.com>

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Package plogr updated to version 0.2.0 with previous version 0.1-1 dated 2016-09-24

Title: The 'plog' C++ Logging Library
Description: A simple header-only logging library for C++. Add 'LinkingTo: plogr' to 'DESCRIPTION', and '#include <plogr.h>' in your C++ modules to use it.
Author: Kirill Müller [aut, cre], Sergey Podobry [cph] (Author of the bundled plog library)
Maintainer: Kirill Müller <krlmlr+r@mailbox.org>

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Package orthoDr updated to version 0.5 with previous version 0.3.0 dated 2017-11-02

Title: An Orthogonality Constrained Optimization Approach for Semi-Parametric Dimension Reduction Problems
Description: Utilize an orthogonality constrained optimization algorithm of Wen & Yin (2013) <DOI:10.1007/s10107-012-0584-1> to solve a variety of dimension reduction problems in the semiparametric framework, such as Ma & Zhu (2012) <DOI:10.1080/01621459.2011.646925>, Ma & Zhu (2013) <DOI:10.1214/12-AOS1072>, and Sun, Zhu, Wang & Zeng (2017) <arXiv:1704.05046>. It also serves as a general purpose optimization solver for problems with orthogonality constraints. Parallel computing is enabled through OpenMP.
Author: Ruilin Zhao, Jiyang Zhang and Ruoqing Zhu
Maintainer: Ruoqing Zhu <teazrq@gmail.com>

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Package nimble updated to version 0.6-10 with previous version 0.6-9 dated 2018-02-06

Title: MCMC, Particle Filtering, and Programmable Hierarchical Modeling
Description: A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, particle filtering, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <http://r-nimble.org>.
Author: Perry de Valpine, Christopher Paciorek, Daniel Turek, Cliff Anderson-Bergman, Nick Michaud, Fritz Obermeyer, Duncan Temple Lang, and see AUTHORS file for additional contributors.
Maintainer: Christopher Paciorek <paciorek@stat.berkeley.edu>

Diff between nimble versions 0.6-9 dated 2018-02-06 and 0.6-10 dated 2018-03-25

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Package NetworkToolbox updated to version 1.1.1 with previous version 1.1.0 dated 2018-03-21

Title: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis
Description: Implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershogoren, Mantegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.
Author: Alexander Christensen
Maintainer: Alexander Christensen <alexpaulchristensen@gmail.com>

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Package mstR updated to version 1.1 with previous version 1.0 dated 2017-03-30

Title: Procedures to Generate Patterns under Multistage Testing
Description: Generation of response patterns under dichotomous and polytomous computerized multistage testing (MST) framework. It holds various item response theory (IRT) and score-based methods to select the next module and estimate ability levels (Magis, Yan and von Davier (2017, ISBN:978-3-319-69218-0)).
Author: David Magis (U Liege, Belgium), Duanli Yan (ETS, USA), Alina von Davier (ACTNext, USA)
Maintainer: David Magis <david.magis@uliege.be>

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Package miceFast updated to version 0.0.2 with previous version 0.0.1 dated 2018-03-19

Title: Fast Imputations Using 'Rcpp' and 'Armadillo'
Description: Fast imputations under the object-oriented programming paradigm. There was used quantitative models with a closed-form solution. Thus package is based on linear algebra operations. The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used.
Author: Maciej Nasinski [aut, cre]
Maintainer: Maciej Nasinski <nasinski.maciej@gmail.com>

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Package keras updated to version 2.1.5 with previous version 2.1.4 dated 2018-02-24

Title: R Interface to 'Keras'
Description: Interface to 'Keras' <https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.
Author: JJ Allaire [aut, cre], François Chollet [aut, cph], RStudio [ctb, cph, fnd], Google [ctb, cph, fnd], Yuan Tang [ctb, cph] (<https://orcid.org/0000-0001-5243-233X>), Daniel Falbel [ctb, cph], Wouter Van Der Bijl [ctb, cph], Martin Studer [ctb, cph]
Maintainer: JJ Allaire <jj@rstudio.com>

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Package flare updated to version 1.6.0 with previous version 1.5.0 dated 2014-10-18

Title: Family of Lasso Regression
Description: Provide the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. A multi-stage screening approach is adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. Missing values can be tolerated for Dantzig selector and CLIME. The computation is memory-optimized using the sparse matrix output.
Author: Xingguo Li, Tuo Zhao, Lie Wang, Xiaoming Yuan, and Han Liu
Maintainer: Xingguo Li <xingguo.leo@gmail.com>

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Package blob updated to version 1.1.1 with previous version 1.1.0 dated 2017-06-17

Title: A Simple S3 Class for Representing Vectors of Binary Data ('BLOBS')
Description: R's raw vector is useful for storing a single binary object. What if you want to put a vector of them in a data frame? The 'blob' package provides the blob object, a list of raw vectors, suitable for use as a column in data frame.
Author: Hadley Wickham [aut], Kirill Müller [cre], RStudio [cph]
Maintainer: Kirill Müller <krlmlr+r@mailbox.org>

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Package bizdays updated to version 1.0.5 with previous version 1.0.4 dated 2017-09-14

Title: Business Days Calculations and Utilities
Description: Business days calculations based on a list of holidays and nonworking weekdays. Quite useful for fixed income and derivatives pricing.
Author: Wilson Freitas <wilson.freitas@gmail.com>
Maintainer: Wilson Freitas <wilson.freitas@gmail.com>

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Package berryFunctions updated to version 1.17.0 with previous version 1.16.3 dated 2017-11-10

Title: Function Collection Related to Plotting and Hydrology
Description: Draw horizontal histograms, color scattered points by 3rd dimension, enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, fit multiple functions.
Author: Berry Boessenkool
Maintainer: Berry Boessenkool <berry-b@gmx.de>

Diff between berryFunctions versions 1.16.3 dated 2017-11-10 and 1.17.0 dated 2018-03-25

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Package pROC updated to version 1.11.0 with previous version 1.10.0 dated 2017-06-10

Title: Display and Analyze ROC Curves
Description: Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
Author: Xavier Robin [cre, aut], Natacha Turck [aut], Alexandre Hainard [aut], Natalia Tiberti [aut], Frédérique Lisacek [aut], Jean-Charles Sanchez [aut], Markus Müller [aut], Stefan Siegert [ctb] (Fast DeLong code)
Maintainer: Xavier Robin <pROC-cran@xavier.robin.name>

Diff between pROC versions 1.10.0 dated 2017-06-10 and 1.11.0 dated 2018-03-25

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 27 files changed, 137 insertions(+), 71 deletions(-)

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Package mefa4 updated to version 0.3-5 with previous version 0.3-4 dated 2016-10-14

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 0.3-4 dated 2016-10-14 and 0.3-5 dated 2018-03-25

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 35 files changed, 59 insertions(+), 34 deletions(-)

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Package glmmML updated to version 1.0.3 with previous version 1.0.2 dated 2017-05-03

Title: Generalized Linear Models with Clustering
Description: Binomial and Poisson regression for clustered data, fixed and random effects with bootstrapping.
Author: Göran Broström
Maintainer: Göran Broström <goran.brostrom@umu.se>

Diff between glmmML versions 1.0.2 dated 2017-05-03 and 1.0.3 dated 2018-03-25

 ChangeLog      |    3 +++
 DESCRIPTION    |    8 ++++----
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 6 files changed, 18 insertions(+), 15 deletions(-)

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Package BAS updated to version 1.4.9 with previous version 1.4.8 dated 2018-03-12

Title: Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling
Description: Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) <DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors in GLMs of Li and Clyde (2015) <arXiv:1503.06913>. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using Sampling w/out Replacement or an efficient MCMC algorithm samples models using the BAS tree structure as an efficient hash table. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used. The user may force variables to always be included. Details behind the sampling algorithm are provided in Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049>. This material is based upon work supported by the National Science Foundation under Grant DMS-1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Author: Merlise Clyde [aut, cre, cph] (<https://orcid.org/-5469>), Michael Littman [ctb], Quanli Wang [ctb], Joyee Ghosh [ctb], Yingbo Li [ctb]
Maintainer: Merlise Clyde <clyde@duke.edu>

Diff between BAS versions 1.4.8 dated 2018-03-12 and 1.4.9 dated 2018-03-25

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 9 files changed, 129 insertions(+), 81 deletions(-)

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Package corcounts (with last version 1.4) was removed from CRAN

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

2009-11-13 1.4
2009-10-09 1.3
2008-11-27 1.2
2008-11-13 1.1
2008-11-09 1.0

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Package spatcounts (with last version 1.1) was removed from CRAN

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

2009-06-29 1.1
2009-06-08 1.0

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Package bifactorial (with last version 1.4.7) was removed from CRAN

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

2013-03-04 1.4.7
2010-12-21 1.4.6
2010-11-22 1.4.5
2010-06-17 1.4.4
2010-02-16 1.4.2
2008-09-29 1.4.1
2007-12-08 1.4
2007-12-07 1.0

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