Tue, 18 Feb 2020

Package stratamatch updated to version 0.1.4 with previous version 0.1.3 dated 2020-01-14

Title: Stratification and Matching for Large Observational Data Sets
Description: A pilot matching design to automatically stratify and match large datasets. The manual_stratify() function allows users to manually stratify a dataset based on categorical variables of interest, while the auto_stratify() function does automatically by allocating a held-aside (pilot) data set, fitting a prognostic score (see Hansen (2008) <doi:10.1093/biomet/asn004>) on the pilot set, and stratifying the data set based on prognostic score quantiles. The strata_match() function then does optimal matching of the data set in parallel within strata.
Author: Rachael C. Aikens [aut, cre], Joseph Rigdon [aut], Justin Lee [aut], Michael Baiocchi [aut], Jonathan Chen [aut]
Maintainer: Rachael C. Aikens <rockyaikens@gmail.com>

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Package gdistance updated to version 1.3-0 with previous version 1.2-2 dated 2018-05-06

Title: Distances and Routes on Geographical Grids
Description: Calculate distances and routes on geographic grids.
Author: Jacob van Etten [aut] (<https://orcid.org/0000-0001-7554-2558>), Kaue de Sousa [cre, ctb] (<https://orcid.org/0000-0002-7571-7845>)
Maintainer: Kaue de Sousa <kaue.desousa@inn.no>

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Package RMaCzek updated to version 1.3.1 with previous version 1.3 dated 2020-01-29

Title: Czekanowski's Diagrams
Description: Allows for production of Czekanowski's Diagrams. See A. Vasterlund (2019) Master thesis, Linkoping University.
Author: Albin Vasterlund [aut], Krzysztof Bartoszek [cre, aut, ths]
Maintainer: Krzysztof Bartoszek <krzbar@protonmail.ch>

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Package EIX updated to version 1.1 with previous version 1.0 dated 2019-05-31

Title: Explain Interactions in 'XGBoost'
Description: Structure mining from 'XGBoost' and 'LightGBM' models. Key functionalities of this package cover: visualisation of tree-based ensembles models, identification of interactions, measuring of variable importance, measuring of interaction importance, explanation of single prediction with break down plots (based on 'xgboostExplainer' and 'iBreakDown' packages). To download the 'LightGBM' use the following link: <https://github.com/Microsoft/LightGBM>. 'EIX' is a part of the 'DrWhy.AI' universe.
Author: Ewelina Karbowiak [aut, cre], Przemyslaw Biecek [aut, ths]
Maintainer: Ewelina Karbowiak <ewelina.karbowiak12@gmail.com>

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Package rquery updated to version 1.4.4 with previous version 1.4.3 dated 2020-02-01

Title: Relational Query Generator for Data Manipulation at Scale
Description: A piped query generator based on Edgar F. Codd's relational algebra, and on production experience using 'SQL' and 'dplyr' at big data scale. The design represents an attempt to make 'SQL' more teachable by denoting composition by a sequential pipeline notation instead of nested queries or functions. The implementation delivers reliable high performance data processing on large data systems such as 'Spark', databases, and 'data.table'. Package features include: data processing trees or pipelines as observable objects (able to report both columns produced and columns used), optimized 'SQL' generation as an explicit user visible table modeling step, plus explicit query reasoning and checking.
Author: John Mount [aut, cre], Win-Vector LLC [cph]
Maintainer: John Mount <jmount@win-vector.com>

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Package simpleNeural updated to version 0.1.3 with previous version 0.1.1 dated 2015-04-28

Title: An Easy to Use Multilayer Perceptron
Description: Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or multi-class classification.
Author: David Dernoncourt [aut, cre] (LUNAM Universite, Universite Angers, Laboratoire d'ergonomie et d'epidemiologie en sante au travail (LEEST), w/ support from the French National Research Program for Environmental and Occupational Health of Anses (2012/18))
Maintainer: David Dernoncourt <me@daviddernoncourt.com>

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Package ralger updated to version 1.1.0 with previous version 1.0.0 dated 2019-12-22

Title: Easy Web Scraping
Description: The goal of 'ralger' is to facilitate web scraping in R. The user has the ability to extract a vector with scrap(), a tidy dataframe using tidy_scrap(), a table with table_scrap() and web links with weblink_scrap().
Author: Mohamed El Fodil Ihaddaden
Maintainer: Mohamed El Fodil Ihaddaden <ihaddaden.fodeil@gmail.com>

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Package openVA updated to version 1.0.9 with previous version 1.0.8 dated 2019-02-18

Title: Automated Method for Verbal Autopsy
Description: Implements multiple existing open-source algorithms for coding cause of death from verbal autopsies. The methods implemented include 'InterVA4' by Byass et al (2012) <doi:10.3402/gha.v5i0.19281>, 'InterVA5' by Byass at al (2019) <doi:10.1186/s12916-019-1333-6>, 'InSilicoVA' by McCormick et al (2016) <doi:10.1080/01621459.2016.1152191>, 'NBC' by Miasnikof et al (2015) <doi:10.1186/s12916-015-0521-2>, and a replication of 'Tariff' method by James et al (2011) <doi:10.1186/1478-7954-9-31> and Serina, et al. (2015) <doi:10.1186/s12916-015-0527-9>. It also provides tools for data manipulation tasks commonly used in Verbal Autopsy analysis and implements easy graphical visualization of individual and population level statistics. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist in the implementation of the 'Tariff' method.
Author: Zehang Richard Li, Tyler McCormick, Sam Clark
Maintainer: Zehang Richard Li <lizehang@gmail.com>

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Package InSilicoVA updated to version 1.2.7 with previous version 1.2.6 dated 2020-02-18

Title: Probabilistic Verbal Autopsy Coding with 'InSilicoVA' Algorithm
Description: Computes individual causes of death and population cause-specific mortality fractions using the 'InSilicoVA' algorithm from McCormick et al. (2016) <DOI:10.1080/01621459.2016.1152191>. It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used 'InterVA4' method. This package provides general model fitting and customization for 'InSilicoVA' algorithm and basic graphical visualization of the output.
Author: Zehang Richard Li, Tyler McCormick, Sam Clark
Maintainer: Zehang Richard Li <lizehang@gmail.com>

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Package GWASinspector updated to version 1.4.3 with previous version 1.4.2 dated 2020-01-19

Title: Comprehensive and Easy to Use Quality Control of GWAS Results
Description: When evaluating the results of a genome-wide association study (GWAS), it is important to perform a quality control to ensure that the results are valid, complete, correctly formatted, and, in case of meta-analysis, consistent with other studies that have applied the same analysis. This package was developed to facilitate and streamline this process and provide the user with a comprehensive report.
Author: Alireza Ani [aut, cre], Peter J. van der Most [aut], Ahmad Vaez [aut], Ilja M. Nolte [aut]
Maintainer: Alireza Ani <a.ani@umcg.nl>

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Package text2vec updated to version 0.6 with previous version 0.5.1 dated 2018-01-11

Title: Modern Text Mining Framework for R
Description: Fast and memory-friendly tools for text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), similarities. This package provides a source-agnostic streaming API, which allows researchers to perform analysis of collections of documents which are larger than available RAM. All core functions are parallelized to benefit from multicore machines.
Author: Dmitriy Selivanov [aut, cre, cph], Manuel Bickel [aut, cph] (Coherence measures for topic models), Qing Wang [aut, cph] (Author of the WaprLDA C++ code)
Maintainer: Dmitriy Selivanov <selivanov.dmitriy@gmail.com>

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Package statgenSTA updated to version 1.0.3 with previous version 1.0.2 dated 2020-01-14

Title: Single Trial Analysis (STA) of Field Trials
Description: Phenotypic analysis of field trials using mixed models with and without spatial components. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (<http://www.vsni.co.uk/software/asreml-r>).
Author: Bart-Jan van Rossum [aut, cre], Fred van Eeuwijk [ctb] (<https://orcid.org/0000-0003-3672-2921>), Martin Boer [ctb], Marcos Malosetti [ctb] (<https://orcid.org/0000-0002-8150-1397>), Daniela Bustos-Korts [ctb] (<https://orcid.org/0000-0003-3827-6726>), Emilie Millet [ctb] (<https://orcid.org/0000-0002-2913-4892>), Joao Paulo [ctb] (<https://orcid.org/0000-0002-4180-0763>), Maikel Verouden [ctb] (<https://orcid.org/0000-0002-4893-3323>), Willem Kruijer [ctb] (<https://orcid.org/0000-0001-7179-1733>), Ron Wehrens [ctb] (<https://orcid.org/0000-0002-8798-5599>), Choazhi Zheng [ctb] (<https://orcid.org/0000-0001-6030-3933>)
Maintainer: Bart-Jan van Rossum <bart-jan.vanrossum@wur.nl>

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Package iBreakDown updated to version 1.0 with previous version 0.9.9 dated 2019-08-26

Title: Model Agnostic Instance Level Variable Attributions
Description: Model agnostic tool for decomposition of predictions from black boxes. Supports additive attributions and attributions with interactions. The Break Down Table shows contributions of every variable to a final prediction. The Break Down Plot presents variable contributions in a concise graphical way. This package works for classification and regression models. It is an extension of the 'breakDown' package (Staniak and Biecek 2018) <doi:10.32614/RJ-2018-072>, with new and faster strategies for orderings. It supports interactions in explanations and has interactive visuals (implemented with 'D3.js' library). The methodology behind is described in the 'iBreakDown' article (Gosiewska and Biecek 2019) <arXiv:1903.11420> This package is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Author: Przemyslaw Biecek [aut, cre], Alicja Gosiewska [aut], Hubert Baniecki [aut], Adam Izdebski [aut], Dariusz Komosinski [ctb]
Maintainer: Przemyslaw Biecek <przemyslaw.biecek@gmail.com>

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Package gRbase updated to version 1.8-6.4 with previous version 1.8-5.1 dated 2020-01-10

Title: A Package for Graphical Modelling in R
Description: The 'gRbase' package provides graphical modelling features used by e.g. the packages 'gRain', 'gRim' and 'gRc'. 'gRbase' contains data sets relevant for use in connection with graphical models (in particular all data sets used in the book Graphical Models with R (2012)). 'gRbase' implements graph algorithms including (i) maximum cardinality search (for marked and unmarked graphs). (ii) moralize. (iii) triangulate. (iv) junction tree. 'gRbase' facilitates array operations, 'gRbase' implements functions for testing for conditional independence. 'gRbase' illustrates how hierarchical log-linear models may be implemented and describes concept of graphical meta data. These features, however, are not maintained anymore and remains in 'gRbase' only because there exists a paper describing these facilities: A Common Platform for Graphical Models in R: The 'gRbase' Package, Journal of Statistical Software, Vol 14, No 17, 2005. NOTICE 'gRbase' requires that the packages graph, 'Rgraphviz' and 'RBGL' are installed from 'bioconductor'; for installation instructions please refer to the web page given below.
Author: Søren Højsgaard <sorenh@math.aau.dk>
Maintainer: Søren Højsgaard <sorenh@math.aau.dk>

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Permanent link

Package GPoM updated to version 1.3 with previous version 1.2 dated 2018-07-26

Title: Generalized Polynomial Modelling
Description: Platform dedicated to the Global Modelling technique. Its aim is to obtain ordinary differential equations of polynomial form directly from time series. It can be applied to single or multiple time series under various conditions of noise, time series lengths, sampling, etc. This platform is developped at the Centre d'Etudes Spatiales de la Biosphere (CESBIO), UMR 5126 UPS/CNRS/CNES/IRD, 18 av. Edouard Belin, 31401 TOULOUSE, FRANCE. The developments were funded by the French program Les Enveloppes Fluides et l'Environnement (LEFE, MANU, projets GloMo, SpatioGloMo and MoMu). The French program Defi InFiNiTi (CNRS) and PNTS are also acknowledged (projects Crops'IChaos and Musc & SlowFast).
Author: Sylvain Mangiarotti [aut], Mireille Huc [cre, aut], Flavie Le Jean [ctb], Malika Chassan [ctb], Laurent Drapeau [ctb], Institut de Recherche pour le Développement [fnd], Centre National de la Recherche Scientifique [fnd]
Maintainer: Mireille Huc <mireille.huc@cesbio.cnes.fr>

Diff between GPoM versions 1.2 dated 2018-07-26 and 1.3 dated 2020-02-18

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Permanent link

New package donut with initial version 1.0.1
Package: donut
Title: Nearest Neighbour Search with Variables on a Torus
Version: 1.0.1
Date: 2020-02-18
Authors@R: person(c("Paul", "J."), "Northrop", email = "p.northrop@ucl.ac.uk", role = c("aut", "cre", "cph"))
Description: Finds the k nearest neighbours in a dataset of specified points, adding the option to wrap certain variables on a torus. The user chooses the algorithm to use to find the nearest neighbours. Two such algorithms, provided by the packages 'RANN' <https://cran.r-project.org/package=RANN>, and 'nabor' <https://cran.r-project.org/package=nabor>, are suggested.
Imports: graphics
License: GPL (>= 2)
LazyData: TRUE
Encoding: UTF-8
Depends: R (>= 3.3.0)
RoxygenNote: 7.0.1
Suggests: knitr, nabor, RANN, rmarkdown, testthat (>= 2.1.0)
VignetteBuilder: knitr
URL: http://github.com/paulnorthrop/donut, https://paulnorthrop.github.io/donut/
BugReports: http://github.com/paulnorthrop/donut/issues
NeedsCompilation: no
Packaged: 2020-02-18 13:03:03 UTC; paul
Author: Paul J. Northrop [aut, cre, cph]
Maintainer: Paul J. Northrop <p.northrop@ucl.ac.uk>
Repository: CRAN
Date/Publication: 2020-02-18 14:30:07 UTC

More information about donut at CRAN
Permanent link

Package bayes4psy updated to version 1.2.2 with previous version 1.2.0 dated 2019-12-13

Title: User Friendly Bayesian Data Analysis for Psychology
Description: Contains several Bayesian models for data analysis of psychological tests. A user friendly interface for these models should enable students and researchers to perform professional level Bayesian data analysis without advanced knowledge in programming and Bayesian statistics. This package is based on the Stan platform (Carpenter et el. 2017 <doi:10.18637/jss.v076.i01>).
Author: Jure Demšar [cre, aut], Grega Repovš [aut], Erik Štrumbelj [aut], Trustees of Columbia University [cph], John Kruschke [cph] (R/shared_functions.R - mcmc_hdi, src/stan_files/ttest.stan), Rasmus Baath [cph] (R/b_bootstrap.R)
Maintainer: Jure Demšar <jure.demsar@fri.uni-lj.si>

Diff between bayes4psy versions 1.2.0 dated 2019-12-13 and 1.2.2 dated 2020-02-18

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More information about bayes4psy at CRAN
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Package classyfireR updated to version 0.3.3 with previous version 0.3.0 dated 2019-11-18

Title: R Interface to the ClassyFire RESTful API
Description: Access to the ClassyFire RESTful API <http://classyfire.wishartlab.com>. Retrieve existing entity classifications and submit new entities for classification.
Author: Tom Wilson [aut, cre] (<https://orcid.org/0000-0003-3112-4682>), Jasen Finch [aut]
Maintainer: Tom Wilson <tpw2@aber.ac.uk>

Diff between classyfireR versions 0.3.0 dated 2019-11-18 and 0.3.3 dated 2020-02-18

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More information about classyfireR at CRAN
Permanent link

Package auditor updated to version 1.2.0 with previous version 1.1.0 dated 2019-09-24

Title: Model Audit - Verification, Validation, and Error Analysis
Description: Provides an easy to use unified interface for creating validation plots for any model. The 'auditor' helps to avoid repetitive work consisting of writing code needed to create residual plots. This visualizations allow to asses and compare the goodness of fit, performance, and similarity of models.
Author: Alicja Gosiewska [aut, cre] (<https://orcid.org/0000-0001-6563-5742>), Przemyslaw Biecek [aut, ths] (<https://orcid.org/0000-0001-8423-1823>), Hubert Baniecki [aut] (<https://orcid.org/0000-0001-6661-5364>), Tomasz Mikołajczyk [aut], Michal Burdukiewicz [ctb], Szymon Maksymiuk [ctb]
Maintainer: Alicja Gosiewska <alicjagosiewska@gmail.com>

Diff between auditor versions 1.1.0 dated 2019-09-24 and 1.2.0 dated 2020-02-18

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Package parglm (with last version 0.1.4) was removed from CRAN

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

2020-01-07 0.1.4
2019-03-18 0.1.3
2019-03-14 0.1.2
2019-01-19 0.1.1
2018-12-09 0.1.0

Permanent link
Package rdwd updated to version 1.3.1 with previous version 1.2.0 dated 2019-10-28

Title: Select and Download Climate Data from 'DWD' (German Weather Service)
Description: Handle climate data from the 'DWD' ('Deutscher Wetterdienst', see <https://www.dwd.de/EN/climate_environment/cdc/cdc.html> for more information). Choose files with 'selectDWD()', download and process data sets with 'dataDWD()' and 'readDWD()'.
Author: Berry Boessenkool
Maintainer: Berry Boessenkool <berry-b@gmx.de>

Diff between rdwd versions 1.2.0 dated 2019-10-28 and 1.3.1 dated 2020-02-18

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Package ingredients updated to version 1.0 with previous version 0.5.0 dated 2019-12-20

Title: Effects and Importances of Model Ingredients
Description: Collection of tools for assessment of feature importance and feature effects. Key functions are: feature_importance() for assessment of global level feature importance, ceteris_paribus() for calculation of the what-if plots, partial_dependence() for partial dependence plots, conditional_dependence() for conditional dependence plots, accumulated_dependence() for accumulated local effects plots, aggregate_profiles() and cluster_profiles() for aggregation of ceteris paribus profiles, generic print() and plot() for better usability of selected explainers, generic plotD3() for interactive, D3 based explanations, and generic describe() for explanations in natural language. The package 'ingredients' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Author: Przemyslaw Biecek [aut, cre] (<https://orcid.org/0000-0001-8423-1823>), Hubert Baniecki [aut] (<https://orcid.org/0000-0001-6661-5364>), Adam Izdebski [aut]
Maintainer: Przemyslaw Biecek <przemyslaw.biecek@gmail.com>

Diff between ingredients versions 0.5.0 dated 2019-12-20 and 1.0 dated 2020-02-18

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Package ggroups updated to version 2.0.1 with previous version 1.3.0 dated 2020-02-09

Title: Pedigree and Genetic Groups
Description: Calculates additive and dominance genetic relationship matrices and their inverses, in matrix and tabular-sparse formats. It includes functions for checking and processing pedigree, as well as functions to calculate the matrix of genetic group contributions (Q), and adding those contributions to the genetic merit of animals (Quaas (1988) <doi:10.3168/jds.S0022-0302(88)79691-5>). Calculation of Q is computationally extensive. There are computationally optimized functions to calculate Q.
Author: Mohammad Ali Nilforooshan [aut, cre] (<https://orcid.org/0000-0003-0339-5442>)
Maintainer: Mohammad Ali Nilforooshan <m.a.nilforooshan@gmail.com>

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Package smoof updated to version 1.6.0.2 with previous version 1.6.0.1 dated 2020-01-31

Title: Single and Multi-Objective Optimization Test Functions
Description: Provides generators for a high number of both single- and multi- objective test functions which are frequently used for the benchmarking of (numerical) optimization algorithms. Moreover, it offers a set of convenient functions to generate, plot and work with objective functions.
Author: Jakob Bossek [aut, cre], Pascal Kerschke [ctb]
Maintainer: Jakob Bossek <j.bossek@gmail.com>

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Package restatapi updated to version 0.6.9 with previous version 0.6.2 dated 2019-12-10

Title: Search and Retrieve Data from Eurostat Database
Description: Eurostat is the statistical office of the European Union and provides high quality statistics for Europe. Large set of the data is disseminated through the Eurostat database (<https://ec.europa.eu/eurostat/data/database>). The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX <https://sdmx.org>) Web Services (<https://ec.europa.eu/eurostat/web/sdmx-web-services/about-this-service>) to search and download data from the Eurostat database using the SDMX standard.
Author: Mátyás Mészáros [aut, cre]
Maintainer: Mátyás Mészáros <matyas.meszaros@ec.europa.eu>

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Package mboost updated to version 2.9-2 with previous version 2.9-1 dated 2018-08-22

Title: Model-Based Boosting
Description: Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.
Author: Torsten Hothorn [aut] (<https://orcid.org/0000-0001-8301-0471>), Peter Buehlmann [aut], Thomas Kneib [aut], Matthias Schmid [aut], Benjamin Hofner [aut, cre] (<https://orcid.org/0000-0003-2810-3186>), Fabian Sobotka [ctb], Fabian Scheipl [ctb], Andreas Mayr [ctb]
Maintainer: Benjamin Hofner <benjamin.hofner@pei.de>

Diff between mboost versions 2.9-1 dated 2018-08-22 and 2.9-2 dated 2020-02-18

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Package BayesVarSel updated to version 2.0.1 with previous version 1.8.0 dated 2017-12-04

Title: Bayes Factors, Model Choice and Variable Selection in Linear Models
Description: Conceived to calculate Bayes factors in Linear models and then to provide a formal Bayesian answer to testing and variable selection problems. From a theoretical side, the emphasis in this package is placed on the prior distributions and it allows a wide range of them: Jeffreys (1961); Zellner and Siow(1980)<DOI:10.1007/bf02888369>; Zellner and Siow(1984); Zellner (1986)<DOI:10.2307/2233941>; Fernandez et al. (2001)<DOI:10.1016/s0304-4076(00)00076-2>; Liang et al. (2008)<DOI:10.1198/016214507000001337> and Bayarri et al. (2012)<DOI:10.1214/12-aos1013>. The interaction with the package is through a friendly interface that syntactically mimics the well-known lm() command of R. The resulting objects can be easily explored providing the user very valuable information (like marginal, joint and conditional inclusion probabilities of potential variables; the highest posterior probability model, HPM; the median probability model, MPM) about the structure of the true -data generating- model. Additionally, this package incorporates abilities to handle problems with a large number of potential explanatory variables through parallel and heuristic versions of the main commands, Garcia-Donato and Martinez-Beneito (2013)<DOI:10.1080/01621459.2012.742443>. It also allows problems with p>n and p>>n and also incorporates routines to handle problems with variable selection with factors.
Author: Gonzalo Garcia-Donato [aut], Anabel Forte [aut, cre], Carlos Vergara-Hernández [ctb]
Maintainer: Anabel Forte <anabel.forte@uv.es>

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Package vein updated to version 0.8.7 with previous version 0.8.5 dated 2020-01-17

Title: Vehicular Emissions Inventories
Description: Elaboration of vehicular emissions inventories, consisting in four stages, pre-processing activity data, preparing emissions factors, estimating the emissions and post-processing of emissions in maps and databases. More details in Ibarra-Espinosa et al (2018) <doi:10.5194/gmd-11-2209-2018>. Before using VEIN you need to know the vehicular composition of your study area, in other words, the combination of of type of vehicles, size and fuel of the fleet. Then, it is recommended to start with the function inventory to create a structure of directories and template scripts.
Author: Sergio Ibarra-Espinosa [aut, cre] (<https://orcid.org/0000-0002-3162-1905>), Daniel Schuch [ctb] (<https://orcid.org/0000-0001-5977-4519>)
Maintainer: Sergio Ibarra-Espinosa <sergio.ibarra@usp.br>

Diff between vein versions 0.8.5 dated 2020-01-17 and 0.8.7 dated 2020-02-18

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Package InSilicoVA updated to version 1.2.6 with previous version 1.2.5 dated 2018-10-29

Title: Probabilistic Verbal Autopsy Coding with 'InSilicoVA' Algorithm
Description: Computes individual causes of death and population cause-specific mortality fractions using the 'InSilicoVA' algorithm from McCormick et al. (2016) <DOI:10.1080/01621459.2016.1152191>. It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used 'InterVA4' method. This package provides general model fitting and customization for 'InSilicoVA' algorithm and basic graphical visualization of the output.
Author: Zehang Li, Tyler McCormick, Sam Clark
Maintainer: Zehang Li <lizehang@gmail.com>

Diff between InSilicoVA versions 1.2.5 dated 2018-10-29 and 1.2.6 dated 2020-02-18

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Package CircNNTSR updated to version 2.2-1 with previous version 2.2 dated 2016-05-10

Title: Statistical Analysis of Circular Data using Nonnegative Trigonometric Sums (NNTS) Models
Description: Includes functions for the analysis of circular data using distributions based on Nonnegative Trigonometric Sums (NNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, for plotting and more.
Author: Juan Jose Fernandez-Duran and Maria Mercedes Gregorio-Dominguez
Maintainer: Maria Mercedes Gregorio-Dominguez <mercedes@itam.mx>

Diff between CircNNTSR versions 2.2 dated 2016-05-10 and 2.2-1 dated 2020-02-18

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Package biclustermd updated to version 0.2.1 with previous version 0.2.0 dated 2019-12-07

Title: Biclustering with Missing Data
Description: Biclustering is a statistical learning technique that simultaneously partitions and clusters rows and columns of a data matrix. Since the solution space of biclustering is in infeasible to completely search with current computational mechanisms, this package uses a greedy heuristic. The algorithm featured in this package is, to the best our knowledge, the first biclustering algorithm to work on data with missing values. Li, J., Reisner, J., Pham, H., Olafsson, S., and Vardeman, S. (2020) Biclustering with Missing Data. Information Sciences, 510, 304–316.
Author: John Reisner [cre, aut, cph], Hieu Pham [ctb, cph], Jing Li [ctb, cph]
Maintainer: John Reisner <johntreisner@gmail.com>

Diff between biclustermd versions 0.2.0 dated 2019-12-07 and 0.2.1 dated 2020-02-18

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