Sat, 12 Oct 2019

Package WVPlots updated to version 1.2.1 with previous version 1.2.0 dated 2019-10-04

Title: Common Plots for Analysis
Description: Select data analysis plots, under a standardized calling interface implemented on top of 'ggplot2' and 'plotly'. Plots of interest include: 'ROC', gain curve, scatter plot with marginal distributions, conditioned scatter plot with marginal densities, box and stem with matching theoretical distribution, and density with matching theoretical distribution.
Author: John Mount [aut, cre], Nina Zumel [aut], Win-Vector LLC [cph]
Maintainer: John Mount <jmount@win-vector.com>

Diff between WVPlots versions 1.2.0 dated 2019-10-04 and 1.2.1 dated 2019-10-12

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Package strucchange updated to version 1.5-2 with previous version 1.5-1 dated 2015-06-06

Title: Testing, Monitoring, and Dating Structural Changes
Description: Testing, monitoring and dating structural changes in (linear) regression models. strucchange features tests/methods from the generalized fluctuation test framework as well as from the F test (Chow test) framework. This includes methods to fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM, recursive/moving estimates) and F statistics, respectively. It is possible to monitor incoming data online using fluctuation processes. Finally, the breakpoints in regression models with structural changes can be estimated together with confidence intervals. Emphasis is always given to methods for visualizing the data.
Author: Achim Zeileis [aut, cre] (<https://orcid.org/0000-0003-0918-3766>), Friedrich Leisch [aut], Kurt Hornik [aut], Christian Kleiber [aut], Bruce Hansen [ctb], Edgar C. Merkle [ctb]
Maintainer: Achim Zeileis <Achim.Zeileis@R-project.org>

Diff between strucchange versions 1.5-1 dated 2015-06-06 and 1.5-2 dated 2019-10-12

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Package shinyglide updated to version 0.1.2 with previous version 0.1.1 dated 2019-07-10

Title: Glide Component for Shiny Applications
Description: Insert Glide JavaScript component into Shiny applications for carousel or assistant-like user interfaces.
Author: Julien Barnier [aut, cre]
Maintainer: Julien Barnier <julien.barnier@ens-lyon.fr>

Diff between shinyglide versions 0.1.1 dated 2019-07-10 and 0.1.2 dated 2019-10-12

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Package BatchGetSymbols updated to version 2.5.4 with previous version 2.5.3 dated 2019-08-05

Title: Downloads and Organizes Financial Data for Multiple Tickers
Description: Makes it easy to download a large number of trade data from Yahoo Finance <https://finance.yahoo.com/>.
Author: Marcelo Perlin [aut, cre]
Maintainer: Marcelo Perlin <marceloperlin@gmail.com>

Diff between BatchGetSymbols versions 2.5.3 dated 2019-08-05 and 2.5.4 dated 2019-10-12

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Package rLandsat (with last version 0.1.1) was removed from CRAN

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

2019-07-06 0.1.1

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Package refimpact (with last version 1.0.0) was removed from CRAN

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

2017-07-19 1.0.0
2016-09-26 0.1.0

Permanent link
Package CollessLike updated to version 2.0 with previous version 1.0 dated 2018-04-03

Title: Distribution and Percentile of Sackin, Cophenetic and Colless-Like Balance Indices of Phylogenetic Trees
Description: Computation of Colless-Like, Sackin and cophenetic balance indices of a phylogenetic tree and study of the distribution of these balance indices under the alpha-gamma model. For more details see A. Mir, F. Rossello, L. Rotger (2013) <doi:10.1016/j.mbs.2012.10.005> and (2018) <doi:10.1371/journal.pone.0203401>, M. J. Sackin (1972) <doi:10.1093/sysbio/21.2.225>, D. H. Colless (1982) <doi:10.2307/2413420>.
Author: Arnau Mir, Francesc Rossello, Lucia Rotger
Maintainer: Lucia Rotger <lucia.rotger@uib.es>

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Package fusedest updated to version 1.3.1 with previous version 1.3 dated 2019-09-20

Title: Block Splitting Algorithm for Estimation with Fused Penalty Functions
Description: Provides methods fusedest_normal() and fusedest_logit() for carrying out block splitting algorithms for fused penalty estimation. For details, please see Tso-Jung Yen (2019) <doi.10618600.2019.1660178>.
Author: Tso-Jung Yen
Maintainer: Tso-Jung Yen <tjyen@stat.sinica.edu.tw>

Diff between fusedest versions 1.3 dated 2019-09-20 and 1.3.1 dated 2019-10-12

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Package BayesNSGP updated to version 0.1.1 with previous version 0.1.0 dated 2019-08-29

Title: Bayesian Analysis of Non-Stationary Gaussian Process Models
Description: Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <arXiv:1702.00434v2>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the 'nimble' package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.
Author: Daniel Turek, Mark Risser
Maintainer: Daniel Turek <dbt1@williams.edu>

Diff between BayesNSGP versions 0.1.0 dated 2019-08-29 and 0.1.1 dated 2019-10-12

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Package soundgen updated to version 1.5.1 with previous version 1.5.0 dated 2019-09-11

Title: Parametric Voice Synthesis
Description: Tools for sound synthesis and acoustic analysis. Performs parametric synthesis of sounds with harmonic and noise components such as animal vocalizations or human voice. Also includes tools for spectral analysis, pitch tracking, audio segmentation, self-similarity matrices, morphing, etc.
Author: Andrey Anikin [aut, cre]
Maintainer: Andrey Anikin <rty.anik@rambler.ru>

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Package MetaUtility updated to version 2.0.1 with previous version 2.0.0 dated 2019-10-07

Title: Utility Functions for Conducting and Interpreting Meta-Analyses
Description: Contains functions to estimate the proportion of effects stronger than a threshold of scientific importance (function prop_stronger), to nonparametrically characterize the distribution of effects in a meta-analysis (calib_ests, pct_pval), to make effect size conversions (r_to_d, r_to_z, z_to_r), to compute and format inference in a meta-analysis (format_CI, format_stat, tau_CI), to scrape results from existing meta-analyses for re-analysis (scrape_meta, parse_CI_string).
Author: Maya B. Mathur, Rui Wang, Tyler J. VanderWeele
Maintainer: Maya B. Mathur <mmathur@stanford.edu>

Diff between MetaUtility versions 2.0.0 dated 2019-10-07 and 2.0.1 dated 2019-10-12

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Package brglm2 updated to version 0.5.2 with previous version 0.5.1 dated 2019-02-14

Title: Bias Reduction in Generalized Linear Models
Description: Estimation and inference from generalized linear models based on various methods for bias reduction. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2016) <arXiv:1604.04768>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <http://www.jstor.org/stable/2345592>. See Kosmidis et al (2019) <doi:10.1007/s11222-019-09860-6> for more details. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation). 'brglm2' also provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in binomial response generalized linear models.
Author: Ioannis Kosmidis [aut, cre] (<https://orcid.org/0000-0003-1556-0302>), Kjell Konis [ctb], Euloge Clovis Kenne Pagui [ctb], Nicola Sartori [ctb]
Maintainer: Ioannis Kosmidis <ioannis.kosmidis@warwick.ac.uk>

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New package spectralGraphTopology with initial version 0.2.0
Package: spectralGraphTopology
Title: Learning Graphs from Data via Spectral Constraints
Version: 0.2.0
Date: 2019-10-08
Description: In the era of big data and hyperconnectivity, learning high-dimensional structures such as graphs from data has become a prominent task in machine learning and has found applications in many fields such as finance, health care, and networks. 'spectralGraphTopology' is an open source, documented, and well-tested R package for learning graphs from data. It provides implementations of state of the art algorithms such as Combinatorial Graph Laplacian Learning (CGL), Spectral Graph Learning (SGL), Graph Estimation based on Majorization-Minimization (GLE-MM), and Graph Estimation based on Alternating Direction Method of Multipliers (GLE-ADMM). In addition, graph learning has been widely employed for clustering, where specific algorithms are available in the literature. To this end, we provide an implementation of the Constrained Laplacian Rank (CLR) algorithm.
Authors@R: c( person("Ze", "Vinicius", role = c("cre", "aut"), email = "jvmirca@gmail.com"), person(c("Daniel", "P."), "Palomar", role = "aut", email = "daniel.p.palomar@gmail.com") )
Maintainer: Ze Vinicius <jvmirca@gmail.com>
URL: https://github.com/dppalomar/spectralGraphTopology, https://mirca.github.io/spectralGraphTopology, https://www.danielppalomar.com
BugReports: https://github.com/dppalomar/spectralGraphTopology/issues
Depends:
License: GPL-3
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp, RcppArmadillo, RcppEigen
Imports: Rcpp, MASS, Matrix, progress, rlist
RoxygenNote: 6.1.1
Suggests: bookdown, knitr, prettydoc, rmarkdown, R.rsp, testthat, patrick, corrplot, igraph, kernlab, pals, clusterSim, viridis, quadprog, matrixcalc
VignetteBuilder: knitr, rmarkdown, R.rsp
NeedsCompilation: yes
Packaged: 2019-10-10 15:50:11 UTC; mirca
Author: Ze Vinicius [cre, aut], Daniel P. Palomar [aut]
Repository: CRAN
Date/Publication: 2019-10-12 08:00:03 UTC

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Package PNADcIBGE updated to version 0.5.0 with previous version 0.4.3 dated 2018-08-23

Title: Downloading, Reading and Analysing PNADc Microdata
Description: Provides tools for download, read, and analyse the PNADc household survey from Brazilian Institute of Geography and Statistics. The data must be downloaded from the official website <https://www.ibge.gov.br/>. Further analyses must be made using package 'survey'.
Author: Douglas Braga [aut], Gabriel Assuncao [aut, cre]
Maintainer: Gabriel Assuncao <pacotepnadc@ibge.gov.br>

Diff between PNADcIBGE versions 0.4.3 dated 2018-08-23 and 0.5.0 dated 2019-10-12

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New package phaseR with initial version 2.1.3
Package: phaseR
Type: Package
Title: Phase Plane Analysis of One- And Two-Dimensional Autonomous ODE Systems
Version: 2.1.3
Authors@R: c(person(given = "Michael J", family = "Grayling", email = "michael.grayling@newcastle.ac.uk", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-0680-6668")), person(given = "Gerhard", family = "Burger", email = "burger.ga@gmail.com", role = "ctb", comment = c(ORCID = "0000-0003-1062-5576")), person(given = "Stephen P", family = "Ellner", role = "ctb"), person(given = "John M", family = "Guckenheimer", role = "ctb"))
Imports: deSolve, graphics, grDevices, utils
Description: Performs a qualitative analysis of one- and two-dimensional autonomous ordinary differential equation systems, using phase plane methods. Programs are available to identify and classify equilibrium points, plot the direction field, and plot trajectories for multiple initial conditions. In the one-dimensional case, a program is also available to plot the phase portrait. Whilst in the two-dimensional case, programs are additionally available to plot nullclines and stable/unstable manifolds of saddle points. Many example systems are provided for the user. For further details can be found in Grayling (2014) <doi:10.32614/RJ-2014-023>.
License: MIT + file LICENSE
LazyData: TRUE
Suggests: knitr, rmarkdown, testthat
Date: 2019-09-10
URL: https://github.com/mjg211/phaseR
BugReports: https://github.com/mjg211/phaseR/issues
RoxygenNote: 6.1.1
Encoding: UTF-8
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-10-11 08:35:37 UTC; michaelgrayling
Author: Michael J Grayling [aut, cre] (<https://orcid.org/0000-0002-0680-6668>), Gerhard Burger [ctb] (<https://orcid.org/0000-0003-1062-5576>), Stephen P Ellner [ctb], John M Guckenheimer [ctb]
Maintainer: Michael J Grayling <michael.grayling@newcastle.ac.uk>
Repository: CRAN
Date/Publication: 2019-10-12 07:30:02 UTC

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Package NACHO updated to version 0.6.1 with previous version 0.6.0 dated 2019-10-07

Title: NanoString Quality Control Dashboard
Description: NanoString nCounter data are gene expression assays where there is no need for the use of enzymes or amplification protocols and work with fluorescent barcodes (Geiss et al. (2018) <doi:10.1038/nbt1385>). Each barcode is assigned a messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its target can be counted. As a result each count of a specific barcode represents the presence of its target mRNA/miRNA. 'NACHO' (NAnoString quality Control dasHbOard) is able to analyse the exported NanoString nCounter data and facilitates the user in performing a quality control. 'NACHO' does this by visualising quality control metrics, expression of control genes, principal components and sample specific size factors in an interactive web application.
Author: Mickaël Canouil [aut, cre] (<https://orcid.org/0000-0002-3396-4549>), Roderick Slieker [aut] (<https://orcid.org/0000-0003-0961-9152>), Gerard Bouland [aut]
Maintainer: Mickaël Canouil <mickael.canouil@cnrs.fr>

Diff between NACHO versions 0.6.0 dated 2019-10-07 and 0.6.1 dated 2019-10-12

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New package maotai with initial version 0.1.0
Package: maotai
Type: Package
Title: Tools for Matrix Algebra, Optimization and Inference
Version: 0.1.0
Authors@R: c(person("Kisung", "You", role = c("aut", "cre"),email = "kyoustat@gmail.com",comment=c(ORCID="0000-0002-8584-459X")))
Description: Matrix is an universal and sometimes primary object/unit in applied mathematics and statistics. We provide a number of algorithms for selected problems in optimization and statistical inference. For general exposition to the topic with focus on statistical context, see the book by Banerjee and Roy (2014, ISBN:9781420095388).
Encoding: UTF-8
License: GPL (>= 3)
Suggests: igraph, rstiefel
Imports: Rcpp, Rdpack, RSpectra, Matrix, shapes, stats, utils
LinkingTo: Rcpp, RcppArmadillo
RdMacros: Rdpack
RoxygenNote: 6.1.1
URL: http://github.com/kyoustat/maotai
BugReports: http://github.com/kyoustat/maotai/issues
NeedsCompilation: yes
Packaged: 2019-10-09 18:39:30 UTC; kisung
Author: Kisung You [aut, cre] (<https://orcid.org/0000-0002-8584-459X>)
Maintainer: Kisung You <kyoustat@gmail.com>
Repository: CRAN
Date/Publication: 2019-10-12 07:50:02 UTC

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Package EBPRS updated to version 1.2.1 with previous version 1.2.0 dated 2019-10-10

Title: Derive Polygenic Risk Score Based on Emprical Bayes Theory
Description: EB-PRS is a novel method that leverages information for effect sizes across all the markers to improve the prediction accuracy. No parameter tuning is needed in the method, and no external information is needed. This R-package provides the calculation of polygenic risk scores from the given training summary statistics and testing data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for each individual in testing data, and evaluate the PRS according to AUC and predictive r2.
Author: Shuang Song [aut, cre], Wei Jiang [aut], Lin Hou [aut] and Hongyu Zhao [aut]
Maintainer: Shuang Song <song-s19@mails.tsinghua.edu.cn>

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Package restatapi updated to version 0.4.0 with previous version 0.3.6 dated 2019-09-29

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 politeness updated to version 0.4.1 with previous version 0.4.0 dated 2019-10-09

Title: Detecting Politeness Features in Text
Description: Detecting markers of politeness in English natural language. This package allows researchers to easily visualize and quantify politeness between groups of documents. This package combines prior research on the linguistic markers of politeness (Brown & Levinson, 1987 <http://psycnet.apa.org/record/1987-97641-000>; Danescu-Niculescu-Mizil et al., 2013 <arXiv:1306.6078>; Voigt et al., 2017 <doi:10.1073/pnas.1702413114>). We thank the Spencer Foundation, the Hewlett Foundation, and Harvard's Institute for Quantitative Social Science for support.
Author: Mike Yeomans, Alejandro Kantor, Dustin Tingley
Maintainer: Mike Yeomans <myeomans@hbs.edu>

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Package GMMAT updated to version 1.1.2 with previous version 1.1.1 dated 2019-08-26

Title: Generalized Linear Mixed Model Association Tests
Description: Perform association tests using generalized linear mixed models (GLMMs) in genome-wide association studies (GWAS) and sequencing association studies. First, GMMAT fits a GLMM with covariate adjustment and random effects to account for population structure and familial or cryptic relatedness. For GWAS, GMMAT performs score tests for each genetic variant as proposed in Chen et al. (2016) <DOI:10.1016/j.ajhg.2016.02.012>. For candidate gene studies, GMMAT can also perform Wald tests to get the effect size estimate for each genetic variant. For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT) as proposed in Chen et al. (2019) <DOI:10.1016/j.ajhg.2018.12.012>, including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets.
Author: Han Chen, Matthew P. Conomos
Maintainer: Han Chen <Han.Chen.2@uth.tmc.edu>

Diff between GMMAT versions 1.1.1 dated 2019-08-26 and 1.1.2 dated 2019-10-12

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