Mon, 29 Jul 2019

New package MSEtool with initial version 1.2.1
Package: MSEtool
Type: Package
Title: Management Strategy Evaluation Toolkit
Version: 1.2.1
Authors@R: c(person("Quang", "Huynh", email = "q.huynh@oceans.ubc.ca", role = "aut"), person("Tom", "Carruthers", email = "t.carruthers@oceans.ubc.ca", role = c("aut", "cre")), person("Adrian", "Hordyk", email = "a.hordyk@oceans.ubc.ca", role = "aut"), person("Chris", "Grandin", rol = "ctb", comment = "iSCAM functions"))
Date: 2019-07-29
Maintainer: Tom Carruthers <t.carruthers@oceans.ubc.ca>
Description: Simulation tools for management strategy evaluation are provided for the 'DLMtool' operating model to inform data-rich fisheries. 'MSEtool' provides complementary assessment models of varying complexity with standardized reporting, diagnostic tools for evaluating assessment models within closed-loop simulation, and helper functions for building more complex operating models and management procedures.
License: GPL-3
Depends: R (>= 3.3.0), DLMtool (>= 5.3.1)
Imports: MASS, TMB, coda, corpcor, gplots, grDevices, graphics, methods, mvtnorm, pryr, reshape2, snowfall, stats, utils, abind, rmarkdown
LinkingTo: TMB, RcppEigen
LazyData: yes
LazyLoad: yes
RoxygenNote: 6.1.1
Suggests: knitr, testthat, r4ss, shiny
VignetteBuilder: knitr
URL: http://www.datalimitedtoolkit.org
BugReports: https://github.com/tcarruth/MSEtool/issues
NeedsCompilation: yes
Packaged: 2019-07-29 21:26:04 UTC; qhuynh
Author: Quang Huynh [aut], Tom Carruthers [aut, cre], Adrian Hordyk [aut], Chris Grandin [ctb] (iSCAM functions)
Repository: CRAN
Date/Publication: 2019-07-29 23:10:02 UTC

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Package JointNets updated to version 2.0.1 with previous version 2.0.0 dated 2019-07-28

Title: End-to-End Sparse Gaussian Graphical Model Simulation, Estimation, Visualization, Evaluation and Application
Description: An end-to-end package for learning multiple sparse Gaussian graphical models and nonparanormal models from Heterogeneous Data with Additional Knowledge. It is able to simulate multiple related graphs as well as produce samples drawn from them. Multiple state-of-the-art sparse Gaussian graphical model estimators are included to both multiple and difference estimation. Graph visualization is available in 2D as well as 3D, designed specifically for brain. Moreover, a set of evaluation metrics are integrated for easy exploration with model validity. Finally, classification using graphical model is achieved with Quadratic Discriminant Analysis. The package comes with multiple demos with datasets from various fields. Methods references: SIMULE (Wang B et al. (2017) <doi:10.1007/s10994-017-5635-7>), WSIMULE (Singh C et al. (2017) <arXiv:1709.04090v2>), DIFFEE (Wang B et al. (2018) <arXiv:1710.11223>), JEEK (Wang B et al. (2018) <arXiv:1806.00548>), JGL(Danaher P et al. (2012) <arXiv:1111.0324>) and kdiffnet (Sekhon A et al, preprint for publication).
Author: Zhaoyang Wang [aut], Beilun Wang [aut], Arshdeep Sekhon [aut, cre], Yanjun Qi [aut]
Maintainer: Arshdeep Sekhon <as5cu@virginia.edu>

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Package castor updated to version 1.4.2 with previous version 1.4.1 dated 2019-06-15

Title: Efficient Phylogenetics on Large Trees
Description: Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.
Author: Stilianos Louca
Maintainer: Stilianos Louca <louca@zoology.ubc.ca>

Diff between castor versions 1.4.1 dated 2019-06-15 and 1.4.2 dated 2019-07-29

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Package bayesm updated to version 3.1-3 with previous version 3.1-2 dated 2019-07-13

Title: Bayesian Inference for Marketing/Micro-Econometrics
Description: Covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, Analysis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009) For further reference, consult our book, Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch (Wiley 2005) and Bayesian Non- and Semi-Parametric Methods and Applications (Princeton U Press 2014).
Author: Peter Rossi <perossichi@gmail.com>
Maintainer: Peter Rossi <perossichi@gmail.com>

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Package gradeR updated to version 1.0.1 with previous version 1.0.0 dated 2019-06-05

Title: Helps Grade Assignment Submissions that are R Scripts
Description: After being given the location of your students' submissions and a test file, the function runs each .R file, and evaluates the results from all the given tests. Results are neatly returned in a data frame that has a row for each student, and a column for each test.
Author: Taylor Brown [aut, cre]
Maintainer: Taylor Brown <trb5me@virginia.edu>

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Package bhm updated to version 1.15 with previous version 1.13 dated 2017-12-20

Title: Biomarker Threshold Models
Description: Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method.
Author: Bingshu E. Chen
Maintainer: Bingshu E. Chen <bingshu.chen@queensu.ca>

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Package LAGOSNE updated to version 2.0.1 with previous version 2.0.0 dated 2019-07-24

Title: Interface to the Lake Multi-Scaled Geospatial and Temporal Database
Description: Client for programmatic access to the Lake Multi-scaled Geospatial and Temporal database <https://lagoslakes.org>, with functions for accessing lake water quality and ecological context data for the US.
Author: Joseph Stachelek [aut, cre] (<https://orcid.org/0000-0002-5924-2464>), Samantha Oliver [aut] (<https://orcid.org/0000-0001-5668-1165>), Farzan Masrour [aut]
Maintainer: Joseph Stachelek <stachel2@msu.edu>

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New package fieldRS with initial version 0.2.2
Package: fieldRS
Type: Package
Title: Remote Sensing Field Work Tools
Version: 0.2.2
Date: 2019-07-29
Authors@R: person("Ruben", "Remelgado", role = c("aut", "cre"), email="remelgado.ruben@gmail.com")
URL: https://github.com/RRemelgado/fieldRS/
BugReports: https://github.com/RRemelgado/fieldRS/issues/
Maintainer: Ruben Remelgado <remelgado.ruben@gmail.com>
Description: In remote sensing, designing a field campaign to collect ground-truth data can be a challenging task. We need to collect representative samples while accounting for issues such as budget constraints and limited accessibility created by e.g. poor infrastructure. As suggested by Olofsson et al. (2014) <doi:10.1016/j.rse.2014.02.015>, this demands the establishment of best-practices to collect ground-truth data that avoid the waste of time and funds. 'fieldRS' addresses this issue by helping scientists and practitioners design field campaigns through the identification of priority sampling sites, the extraction of potential sampling plots and the conversion of plots into consistent training and validation samples that can be used in e.g. land cover classification.
Encoding: UTF-8
LazyData: TRUE
Imports: raster, sp, caret, ggplot2, grDevices, spatialEco, rgeos, stringdist, concaveman
RoxygenNote: 6.1.1
License: GPL (>= 3)
Suggests: knitr, rmarkdown, kableExtra, imager, randomForest, rgdal, RStoolbox
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-07-29 15:32:33 UTC; rr70wedu
Author: Ruben Remelgado [aut, cre]
Repository: CRAN
Date/Publication: 2019-07-29 18:30:02 UTC

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Package tsibble updated to version 0.8.3 with previous version 0.8.2 dated 2019-06-10

Title: Tidy Temporal Data Frames and Tools
Description: Provides a 'tbl_ts' class (the 'tsibble') for temporal data in an explicit data- and model-oriented format. The 'tsibble' provides tools to easily manipulate and analyse temporal data, such as filling in time gaps, aggregating over calendar periods, performing rolling window calculations, and etc.
Author: Earo Wang [aut, cre] (<https://orcid.org/0000-0001-6448-5260>), Di Cook [aut, ths] (<https://orcid.org/0000-0002-3813-7155>), Rob Hyndman [aut, ths] (<https://orcid.org/0000-0002-2140-5352>), Mitchell O'Hara-Wild [aut] (<https://orcid.org/0000-0001-6729-7695>)
Maintainer: Earo Wang <earo.wang@gmail.com>

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Package rquery updated to version 1.3.7 with previous version 1.3.6 dated 2019-07-04

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 reportROC updated to version 3.4 with previous version 3.3 dated 2019-04-08

Title: An Easy Way to Report ROC Analysis
Description: Provides an easy way to report the results of ROC analysis, including: 1. an ROC curve. 2. the value of Cutoff, AUC (Area Under Curve), ACC (accuracy), SEN (sensitivity), SPE (specificity), PLR (positive likelihood ratio), NLR (negative likelihood ratio), PPV (positive predictive value), NPV (negative predictive value).
Author: Zhicheng Du, Yuantao Hao
Maintainer: Zhicheng Du<dgdzc@hotmail.com>

Diff between reportROC versions 3.3 dated 2019-04-08 and 3.4 dated 2019-07-29

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New package gitignore with initial version 0.1.3
Type: Package
Package: gitignore
Title: Create Useful .gitignore Files for your Project
Version: 0.1.3
Authors@R: c(person(given = "Philippe", family = "Massicotte", role = c("aut", "cre"), email = "pmassicotte@hotmail.com", comment = c(ORCID = "0000-0002-5919-4116")), person(given = "Amanda", family = "Dobbyn", role = "rev", email = "amanda.e.dobbyn@gmail.com"), person(given = "Mauro", family = "Lepore", role = "rev", email = "maurolepore@gmail.com", comment = c(ORCID = "0000-0002-1986-7988")))
Description: Simple interface to query gitignore.io to fetch gitignore templates that can be included in the .gitignore file. More than 450 templates are currently available.
License: GPL-3
URL: https://github.com/ropensci/gitignore
BugReports: https://github.com/ropensci/gitignore/issues
Imports: clipr, clisymbols, crayon, curl, glue, here, jsonlite, purrr, xfun
Suggests: covr, knitr, rmarkdown, spelling, testthat (>= 2.1.0)
VignetteBuilder: knitr
Encoding: UTF-8
Language: en-US
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-07-19 18:26:37 UTC; pmassicotte
Author: Philippe Massicotte [aut, cre] (<https://orcid.org/0000-0002-5919-4116>), Amanda Dobbyn [rev], Mauro Lepore [rev] (<https://orcid.org/0000-0002-1986-7988>)
Maintainer: Philippe Massicotte <pmassicotte@hotmail.com>
Repository: CRAN
Date/Publication: 2019-07-29 14:20:02 UTC

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Package bsamGP updated to version 1.2.2 with previous version 1.2.1 dated 2018-06-15

Title: Bayesian Spectral Analysis Models using Gaussian Process Priors
Description: Contains functions to perform Bayesian inference using a spectral analysis of Gaussian process priors. Gaussian processes are represented with a Fourier series based on cosine basis functions. Currently the package includes parametric linear models, partial linear additive models with/without shape restrictions, generalized linear additive models with/without shape restrictions, and density estimation model. To maximize computational efficiency, the actual Markov chain Monte Carlo sampling for each model is done using codes written in FORTRAN 90. This software has been developed using funding supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (no. NRF-2016R1D1A1B03932178 and no. NRF-2017R1D1A3B03035235).
Author: Seongil Jo [aut, cre], Taeryon Choi [aut], Beomjo Park [aut, cre], Peter J. Lenk [ctb]
Maintainer: Beomjo Park <beomjop@andrew.cmu.edu>

Diff between bsamGP versions 1.2.1 dated 2018-06-15 and 1.2.2 dated 2019-07-29

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Package tidytext updated to version 0.2.2 with previous version 0.2.1 dated 2019-06-14

Title: Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools
Description: Text mining for word processing and sentiment analysis using 'dplyr', 'ggplot2', and other tidy tools.
Author: Gabriela De Queiroz [ctb], Emil Hvitfeldt [ctb], Os Keyes [ctb] (<https://orcid.org/0000-0001-5196-609X>), Kanishka Misra [ctb], Tim Mastny [ctb], Jeff Erickson [ctb], David Robinson [aut], Julia Silge [aut, cre] (<https://orcid.org/0000-0002-3671-836X>)
Maintainer: Julia Silge <julia.silge@gmail.com>

Diff between tidytext versions 0.2.1 dated 2019-06-14 and 0.2.2 dated 2019-07-29

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Package spatialrisk updated to version 0.6.2 with previous version 0.6.1 dated 2019-04-30

Title: Calculating Concentration Risk under Solvency II
Description: Methods for determining spatial risk, in particular calculating the maximum value of insured fire risk policies of all buildings that are partly or fully located within circle of a radius of 200m.
Author: Martin Haringa
Maintainer: Martin Haringa <mtharinga@gmail.com>

Diff between spatialrisk versions 0.6.1 dated 2019-04-30 and 0.6.2 dated 2019-07-29

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Package jomo updated to version 2.6-9 with previous version 2.6-8 dated 2019-05-21

Title: Multilevel Joint Modelling Multiple Imputation
Description: Similarly to Schafer's package 'pan', 'jomo' is a package for multilevel joint modelling multiple imputation (Carpenter and Kenward, 2013) <doi: 10.1002/9781119942283>. Novel aspects of 'jomo' are the possibility of handling binary and categorical data through latent normal variables, the option to use cluster-specific covariance matrices and to impute compatibly with the substantive model.
Author: Matteo Quartagno, James Carpenter
Maintainer: Matteo Quartagno <m.quartagno@ucl.ac.uk>

Diff between jomo versions 2.6-8 dated 2019-05-21 and 2.6-9 dated 2019-07-29

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Package Spectrum updated to version 0.7 with previous version 0.6 dated 2019-05-28

Title: Fast Adaptive Spectral Clustering for Single and Multi-View Data
Description: A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens local connections in the graph. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. The method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K.
Author: Christopher R John, David Watson
Maintainer: Christopher R John <chris.r.john86@gmail.com>

Diff between Spectrum versions 0.6 dated 2019-05-28 and 0.7 dated 2019-07-29

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New package mixmeta with initial version 0.2.2
Package: mixmeta
Type: Package
Version: 0.2.2
Date: 2019-07-25
Title: An Extended Mixed-Effects Framework for Meta-Analysis
Description: A collection of functions to perform various meta-analytical models through a unified mixed-effects framework, including standard univariate fixed and random-effects meta-analysis and meta-regression, and non-standard extensions such as multivariate, multilevel, longitudinal, and dose-response models.
Author: Antonio Gasparrini [aut, cre], Francesco Sera [aut]
Maintainer: Antonio Gasparrini <antonio.gasparrini@lshtm.ac.uk>
Authors@R: c( person("Antonio","Gasparrini",role=c("aut","cre"),email="antonio.gasparrini@lshtm.ac.uk"), person("Francesco","Sera",role="aut",email="francesco.sera@lshtm.ac.uk"))
Imports: stats, graphics, grDevices, utils
Depends: R (>= 3.5.0)
Suggests: metafor, meta, rmeta, dosresmeta, nlme, MASS, dlnm
URL: https://github.com/gasparrini/mixmeta, http://www.ag-myresearch.com/package-mixmeta
License: GPL (>= 2)
LazyData: yes
NeedsCompilation: no
Packaged: 2019-07-25 23:14:09 UTC; gaspa
Repository: CRAN
Date/Publication: 2019-07-29 12:40:02 UTC

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Package corr2D updated to version 1.0.0 with previous version 0.4.0 dated 2019-07-01

Title: Implementation of 2D Correlation Analysis in R
Description: Implementation of two-dimensional (2D) correlation analysis based on the Fourier-transformation approach described by Isao Noda (I. Noda (1993) <DOI:10.1366/0003702934067694>). Additionally there are two plot functions for the resulting correlation matrix: The first one creates colored 2D plots, while the second one generates 3D plots.
Author: Robert Geitner [cre, aut], Robby Fritzsch [aut], Thomas Bocklitz [aut], Juergen Popp [ctb, cph]
Maintainer: Robert Geitner <r.geitner@uu.nl>

Diff between corr2D versions 0.4.0 dated 2019-07-01 and 1.0.0 dated 2019-07-29

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Package AzureML (with last version 0.2.15) was removed from CRAN

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

2019-07-27 0.2.15
2017-10-23 0.2.14
2016-08-21 0.2.13
2016-07-13 0.2.12
2016-02-01 0.2.10
2015-08-15 0.1.1

Permanent link
New package tricolore with initial version 1.2.1
Package: tricolore
Type: Package
Title: A Flexible Color Scale for Ternary Compositions
Version: 1.2.1
Author: Jonas Schöley, Ilya Kashnitsky
Maintainer: Jonas Schöley <jschoeley@gmail.com>
Description: A flexible color scale for ternary compositions with options for discretization, centering and scaling.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 2.10)
Imports: grDevices, ggplot2 (>= 3.0.0), ggtern (>= 3.0.0), shiny, assertthat
RoxygenNote: 6.1.1
Suggests: testthat, knitr, rmarkdown, sf, leaflet, httpuv, dplyr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-07-19 10:18:46 UTC; jon
Repository: CRAN
Date/Publication: 2019-07-29 11:00:02 UTC

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Package RootsExtremaInflections updated to version 1.2.1 with previous version 1.1 dated 2017-05-10

Title: Finds Roots, Extrema and Inflection Points of a Curve
Description: Implementation of Taylor Regression Estimator (TRE), Tulip Extreme Finding Estimator (TEFE), Bell Extreme Finding Estimator (BEFE), Integration Extreme Finding Estimator (IEFE) and Integration Root Finding Estimator (IRFE) for roots, extrema and inflections of a curve . Christopoulos, DT (2019) <doi:10.13140/RG.2.2.17158.32324> . Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> . Christopoulos, DT (2016) <https://veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> . Christopoulos, DT (2014) <arXiv:1206.5478v2 [math.NA]> .
Author: Demetris T. Christopoulos
Maintainer: Demetris T. Christopoulos <dchristop@econ.uoa.gr>

Diff between RootsExtremaInflections versions 1.1 dated 2017-05-10 and 1.2.1 dated 2019-07-29

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Package RNewsflow updated to version 1.1.1 with previous version 1.1.0 dated 2019-04-01

Title: Tools for Comparing Text Messages Across Time and Media
Description: A collection of tools for measuring the similarity of text messages and tracing the flow of messages over time and across media.
Author: Kasper Welbers & Wouter van Atteveldt
Maintainer: Kasper Welbers <kasperwelbers@gmail.com>

Diff between RNewsflow versions 1.1.0 dated 2019-04-01 and 1.1.1 dated 2019-07-29

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Package ragg updated to version 0.1.1 with previous version 0.1.0 dated 2019-07-15

Title: Graphic Devices Based on AGG
Description: Anti-Grain Geometry (AGG) is a high-quality and high-performance 2D drawing library. The 'ragg' package provides a set of graphic devices based on AGG to use as alternative to the raster devices provided through the 'grDevices' package.
Author: Thomas Lin Pedersen [cre, aut] (<https://orcid.org/0000-0002-5147-4711>), Maxim Shemanarev [aut, cph] (Author of AGG), Tony Juricic [ctb, cph] (Contributor to AGG), Milan Marusinec [ctb, cph] (Contributor to AGG), Spencer Garrett [ctb] (Contributor to AGG), RStudio [cph]
Maintainer: Thomas Lin Pedersen <thomas.pedersen@rstudio.com>

Diff between ragg versions 0.1.0 dated 2019-07-15 and 0.1.1 dated 2019-07-29

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Package pvaluefunctions updated to version 1.4.0 with previous version 1.3.0 dated 2019-07-10

Title: Creates and Plots P-Value Functions, S-Value Functions, Confidence Distributions and Confidence Densities
Description: Contains functions to compute and plot confidence distributions, confidence densities, p-value functions and s-value (surprisal) functions for several commonly used estimates. Instead of just calculating one p-value and one confidence interval, p-value functions display p-values and confidence intervals for many levels thereby allowing to gauge the compatibility of several parameter values with the data. These methods are discussed by Infanger D, Schmidt-Trucksäss A. (2019) <doi:10.1002/sim.8293>; Poole C. (1987) <doi:10.2105/AJPH.77.2.195>; Schweder T, Hjort NL. (2002) <doi:10.1111/1467-9469.00285>; Bender R, Berg G, Zeeb H. (2005) <doi:10.1002/bimj.200410104> ; Singh K, Xie M, Strawderman WE. (2007) <doi:10.1214/074921707000000102>; Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052); Amrhein V, Trafimow D, Greenland S. (2019) <doi:10.1080/00031305.2018.1543137>; and Greenland S. (2019) <doi:10.1080/00031305.2018.1529625>.
Author: Denis Infanger [aut, cre] (<https://orcid.org/0000-0001-9028-7110>)
Maintainer: Denis Infanger <denis.infanger@unibas.ch>

Diff between pvaluefunctions versions 1.3.0 dated 2019-07-10 and 1.4.0 dated 2019-07-29

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Package MoEClust updated to version 1.2.3 with previous version 1.2.2 dated 2019-05-15

Title: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component
Description: Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2018) <arXiv:1711.05632v2>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.
Author: Keefe Murphy [aut, cre], Thomas Brendan Murphy [ctb]
Maintainer: Keefe Murphy <keefe.murphy@ucd.ie>

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Package newsmap updated to version 0.6.9 with previous version 0.6.8 dated 2019-07-25

Title: Semi-Supervised Model for Geographical Document Classification
Description: Semi-supervised model for geographical document classification (Watanabe 2018) <doi:10.1080/21670811.2017.1293487>. This package currently contains seed dictionaries in English, German, French, Spanish, Japanese, Russian and Chinese (Simplified and Traditional).
Author: Kohei Watanabe [aut, cre, cph], Stefan Müller [aut], Dani Madrid-Morales [aut], Katerina Tertytchnaya [aut], Ke Cheng [aut], Chung-hong Chan [aut], Claude Grasland [aut], Giuseppe Carteny [aut]
Maintainer: Kohei Watanabe <watanabe.kohei@gmail.com>

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Package clr updated to version 0.1.2 with previous version 0.1.1 dated 2019-01-11

Title: Curve Linear Regression via Dimension Reduction
Description: A new methodology for linear regression with both curve response and curve regressors, which is described in Cho, Goude, Brossat and Yao (2013) <doi:10.1080/01621459.2012.722900> and (2015) <doi:10.1007/978-3-319-18732-7_3>. The key idea behind this methodology is dimension reduction based on a singular value decomposition in a Hilbert space, which reduces the curve regression problem to several scalar linear regression problems.
Author: Amandine Pierrot with contributions and/or help from Qiwei Yao, Haeran Cho, Yannig Goude and Tony Aldon.
Maintainer: Amandine Pierrot <amandine.m.pierrot@gmail.com>

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Package xml2 updated to version 1.2.1 with previous version 1.2.0 dated 2018-01-24

Title: Parse XML
Description: Work with XML files using a simple, consistent interface. Built on top of the 'libxml2' C library.
Author: Hadley Wickham [aut], Jim Hester [aut, cre], Jeroen Ooms [aut], RStudio [cph], R Foundation [ctb] (Copy of R-project homepage cached as example)
Maintainer: Jim Hester <jim.hester@rstudio.com>

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Package seewave updated to version 2.1.4 with previous version 2.1.3 dated 2019-03-30

Title: Sound Analysis and Synthesis
Description: Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound). This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses.
Author: Jerome Sueur <sueur@mnhn.fr> [cre, au], Thierry Aubin [au], Caroline Simonis [au], Laurent Lellouch [main ctrb], Ethan C. Brown [ctrb], Marion Depraetere [ctrb], Camille Desjonqueres [ctrb], Francois Fabianek [ctrb], Amandine Gasc [ctrb], Eric Kasten [ctrb], Stefanie LaZerte [ctrb], Jonathan Lees [ctrb], Jean Marchal [ctrb], Andre Mikulec [ctrb], Sandrine Pavoine [ctrb], David Pinaud [ctrb], Alicia Stotz [ctrb], Luis J. Villanueva-Rivera [ctrb], Zev Ross [ctrb], Carl G. Witthoft [ctrb], Hristo Zhivomirov [ctrb].
Maintainer: Jerome Sueur <sueur@mnhn.fr>

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Package aplpack updated to version 1.3.3 with previous version 1.3.2 dated 2018-06-12

Title: Another Plot Package: 'Bagplots', 'Iconplots', 'Summaryplots', Slider Functions and Others
Description: Some functions for drawing some special plots: The function 'bagplot' plots a bagplot, 'faces' plots chernoff faces, 'iconplot' plots a representation of a frequency table or a data matrix, 'plothulls' plots hulls of a bivariate data set, 'plotsummary' plots a graphical summary of a data set, 'puticon' adds icons to a plot, 'skyline.hist' combines several histograms of a one dimensional data set in one plot, 'slider' functions supports some interactive graphics, 'spin3R' helps an inspection of a 3-dim point cloud, 'stem.leaf' plots a stem and leaf plot, 'stem.leaf.backback' plots back-to-back versions of stem and leaf plot.
Author: Hans Peter Wolf [aut, cre]
Maintainer: Hans Peter Wolf <pwolf@wiwi.uni-bielefeld.de>

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Package Rgretl (with last version 0.2.2) was removed from CRAN

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

2018-03-18 0.2.2
2018-03-06 0.2.1
2017-11-17 0.1.6
2017-11-14 0.1.5

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Package rsvd updated to version 1.0.2 with previous version 1.0.1 dated 2019-06-02

Title: Randomized Singular Value Decomposition
Description: Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.
Author: N. Benjamin Erichson [aut, cre]
Maintainer: N. Benjamin Erichson <erichson@berkeley.edu>

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Package PKNCA updated to version 0.9.1 with previous version 0.8.5 dated 2018-06-13

Title: Perform Pharmacokinetic Non-Compartmental Analysis
Description: Compute standard Non-Compartmental Analysis (NCA) parameters and summarize them. In addition to this core work, it also provides standardized plotting routines, basic assessments for biocomparison or drug interaction, and model-based estimation routines for calculating doses to reach specific values of AUC or Cmax.
Author: Bill Denney [aut, cre] (<https://orcid.org/0000-0002-5759-428X>), Clare Buckeridge [aut], Sridhar Duvvuri [ctb]
Maintainer: Bill Denney <wdenney@humanpredictions.com>

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 PKNCA-0.9.1/PKNCA/R/pk.calc.urine.R                                                    |   43 
 PKNCA-0.9.1/PKNCA/R/tss.R                                                              |    3 
 PKNCA-0.9.1/PKNCA/R/tss.monoexponential.R                                              |    2 
 PKNCA-0.9.1/PKNCA/R/tss.stepwise.linear.R                                              |    2 
 PKNCA-0.9.1/PKNCA/README.md                                                            |    1 
 PKNCA-0.9.1/PKNCA/build/vignette.rds                                                   |binary
 PKNCA-0.9.1/PKNCA/inst/doc/AUC-Calculation-with-PKNCA.R                                |   25 
 PKNCA-0.9.1/PKNCA/inst/doc/AUC-Calculation-with-PKNCA.Rmd                              |   35 
 PKNCA-0.9.1/PKNCA/inst/doc/AUC-Calculation-with-PKNCA.html                             |  619 ++++--
 PKNCA-0.9.1/PKNCA/inst/doc/Data-Cleaning-in-PKNCA.html                                 |  302 ++-
 PKNCA-0.9.1/PKNCA/inst/doc/Example-theophylline.R                                      |only
 PKNCA-0.9.1/PKNCA/inst/doc/Example-theophylline.Rmd                                    |only
 PKNCA-0.9.1/PKNCA/inst/doc/Example-theophylline.html                                   |only
 PKNCA-0.9.1/PKNCA/inst/doc/Introduction-and-Usage.R                                    |   78 
 PKNCA-0.9.1/PKNCA/inst/doc/Introduction-and-Usage.Rmd                                  |   81 
 PKNCA-0.9.1/PKNCA/inst/doc/Introduction-and-Usage.html                                 |  567 ++++-
 PKNCA-0.9.1/PKNCA/inst/doc/Methods-for-Dose-Aware-Interpolation-and-Extrapolation.R    |   18 
 PKNCA-0.9.1/PKNCA/inst/doc/Methods-for-Dose-Aware-Interpolation-and-Extrapolation.Rmd  |   25 
 PKNCA-0.9.1/PKNCA/inst/doc/Methods-for-Dose-Aware-Interpolation-and-Extrapolation.html |  215 ++
 PKNCA-0.9.1/PKNCA/inst/doc/Options-for-Controlling-PKNCA.Rmd                           |    7 
 PKNCA-0.9.1/PKNCA/inst/doc/Options-for-Controlling-PKNCA.html                          |  217 ++
 PKNCA-0.9.1/PKNCA/inst/doc/PKNCA-validation.Rmd                                        |    2 
 PKNCA-0.9.1/PKNCA/inst/doc/PKNCA-validation.html                                       |  349 +++
 PKNCA-0.9.1/PKNCA/inst/doc/Selection-of-Calculation-Intervals.R                        |  151 +
 PKNCA-0.9.1/PKNCA/inst/doc/Selection-of-Calculation-Intervals.Rmd                      |  163 +
 PKNCA-0.9.1/PKNCA/inst/doc/Selection-of-Calculation-Intervals.html                     |  964 +++++++++-
 PKNCA-0.9.1/PKNCA/inst/doc/Superposition.R                                             |   63 
 PKNCA-0.9.1/PKNCA/inst/doc/Superposition.Rmd                                           |   74 
 PKNCA-0.9.1/PKNCA/inst/doc/Superposition.html                                          |  415 +++-
 PKNCA-0.9.1/PKNCA/inst/doc/Writing-Parameter-Functions.Rmd                             |   16 
 PKNCA-0.9.1/PKNCA/inst/doc/Writing-Parameter-Functions.html                            |  230 ++
 PKNCA-0.9.1/PKNCA/man/PKNCA.choose.option.Rd                                           |    4 
 PKNCA-0.9.1/PKNCA/man/PKNCA.options.Rd                                                 |    4 
 PKNCA-0.9.1/PKNCA/man/PKNCA.set.summary.Rd                                             |   21 
 PKNCA-0.9.1/PKNCA/man/PKNCAconc.Rd                                                     |   15 
 PKNCA-0.9.1/PKNCA/man/PKNCAdata.Rd                                                     |    7 
 PKNCA-0.9.1/PKNCA/man/PKNCAdose.Rd                                                     |    4 
 PKNCA-0.9.1/PKNCA/man/PKNCAresults.Rd                                                  |    5 
 PKNCA-0.9.1/PKNCA/man/add.interval.col.Rd                                              |   12 
 PKNCA-0.9.1/PKNCA/man/as.data.frame.PKNCAresults.Rd                                    |    3 
 PKNCA-0.9.1/PKNCA/man/check.interval.deps.Rd                                           |    7 
 PKNCA-0.9.1/PKNCA/man/check.interval.specification.Rd                                  |   10 
 PKNCA-0.9.1/PKNCA/man/choose.auc.intervals.Rd                                          |   13 
 PKNCA-0.9.1/PKNCA/man/clean.conc.blq.Rd                                                |    3 
 PKNCA-0.9.1/PKNCA/man/clean.conc.na.Rd                                                 |    4 
 PKNCA-0.9.1/PKNCA/man/exclude.Rd                                                       |    4 
 PKNCA-0.9.1/PKNCA/man/exclude_nca.Rd                                                   |    4 
 PKNCA-0.9.1/PKNCA/man/find.tau.Rd                                                      |    7 
 PKNCA-0.9.1/PKNCA/man/findOperator.Rd                                                  |    5 
 PKNCA-0.9.1/PKNCA/man/formula.parseFormula.Rd                                          |    9 
 PKNCA-0.9.1/PKNCA/man/geomean.Rd                                                       |    8 
 PKNCA-0.9.1/PKNCA/man/get.interval.cols.Rd                                             |   11 
 PKNCA-0.9.1/PKNCA/man/get.parameter.deps.Rd                                            |    8 
 PKNCA-0.9.1/PKNCA/man/getDataName.Rd                                                   |    5 
 PKNCA-0.9.1/PKNCA/man/getDepVar.Rd                                                     |    5 
 PKNCA-0.9.1/PKNCA/man/getGroups.PKNCAconc.Rd                                           |    4 
 PKNCA-0.9.1/PKNCA/man/getIndepVar.Rd                                                   |    5 
 PKNCA-0.9.1/PKNCA/man/interp.extrap.conc.Rd                                            |   14 
 PKNCA-0.9.1/PKNCA/man/parseFormula.Rd                                                  |    5 
 PKNCA-0.9.1/PKNCA/man/pk.calc.ae.Rd                                                    |    2 
 PKNCA-0.9.1/PKNCA/man/pk.calc.aucint.Rd                                                |   47 
 PKNCA-0.9.1/PKNCA/man/pk.calc.aucpext.Rd                                               |    3 
 PKNCA-0.9.1/PKNCA/man/pk.calc.auxc.Rd                                                  |   21 
 PKNCA-0.9.1/PKNCA/man/pk.calc.c0.Rd                                                    |   10 
 PKNCA-0.9.1/PKNCA/man/pk.calc.cav.Rd                                                   |    3 
 PKNCA-0.9.1/PKNCA/man/pk.calc.cl.Rd                                                    |    2 
 PKNCA-0.9.1/PKNCA/man/pk.calc.clr.Rd                                                   |    2 
 PKNCA-0.9.1/PKNCA/man/pk.calc.ctrough.Rd                                               |    6 
 PKNCA-0.9.1/PKNCA/man/pk.calc.deg.fluc.Rd                                              |    3 
 PKNCA-0.9.1/PKNCA/man/pk.calc.f.Rd                                                     |    3 
 PKNCA-0.9.1/PKNCA/man/pk.calc.fe.Rd                                                    |    2 
 PKNCA-0.9.1/PKNCA/man/pk.calc.half.life.Rd                                             |   21 
 PKNCA-0.9.1/PKNCA/man/pk.calc.kel.Rd                                                   |    2 
 PKNCA-0.9.1/PKNCA/man/pk.calc.mrt.Rd                                                   |   12 
 PKNCA-0.9.1/PKNCA/man/pk.calc.mrt.md.Rd                                                |   11 
 PKNCA-0.9.1/PKNCA/man/pk.calc.ptr.Rd                                                   |    9 
 PKNCA-0.9.1/PKNCA/man/pk.calc.swing.Rd                                                 |    3 
 PKNCA-0.9.1/PKNCA/man/pk.calc.thalf.eff.Rd                                             |    3 
 PKNCA-0.9.1/PKNCA/man/pk.calc.vd.Rd                                                    |    2 
 PKNCA-0.9.1/PKNCA/man/pk.calc.vss.Rd                                                   |    3 
 PKNCA-0.9.1/PKNCA/man/pk.calc.vz.Rd                                                    |    3 
 PKNCA-0.9.1/PKNCA/man/pk.nca.Rd                                                        |   20 
 PKNCA-0.9.1/PKNCA/man/pk.nca.interval.Rd                                               |    6 
 PKNCA-0.9.1/PKNCA/man/pk.tss.Rd                                                        |    8 
 PKNCA-0.9.1/PKNCA/man/pk.tss.data.prep.Rd                                              |    5 
 PKNCA-0.9.1/PKNCA/man/pk.tss.monoexponential.Rd                                        |    9 
 PKNCA-0.9.1/PKNCA/man/pk.tss.monoexponential.individual.Rd                             |    4 
 PKNCA-0.9.1/PKNCA/man/pk.tss.monoexponential.population.Rd                             |    4 
 PKNCA-0.9.1/PKNCA/man/pk.tss.stepwise.linear.Rd                                        |    8 
 PKNCA-0.9.1/PKNCA/man/print.summary_PKNCAresults.Rd                                    |only
 PKNCA-0.9.1/PKNCA/man/roundString.Rd                                                   |   10 
 PKNCA-0.9.1/PKNCA/man/setAttributeColumn.Rd                                            |    4 
 PKNCA-0.9.1/PKNCA/man/signifString.Rd                                                  |   17 
 PKNCA-0.9.1/PKNCA/man/summary.PKNCAresults.Rd                                          |   25 
 PKNCA-0.9.1/PKNCA/man/superposition.Rd                                                 |    8 
 PKNCA-0.9.1/PKNCA/tests/testthat/test-PKNCA.options.R                                  |  196 +-
 PKNCA-0.9.1/PKNCA/tests/testthat/test-aucint.R                                         |   36 
 PKNCA-0.9.1/PKNCA/tests/testthat/test-check.intervals.R                                |   10 
 PKNCA-0.9.1/PKNCA/tests/testthat/test-class-PKNCAdata.R                                |   84 
 PKNCA-0.9.1/PKNCA/tests/testthat/test-class-PKNCAresults.R                             |  340 ++-
 PKNCA-0.9.1/PKNCA/tests/testthat/test-class-general.R                                  |   68 
 PKNCA-0.9.1/PKNCA/tests/testthat/test-exclude.R                                        |   20 
 PKNCA-0.9.1/PKNCA/tests/testthat/test-general.functions.R                              |   68 
 PKNCA-0.9.1/PKNCA/tests/testthat/test-parseFormula.R                                   |    8 
 PKNCA-0.9.1/PKNCA/tests/testthat/test-pk.calc.simple.R                                 |    2 
 PKNCA-0.9.1/PKNCA/tests/testthat/test-provenance.R                                     |    6 
 PKNCA-0.9.1/PKNCA/tests/testthat/test-superpostion.R                                   |   30 
 PKNCA-0.9.1/PKNCA/tests/testthat/test-time.to.steady.state.R                           |   92 
 PKNCA-0.9.1/PKNCA/vignettes/AUC-Calculation-with-PKNCA.Rmd                             |   35 
 PKNCA-0.9.1/PKNCA/vignettes/Example-theophylline.Rmd                                   |only
 PKNCA-0.9.1/PKNCA/vignettes/Introduction-and-Usage.Rmd                                 |   81 
 PKNCA-0.9.1/PKNCA/vignettes/Methods-for-Dose-Aware-Interpolation-and-Extrapolation.Rmd |   25 
 PKNCA-0.9.1/PKNCA/vignettes/Options-for-Controlling-PKNCA.Rmd                          |    7 
 PKNCA-0.9.1/PKNCA/vignettes/PKNCA-validation.Rmd                                       |    2 
 PKNCA-0.9.1/PKNCA/vignettes/Selection-of-Calculation-Intervals.Rmd                     |  163 +
 PKNCA-0.9.1/PKNCA/vignettes/Superposition.Rmd                                          |   74 
 PKNCA-0.9.1/PKNCA/vignettes/Writing-Parameter-Functions.Rmd                            |   16 
 148 files changed, 6227 insertions(+), 1973 deletions(-)

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