Wed, 05 Feb 2020

Package TestDesign updated to version 1.0.2 with previous version 1.0.1 dated 2020-01-24

Title: Optimal Test Design Approach to Fixed and Adaptive Test Construction
Description: Use the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and van der Linden (2018) <doi:10.1201/9781315117430> in constructing fixed and adaptive tests. Supports the following mixed-integer programming (MIP) solver packages: 'lpsymphony', 'Rsymphony', 'gurobi', 'lpSolve', and 'Rglpk'. The 'gurobi' package is not available from CRAN; see <https://www.gurobi.com/downloads>. See vignette for installing 'Rsymphony' package on Mac systems.
Author: Seung W. Choi [aut, cre] (<https://orcid.org/0000-0003-4777-5420>), Sangdon Lim [aut] (<https://orcid.org/0000-0002-2988-014X>)
Maintainer: Seung W. Choi <schoi@austin.utexas.edu>

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Package stockR updated to version 1.0.73 with previous version 1.0.68 dated 2018-06-14

Title: Identifying Stocks in Genetic Data
Description: Provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after find good starting values via a K-means clustering of the genetic data. Details can be found in Foster, Feutry, Grewe, Berry, Hui, Davies (2019) Reliably Discriminating Stock Structure with Genetic Markers: Mixture Models with Robust and Fast Computation. Molecular Ecology Resources.
Author: Scott D. Foster [aut, cre]
Maintainer: Scott D. Foster <scott.foster@data61.csiro.au>

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New package PwrGSD with initial version 2.3.2
Package: PwrGSD
Title: Power in a Group Sequential Design
Version: 2.3.2
Author: Grant Izmirlian <izmirlig@mail.nih.gov>
Depends: survival
Description: Tools for the evaluation of interim analysis plans for sequentially monitored trials on a survival endpoint; tools to construct efficacy and futility boundaries, for deriving power of a sequential design at a specified alternative, template for evaluating the performance of candidate plans at a set of time varying alternatives. See Izmirlian, G. (2014) <doi:10.4310/SII.2014.v7.n1.a4>.
Maintainer: Grant Izmirlian <izmirlig@mail.nih.gov>
License: GPL (>= 2)
NeedsCompilation: yes
Packaged: 2020-01-21 23:23:36 UTC; izmirlig
Repository: CRAN
Date/Publication: 2020-02-06 00:40:06 UTC

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Package prioritizr updated to version 4.1.5 with previous version 4.1.4 dated 2019-10-03

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], Joseph Bennett [aut], Hugh P Possingham [aut]
Maintainer: Richard Schuster <richard.schuster@glel.carleton.ca>

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Package gaselect updated to version 1.0.9 with previous version 1.0.7 dated 2019-03-13

Title: Genetic Algorithm (GA) for Variable Selection from High-Dimensional Data
Description: Provides a genetic algorithm for finding variable subsets in high dimensional data with high prediction performance. The genetic algorithm can use ordinary least squares (OLS) regression models or partial least squares (PLS) regression models to evaluate the prediction power of variable subsets. By supporting different cross-validation schemes, the user can fine-tune the tradeoff between speed and quality of the solution.
Author: David Kepplinger
Maintainer: David Kepplinger <david.kepplinger@gmail.com>

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Package DMMF updated to version 0.5.1.1 with previous version 0.5.0.2 dated 2018-08-17

Title: Daily Based Morgan-Morgan-Finney (DMMF) Soil Erosion Model
Description: Implements the daily based Morgan-Morgan-Finney (DMMF) soil erosion model (Choi et al., 2017 <doi:10.3390/w9040278>) for estimating surface runoff and sediment budgets from a field or a catchment on a daily basis.
Author: Kwanghun Choi [aut, cre], Sebastian Arnhold [dtc, ctb], Bjoern Reineking [ths, ctb]
Maintainer: Kwanghun Choi <kwanghun.choi@yahoo.com>

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Package checkmate updated to version 2.0.0 with previous version 1.9.4 dated 2019-07-04

Title: Fast and Versatile Argument Checks
Description: Tests and assertions to perform frequent argument checks. A substantial part of the package was written in C to minimize any worries about execution time overhead.
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Bernd Bischl [ctb]
Maintainer: Michel Lang <michellang@gmail.com>

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Package bisque updated to version 1.0.2 with previous version 1.0.1 dated 2019-04-26

Title: Approximate Bayesian Inference via Sparse Grid Quadrature Evaluation (BISQuE) for Hierarchical Models
Description: Implementation of the 'bisque' strategy for approximate Bayesian posterior inference. See Hewitt and Hoeting (2019) <arXiv:1904.07270> for complete details. 'bisque' combines conditioning with sparse grid quadrature rules to approximate marginal posterior quantities of hierarchical Bayesian models. The resulting approximations are computationally efficient for many hierarchical Bayesian models. The 'bisque' package allows approximate posterior inference for custom models; users only need to specify the conditional densities required for the approximation.
Author: Joshua Hewitt
Maintainer: Joshua Hewitt <joshua.hewitt@duke.edu>

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Package azuremlsdk updated to version 0.6.85 with previous version 0.5.7 dated 2019-11-15

Title: Interface to the 'Azure Machine Learning' 'SDK'
Description: Interface to the 'Azure Machine Learning' Software Development Kit ('SDK'). Data scientists can use the 'SDK' to train, deploy, automate, and manage machine learning models on the 'Azure Machine Learning' service. To learn more about 'Azure Machine Learning' visit the website: <https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml>.
Author: Heemanshu Suri [cre, aut], Billy Hu [aut], Diondra Peck [aut], Minna Xiao [aut], Himanshu Chandola [ctb], Abe Omorogbe [ctb], Microsoft [cph, fnd], Google Inc. [cph] (Examples and Tutorials), The TensorFlow Authors [cph] (Examples and Tutorials), RStudio Inc. [cph] (Examples and Tutorials)
Maintainer: Heemanshu Suri <Heemanshu.Suri@microsoft.com>

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Package assertr updated to version 2.7 with previous version 2.6 dated 2019-01-22

Title: Assertive Programming for R Analysis Pipelines
Description: Provides functionality to assert conditions that have to be met so that errors in data used in analysis pipelines can fail quickly. Similar to 'stopifnot()' but more powerful, friendly, and easier for use in pipelines.
Author: Tony Fischetti [aut, cre]
Maintainer: Tony Fischetti <tony.fischetti@gmail.com>

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Package stringb updated to version 0.1.14 with previous version 0.1.13 dated 2016-11-01

Title: Convenient Base R String Handling
Description: Base R already ships with string handling capabilities 'out- of-the-box' but lacks streamlined function names and workflow. The 'stringi' ('stringr') package on the other hand has well named functions, extensive Unicode support and allows for a streamlined workflow. On the other hand it adds dependencies and regular expression interpretation between base R functions and 'stringi' functions might differ. This packages aims at providing a solution to the use case of unwanted dependencies on the one hand but the need for streamlined text processing on the other. The packages' functions are solely based on wrapping base R functions into 'stringr'/'stringi' like function names. Along the way it adds one or two extra functions and last but not least provides all functions as generics, therefore allowing for adding methods for other text structures besides plain character vectors.
Author: Peter Meissner [aut, cre]
Maintainer: Peter Meissner <retep.meissner@gmail.com>

Diff between stringb versions 0.1.13 dated 2016-11-01 and 0.1.14 dated 2020-02-05

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Package rsf updated to version 0.2.1 with previous version 0.2.0 dated 2019-10-05

Title: Report of Statistical Findings in 'bookdown'
Description: A report of statistical findings (RSF) project template is generated using a 'bookdown' format. 'YAML' fields can be further customized. Additional helper functions provide extra features to the RSF.
Author: Derek Chiu [aut, cre]
Maintainer: Derek Chiu <dchiu@bccrc.ca>

Diff between rsf versions 0.2.0 dated 2019-10-05 and 0.2.1 dated 2020-02-05

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Package GenEst updated to version 1.4.0.2 with previous version 1.4.0.1 dated 2019-11-22

Title: Generalized Mortality Estimator
Description: Command-line and 'shiny' GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) <doi:10.3133/tm7A2>.
Author: Daniel Dalthorp [aut, cre], Juniper Simonis [aut], Lisa Madsen [aut], Manuela Huso [aut], Paul Rabie [aut], Jeffrey Mintz [aut], Robert Wolpert [aut], Jared Studyvin [aut], Franzi Korner-Nievergelt [aut]
Maintainer: Daniel Dalthorp <ddalthorp@usgs.gov>

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Package RODBCext updated to version 0.3.2 with previous version 0.3.1 dated 2017-07-31

Title: Parameterized Queries Extension for RODBC
Description: An extension for RODBC package adding support for parameterized queries.
Author: Mateusz Zoltak [aut, cre], Brian Ripley [aut], Michael Lapsley [aut], Will Beasley [ctb], Juergen Altfeld [ctb]
Maintainer: Mateusz Zoltak <zozlak@zozlak.org>

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Package SplitReg updated to version 1.0.2 with previous version 1.0.1 dated 2020-01-16

Title: Split Regularized Regression
Description: Functions for computing split regularized estimators defined in Christidis, Lakshmanan, Smucler and Zamar (2019) <arXiv:1712.03561>. The approach fits linear regression models that split the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regression coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. The estimated coefficients are then pooled together to form the final fit.
Author: Anthony Christidis <anthony.christidis@stat.ubc.ca>, Ezequiel Smucler <ezequiels.90@gmail.com>, Ruben Zamar <ruben@stat.ubc.ca>
Maintainer: Anthony Christidis <anthony.christidis@stat.ubc.ca>

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Package simTargetCov updated to version 1.0.1 with previous version 1.0 dated 2019-10-10

Title: Data Transformation or Simulation with Empirical Covariance Matrix
Description: Transforms or simulates data with a target empirical covariance matrix supplied by the user. The method to obtain the data with the target empirical covariance matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019) <arXiv:1812.05678>.
Author: Anthony Christidis <anthony.christidis@stat.ubc.ca>, Stefan Van Aelst <stefan.vanaelst@kuleuven.be>, Ruben Zamar <ruben@stat.ubc.ca>
Maintainer: Anthony Christidis <anthony.christidis@stat.ubc.ca>

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Package rdbnomics updated to version 0.5.2 with previous version 0.5.1 dated 2020-01-07

Title: Download DBnomics Data
Description: R access to hundreds of millions data series from DBnomics API (<https://db.nomics.world/>).
Author: Sebastien Galais [cre, ctb], Thomas Brand [aut]
Maintainer: Sebastien Galais <s915.stem@gmail.com>

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Package yuima updated to version 1.9.6 with previous version 1.8.1 dated 2018-05-21

Title: The YUIMA Project Package for SDEs
Description: Simulation and Inference for SDEs and Other Stochastic Processes.
Author: YUIMA Project Team
Maintainer: Stefano M. Iacus <stefano.iacus@unimi.it>

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Package pksensi updated to version 1.1.5 with previous version 1.1.4 dated 2019-09-19

Title: Global Sensitivity Analysis in Physiologically Based Kinetic Modeling
Description: Applying the global sensitivity analysis workflow to investigate the parameter uncertainty and sensitivity in physiologically based kinetic (PK) models, especially the physiologically based pharmacokinetic/toxicokinetic model with multivariate outputs. The package also provides some functions to check the convergence and sensitivity of model parameters.
Author: Nan-Hung Hsieh [aut, cre] (<https://orcid.org/0000-0003-0163-2766>), Brad Reisfeld [aut], Weihsueh A. Chiu [aut] (<https://orcid.org/0000-0002-7575-2368>)
Maintainer: Nan-Hung Hsieh <nhsieh@cvm.tamu.edu>

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Package mlr3db updated to version 0.1.4 with previous version 0.1.3 dated 2019-10-29

Title: Data Base Backend for 'mlr3'
Description: Extends the 'mlr3' package with a backend to transparently work with data bases. Internally relies on the abstraction of package 'dbplyr' to interact with one of the many supported data base management systems (DBMS).
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>)
Maintainer: Michel Lang <michellang@gmail.com>

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Package manymodelr updated to version 0.2.3 with previous version 0.2.2 dated 2019-07-08

Title: Build and Tune Several Models
Description: Frequently one needs a convenient way to build and tune several models in one go.The goal is to provide a number of convenience functions useful in machine learning applications. It provides the ability to build, tune and obtain predictions of several models in one function. The models are built using 'caret' functions with easier to read syntax. Kuhn(2014) <arXiv:1405.6974v14>. Kuhn(2008) <doi10.18637/jss.v028.i05>. Chambers,J.M.(1992) <doi:10.1007/978-3-642-50096-1_48>. Wilkinson,G.N. and Rogers, C. E. (1973) <doi:10.2307/2346786>.
Author: Nelson Gonzabato [aut, cre]
Maintainer: Nelson Gonzabato <gonzabato@hotmail.com>

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Package FactoMineR updated to version 2.2 with previous version 2.1 dated 2020-01-17

Title: Multivariate Exploratory Data Analysis and Data Mining
Description: Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).
Author: Francois Husson, Julie Josse, Sebastien Le, Jeremy Mazet
Maintainer: Francois Husson <francois.husson@agrocampus-ouest.fr>

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Package date updated to version 1.2-39 with previous version 1.2-38 dated 2018-01-06

Title: Functions for Handling Dates
Description: Functions for handling dates.
Author: Terry Therneau [aut], Thomas Lumley [trl] (R port), Kjetil Halvorsen [trl] (R port), Kurt Hornik [trl, ctb, cre] (<https://orcid.org/0000-0003-4198-9911>, R port), R Core Team [ctb]
Maintainer: Kurt Hornik <Kurt.Hornik@R-project.org>

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Package chk updated to version 0.3.1 with previous version 0.3.0 dated 2020-01-15

Title: Check User-Supplied Function Arguments
Description: For developers to check user-supplied function arguments. It is designed to be simple, fast and customizable. Error messages follow the tidyverse style guide.
Author: Joe Thorley [aut, cre] (<https://orcid.org/0000-0002-7683-4592>), Kirill Müller [aut] (<https://orcid.org/0000-0002-1416-3412>), Ayla Pearson [ctb] (<https://orcid.org/0000-0001-7388-1222>), Poisson Consulting [cph, fnd]
Maintainer: Joe Thorley <joe@poissonconsulting.ca>

Diff between chk versions 0.3.0 dated 2020-01-15 and 0.3.1 dated 2020-02-05

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

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

2017-08-30 1.1
2013-12-30 0.9

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Package bioplots (with last version 0.0.1) was removed from CRAN

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

2016-06-06 0.0.1

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Package ARTP (with last version 2.0.5) was removed from CRAN

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

2018-04-06 2.0.5
2014-02-20 2.0.4
2013-07-30 2.0.3

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Package VIM updated to version 5.1.0 with previous version 4.8.0 dated 2019-02-11

Title: Visualization and Imputation of Missing Values
Description: New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods (<doi:10.1007/s11634-011-0102-y>). A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods. Fast imputation methods such as k-nearest neighbor imputation (<doi:10.18637/jss.v074.i07>) and imputation using robust methods are provided (<doi:10.1016/j.csda.2011.04.012>).
Author: Matthias Templ [aut, cre] (<https://orcid.org/0000-0002-8638-5276>), Alexander Kowarik [aut] (<https://orcid.org/0000-0001-8598-4130>), Andreas Alfons [aut], Bernd Prantner [ctb]
Maintainer: Matthias Templ <matthias.templ@gmail.com>

Diff between VIM versions 4.8.0 dated 2019-02-11 and 5.1.0 dated 2020-02-05

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Package symmoments updated to version 1.2.1 with previous version 1.2 dated 2014-08-04

Title: Symbolic Central and Noncentral Moments of the Multivariate Normal Distribution
Description: Symbolic central and non-central moments of the multivariate normal distribution. Computes a standard representation, LateX code, and values at specified mean and covariance matrices.
Author: Kem Phillips
Maintainer: Kem Phillips <kemphillips@comcast.net>

Diff between symmoments versions 1.2 dated 2014-08-04 and 1.2.1 dated 2020-02-05

 DESCRIPTION               |   14 +++++-----
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 man/symmoments-package.Rd |   12 ++++----
 vignettes/symmoments.rnw  |   33 +++++++++++++-----------
 12 files changed, 121 insertions(+), 110 deletions(-)

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New package shinyNotes with initial version 0.0.1
Package: shinyNotes
Title: Shiny Module for Taking Free-Form Notes
Version: 0.0.1
Authors@R: person('Daniel', 'Kovtun', email = 'quantumfusetrader@gmail.com', role = c('cre', 'aut'))
Description: An enterprise-targeted scalable and customizable 'shiny' module providing an easy way to incorporate free-form note taking or discussion boards into applications. The package includes a 'shiny' module that can be included in any 'shiny' application to create a panel containing searchable, editable text broken down by section headers. Can be used with a local 'SQLite' database, or a compatible remote database of choice.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: shinyjs, shiny, shinyWidgets, dplyr, DBI, dbplyr, RSQLite, magrittr, stringr
RoxygenNote: 6.1.1
URL: https://github.com/danielkovtun/shinyNotes
BugReports: https://github.com/danielkovtun/shinyNotes/issues
Suggests: testthat (>= 2.1.0), knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-01-24 01:33:06 UTC; danny
Author: Daniel Kovtun [cre, aut]
Maintainer: Daniel Kovtun <quantumfusetrader@gmail.com>
Repository: CRAN
Date/Publication: 2020-02-05 16:30:05 UTC

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New package RSQL with initial version 0.1.1
Package: RSQL
Type: Package
Title: Database Agnostic Package to Generate and Process 'SQL' Queries in R
Version: 0.1.1
Language: en-US
Authors@R: c( person("Alejandro", "Baranek",,"abaranek@dc.uba.ar", c("cre", "aut")), person("Leonardo","Belen",,"leobelen@gmail.com", c("aut")))
Maintainer: Alejandro Baranek <abaranek@dc.uba.ar>
Description: Allows the user to generate and execute select, insert, update and delete 'SQL' queries the underlying database without having to explicitly write 'SQL' code.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
Collate: 'sql-lib.R' 'zzz.R'
Imports: lgr, R6, DBI, RSQLite
Suggests: knitr, testthat, rmarkdown, dplyr, covr
VignetteBuilder: knitr
BugReports: https://github.com/rOpenStats/rsql/issues
URL: https://github.com/rOpenStats/rsql
NeedsCompilation: no
Packaged: 2020-01-23 17:24:15 UTC; kenarab
Author: Alejandro Baranek [cre, aut], Leonardo Belen [aut]
Repository: CRAN
Date/Publication: 2020-02-05 16:10:11 UTC

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New package rpubs with initial version 0.2.2
Package: rpubs
Type: Package
Title: Extract Code Block from Rpubs Article
Version: 0.2.2
Author: Aep Hidayatuloh [aut, cre] (<https://orcid.org/0000-0002-4669-3354>)
Authors@R: person(given = "Aep", family = "Hidayatuloh", email = "aef.stk@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-4669-3354"))
Maintainer: Aep Hidayatuloh <aef.stk@gmail.com>
Description: Extract code only or with the output from an Rpubs <https://rpubs.com/> article and write the code-block to R script file.
License: GPL-2
URL: https://github.com/aephidayatuloh/rpubs
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0)
Imports: rvest (>= 0.3.5), xml2 (>= 1.2.2)
Suggests: testthat (>= 2.1.0)
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-01-24 12:11:23 UTC; rstudio-user
Repository: CRAN
Date/Publication: 2020-02-05 16:50:02 UTC

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New package r2pmml with initial version 0.24.0
Package: r2pmml
Version: 0.24.0
Date: 2020-02-05
Type: Package
License: AGPL-3
Title: Convert R Models to PMML
Description: R wrapper for the JPMML-R library <https://github.com/jpmml/jpmml-r>, which converts R models to Predictive Model Markup Language (PMML).
Author: Villu Ruusmann <villu.ruusmann@gmail.com>
Maintainer: Villu Ruusmann <villu.ruusmann@gmail.com>
URL: https://github.com/jpmml/r2pmml
LazyLoad: yes
NeedsCompilation: no
RoxygenNote: 6.1.1
Suggests: caret, e1071, earth, evtree, glmnet, mlbench, partykit, randomForest, ranger, xgboost
SystemRequirements: Java (>= 8.0)
Packaged: 2020-02-05 14:46:13 UTC; vfed
Repository: CRAN
Date/Publication: 2020-02-05 16:30:08 UTC

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Package PAFway updated to version 0.1.3 with previous version 0.1.2 dated 2019-12-16

Title: Pairwise Association of Functional Annotations
Description: Finds pairs of functional annotations or gene ontology (GO) terms that are enriched within a directed network (such as a gene regulatory network). This works with or without edge weights and includes visualizations (both as a network where the functions are nodes and as a heatmap). PAFway is an acronym for Pairwise Associations of Functional annotations in biological networks and pathWAYs.
Author: Daphne Ezer [aut, cre], Mahair Mahjoub [aut]
Maintainer: Daphne Ezer <daphne.ezer@york.ac.uk>

Diff between PAFway versions 0.1.2 dated 2019-12-16 and 0.1.3 dated 2020-02-05

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 NAMESPACE                  |    1 
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Package hsdar updated to version 1.0.1 with previous version 1.0.0 dated 2019-05-25

Title: Manage, Analyse and Simulate Hyperspectral Data
Description: Transformation of reflectance spectra, calculation of vegetation indices and red edge parameters, spectral resampling for hyperspectral remote sensing, simulation of reflectance and transmittance using the leaf reflectance model PROSPECT and the canopy reflectance model PROSAIL.
Author: Lukas W. Lehnert [cre, aut] (<https://orcid.org/0000-0002-5229-2282>), Hanna Meyer [ctb], Joerg Bendix [ctb]
Maintainer: Lukas W. Lehnert <lukaslehnert@googlemail.com>

Diff between hsdar versions 1.0.0 dated 2019-05-25 and 1.0.1 dated 2020-02-05

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Package GLDEX updated to version 2.0.0.7 with previous version 2.0.0.6 dated 2019-08-23

Title: Fitting Single and Mixture of Generalised Lambda Distributions (RS and FMKL) using Various Methods
Description: The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution.
Author: Steve Su, with contributions from: Diethelm Wuertz, Martin Maechler and Rmetrics core team members for low discrepancy algorithm, Juha Karvanen for L moments codes, Robert King for gld C codes and starship codes, Benjamin Dean for corrections and input in ks.gof code and R core team for histsu function.
Maintainer: Steve Su <allegro.su@gmail.com>

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Package gamCopula updated to version 0.0-7 with previous version 0.0-6 dated 2019-12-04

Title: Generalized Additive Models for Bivariate Conditional Dependence Structures and Vine Copulas
Description: Implementation of various inference and simulation tools to apply generalized additive models to bivariate dependence structures and non-simplified vine copulas.
Author: Thomas Nagler [aut], Thibault Vatter [aut, cre]
Maintainer: Thibault Vatter <thibault.vatter@gmail.com>

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Package beadplexr updated to version 0.3.0 with previous version 0.2.0 dated 2018-12-07

Title: Analysis of Multiplex Cytometric Bead Assays
Description: Reproducible and automated analysis of multiplex bead assays such as CBA (Morgan et al. 2004; <doi: 10.1016/j.clim.2003.11.017>), LEGENDplex (Yu et al. 2015; <doi: 10.1084/jem.20142318>), and MACSPlex (Miltenyi Biotec 2014; Application note: Data acquisition and analysis without the MACSQuant analyzer; <https://www.miltenyibiotec.com/upload/assets/IM0021608.PDF>). The package provides functions for streamlined reading of fcs files, and identification of bead clusters and analyte expression. The package eases the calculation of standard curves and the subsequent calculation of the analyte concentration.
Author: Ulrik Stervbo [aut, cre] (<https://orcid.org/0000-0002-2831-8868>)
Maintainer: Ulrik Stervbo <ulrik.stervbo@gmail.com>

Diff between beadplexr versions 0.2.0 dated 2018-12-07 and 0.3.0 dated 2020-02-05

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 R/read_fcs.R                                                                        |    2 
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 man/calc_analyte_mfi.Rd                                                             |    8 
 man/calc_std_conc.Rd                                                                |    3 
 man/calculate_concentration.Rd                                                      |    8 
 man/cluster_events.Rd                                                               |   29 
 man/do_find_turning_points.Rd                                                       |    3 
 man/facs_plot.Rd                                                                    |   18 
 man/fit_standard_curve.Rd                                                           |    9 
 man/ident_bead_pop.Rd                                                               |    9 
 man/identify_analyte.Rd                                                             |   15 
 man/identify_assay_analyte.Rd                                                       |   18 
 man/load_panel.Rd                                                                   |   12 
 man/plot_concentrations.Rd                                                          |   27 
 man/read_fcs.Rd                                                                     |   13 
 man/trim_population.Rd                                                              |    3 
 man/turning_point.Rd                                                                |   23 
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New package allestimates with initial version 0.1.6
Package: allestimates
Type: Package
Title: Effect Estimates from All Models
Version: 0.1.6
Authors@R: person("Zhiqiang", "Wang", email = "zhiqiang.wang@menzies.edu.au", role = c("aut", "cre"))
Description: Estimates and plots effect estimates from models with all possible combinations of a list of variables. It can be used for assessing treatment effects in clinical trials or risk factors in bio-medical and epidemiological research. Like Stata command 'confall' (Wang Z (2007) <doi:10.1177/1536867X0700700203> ), 'allestimates' calculates and stores all effect estimates, and plots them against p values or Akaike information criterion (AIC) values. It currently has functions for linear regression: all_lm(), logistic and Poisson regression: all_glm() and all_speedglm(), and Cox proportional hazards regression: all_cox().
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: broom, ggplot2, MASS, speedglm, survival, tibble, tidyr, utils, stringr, dplyr
Depends: R (>= 2.10)
RoxygenNote: 7.0.2
Suggests: spelling, knitr, rmarkdown
VignetteBuilder: knitr
Language: en-US
NeedsCompilation: no
Packaged: 2020-01-24 01:49:22 UTC; uqzwang
Author: Zhiqiang Wang [aut, cre]
Maintainer: Zhiqiang Wang <zhiqiang.wang@menzies.edu.au>
Repository: CRAN
Date/Publication: 2020-02-05 16:30:12 UTC

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Package SGB updated to version 1.0.1 with previous version 1.0 dated 2019-05-13

Title: Simplicial Generalized Beta Regression
Description: Main properties and regression procedures using a generalization of the Dirichlet distribution called Simplicial Generalized Beta distribution. It is a new distribution on the simplex (i.e. on the space of compositions or positive vectors with sum of components equal to 1). The Dirichlet distribution can be constructed from a random vector of independent Gamma variables divided by their sum. The SGB follows the same construction with generalized Gamma instead of Gamma variables. The Dirichlet exponents are supplemented by an overall shape parameter and a vector of scales. The scale vector is itself a composition and can be modeled with auxiliary variables through a log-ratio transformation. Graf, M. (2017, ISBN: 978-84-947240-0-8). See also the vignette enclosed in the package.
Author: Monique Graf
Maintainer: Monique Graf <monique.p.n.graf@bluewin.ch>

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Package PoissonBinomial updated to version 1.0.1 with previous version 1.0.0 dated 2020-01-22

Title: Exact and Approximate Implementations of the Poisson Binomial Distribution
Description: Implementations of multiple exact and approximate methods as described in Hong (2013) <doi:10.1016/j.csda.2012.10.006> and Biscarri et al. (2018) <doi:10.1016/j.csda.2018.01.007> for computing the probability mass, cumulative distribution and quantile functions, as well as generating random numbers for the Poisson binomial distribution.
Author: Florian Junge [aut, cre]
Maintainer: Florian Junge <florian.junge@h-da.de>

Diff between PoissonBinomial versions 1.0.0 dated 2020-01-22 and 1.0.1 dated 2020-02-05

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New package pewdata with initial version 0.3.1
Package: pewdata
Type: Package
Title: Reproducible Retrieval of Pew Research Center Datasets
Version: 0.3.1
Date: 2020-02-03
Authors@R: c( person("Frederick", "Solt", email = "frederick-solt@uiowa.edu", role = c("aut", "cre")), person("Yue", "Hu", email = "yuehu@tsinghua.edu.cn", role = c("aut")), person("Eric", "Persson", email = "expersso5@gmail.com", role = c("ctb")))
URL: https://github.com/fsolt/pewdata
BugReports: https://github.com/fsolt/pewdata/issues
Description: Reproducible, programmatic retrieval of survey datasets from the Pew Research Center.
License: MIT + file LICENSE
LazyData: TRUE
Encoding: UTF-8
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
Imports: RSelenium, foreign, magrittr, purrr, rio, stringr, tools, utils
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2020-02-05 13:58:02 UTC; fredsolt
Author: Frederick Solt [aut, cre], Yue Hu [aut], Eric Persson [ctb]
Maintainer: Frederick Solt <frederick-solt@uiowa.edu>
Repository: CRAN
Date/Publication: 2020-02-05 15:50:02 UTC

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Package NeuralSens updated to version 0.1.4 with previous version 0.1.3 dated 2020-01-11

Title: Sensitivity Analysis of Neural Networks
Description: Analysis functions to quantify inputs importance in neural network models. Functions are available for calculating and plotting the inputs importance and obtaining the activation function of each neuron layer and its derivatives. The importance of a given input is defined as the distribution of the derivatives of the output with respect to that input in each training data point.
Author: José Portela González [aut], Antonio Muñoz San Roque [aut], Jaime Pizarroso Gonzalo [ctb, cre]
Maintainer: Jaime Pizarroso Gonzalo <jpizarroso@comillas.edu>

Diff between NeuralSens versions 0.1.3 dated 2020-01-11 and 0.1.4 dated 2020-02-05

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Package fuzzyforest updated to version 1.0.7 with previous version 1.0.6 dated 2019-10-26

Title: Fuzzy Forests
Description: Fuzzy forests, a new algorithm based on random forests, is designed to reduce the bias seen in random forest feature selection caused by the presence of correlated features. Fuzzy forests uses recursive feature elimination random forests to select features from separate blocks of correlated features where the correlation within each block of features is high and the correlation between blocks of features is low. One final random forest is fit using the surviving features. This package fits random forests using the 'randomForest' package and allows for easy use of 'WGCNA' to split features into distinct blocks. See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) <doi:10.18637/jss.v091.i09> for further details.
Author: Daniel Conn [aut, cre], Tuck Ngun [aut], Christina M. Ramirez [aut]
Maintainer: Daniel Conn <djconn17@gmail.com>

Diff between fuzzyforest versions 1.0.6 dated 2019-10-26 and 1.0.7 dated 2020-02-05

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Package Factoshiny updated to version 2.2 with previous version 2.1 dated 2020-01-17

Title: Perform Factorial Analysis from 'FactoMineR' with a Shiny Application
Description: Perform factorial analysis with a menu and draw graphs interactively thanks to 'FactoMineR' and a Shiny application.
Author: Pauline Vaissie, Astrid Monge, Francois Husson
Maintainer: Francois Husson <husson@agrocampus-ouest.fr>

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New package weathercan with initial version 0.3.3
Package: weathercan
Type: Package
Title: Download Weather Data from the Environment and Climate Change Canada Website
Version: 0.3.3
Authors@R: c( person("Steffi", "LaZerte", email = "steffi@steffi.ca", role = c("aut","cre"), comment = c(ORCID = "0000-0002-7690-8360")), person("Sam", "Albers", email = "sam.albers@gmail.com", role = c("ctb"), comment = c(ORCID = "0000-0002-9270-7884")), person("Nick", "Brown", email = "nicholas512@gmail.com", role = c("ctb"), comment = c(ORCID = "0000-0002-2719-0671")))
Description: Provides means for downloading historical weather data from the Environment and Climate Change Canada website (<http://climate.weather.gc.ca/historical_data/search_historic_data_e.html>). Data can be downloaded from multiple stations and over large date ranges and automatically processed into a single dataset. Tools are also provided to identify stations either by name or proximity to a location.
License: GPL-3
Language: en-CA
BugReports: https://github.com/ropensci/weathercan/issues
LazyData: TRUE
URL: https://docs.ropensci.org/weathercan, https://github.com/ropensci/weathercan
Depends: R (>= 3.1.2)
Imports: dplyr (>= 0.7.0), httr (>= 1.1.0), lubridate (>= 1.7.1), methods (>= 3.2.2), purrr (>= 0.2.4), rlang (>= 0.1.4), rvest (>= 0.3.4), stringi (>= 1.1.2), stringr (>= 1.4.0), tidyr (>= 0.4.1), xml2 (>= 0.1.2)
RoxygenNote: 6.1.1
Suggests: devtools, ggplot2, ggmap, htmltools, knitr, leaflet, lutz, mockery, naniar, rmarkdown, sf, sp, testthat
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2020-02-04 20:26:41 UTC; steffi
Author: Steffi LaZerte [aut, cre] (<https://orcid.org/0000-0002-7690-8360>), Sam Albers [ctb] (<https://orcid.org/0000-0002-9270-7884>), Nick Brown [ctb] (<https://orcid.org/0000-0002-2719-0671>)
Maintainer: Steffi LaZerte <steffi@steffi.ca>
Repository: CRAN
Date/Publication: 2020-02-05 14:10:02 UTC

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New package tree.interpreter with initial version 0.1.1
Package: tree.interpreter
Type: Package
Title: Random Forest Prediction Decomposition and Feature Importance Measure
Version: 0.1.1
Date: 2020-01-28
Author: Qingyao Sun
Maintainer: Qingyao Sun <sunqingyao19970825@gmail.com>
Description: An R re-implementation of the 'treeinterpreter' package on PyPI <https://pypi.org/project/treeinterpreter/>. Each prediction can be decomposed as 'prediction = bias + feature_1_contribution + ... + feature_n_contribution'. This decomposition is then used to calculate the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using out-of-bag samples (MDI-oob) feature importance measures based on the work of Li et al. (2019) <arXiv:1906.10845>.
Encoding: UTF-8
License: MIT + file LICENSE
Imports: Rcpp (>= 1.0.2)
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.0.2
Suggests: MASS, randomForest, ranger, testthat (>= 2.1.0), knitr, rmarkdown, covr
URL: https://github.com/nalzok/tree.interpreter
BugReports: https://github.com/nalzok/tree.interpreter/issues
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2020-01-28 21:58:01 UTC; nalzok
Repository: CRAN
Date/Publication: 2020-02-05 14:10:06 UTC

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Package surveysd updated to version 1.2.0 with previous version 1.1.0 dated 2019-07-15

Title: Survey Standard Error Estimation for Cumulated Estimates and their Differences in Complex Panel Designs
Description: Calculate point estimates and their standard errors in complex household surveys using bootstrap replicates. Bootstrapping considers survey design with a rotating panel. A comprehensive description of the methodology can be found under <https://statistikat.github.io/surveysd/articles/methodology.html>.
Author: Johannes Gussenbauer [aut, cre], Alexander Kowarik [aut] (<https://orcid.org/0000-0001-8598-4130>), Gregor de Cillia [aut], Matthias Till [ctb]
Maintainer: Johannes Gussenbauer <Johannes.Gussenbauer@statistik.gv.at>

Diff between surveysd versions 1.1.0 dated 2019-07-15 and 1.2.0 dated 2020-02-05

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Package season updated to version 0.3.9 with previous version 0.3.8 dated 2018-09-10

Title: Seasonal Analysis of Health Data
Description: Routines for the seasonal analysis of health data, including regression models, time-stratified case-crossover, plotting functions and residual checks, see Barnett and Dobson (2010) ISBN 978-3-642-10748-1. Thanks to Yuming Guo for checking the case-crossover code.
Author: Adrian Barnett and Peter Baker
Maintainer: Adrian Barnett <a.barnett@qut.edu.au>

Diff between season versions 0.3.8 dated 2018-09-10 and 0.3.9 dated 2020-02-05

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Permanent link

Package rticles updated to version 0.14 with previous version 0.13 dated 2019-12-19

Title: Article Formats for R Markdown
Description: A suite of custom R Markdown formats and templates for authoring journal articles and conference submissions.
Author: JJ Allaire [aut], Yihui Xie [aut, cre] (<https://orcid.org/0000-0003-0645-5666>), R Foundation [aut, cph], Hadley Wickham [aut], Journal of Statistical Software [aut, cph], RStudio [cph], Ramnath Vaidyanathan [aut, cph], Association for Computing Machinery [aut, cph], Carl Boettiger [aut, cph], Elsevier [aut, cph], Karl Broman [aut, cph], Kirill Mueller [aut, cph], Bastiaan Quast [aut, cph], Randall Pruim [aut, cph], Ben Marwick [aut, cph], Charlotte Wickham [aut, cph], Oliver Keyes [aut, cph], Miao Yu [aut, cph], Daniel Emaasit [aut, cph], Thierry Onkelinx [aut, cph], Alessandro Gasparini [aut, cph] (<https://orcid.org/0000-0002-8319-7624>), Marc-Andre Desautels [aut, cph], Dominik Leutnant [aut, cph] (<https://orcid.org/0000-0003-3293-2315>), MDPI [aut, cph], Taylor and Francis [aut, cph], Oğuzhan Öğreden [aut] (<https://orcid.org/0000-0002-9949-3348>), Dalton Hance [aut], Daniel Nüst [aut, cph] (<https://orcid.org/0000-0002-0024-5046>), Petter Uvesten [aut, cph], Elio Campitelli [aut, cph] (<https://orcid.org/0000-0002-7742-9230>), John Muschelli [aut, cph] (<https://orcid.org/0000-0001-6469-1750>), Zhian N. Kamvar [aut, cph] (<https://orcid.org/0000-0003-1458-7108>), Noam Ross [aut, cph] (<https://orcid.org/0000-0002-2136-0000>), Robrecht Cannoodt [aut, cph] (<https://orcid.org/0000-0003-3641-729X>, rcannood), Duncan Luguern [aut], David M. Kaplan [aut, ctb] (<https://orcid.org/0000-0001-6087-359X>, dmkaplan2000)
Maintainer: Yihui Xie <xie@yihui.name>

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 rticles-0.14/rticles/NAMESPACE                                                        |    1 
 rticles-0.14/rticles/NEWS.md                                                          |   13 +++
 rticles-0.14/rticles/R/article.R                                                      |   16 ++++
 rticles-0.14/rticles/README.md                                                        |    2 
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 rticles-0.14/rticles/man/article.Rd                                                   |   15 ++++
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Package mcompanion updated to version 0.5-2 with previous version 0.5-1 dated 2019-12-18

Title: Objects and Methods for Multi-Companion Matrices
Description: Provides a class for multi-companion matrices with methods for arithmetic and factorization. A method for generation of multi-companion matrices with prespecified spectral properties is provided, as well as some utilities for periodically correlated and multivariate time series models. See Boshnakov (2002) <doi:10.1016/S0024-3795(01)00475-X> and Boshnakov & Iqelan (2009) <doi:10.1111/j.1467-9892.2009.00617.x>.
Author: Georgi N. Boshnakov [aut, cre]
Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk>

Diff between mcompanion versions 0.5-1 dated 2019-12-18 and 0.5-2 dated 2020-02-05

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More information about mcompanion at CRAN
Permanent link

Package laeken updated to version 0.5.1 with previous version 0.5.0 dated 2019-01-10

Title: Estimation of Indicators on Social Exclusion and Poverty
Description: Estimation of indicators on social exclusion and poverty, as well as Pareto tail modeling for empirical income distributions.
Author: Andreas Alfons [aut, cre], Josef Holzer [aut], Matthias Templ [aut], Alexander Haider [ctb]
Maintainer: Andreas Alfons <alfons@ese.eur.nl>

Diff between laeken versions 0.5.0 dated 2019-01-10 and 0.5.1 dated 2020-02-05

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Package kergp updated to version 0.5.1 with previous version 0.5.0 dated 2019-09-30

Title: Gaussian Process Laboratory
Description: Gaussian process regression with an emphasis on kernels. Quantitative and qualitative inputs are accepted. Some pre-defined kernels are available, such as radial or tensor-sum for quantitative inputs, and compound symmetry, low rank, group kernel for qualitative inputs. The user can define new kernels and composite kernels through a formula mechanism. Useful methods include parameter estimation by maximum likelihood, simulation, prediction and leave-one-out validation.
Author: Yves Deville, David Ginsbourger, Olivier Roustant. Contributors: Nicolas Durrande.
Maintainer: Olivier Roustant <roustant@insa-toulouse.fr>

Diff between kergp versions 0.5.0 dated 2019-09-30 and 0.5.1 dated 2020-02-05

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Package DT updated to version 0.12 with previous version 0.11 dated 2019-12-19

Title: A Wrapper of the JavaScript Library 'DataTables'
Description: Data objects in R can be rendered as HTML tables using the JavaScript library 'DataTables' (typically via R Markdown or Shiny). The 'DataTables' library has been included in this R package. The package name 'DT' is an abbreviation of 'DataTables'.
Author: Yihui Xie [aut, cre], Joe Cheng [aut], Xianying Tan [aut], JJ Allaire [ctb], Maximilian Girlich [ctb], Greg Freedman Ellis [ctb], Johannes Rauh [ctb], jQuery contributors [ctb, cph] (jQuery in htmlwidgets/lib), SpryMedia Limited [ctb, cph] (DataTables in htmlwidgets/lib), Brian Reavis [ctb, cph] (selectize.js in htmlwidgets/lib), Leon Gersen [ctb, cph] (noUiSlider in htmlwidgets/lib), Bartek Szopka [ctb, cph] (jquery.highlight.js in htmlwidgets/lib), RStudio Inc [cph]
Maintainer: Yihui Xie <xie@yihui.name>

Diff between DT versions 0.11 dated 2019-12-19 and 0.12 dated 2020-02-05

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 DT-0.12/DT/R/datatables.R                                                                              |   13 
 DT-0.12/DT/R/package.R                                                                                 |    2 
 DT-0.12/DT/R/shiny.R                                                                                   |   24 
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Package tolerance updated to version 2.0.0 with previous version 1.3.0 dated 2017-02-04

Title: Statistical Tolerance Intervals and Regions
Description: Statistical tolerance limits provide the limits between which we can expect to find a specified proportion of a sampled population with a given level of confidence. This package provides functions for estimating tolerance limits (intervals) for various univariate distributions (binomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hypergeometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Poisson-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings (linear regression, nonlinear regression, nonparametric regression, and multivariate regression), and analysis of variance tolerance intervals. Visualizations are also available for most of these settings.
Author: Derek S. Young [aut, cre]
Maintainer: Derek S. Young <derek.young@uky.edu>

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Package pomp updated to version 2.6 with previous version 2.5 dated 2020-01-28

Title: Statistical Inference for Partially Observed Markov Processes
Description: Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.
Author: Aaron A. King [aut, cre], Edward L. Ionides [aut], Carles Breto [aut], Stephen P. Ellner [ctb], Matthew J. Ferrari [ctb], Bruce E. Kendall [ctb], Michael Lavine [ctb], Dao Nguyen [ctb], Daniel C. Reuman [ctb], Helen Wearing [ctb], Simon N. Wood [ctb], Sebastian Funk [ctb], Steven G. Johnson [ctb], Eamon O'Dea [ctb]
Maintainer: Aaron A. King <kingaa@umich.edu>

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Package jmvcore updated to version 1.2.5 with previous version 1.0.8 dated 2019-10-21

Title: Dependencies for the 'jamovi' Framework
Description: A framework for creating rich interactive analyses for the jamovi platform (see <https://www.jamovi.org> for more information).
Author: Jonathon Love
Maintainer: Jonathon Love <jon@thon.cc>

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Package hierNet updated to version 1.9 with previous version 1.8 dated 2019-12-08

Title: A Lasso for Hierarchical Interactions
Description: Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect. For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.
Author: Jacob Bien and Rob Tibshirani
Maintainer: Jacob Bien <jbien@usc.edu>

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Package emld updated to version 0.4.0 with previous version 0.3.0 dated 2019-10-11

Title: Ecological Metadata as Linked Data
Description: This is a utility for transforming Ecological Metadata Language ('EML') files into 'JSON-LD' and back into 'EML.' Doing so creates a list-based representation of 'EML' in R, so that 'EML' data can easily be manipulated using standard 'R' tools. This makes this package an effective backend for other 'R'-based tools working with 'EML.' By abstracting away the complexity of 'XML' Schema, developers can build around native 'R' list objects and not have to worry about satisfying many of the additional constraints of set by the schema (such as element ordering, which is handled automatically). Additionally, the 'JSON-LD' representation enables the use of developer-friendly 'JSON' parsing and serialization that may facilitate the use of 'EML' in contexts outside of 'R,' as well as the informatics-friendly serializations such as 'RDF' and 'SPARQL' queries.
Author: Carl Boettiger [aut, cre, cph] (<https://orcid.org/0000-0002-1642-628X>), Matthew B. Jones [aut, cph] (<https://orcid.org/0000-0003-0077-4738>), Bryce Mecum [aut, cph] (<https://orcid.org/0000-0002-0381-3766>)
Maintainer: Carl Boettiger <cboettig@gmail.com>

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Package BAwiR updated to version 1.2.1 with previous version 1.2 dated 2019-02-17

Title: Analysis of Basketball Data
Description: Collection of tools to work with basketball data. Functions available are related to friendly web scraping and visualization. Data were obtained from <http://www.euroleague.net/>, <http://www.eurocupbasketball.com/> and <http://www.acb.com>, following the instructions of their respectives robots.txt files, when available. Tools for visualization include a population pyramid, 2D plots, circular plots of players' percentiles, plots of players' monthly/yearly stats, team heatmaps, team shooting plots, team four factors plots, cross-tables with the results of regular season games and maps of nationalities. Please see Vinue (2020) <doi:10.1089/big.2018.0124>.
Author: Guillermo Vinue
Maintainer: Guillermo Vinue <Guillermo.Vinue@uv.es>

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Package serrsBayes updated to version 0.4-1 with previous version 0.4-0 dated 2019-04-29

Title: Bayesian Modelling of Raman Spectroscopy
Description: Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS), using the method of Moores et al. (2016) <arXiv:1604.07299>. Multivariate observations of SERRS are highly collinear and lend themselves to a reduced-rank representation. The GAMM separates the SERRS signal into three components: a sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline; and additive white noise. The parameters of each component of the model are estimated iteratively using SMC. The posterior distributions of the parameters given the observed spectra are represented as a population of weighted particles.
Author: Matt Moores [aut, cre] (<https://orcid.org/0000-0003-4531-3572>), Jake Carson [aut] (<https://orcid.org/0000-0002-7896-0971>), Benjamin Moskowitz [ctb], Kirsten Gracie [dtc], Karen Faulds [dtc] (<https://orcid.org/0000-0002-5567-7399>), Mark Girolami [aut], Engineering and Physical Sciences Research Council [fnd] (EPSRC programme grant ref: EP/L014165/1), University of Warwick [cph]
Maintainer: Matt Moores <mmoores@gmail.com>

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New package GRANBase with initial version 2.6.20
Package: GRANBase
Type: Package
Title: Creating Continuously Integrated Package Repositories from Manifests
Version: 2.6.20
Encoding: UTF-8
Author: Gabriel Becker[aut,cre], Cory Barr [cre,ctb], Dinakar Kulkarni [aut,ctb]
Maintainer: Gabriel Becker <gabembecker@gmail.com>
Copyright: Genentech Inc
Description: Repository based tools for department and analysis level reproducibility. 'GRANBase' allows creation of custom branched, continuous integration-ready R repositories, including incremental testing of only packages which have changed versions since the last repository build.
License: Artistic-2.0
Depends: GRANCore, switchr (>= 0.13.4), methods
Imports: tools, utils, htmlTable (>= 1.11.0), dplyr, sendmailR, covr, RCurl, jsonlite, stringi, stats, markdown, desc
Suggests: parallel, rmarkdown, hexSticker, knitr, DT (>= 0.2)
VignetteBuilder: knitr
SystemRequirements: svn, git
URL: https://github.com/gmbecker/gRAN
BugReports: https://github.com/gmbecker/gRAN/issues
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2020-02-05 02:44:33 UTC; gabrielbecker
Repository: CRAN
Date/Publication: 2020-02-05 12:10:02 UTC

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Package FSSF updated to version 0.1.1 with previous version 0.1.0 dated 2020-01-31

Title: Generate Fully-Sequential Space-Filling Designs Inside a Unit Hypercube
Description: Provides three methods proposed by Shang and Apley (2019) <doi:10.1080/00224065.2019.1705207> to generate fully-sequential space-filling designs inside a unit hypercube. A 'fully-sequential space-filling design' means a sequence of nested designs (as the design size varies from one point up to some maximum number of points) with the design points added one at a time and such that the design at each size has good space-filling properties. Two methods target the minimum pairwise distance criterion and generate maximin designs, among which one method is more efficient when design size is large. One method targets the maximum hole size criterion and uses a heuristic to generate what is closer to a minimax design.
Author: Boyang Shang and Daniel W. Apley
Maintainer: Boyang Shang <boyangshang2015@u.northwestern.edu>

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Package gdm updated to version 1.4 with previous version 1.3.11 dated 2018-06-05

Title: Generalized Dissimilarity Modeling
Description: A toolkit with functions to fit, plot, summarize, and apply Generalized Dissimilarity Models.
Author: Matthew C. Fitzpatrick, Karel Mokany, Glenn Manion, Matthew Lisk, Simon Ferrier, Diego Nieto-Lugilde
Maintainer: Matthew C. Fitzpatrick <mfitzpatrick@umces.edu>

Diff between gdm versions 1.3.11 dated 2018-06-05 and 1.4 dated 2020-02-05

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Package dttr2 updated to version 0.0.2 with previous version 0.0.1 dated 2019-06-28

Title: Manipulate Dates, DateTimes and Times
Description: Manipulates date (Date), datetime (POSIXct) and time (hms) vectors. Date/times are considered discrete and are floored whenever encountered. Times are wrapped and time zones are maintained unless explicitly altered by the user.
Author: Joe Thorley [aut, cre] (<https://orcid.org/0000-0002-7683-4592>)
Maintainer: Joe Thorley <joe@poissonconsulting.ca>

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Package TRES updated to version 1.1.1 with previous version 1.1.0 dated 2019-11-17

Title: Tensor Regression with Envelope Structure and Three Generic Envelope Estimation Approaches
Description: Provides three estimators for tensor response regression (TRR) and tensor predictor regression (TPR) models with tensor envelope structure. The three types of estimation approaches are generic and can be applied to any envelope estimation problems. The full Grassmannian (FG) optimization is often associated with likelihood-based estimation but requires heavy computation and good initialization; the one-directional optimization approaches (1D and ECD algorithms) are faster, stable and does not require carefully chosen initial values; the SIMPLS-type is motivated by the partial least squares regression and is computationally the least expensive. For details of TRR, see Li L, Zhang X (2017) <doi:10.1080/01621459.2016.1193022>. For details of TPR, see Zhang X, Li L (2017) <doi:10.1080/00401706.2016.1272495>. For details of 1D algorithm, see Cook RD, Zhang X (2016) <doi:10.1080/10618600.2015.1029577>. For details of ECD algorithm, see Cook RD, Zhang X (2018) <doi:10.5705/ss.202016.0037>.
Author: Wenjing Wang [aut], Jing Zeng [aut, cre], Xin Zhang [aut]
Maintainer: Jing Zeng <jing.zeng@stat.fsu.edu>

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Package Numero updated to version 1.4.1 with previous version 1.4.0 dated 2019-12-14

Title: Statistical Framework to Define Subgroups in Complex Datasets
Description: High-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues, please see Gao S, Mutter S, Casey A, Makinen V-P (2019) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, 48:369-37, <doi:10.1093/ije/dyy113>. The framework includes the necessary functions to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.
Author: Song Gao [aut], Stefan Mutter [aut], Aaron E. Casey [aut], Ville-Petteri Makinen [aut, cre]
Maintainer: Ville-Petteri Makinen <vpmakine@gmail.com>

Diff between Numero versions 1.4.0 dated 2019-12-14 and 1.4.1 dated 2020-02-05

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Package NNLM (with last version 0.4.3) was removed from CRAN

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

2019-07-02 0.4.3

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