Wed, 28 Jul 2021

Package DSAIRM updated to version 0.9.3 with previous version 0.8.7 dated 2021-03-20

Title: Dynamical Systems Approach to Immune Response Modeling
Description: Simulation models (apps) of various within-host immune response scenarios. The purpose of the package is to help individuals learn about within-host infection and immune response modeling from a dynamical systems perspective. All apps include explanations of the underlying models and instructions on what to do with the models.
Author: Andreas Handel [aut, cre] (<https://orcid.org/0000-0002-4622-1146>), Cody Dailey [ctb], Yang Ge [ctb], Spencer Hall [ctb], Brian McKay [ctb], Sina Solaimanpour [ctb], Alexis Vittengl [ctb], Henok Woldu [ctb]
Maintainer: Andreas Handel <ahandel@uga.edu>

Diff between DSAIRM versions 0.8.7 dated 2021-03-20 and 0.9.3 dated 2021-07-28

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 DSAIRM-0.9.3/DSAIRM/DESCRIPTION                                                       |   15 
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 DSAIRM-0.9.3/DSAIRM/NAMESPACE                                                         |    5 
 DSAIRM-0.9.3/DSAIRM/NEWS.md                                                           |   11 
 DSAIRM-0.9.3/DSAIRM/R/DSAIRM.R                                                        |   31 
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 DSAIRM-0.9.3/DSAIRM/R/generate_ggplot.R                                               |   26 
 DSAIRM-0.9.3/DSAIRM/R/generate_shinyinput.R                                           |   21 
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 DSAIRM-0.9.3/DSAIRM/R/simulate_basicvirus_modelexploration.R                          |  245 ++--
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 DSAIRM-0.9.3/DSAIRM/R/simulate_pkpdmodel_ode.R                                        |   12 
 DSAIRM-0.9.3/DSAIRM/R/simulate_usanalysis.R                                           |    8 
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Package mgsub updated to version 1.7.3 with previous version 1.7.2 dated 2020-07-22

Title: Safe, Multiple, Simultaneous String Substitution
Description: Designed to enable simultaneous substitution in strings in a safe fashion. Safe means it does not rely on placeholders (which can cause errors in same length matches).
Author: Mark Ewing [aut, cre]
Maintainer: Mark Ewing <b.mark@ewingsonline.com>

Diff between mgsub versions 1.7.2 dated 2020-07-22 and 1.7.3 dated 2021-07-28

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 man/censor_worker.Rd            |   10 +----
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More information about mgsub at CRAN
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Package rscorecard updated to version 0.20.0 with previous version 0.19.1 dated 2021-07-20

Title: A Method to Download Department of Education College Scorecard Data
Description: A method to download Department of Education College Scorecard data using the public API <https://collegescorecard.ed.gov/data/documentation/>. It is based on the 'dplyr' model of piped commands to select and filter data in a single chained function call. An API key from the U.S. Department of Education is required.
Author: Benjamin Skinner [aut, cre] (<https://orcid.org/0000-0002-0337-7415>)
Maintainer: Benjamin Skinner <btskinner@coe.ufl.edu>

Diff between rscorecard versions 0.19.1 dated 2021-07-20 and 0.20.0 dated 2021-07-28

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Package gllvm updated to version 1.3.1 with previous version 1.3.0 dated 2021-04-30

Title: Generalized Linear Latent Variable Models
Description: Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either Laplace approximation method or variational approximation method implemented via TMB (Kristensen et al., (2016), <doi:10.18637/jss.v070.i05>). For details see Niku et al. (2019a) <doi:10.1371/journal.pone.0216129> and Niku et al. (2019b) <doi:10.1111/2041-210X.13303>.
Author: Jenni Niku [aut, cre], Wesley Brooks [aut], Riki Herliansyah [aut], Francis K.C. Hui [aut], Sara Taskinen [aut], David I. Warton [aut], Bert van der Veen [aut]
Maintainer: Jenni Niku <jenni.m.e.niku@jyu.fi>

Diff between gllvm versions 1.3.0 dated 2021-04-30 and 1.3.1 dated 2021-07-28

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Package Qtools updated to version 1.5.5 with previous version 1.5.4 dated 2021-02-07

Title: Utilities for Quantiles
Description: Functions for unconditional and conditional quantiles. These include methods for transformation-based quantile regression, quantile-based measures of location, scale and shape, methods for quantiles of discrete variables, quantile-based multiple imputation, restricted quantile regression, and directional quantile classification. A vignette is given in Geraci (2016, The R Journal) <doi:10.32614/RJ-2016-037> and included in the package.
Author: Marco Geraci [aut, cph, cre] (<https://orcid.org/0000-0002-6311-8685>), Alessio Farcomeni [ctb] (Contributions to midrq code, <https://orcid.org/0000-0002-7104-5826>), Cinzia Viroli [ctb] (Contributions to dqc code, <https://orcid.org/0000-0002-3278-5266>)
Maintainer: Marco Geraci <marco.geraci@uniroma1.it>

Diff between Qtools versions 1.5.4 dated 2021-02-07 and 1.5.5 dated 2021-07-28

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 MD5                   |   24 ++++++++++----------
 NEWS                  |    8 ++++++
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 man/cmidecdf.Rd       |    2 -
 man/midq2q.Rd         |   14 +++++++++--
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Permanent link

Package AlphaSimR updated to version 1.0.2 with previous version 1.0.1 dated 2021-05-14

Title: Breeding Program Simulations
Description: The successor to the 'AlphaSim' software for breeding program simulation [Faux et al. (2016) <doi:10.3835/plantgenome2016.02.0013>]. Used for stochastic simulations of breeding programs to the level of DNA sequence for every individual. Contained is a wide range of functions for modeling common tasks in a breeding program, such as selection and crossing. These functions allow for constructing simulations of highly complex plant and animal breeding programs via scripting in the R software environment. Such simulations can be used to evaluate overall breeding program performance and conduct research into breeding program design, such as implementation of genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') for fast simulation of biallelic sequences according to a population demographic history [Chen et al. (2009) <doi:10.1101/gr.083634.108>].
Author: Chris Gaynor [aut, cre] (<https://orcid.org/0000-0003-0558-6656>), Gregor Gorjanc [ctb] (<https://orcid.org/0000-0001-8008-2787>), John Hickey [ctb] (<https://orcid.org/0000-0001-5675-3974>), Daniel Money [ctb] (<https://orcid.org/0000-0001-5151-3648>), David Wilson [ctb]
Maintainer: Chris Gaynor <gaynor.robert@hotmail.com>

Diff between AlphaSimR versions 1.0.1 dated 2021-05-14 and 1.0.2 dated 2021-07-28

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Package apsimx (with last version 1.995) was removed from CRAN

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

2021-07-10 1.995
2021-07-01 1.993
2021-03-15 1.977
2021-01-14 1.971
2020-10-16 1.964
2020-07-09 1.946
2020-07-01 1.941

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Package pomp updated to version 3.5 with previous version 3.4 dated 2021-05-13

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 B. O'Dea [ctb]
Maintainer: Aaron A. King <kingaa@umich.edu>

Diff between pomp versions 3.4 dated 2021-05-13 and 3.5 dated 2021-07-28

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Package insurancerating updated to version 0.6.7 with previous version 0.6.6 dated 2021-05-19

Title: Analytic Insurance Rating Techniques
Description: Methods for insurance rating. It helps actuaries to implement GLMs within all relevant steps needed to construct a risk premium from raw data. It provides a data driven strategy for the construction of insurance tariff classes. This strategy is based on the work by Antonio and Valdez (2012) <doi:10.1007/s10182-011-0152-7>. It also provides recipes on how to easily perform one-way, or univariate, analyses on an insurance portfolio. In addition it adds functionality to include reference categories in the levels of the coefficients in the output of a generalized linear regression analysis.
Author: Martin Haringa [aut, cre]
Maintainer: Martin Haringa <mtharinga@gmail.com>

Diff between insurancerating versions 0.6.6 dated 2021-05-19 and 0.6.7 dated 2021-07-28

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Package fastai updated to version 2.0.9 with previous version 2.0.8 dated 2021-05-16

Title: Interface to 'fastai'
Description: The 'fastai' <https://docs.fast.ai/index.html> library simplifies training fast and accurate neural networks using modern best practices. It is based on research in to deep learning best practices undertaken at 'fast.ai', including 'out of the box' support for vision, text, tabular, audio, time series, and collaborative filtering models.
Author: Turgut Abdullayev [ctb, cre, cph, aut]
Maintainer: Turgut Abdullayev <turqut.a.314@gmail.com>

Diff between fastai versions 2.0.8 dated 2021-05-16 and 2.0.9 dated 2021-07-28

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Package deepNN updated to version 1.1 with previous version 1.0 dated 2020-03-05

Title: Deep Learning
Description: Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) <https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi> Neural Networks YouTube playlist. Michael A. Nielsen <http://neuralnetworksanddeeplearning.com/> Neural Networks and Deep Learning.
Author: Benjamin Taylor [aut, cre]
Maintainer: Benjamin Taylor <benjamin.taylor.software@gmail.com>

Diff between deepNN versions 1.0 dated 2020-03-05 and 1.1 dated 2021-07-28

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New package RcppML with initial version 0.1.0
Package: RcppML
Title: Rcpp Machine Learning Library
Version: 0.1.0
Date: 2021-06-29
Description: High-performance machine learning algorithms implemented in 'Rcpp' and 'RcppEigen', including non-negative factorization of sparse matrices, projection of linear factor models, and non-negative least squares.
License: GPL (>= 2)
Imports: Rcpp, Matrix, methods, stats
LinkingTo: Rcpp, RcppEigen
VignetteBuilder: knitr
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
URL: https://github.com/zdebruine/RcppML
BugReports: https://github.com/zdebruine/RcppML/issues
NeedsCompilation: yes
Packaged: 2021-07-28 11:36:50 UTC; Owner
Author: Zachary DeBruine [aut, cre] (<https://orcid.org/0000-0003-2234-4827>)
Maintainer: Zachary DeBruine <zacharydebruine@gmail.com>
Repository: CRAN
Date/Publication: 2021-07-28 12:10:08 UTC

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Package protolite updated to version 2.1.1 with previous version 2.1 dated 2020-01-13

Title: Highly Optimized Protocol Buffer Serializers
Description: Pure C++ implementations for reading and writing several common data formats based on Google protocol-buffers. Currently supports 'rexp.proto' for serialized R objects, 'geobuf.proto' for binary geojson, and 'mvt.proto' for vector tiles. This package uses the auto-generated C++ code by protobuf-compiler, hence the entire serialization is optimized at compile time. The 'RProtoBuf' package on the other hand uses the protobuf runtime library to provide a general- purpose toolkit for reading and writing arbitrary protocol-buffer data in R.
Author: Jeroen Ooms
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between protolite versions 2.1 dated 2020-01-13 and 2.1.1 dated 2021-07-28

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Package lwgeom updated to version 0.2-7 with previous version 0.2-6 dated 2021-04-02

Title: Bindings to Selected 'liblwgeom' Functions for Simple Features
Description: Access to selected functions found in 'liblwgeom' <https://github.com/postgis/postgis/tree/master/liblwgeom>, the light-weight geometry library used by 'PostGIS' <http://postgis.net/>.
Author: Edzer Pebesma [aut, cre] (<https://orcid.org/0000-0001-8049-7069>), Colin Rundel [ctb], Andy Teucher [ctb], liblwgeom developers [cph]
Maintainer: Edzer Pebesma <edzer.pebesma@uni-muenster.de>

Diff between lwgeom versions 0.2-6 dated 2021-04-02 and 0.2-7 dated 2021-07-28

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Package cld3 updated to version 1.4.2 with previous version 1.4.1 dated 2021-01-11

Title: Google's Compact Language Detector 3
Description: Google's Compact Language Detector 3 is a neural network model for language identification and the successor of 'cld2' (available from CRAN). The algorithm is still experimental and takes a novel approach to language detection with different properties and outcomes. It can be useful to combine this with the Bayesian classifier results from 'cld2'. See <https://github.com/google/cld3#readme> for more information.
Author: Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>), Google Inc [cph] (CLD3 C++ library)
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between cld3 versions 1.4.1 dated 2021-01-11 and 1.4.2 dated 2021-07-28

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New package bdrc with initial version 1.0.0
Package: bdrc
Title: Bayesian Discharge Rating Curves
Version: 1.0.0
Maintainer: Solvi Rognvaldsson <solviro@gmail.com>
Description: Fits a discharge rating curve based on the power-law and the generalized power-law from data on paired stage and discharge measurements in a given river using a Bayesian hierarchical model as described in Hrafnkelsson et al. (2020) <arXiv:2010.04769>.
Depends: R (>= 3.5.0)
License: MIT + file LICENSE
LazyData: true
Imports: ggplot2, grid, gridExtra, rlang, scales
Suggests: testthat, knitr, rmarkdown, covr, vdiffr
VignetteBuilder: knitr
BugReports: https://github.com/sor16/bdrc/issues
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2021-07-28 07:37:21 UTC; solviro
Author: Birgir Hrafnkelsson [aut, cph] (<https://orcid.org/0000-0003-1864-9652>), Solvi Rognvaldsson [aut, cre] (<https://orcid.org/0000-0002-4376-3361>), Axel Orn Jansson [aut], Rafael Vias [aut]
Repository: CRAN
Date/Publication: 2021-07-28 12:10:04 UTC

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New package tldr with initial version 0.2.3
Package: tldr
Title: T Loux Doing R: Functions to Simplify Data Analysis and Reporting
Version: 0.2.3
Description: Gives a number of functions to aid common data analysis processes and reporting statistical results in an 'RMarkdown' file. Data analysis functions combine multiple base R functions used to describe simple bivariate relationships into a single, easy to use function. Reporting functions will return character strings to report p-values, confidence intervals, and hypothesis test and regression results. Strings will be LaTeX-formatted as necessary and will knit pretty in an 'RMarkdown' document. The package also provides a wrapper for the CreateTableOne() function in the 'tableone' package to make the results knit-able.
Depends: R (>= 3.3.1)
Imports: tableone, ggplot2, reshape2
Suggests: knitr
License: GPL-3
Encoding: UTF-8
LazyData: false
NeedsCompilation: no
Packaged: 2021-07-15 19:42:10 UTC; loux
Author: Travis Loux [aut, cre]
Maintainer: Travis Loux <travis.loux@slu.edu>
Repository: CRAN
Date/Publication: 2021-07-28 11:40:02 UTC

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Package rebmix updated to version 2.13.1 with previous version 2.13.0 dated 2021-07-14

Title: Finite Mixture Modeling, Clustering & Classification
Description: Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac or circular von Mises parametric families.
Author: Marko Nagode [aut, cre] (<https://orcid.org/0000-0003-0637-3812>), Branislav Panic [ctb] (<https://orcid.org/0000-0001-8349-8550>), Jernej Klemenc [ctb] (<https://orcid.org/0000-0002-6778-6728>), Simon Oman [ctb] (<https://orcid.org/0000-0001-8213-0818>)
Maintainer: Marko Nagode <marko.nagode@fs.uni-lj.si>

Diff between rebmix versions 2.13.0 dated 2021-07-14 and 2.13.1 dated 2021-07-28

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New package predfairness with initial version 0.1.0
Package: predfairness
Title: Discrimination Mitigation for Machine Learning Models
Version: 0.1.0
Date: 2021-07-14
Maintainer: Thaís de Bessa Gontijo de Oliveira <thais.bgo@gmail.com>
Description: Based on different statistical definitions of discrimination, several methods have been proposed to detect and mitigate social inequality in machine learning models. This package aims to provide an alternative to fairness treatment in predictive models. The ROC method implemented in this package is described by Kamiran, Karim and Zhang (2012) <https://ieeexplore.ieee.org/document/6413831/>.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Suggests: caret, stats
NeedsCompilation: no
Packaged: 2021-07-27 19:51:49 UTC; leonardopv
Author: Thaís de Bessa Gontijo de Oliveira [aut, cre], Leonardo Paes Vieira [aut], Gustavo Rodrigues Lacerda Silva [ctb], Barbara Bianca Alves Cardoso [ctb], Douglas Alexandre Gomes Vieira [ctb]
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2021-07-28 11:50:02 UTC

More information about predfairness at CRAN
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New package MedianaDesigner with initial version 0.2.2
Package: MedianaDesigner
Title: Efficient Simulation-Based Power and Sample Size Calculations for a Broad Class of Late-Stage Clinical Trials
Version: 0.2.2
Date: 2021-07-12
Author: Alex Dmitrienko [aut, cre]
Depends: R (>= 3.1.2)
Imports: Rcpp (>= 0.12.10), officer, flextable, devEMF, shiny, shinydashboard, shinyMatrix
Suggests: testthat
LinkingTo: Rcpp
Maintainer: Alex Dmitrienko <admitrienko@mediana.us>
Description: The following modules are included in the package: Adaptive designs with data-driven sample size or event count re-estimation, Adaptive designs with data-driven treatment selection, Adaptive designs with data-driven population selection, Optimal selection of a futility stopping rule, Event prediction in event-driven trials.
License: GPL-3
LazyLoad: yes
LazyData: true
NeedsCompilation: yes
Packaged: 2021-07-25 12:26:50 UTC; victordmitrienko
Repository: CRAN
Date/Publication: 2021-07-28 11:20:12 UTC
URL: https://github.com/medianasoft/MedianaDesigner
BugReports: https://github.com/medianasoft/MedianaDesigner/issues

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New package ipeadatar with initial version 0.1.4
Package: ipeadatar
Title: API Wrapper for 'Ipeadata'
Version: 0.1.4
Author: Luiz Eduardo S. Gomes [aut, cre], Jessyka A. P. Goltara [ctb]
Maintainer: Luiz Eduardo S. Gomes <gomes.leduardo@gmail.com>
Description: Allows direct access to the macroeconomic, financial and regional database maintained by Brazilian Institute for Applied Economic Research ('Ipea'). This R package uses the 'Ipeadata' API. For more information, see <http://www.ipeadata.gov.br/>.
Depends: R (>= 3.5.0)
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://github.com/gomesleduardo/ipeadatar
BugReports: https://github.com/gomesleduardo/ipeadatar/issues
Imports: stringr, curl, rlang, jsonlite, magrittr, dplyr, lubridate, purrr, sjlabelled
NeedsCompilation: no
LazyData: false
Packaged: 2021-07-24 01:51:40 UTC; Eduardo
Repository: CRAN
Date/Publication: 2021-07-28 11:20:06 UTC

More information about ipeadatar at CRAN
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Package fairadapt updated to version 0.2.0 with previous version 0.1.0 dated 2019-11-28

Title: Fair Data Adaptation with Quantile Preservation
Description: An implementation of the fair data adaptation with quantile preservation described in Plecko & Meinshausen (2019) <arXiv:1911.06685>. The adaptation procedure uses the specified causal graph to pre-process the given training and testing data in such a way to remove the bias caused by the protected attribute. The procedure uses tree ensembles for quantile regression.
Author: Drago Plecko [aut, cre]
Maintainer: Drago Plecko <drago.plecko@stat.math.ethz.ch>

Diff between fairadapt versions 0.1.0 dated 2019-11-28 and 0.2.0 dated 2021-07-28

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Package blockcluster updated to version 4.5.1 with previous version 4.5.0 dated 2021-07-13

Title: Co-Clustering Package for Binary, Categorical, Contingency and Continuous Data-Sets
Description: Simultaneous clustering of rows and columns, usually designated by biclustering, co-clustering or block clustering, is an important technique in two way data analysis. It consists of estimating a mixture model which takes into account the block clustering problem on both the individual and variables sets. The 'blockcluster' package provides a bridge between the C++ core library build on top of the 'STK++' library, and the R statistical computing environment. This package allows to co-cluster binary <doi:10.1016/j.csda.2007.09.007>, contingency <doi:10.1080/03610920903140197>, continuous <doi:10.1007/s11634-013-0161-3> and categorical data-sets <doi:10.1007/s11222-014-9472-2>. It also provides utility functions to visualize the results. This package may be useful for various applications in fields of Data mining, Information retrieval, Biology, computer vision and many more. More information about the project and comprehensive tutorial can be found on the link mentioned in URL.
Author: Serge Iovleff [aut, cre], Parmeet Singh Bhatia [aut], Josselin Demont [ctb], Vincent Brault [ctb], Vincent Kubicki [ctb], Gerard Goavert [ctb], Christophe Biernacki [ctb], Gilles Celeux [ctb]
Maintainer: Serge Iovleff <Serge.Iovleff@stkpp.org>

Diff between blockcluster versions 4.5.0 dated 2021-07-13 and 4.5.1 dated 2021-07-28

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New package assemblerr with initial version 0.1.0
Package: assemblerr
Title: Assembly of Pharmacometric Models
Description: Construct pharmacometric nonlinear mixed effect models by combining predefined model components and automatically generate model code for NONMEM. Models are created by combining parameter and observation models, algebraic relationships, compartments, and flows. Pharmacokinetic models can be assembled from the higher-order components: absorption, distribution, and elimination. The generated code is optimized for performance by recognizing, for example, linear differential equations or differential equations with an analytic solution.
Version: 0.1.0
License: MIT + file LICENSE
Encoding: UTF-8
Suggests: testthat (>= 3.0.2), knitr, rmarkdown, covr, withr, markdown
Imports: purrr, rlang, magrittr, methods, glue, vctrs (>= 0.3.4), cli (>= 2.1.0), tidyselect
VignetteBuilder: knitr
ByteCompile: true
URL: https://github.com/UUPharmacometrics/assemblerr
BugReports: https://github.com/UUPharmacometrics/assemblerr/issues
NeedsCompilation: no
Packaged: 2021-07-27 13:56:14 UTC; seb
Author: Sebastian Ueckert [aut, cre, cph] (<https://orcid.org/0000-0002-3712-0255>), Mats O. Karlsson [sad], Andrew C. Hooker [sad], Rikard Nordgren [sad], Simon Carter [rev], Simon Buatois [rev], João A. Abrantes [rev], F. Hoffmann-La Roche Ltd. [fnd]
Maintainer: Sebastian Ueckert <sebastian.ueckert@gmail.com>
Repository: CRAN
Date/Publication: 2021-07-28 11:40:05 UTC

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New package ADP with initial version 0.1.3
Package: ADP
Title: Adoption Probability, Triers and Users Rate of a New Product
Version: 0.1.3
Date: 2021-07-27
Maintainer: Mickey Kislev <mickeykislev@researchgap.ac>
Description: Calculating users of a product based on the prevalence of triers in the population. The measuring of triers is relatively easy. It is just a question of whether a person tried a product even once in his life or not. While measuring the rate of people who also adopt it as part of their life is more complicated since the adoption of an innovative product is a subjective view of the individual. Mickey Kislev and Shira Kislev developed a formula to calculates the prevalence of users of a product to overcome this difficulty. The current calculator assists in calculating the users of a product based on the prevalence of triers in the population. See for: Kislev, M., and S. Kislev (2020) <doi:10.5539/ijms.v12n4p63>.
License: GPL-3
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2021-07-27 19:20:56 UTC; micke
Author: Mickey Kislev [cre], Shira Kislev [aut]
Repository: CRAN
Date/Publication: 2021-07-28 11:50:05 UTC

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Package tergm updated to version 4.0.2 with previous version 4.0.1 dated 2021-06-24

Title: Fit, Simulate and Diagnose Models for Network Evolution Based on Exponential-Family Random Graph Models
Description: An integrated set of extensions to the 'ergm' package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). 'tergm' is a part of the 'statnet' suite of packages for network analysis. See Krivitsky and Handcock (2014) <doi:10.1111/rssb.12014> and Carnegie, Krivitsky, Hunter, and Goodreau (2015) <doi:10.1080/10618600.2014.903087>.
Author: Pavel N. Krivitsky [aut, cre] (<https://orcid.org/0000-0002-9101-3362>), Mark S. Handcock [aut, ths], David R. Hunter [ctb], Steven M. Goodreau [ctb, ths], Martina Morris [ctb, ths], Nicole Bohme Carnegie [ctb], Carter T. Butts [ctb], Ayn Leslie-Cook [ctb], Skye Bender-deMoll [ctb], Li Wang [ctb], Kirk Li [ctb], Chad Klumb [ctb]
Maintainer: Pavel N. Krivitsky <pavel+tergm@unsw.edu.au>

Diff between tergm versions 4.0.1 dated 2021-06-24 and 4.0.2 dated 2021-07-28

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New package stringx with initial version 0.1.2
Package: stringx
Title: Drop-in Replacements for Base String Functions Powered by 'stringi'
Version: 0.1.2
Date: 2021-07-27
Description: English is the native language for only 5% of the World population. Also, only 17% of us can understand this text. Moreover, the Latin alphabet is the main one for merely 36% of the total. The early computer era, now a very long time ago, was dominated by the US. Due to the proliferation of the internet, smartphones, social media, and other technologies and communication platforms, this is no longer the case. This package replaces base R string functions (such as grep(), tolower(), and sprintf()) with ones that fully support the Unicode standards related to natural language processing, fixes some long-standing inconsistencies, and introduces some new, useful features. Thanks to 'ICU' (International Components for Unicode) and 'stringi', they are fast, reliable, and portable across different platforms.
BugReports: https://github.com/gagolews/stringx/issues
URL: https://stringx.gagolewski.com/
License: GPL (>= 2)
Depends: R (>= 4.1.0)
Imports: stringi (>= 1.7.2)
Suggests: realtest (>= 0.2.1)
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2021-07-27 08:26:13 UTC; gagolews
Author: Marek Gagolewski [aut, cre, cph] (<https://orcid.org/0000-0003-0637-6028>)
Maintainer: Marek Gagolewski <marek@gagolewski.com>
Repository: CRAN
Date/Publication: 2021-07-28 10:50:02 UTC

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New package profoc with initial version 0.8.0
Package: profoc
Title: Probabilistic Forecast Combination Using CRPS Learning
Version: 0.8.0
Date: 2021-07-27
Description: Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <arXiv:2102.00968>. The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) <arXiv:1404.1356>. Quantile regression is also implemented for comparison purposes. Model parameters can be tuned automatically with respect to the loss of the forecast combination. Methods like predict(), update(), plot() and print() are available for convenience. This package utilizes the optim C++ library for numeric optimization <https://github.com/kthohr/optim>.
License: GPL (>= 3)
Encoding: UTF-8
Depends: R (>= 3.0.2)
Imports: Rcpp (>= 1.0.5), Matrix
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, splines2 (>= 0.3.0)
URL: https://profoc.berrisch.biz/, https://github.com/BerriJ/profoc
BugReports: https://github.com/BerriJ/profoc/issues
Suggests: testthat (>= 3.0.0), gamlss.dist, ggplot2
NeedsCompilation: yes
Packaged: 2021-07-27 09:14:49 UTC; vscode
Author: Jonathan Berrisch [cre] (<https://orcid.org/0000-0002-4944-9074>), Florian Ziel [aut] (<https://orcid.org/0000-0002-2974-2660>)
Maintainer: Jonathan Berrisch <Jonathan@Berrisch.biz>
Repository: CRAN
Date/Publication: 2021-07-28 11:00:02 UTC

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Package osmdata updated to version 0.1.6 with previous version 0.1.5 dated 2021-03-22

Title: Import 'OpenStreetMap' Data as Simple Features or Spatial Objects
Description: Download and import of 'OpenStreetMap' ('OSM') data as 'sf' or 'sp' objects. 'OSM' data are extracted from the 'Overpass' web server (<https://overpass-api.de/>) and processed with very fast 'C++' routines for return to 'R'.
Author: Mark Padgham [aut, cre], Bob Rudis [aut], Robin Lovelace [aut], Maëlle Salmon [aut], Andrew Smith [ctb], James Smith [ctb], Andrea Gilardi [ctb], Enrico Spinielli [ctb], Marcin Kalicinski [ctb, cph] (Author of included RapidXML code), Finkelstein Noam [ctb, cph] (Author of included stub.R code), Bartnik Lukasz [ctb, cph] (Author of included stub.R code)
Maintainer: Mark Padgham <mark.padgham@email.com>

Diff between osmdata versions 0.1.5 dated 2021-03-22 and 0.1.6 dated 2021-07-28

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New package LFApp with initial version 1.3
Package: LFApp
Version: 1.3
Date: 2021-07-27
Title: Shiny Apps for Lateral Flow Assays
Author: Filip Paskali [aut, cre], Weronika Schary [aut] (<https://orcid.org/0000-0002-7229-316X>), Matthias Kohl [aut] (<https://orcid.org/0000-0001-9514-8910>)
Maintainer: Filip Paskali <F.Paskali@hs-furtwangen.de>
Description: Shiny apps for the quantitative analysis of images from lateral flow assays (LFAs). The images are segmented and background corrected and color intensities are extracted. The apps can be used to import and export intensity data and to calibrate LFAs by means of linear, loess, or gam models. The calibration models can further be saved and applied to intensity data from new images for determining concentrations.
License: LGPL-3
Depends: R (>= 4.0.0)
Imports: stats, utils, graphics, methods, mgcv, shiny, shinyjs, shinythemes, shinyFiles, shinyMobile (>= 0.9), EBImage, DT, ggplot2, fs
Suggests: knitr, rmarkdown, remotes
VignetteBuilder: knitr
Encoding: UTF-8
URL: https://github.com/fpaskali/LFApp
BugReports: https://github.com/fpaskali/LFApp/issues
NeedsCompilation: no
Packaged: 2021-07-27 09:06:00 UTC; kohlm
Repository: CRAN
Date/Publication: 2021-07-28 11:00:05 UTC

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Package IntCal updated to version 0.2.2 with previous version 0.2.1 dated 2021-07-19

Title: Radiocarbon Calibration Curves
Description: The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided here in a single data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98) and postbomb curves. Also provided are functions to copy the curves into memory, and to plot the curves and their underlying data, as well as functions to calibrate radiocarbon dates.
Author: Maarten Blaauw [aut, cre]
Maintainer: Maarten Blaauw <maarten.blaauw@qub.ac.uk>

Diff between IntCal versions 0.2.1 dated 2021-07-19 and 0.2.2 dated 2021-07-28

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Package fRLR updated to version 1.2 with previous version 1.1 dated 2019-01-08

Title: Fit Repeated Linear Regressions
Description: When fitting a set of linear regressions which have some same variables, we can separate the matrix and reduce the computation cost. This package aims to fit a set of repeated linear regressions faster. More details can be found in this blog Lijun Wang (2017) <https://stats.hohoweiya.xyz//regression/2017/09/26/An-R-Package-Fit-Repeated-Linear-Regressions/>.
Author: Lijun Wang [aut, cre, cph]
Maintainer: Lijun Wang <szcfweiya@gmail.com>

Diff between fRLR versions 1.1 dated 2019-01-08 and 1.2 dated 2021-07-28

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Package findInGit updated to version 0.1.1 with previous version 0.1.0 dated 2021-07-27

Title: Find Pattern in Files of All Branches of a 'git' Repository
Description: Creates a HTML widget which displays the results of searching for a pattern in files in a given 'git' repository, including all its branches. The results can also be returned in a dataframe.
Author: Stéphane Laurent [aut, cre], Rob Burns [cph] ('ansi-to-html' library)
Maintainer: Stéphane Laurent <laurent_step@outlook.fr>

Diff between findInGit versions 0.1.0 dated 2021-07-27 and 0.1.1 dated 2021-07-28

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Package fdaPOIFD updated to version 1.0.1 with previous version 1.0.0 dated 2021-01-13

Title: Partially Observed Integrated Functional Depth
Description: Integrated Depths for Partially Observed Functional Data (PoFD). Applications to visualization, outlier detection and classification. Software companion for Elías, Antonio, Jiménez, Raúl, Paganoni, Anna M. and Sangalli, Laura M., (2020), "Integrated Depth for Partially Observed Functional Data".
Author: Antonio Elías [aut, cre], Raúl Jiménez [ctb], Anna M. Paganoni [ctb], Laura M. Sangalli [ctb]
Maintainer: Antonio Elías <antonioefz91@gmail.com>

Diff between fdaPOIFD versions 1.0.0 dated 2021-01-13 and 1.0.1 dated 2021-07-28

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Package e1071 updated to version 1.7-8 with previous version 1.7-7 dated 2021-05-23

Title: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien
Description: Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, generalized k-nearest neighbour ...
Author: David Meyer [aut, cre], Evgenia Dimitriadou [aut, cph], Kurt Hornik [aut], Andreas Weingessel [aut], Friedrich Leisch [aut], Chih-Chung Chang [ctb, cph] (libsvm C++-code), Chih-Chen Lin [ctb, cph] (libsvm C++-code)
Maintainer: David Meyer <David.Meyer@R-project.org>

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Package DALEX updated to version 2.3.0 with previous version 2.2.1 dated 2021-06-01

Title: moDel Agnostic Language for Exploration and eXplanation
Description: Unverified black box model is the path to the failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection. DALEX package xrays any model and helps to explore and explain its behaviour. Machine Learning (ML) models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance. But such black-box models usually lack of direct interpretability. DALEX package contains various methods that help to understand the link between input variables and model output. Implemented methods help to explore model on the level of a single instance as well as a level of the whole dataset. All model explainers are model agnostic and can be compared across different models. DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration. Find more details in (Biecek 2018) <arXiv:1806.08915>.
Author: Przemyslaw Biecek [aut, cre] (<https://orcid.org/0000-0001-8423-1823>), Szymon Maksymiuk [aut] (<https://orcid.org/0000-0002-3120-1601>), Hubert Baniecki [aut] (<https://orcid.org/0000-0001-6661-5364>)
Maintainer: Przemyslaw Biecek <przemyslaw.biecek@gmail.com>

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Package ctmm updated to version 0.6.1 with previous version 0.6.0 dated 2021-01-10

Title: Continuous-Time Movement Modeling
Description: Functions for identifying, fitting, and applying continuous-space, continuous-time stochastic movement models to animal tracking data. The package is described in Calabrese et al (2016) <doi:10.1111/2041-210X.12559>, with models and methods based on those introduced in Fleming & Calabrese et al (2014) <doi:10.1086/675504>, Fleming et al (2014) <doi:10.1111/2041-210X.12176>, Fleming et al (2015) <doi:10.1103/PhysRevE.91.032107>, Fleming et al (2015) <doi:10.1890/14-2010.1>, Fleming et al (2016) <doi:10.1890/15-1607>, Péron & Fleming et al (2016) <doi:10.1186/s40462-016-0084-7>, Fleming & Calabrese (2017) <doi:10.1111/2041-210X.12673>, Péron et al (2017) <doi:10.1002/ecm.1260>, Fleming et al (2017) <doi:10.1016/j.ecoinf.2017.04.008>, Fleming et al (2018) <doi:10.1002/eap.1704>, Winner & Noonan et al (2018) <doi:10.1111/2041-210X.13027>, Fleming et al (2019) <doi:10.1111/2041-210X.13270>, Noonan & Fleming et al (2019) <doi:10.1186/s40462-019-0177-1>, Fleming et al (2020) <doi:10.1101/2020.06.12.130195>, and Noonan et al (2021) <doi:10.1111/2041-210X.13597>.
Author: Christen H. Fleming [aut, cre], Justin M. Calabrese [aut], Xianghui Dong [ctb], Kevin Winner [ctb], Guillaume Péron [ctb], Michael J. Noonan [ctb], Bart Kranstauber [ctb], Eliezer Gurarie [ctb], Kamran Safi [ctb], Paul C. Cross [dtc], Thomas Mueller [dtc], Rogério C. de Paula [dtc], Thomas Akre [dtc], Jonathan Drescher-Lehman [dtc], Autumn-Lynn Harrison [dtc], Ronaldo G. Morato [dtc]
Maintainer: Christen H. Fleming <flemingc@si.edu>

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Package kindisperse updated to version 0.10.2 with previous version 0.10.1 dated 2021-07-17

Title: Simulate and Estimate Close-Kin Dispersal Kernels
Description: Functions for simulating and estimating kinship-related dispersal. Based on the methods described in M. Jasper, T.L. Schmidt., N.W. Ahmad, S.P. Sinkins & A.A. Hoffmann (2019) <doi:10.1111/1755-0998.13043> "A genomic approach to inferring kinship reveals limited intergenerational dispersal in the yellow fever mosquito". Assumes an additive variance model of dispersal in two dimensions, compatible with Wright's neighbourhood area. Simple and composite dispersal simulations are supplied, as well as the functions needed to estimate parent-offspring dispersal for simulated or empirical data, and to undertake sampling design for future field studies of dispersal. For ease of use an integrated Shiny app is also included.
Author: Moshe-Elijah Jasper [aut, cre] (<https://orcid.org/0000-0003-4541-3223>)
Maintainer: Moshe-Elijah Jasper <moshe.jasper@unimelb.edu.au>

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Package DGVM3D (with last version 1.0.0) was removed from CRAN

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

2018-03-19 1.0.0

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Package stringdist updated to version 0.9.7 with previous version 0.9.6.3 dated 2020-10-09

Title: Approximate String Matching, Fuzzy Text Search, and String Distance Functions
Description: Implements an approximate string matching version of R's native 'match' function. Also offers fuzzy text search based on various string distance measures. Can calculate various string distances based on edits (Damerau-Levenshtein, Hamming, Levenshtein, optimal sting alignment), qgrams (q- gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An implementation of soundex is provided as well. Distances can be computed between character vectors while taking proper care of encoding or between integer vectors representing generic sequences. This package is built for speed and runs in parallel by using 'openMP'. An API for C or C++ is exposed as well. Reference: MPJ van der Loo (2014) <doi:10.32614/RJ-2014-011>.
Author: Mark van der Loo [aut, cre] (<https://orcid.org/0000-0002-9807-4686>), Jan van der Laan [ctb], R Core Team [ctb], Nick Logan [ctb], Chris Muir [ctb], Johannes Gruber [ctb]
Maintainer: Mark van der Loo <mark.vanderloo@gmail.com>

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Package OptCirClust updated to version 0.0.4 with previous version 0.0.3 dated 2020-12-18

Title: Circular, Periodic, or Framed Data Clustering: Fast, Optimal, and Reproducible
Description: Fast, optimal, and reproducible clustering algorithms for circular, periodic, or framed data. The algorithms introduced here are based on a core algorithm for optimal framed clustering the authors have developed (Debnath & Song 2021) <doi:10.1109/TCBB.2021.3077573>. The runtime of these algorithms is O(K N log^2 N), where K is the number of clusters and N is the number of circular data points. On a desktop computer using a single processor core, millions of data points can be grouped into a few clusters within seconds. One can apply the algorithms to characterize events along circular DNA molecules, circular RNA molecules, and circular genomes of bacteria, chloroplast, and mitochondria. One can also cluster climate data along any given longitude or latitude. Periodic data clustering can be formulated as circular clustering. The algorithms offer a general high-performance solution to circular, periodic, or framed data clustering.
Author: Tathagata Debnath [aut] (<https://orcid.org/0000-0001-6445-275X>), Joe Song [aut, cre] (<https://orcid.org/0000-0002-6883-6547>)
Maintainer: Joe Song <joemsong@cs.nmsu.edu>

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Package matrixcalc updated to version 1.0-5 with previous version 1.0-4 dated 2021-06-03

Title: Collection of Functions for Matrix Calculations
Description: A collection of functions to support matrix calculations for probability, econometric and numerical analysis. There are additional functions that are comparable to APL functions which are useful for actuarial models such as pension mathematics. This package is used for teaching and research purposes at the Department of Finance and Risk Engineering, New York University, Polytechnic Institute, Brooklyn, NY 11201. Horn, R.A. (1990) Matrix Analysis. ISBN 978-0521386326. Lancaster, P. (1969) Theory of Matrices. ISBN 978-0124355507. Lay, D.C. (1995) Linear Algebra: And Its Applications. ISBN 978-0201845563.
Author: Frederick Novomestky <fnovomes@poly.edu>
Maintainer: S. Thomas Kelly <tomkellygenetics@gmail.com>

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Package covid19india updated to version 0.1.3 with previous version 0.1.2 dated 2021-07-21

Title: Pulling Clean Data from Covid19india.org
Description: Pull raw and pre-cleaned versions of national and state-level COVID-19 time-series data from covid19india.org <https://www.covid19india.org>. Easily obtain and merge case count data, testing data, and vaccine data. Also assists in calculating the time-varying effective reproduction number with sensible parameters for COVID-19.
Author: Max Salvatore [aut, cre], Michael Kleinsasser [aut]
Maintainer: Max Salvatore <mmsalva@umich.edu>

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Package wcde updated to version 0.0.2 with previous version 0.0.1 dated 2021-06-23

Title: Download Data from the Wittgenstein Human Capital Data Explorer
Description: Download and plot education specific demographic data from the Wittgenstein Human Capital Data Explorer <http://dataexplorer.wittgensteincentre.org/>.
Author: Guy J. Abel [aut, cre, ctb] (<https://orcid.org/0000-0002-4893-5687>), Samir K.C. [ctb] (<https://orcid.org/0000-0002-5213-9181>), Michaela Potancokova [ctb], Claudia Reiter [ctb] (<https://orcid.org/0000-0002-1485-3851>), Andrea Tamburini [ctb], Dilek Yildiz [ctb] (<https://orcid.org/0000-0001-6192-0634>)
Maintainer: Guy J. Abel <g.j.abel@gmail.com>

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Package covid19italy updated to version 0.3.1 with previous version 0.3.0 dated 2020-07-21

Title: The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Italy Dataset
Description: Provides a daily summary of the Coronavirus (COVID-19) cases in Italy by country, region and province level. Data source: Presidenza del Consiglio dei Ministri - Dipartimento della Protezione Civile <https://www.protezionecivile.it/>.
Author: Rami Krispin [aut, cre]
Maintainer: Rami Krispin <rami.krispin@gmail.com>

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Package TestDataImputation updated to version 2.1 with previous version 1.2 dated 2020-11-16

Title: Missing Item Responses Imputation for Test and Assessment Data
Description: Functions for imputing missing item responses for dichotomous and polytomous test and assessment data. This package enables missing imputation methods that are suitable for test and assessment data, including: listwise (LW) deletion (see De Ayala et al. 2001 <doi:10.1111/j.1745-3984.2001.tb01124.x>), treating as incorrect (IN, see Lord, 1974 <doi: 10.1111/j.1745-3984.1974.tb00996.x>; Mislevy & Wu, 1996 <doi: 10.1002/j.2333-8504.1996.tb01708.x>; Pohl et al., 2014 <doi: 10.1177/0013164413504926>), person mean imputation (PM), item mean imputation (IM), two-way (TW) and response function (RF) imputation (see Sijtsma & van der Ark, 2003 <doi: 10.1207/s15327906mbr3804_4>), logistic regression (LR) imputation, and expectation–maximization (EM) imputation (see Finch, 2008 <doi: 10.1111/j.1745-3984.2008.00062.x>).
Author: Shenghai Dai [aut, cre], Xiaolin Wang [aut], Dubravka Svetina [aut]
Maintainer: Shenghai Dai <s.dai@wsu.edu>

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More information about TestDataImputation at CRAN
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Package summarytools updated to version 1.0.0 with previous version 0.9.9 dated 2021-03-19

Title: Tools to Quickly and Neatly Summarize Data
Description: Data frame summaries, cross-tabulations, weight-enabled frequency tables and common descriptive (univariate) statistics in concise tables available in a variety of formats (plain ASCII, Markdown and HTML). A good point-of-entry for exploring data, both for experienced and new R users.
Author: Dominic Comtois [aut, cre]
Maintainer: Dominic Comtois <dominic.comtois@gmail.com>

Diff between summarytools versions 0.9.9 dated 2021-03-19 and 1.0.0 dated 2021-07-28

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More information about summarytools at CRAN
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Package BiDAG updated to version 2.0.3 with previous version 2.0.2 dated 2021-04-30

Title: Bayesian Inference for Directed Acyclic Graphs
Description: Implementation of a collection of MCMC methods for Bayesian structure learning of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient inference on larger DAGs, the space of DAGs is pruned according to the data. To filter the search space, the algorithm employs a hybrid approach, combining constraint-based learning with search and score. A reduced search space is initially defined on the basis of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with search and score. Search and score is then performed following two approaches: Order MCMC, or Partition MCMC. The BGe score is implemented for continuous data and the BDe score is implemented for binary data or categorical data. The algorithms may provide the maximum a posteriori (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data. All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks. References: J. Kuipers, P. Suter and G. Moffa (2018) <arXiv:1803.07859v2>, N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>, J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>, M. Kalisch et al.(2012) <doi:10.18637/jss.v047.i11>.
Author: Polina Suter [aut, cre], Jack Kuipers [aut]
Maintainer: Polina Suter <polina.suter@bsse.ethz.ch>

Diff between BiDAG versions 2.0.2 dated 2021-04-30 and 2.0.3 dated 2021-07-28

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More information about BiDAG at CRAN
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