Sun, 02 Aug 2020

Package vov updated to version 0.1.1 with previous version 0.1.0 dated 2019-12-20

Title: CSS Animations for 'shiny' Elements
Description: A wrapper around a CSS library called 'vov.css', intended for use in 'shiny' applications. Simply wrap a UI element in one of the animation functions to see it move.
Author: Tyler Littlefield [aut, cre] (Creator of Shiny Wrapper), Vaibhav Tandon [ctb, cph] (Author of included CSS code), Huynhle Danube [ctb]
Maintainer: Tyler Littlefield <tylerlittlefield@hey.com>

Diff between vov versions 0.1.0 dated 2019-12-20 and 0.1.1 dated 2020-08-02

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Package tidyfst updated to version 0.9.8 with previous version 0.9.7 dated 2020-05-28

Title: Tidy Verbs for Fast Data Manipulation
Description: A toolkit of tidy data manipulation verbs with 'data.table' as the backend. Combining the merits of syntax elegance from 'dplyr' and computing performance from 'data.table', 'tidyfst' intends to provide users with state-of-the-art data manipulation tools with least pain. This package is an extension of 'data.table'. While enjoying a tidy syntax, it also wraps combinations of efficient functions to facilitate frequently-used data operations.
Author: Tian-Yuan Huang [aut, cre] (<https://orcid.org/0000-0002-3591-4203>)
Maintainer: Tian-Yuan Huang <huang.tian-yuan@qq.com>

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Package RXshrink updated to version 1.4.1 with previous version 1.4 dated 2020-06-26

Title: Maximum Likelihood Shrinkage using Generalized Ridge or Least Angle Regression Methods
Description: Functions are provided to calculate and display ridge TRACE diagnostics for a variety of shrinkage Paths. TRACEs identify the m-Extent of shrinkage most likely, under Normal-theory, to produce optimally biased estimates of beta-coefficients with minimum MSE Risk. The unr.ridge() function implements the "Unrestricted Path" introduced in Obenchain (2020) <arXiv:2005.14291>. This Shrinkage-Path is more efficient than the Paths used by the qm.ridge(), aug.lars() and uc.lars() functions. Optimally biased predictions can be made using RXpredict() for all five types of RXshrink linear model TRACE diagnostics. Functions MLboot(), MLcalc(), MLhist() and MLtrue() provide insights into the true bias and MSE risk characteristics of non-linear Shrinkage estimators. The correct.signs() function provides estimates with "correct" numerical signs when ill-conditioned (nearly multicollinear) models yield OLS estimates that disagree with the signs of the observed correlations between the y-outcome and the selected x-predictor variables.
Author: Bob Obenchain
Maintainer: Bob Obenchain <wizbob@att.net>

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Package NBR updated to version 0.1.3 with previous version 0.1.2 dated 2020-03-26

Title: Network-Based R-Statistics using Mixed Effects Models
Description: An implementation of network-based statistics in R using mixed effects models. Theoretical background for Network-Based Statistics can be found in Zalesky et al. (2010) <doi:10.1016/j.neuroimage.2010.06.041>. For Mixed Effects Models check the R package <https://CRAN.R-project.org/package=nlme>.
Author: Zeus Gracia-Tabuenca [aut, cre], Sarael Alcauter [aut]
Maintainer: Zeus Gracia-Tabuenca <zgtabuenca@comunidad.unam.mx>

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Package modelsummary updated to version 0.5.1 with previous version 0.5.0 dated 2020-07-05

Title: Summary Tables and Plots for Statistical Models and Data: Beautiful, Customizable, and Publication-Ready
Description: Create beautiful and customizable tables to summarize several statistical models side-by-side. Draw coefficient plots, multi-level cross-tabs, dataset summaries, balance tables (a.k.a. "Table 1s"), and correlation matrices. This package supports dozens of statistical models, and it can produce tables in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel, RTF, JPG, or PNG. Tables can easily be embedded in 'Rmarkdown' or 'knitr' dynamic documents.
Author: Vincent Arel-Bundock [aut, cre] (<https://orcid.org/0000-0003-2042-7063>)
Maintainer: Vincent Arel-Bundock <vincent.arel-bundock@umontreal.ca>

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Package LRQMM updated to version 1.2.2 with previous version 1.2.1 dated 2020-02-22

Title: Fitting Linear Quantile Regression Mixed Models with Relationship Matrix
Description: Fit a quantile regression mixed model involved Relationship Matrix using a sparse implementation of the Frisch-Newton interior-point algorithm as described in Portnoy and Koenker (1977, Statistical Science) <https://www.jstor.org/stable/2246216>.
Author: Sayyed Reza Alavian[aut,cre] Majid Sarmad[ths] Mehdi Jabbari Nooghabi[ths] Hani Rezaee[ctb] Saeed Zerehdaran[ctb] Ferdowsi University Of Mashhad [cph]
Maintainer: Sayyed Reza Alavian <s.rezaalavian@mail.um.ac.ir>

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Package insee updated to version 0.8.0 with previous version 0.7.0 dated 2020-07-29

Title: Tools to Easily Download Data from INSEE BDM Database
Description: Using embedded sdmx queries, get the data of more than 140 000 insee series from bdm database. Have a look at the detailed sdmx web service page with the following link : <https://www.insee.fr/en/information/2868055>.
Author: Hadrien Leclerc [aut, cre]
Maintainer: Hadrien Leclerc <leclerc.hadrien@gmail.com>

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Package implied updated to version 0.3.1 with previous version 0.3.0 dated 2020-04-18

Title: Convert Bookmaker Odds to Probabilities
Description: Convert bookmaker odds into proper probabilities. Seven different algorithms are available, including basic normalization, Shin's method (Hyun Song Shin, (1992) <doi:10.2307/2234526>), and others.
Author: Jonas Christoffer Lindstrøm
Maintainer: Jonas Christoffer Lindstrøm <jonaslindstrom88@gmail.com>

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Package ICAOD updated to version 1.0.0 with previous version 0.9.9 dated 2019-04-19

Title: Optimal Designs for Linear and Nonlinear Models
Description: Finds optimal designs for nonlinear models using a metaheuristic algorithm called imperialist competitive algorithm ICA. See, for details, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014> and Masoudi et al. (2019) <doi:10.1080/10618600.2019.1601097>.
Author: Ehsan Masoudi [aut, cre], Heinz Holling [aut], Weng Kee Wong [aut], Seongho Kim [ctb]
Maintainer: Ehsan Masoudi <esn_mud@yahoo.com>

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Package genie updated to version 1.0.5 with previous version 1.0.4 dated 2017-04-27

Title: Fast, Robust, and Outlier Resistant Hierarchical Clustering
Description: Includes the reference implementation of Genie - a hierarchical clustering algorithm that links two point groups in such a way that an inequity measure (namely, the Gini index) of the cluster sizes does not significantly increase above a given threshold. This method most often outperforms many other data segmentation approaches in terms of clustering quality as tested on a wide range of benchmark datasets. At the same time, Genie retains the high speed of the single linkage approach, therefore it is also suitable for analysing larger data sets. For more details see (Gagolewski et al. 2016 <DOI:10.1016/j.ins.2016.05.003>). For an even faster and more feature-rich implementation, including, amongst others, noise point detection, see the 'genieclust' package.
Author: Marek Gagolewski [aut, cre, cph] (<https://orcid.org/0000-0003-0637-6028>), Maciej Bartoszuk [aut] (<https://orcid.org/0000-0001-6088-8273>), Anna Cena [aut] (<https://orcid.org/0000-0001-8697-5383>)
Maintainer: Marek Gagolewski <marek@gagolewski.com>

Diff between genie versions 1.0.4 dated 2017-04-27 and 1.0.5 dated 2020-08-02

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Package BGData updated to version 2.2.0 with previous version 2.1.0 dated 2019-01-25

Title: A Suite of Packages for Analysis of Big Genomic Data
Description: An umbrella package providing a phenotype/genotype data structure and scalable and efficient computational methods for large genomic datasets in combination with several other packages: 'BEDMatrix', 'LinkedMatrix', and 'symDMatrix'.
Author: Gustavo de los Campos [aut], Alexander Grueneberg [aut, cre], Paulino Perez [ctb], Ana Vazquez [ctb]
Maintainer: Alexander Grueneberg <cran@agrueneberg.info>

Diff between BGData versions 2.1.0 dated 2019-01-25 and 2.2.0 dated 2020-08-02

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Package AzureVM updated to version 2.2.1 with previous version 2.2.0 dated 2020-07-28

Title: Virtual Machines in 'Azure'
Description: Functionality for working with virtual machines (VMs) in Microsoft's 'Azure' cloud: <https://azure.microsoft.com/en-us/services/virtual-machines/>. Includes facilities to deploy, startup, shutdown, and cleanly delete VMs and VM clusters. Deployment configurations can be highly customised, and can make use of existing resources as well as creating new ones. A selection of predefined configurations is provided to allow easy deployment of commonly used Linux and Windows images, including Data Science Virtual Machines. With a running VM, execute scripts and install optional extensions. Part of the 'AzureR' family of packages.
Author: Hong Ooi [aut, cre], Microsoft [cph]
Maintainer: Hong Ooi <hongooi@microsoft.com>

Diff between AzureVM versions 2.2.0 dated 2020-07-28 and 2.2.1 dated 2020-08-02

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Package TSS.RESTREND updated to version 0.3.1 with previous version 0.3.0 dated 2020-07-15

Title: Time Series Segmentation of Residual Trends
Description: Time Series Segmented Residual Trends is a method for the automated detection of land degradation from remotely sensed vegetation and climate datasets. TSS-RESTREND incorporates aspects of two existing degradation detection methods: RESTREND which is used to control for climate variability, and BFAST which is used to look for structural changes in the ecosystem. The full details of the testing and justification of the TSS-RESTREND method (version 0.1.02) are published in Burrell et al., (2017). <doi:10.1016/j.rse.2017.05.018>. The changes to the method introduced in version 0.2.03 focus on the inclusion of temperature as an additional climate variable. This allows for land degradation assessment in temperature limited drylands. A paper that details this work is currently under review. There are also a number of bug fixes and speed improvements. Version 0.3.0 introduces additional attribution for eCO2, climate change and climate variability the details of which are in press in Burrell et al., (2020). The version under active development and additional example scripts showing how the package can be applied can be found at <https://github.com/ArdenB/TSSRESTREND>.
Author: Arden Burrell [aut, cre]
Maintainer: Arden Burrell <arden.burrell@gmail.com>

Diff between TSS.RESTREND versions 0.3.0 dated 2020-07-15 and 0.3.1 dated 2020-08-02

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Package brickr updated to version 0.3.5 with previous version 0.3.4 dated 2020-05-09

Title: Emulate LEGO Bricks in 2D and 3D
Description: Generate digital LEGO models using 'tidyverse' functions. Convert image files into 2D and 3D LEGO mosaics, complete with piece counts and instructions. Render 3D models using simple data frame instructions. Developed under the LEGO Group's Fair Play policy <https://www.lego.com/en-us/legal/notices-and-policies/fair-play/>.
Author: Ryan Timpe [aut, cre]
Maintainer: Ryan Timpe <ryan.timpe@gmail.com>

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Package MCMCpack updated to version 1.4-9 with previous version 1.4-8 dated 2020-05-31

Title: Markov Chain Monte Carlo (MCMC) Package
Description: Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.
Author: Andrew D. Martin [aut], Kevin M. Quinn [aut], Jong Hee Park [aut,cre], Ghislain Vieilledent [ctb], Michael Malecki[ctb], Matthew Blackwell [ctb], Keith Poole [ctb], Craig Reed [ctb], Ben Goodrich [ctb], Ross Ihaka [cph], The R Development Core Team [cph], The R Foundation [cph], Pierre L'Ecuyer [cph], Makoto Matsumoto [cph], Takuji Nishimura [cph]
Maintainer: Jong Hee Park <jongheepark@snu.ac.kr>

Diff between MCMCpack versions 1.4-8 dated 2020-05-31 and 1.4-9 dated 2020-08-02

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Package wdm updated to version 0.2.2 with previous version 0.2.1 dated 2019-04-11

Title: Weighted Dependence Measures
Description: Provides efficient implementations of weighted dependence measures and related asymptotic tests for independence. Implemented measures are the Pearson correlation, Spearman's rho, Kendall's tau, Blomqvist's beta, and Hoeffding's D; see, e.g., Nelsen (2006) <doi:10.1007/0-387-28678-0> and Hollander et al. (2015, ISBN:9780470387375).
Author: Thomas Nagler [aut, cre]
Maintainer: Thomas Nagler <mail@tnagler.com>

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Package rayrender updated to version 0.14.0 with previous version 0.9.1 dated 2020-04-19

Title: Build and Raytrace 3D Scenes
Description: Render scenes using pathtracing. Build 3D scenes out of spheres, cubes, planes, disks, triangles, line segments, cylinders, ellipsoids, and 3D models in the 'Wavefront' OBJ file format. Supports several material types, textures, multicore rendering, and tone-mapping. Based on the "Ray Tracing in One Weekend" book series. Peter Shirley (2018) <https://raytracing.github.io>.
Author: Tyler Morgan-Wall [aut, cph, cre] (<https://orcid.org/0000-0002-3131-3814>), Syoyo Fujita [ctb, cph], Melissa O'Neill [ctb, cph]
Maintainer: Tyler Morgan-Wall <tylermw@gmail.com>

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Package mboost updated to version 2.9-2.1 with previous version 2.9-2 dated 2020-02-18

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

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Package fit.models updated to version 0.64 with previous version 0.63 dated 2020-06-19

Title: Compare Fitted Models
Description: The fit.models function and its associated methods (coefficients, print, summary, plot, etc.) were originally provided in the robust package to compare robustly and classically fitted model objects. See chapters 2, 3, and 5 in Insightful (2002) 'Robust Library User's Guide' <http://robust.r-forge.r-project.org/Robust.pdf>). The aim of the fit.models package is to separate this fitted model object comparison functionality from the robust package and to extend it to support fitting methods (e.g., classical, robust, Bayesian, regularized, etc.) more generally.
Author: Kjell Konis [aut, cre]
Maintainer: Kjell Konis <kjellk@gmail.com>

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Package symDMatrix updated to version 2.1.1 with previous version 2.1.0 dated 2020-05-28

Title: Partitioned Symmetric Matrices
Description: A matrix-like class to represent a symmetric matrix partitioned into file-backed blocks.
Author: Gustavo de los Campos [aut], Alexander Grueneberg [aut, cre]
Maintainer: Alexander Grueneberg <cran@agrueneberg.info>

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Package fda updated to version 5.1.5.1 with previous version 5.1.5 dated 2020-07-13

Title: Functional Data Analysis
Description: These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. New York: Springer and in Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009). Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book. Matlab versions are available by ftp from <http://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/>.
Author: J. O. Ramsay <ramsay@psych.mcgill.ca> [aut,cre], Spencer Graves <spencer.graves@effectivedefense.org> [ctb], Giles Hooker <gjh27@cornell.edu> [ctb]
Maintainer: J. O. Ramsay <ramsay@psych.mcgill.ca>

Diff between fda versions 5.1.5 dated 2020-07-13 and 5.1.5.1 dated 2020-08-02

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Package mice updated to version 3.10.0.1 with previous version 3.10.0 dated 2020-07-13

Title: Multivariate Imputation by Chained Equations
Description: Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.
Author: Stef van Buuren [aut, cre], Karin Groothuis-Oudshoorn [aut], Gerko Vink [ctb], Rianne Schouten [ctb], Alexander Robitzsch [ctb], Lisa Doove [ctb], Shahab Jolani [ctb], Margarita Moreno-Betancur [ctb], Ian White [ctb], Philipp Gaffert [ctb], Florian Meinfelder [ctb], Bernie Gray [ctb], Vincent Arel-Bundock [ctb]
Maintainer: Stef van Buuren <stef.vanbuuren@tno.nl>

Diff between mice versions 3.10.0 dated 2020-07-13 and 3.10.0.1 dated 2020-08-02

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Package DCEM updated to version 2.0.4 with previous version 2.0.2 dated 2020-04-07

Title: Clustering Big Data using Expectation Maximization Star (EM*) Algorithm
Description: Implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering big data (gaussian mixture models for both multivariate and univariate datasets). This version implements the faster alternative EM* that avoids revisiting data by leveraging the heap structure. The implementation supports both random and K-means++ based initialization. Reference: Hasan Kurban, Mark Jenne, Mehmet M. Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>. This work is partially supported by NCI Grant 1R01CA213466-01.
Author: Sharma Parichit [aut, cre, ctb], Kurban Hasan [aut, ctb], Jenne Mark [aut, ctb], Dalkilic Mehmet [aut]
Maintainer: Sharma Parichit <parishar@iu.edu>

Diff between DCEM versions 2.0.2 dated 2020-04-07 and 2.0.4 dated 2020-08-02

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Package MALDIrppa (with last version 1.0.4) was removed from CRAN

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

2020-07-19 1.0.4
2020-07-18 1.0.3

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Package rayshader updated to version 0.19.2 with previous version 0.15.1 dated 2020-04-18

Title: Create Maps and Visualize Data in 2D and 3D
Description: Uses a combination of raytracing and multiple hill shading methods to produce 2D and 3D data visualizations and maps. Includes water detection and layering functions, programmable color palette generation, several built-in textures for hill shading, 2D and 3D plotting options, a built-in path tracer, 'Wavefront' OBJ file export, and the ability to save 3D visualizations to a 3D printable format.
Author: Tyler Morgan-Wall
Maintainer: Tyler Morgan-Wall <tylermw@gmail.com>

Diff between rayshader versions 0.15.1 dated 2020-04-18 and 0.19.2 dated 2020-08-02

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Package Umpire updated to version 2.0.9 with previous version 2.0.3 dated 2020-06-01

Title: Simulating Realistic Gene Expression and Clinical Data
Description: The Ultimate Microrray Prediction, Reality and Inference Engine (UMPIRE) is a package to facilitate the simulation of realistic microarray data sets with links to associated outcomes. See Zhang and Coombes (2012) <doi:10.1186/1471-2105-13-S13-S1>. Version 2.0 adds the ability to simulate realistic mixed-typed clinical data.
Author: Kevin R. Coombes, Jiexin Zhang, Caitlin E. Coombes
Maintainer: Kevin R. Coombes <krc@silicovore.com>

Diff between Umpire versions 2.0.3 dated 2020-06-01 and 2.0.9 dated 2020-08-02

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Package hydraulics updated to version 0.2.1 with previous version 0.2.0 dated 2020-07-02

Title: Basic Pipe and Open Channel Hydraulics
Description: Functions for basic hydraulic calculations related to water flow in circular pipes both flowing full (under pressure), and partially full (gravity flow), and trapezoidal open channels. For pressure flow this includes friction loss calculations by solving the Darcy-Weisbach equation for head loss, flow or diameter, and plotting a Moody diagram. The Darcy-Weisbach friction factor is calculated using the Colebrook (or Colebrook-White equation), the basis of the Moody diagram, the original citation being Colebrook (1939) <doi:10.1680/ijoti.1939.13150>. For gravity flow, the Manning equation is used, again solving for missing parameters. The derivation of and solutions using the Darcy-Weisbach equation and the Manning equation are outlined in many fluid mechanics texts such as Finnemore and Franzini (2002, ISBN:978-0072432022). For the Manning equation solutions, this package uses modifications of original code from the 'iemisc' package by Irucka Embry.
Author: Ed Maurer [aut, cre], Irucka Embry [aut, ctb] (iemisc code)
Maintainer: Ed Maurer <emaurer@scu.edu>

Diff between hydraulics versions 0.2.0 dated 2020-07-02 and 0.2.1 dated 2020-08-02

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