Fri, 28 Aug 2020

Package renv updated to version 0.12.0 with previous version 0.11.0 dated 2020-06-26

Title: Project Environments
Description: A dependency management toolkit for R. Using 'renv', you can create and manage project-local R libraries, save the state of these libraries to a 'lockfile', and later restore your library as required. Together, these tools can help make your projects more isolated, portable, and reproducible.
Author: Kevin Ushey [aut, cre], RStudio [cph]
Maintainer: Kevin Ushey <kevin@rstudio.com>

Diff between renv versions 0.11.0 dated 2020-06-26 and 0.12.0 dated 2020-08-28

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Package tables updated to version 0.9.4 with previous version 0.9.3 dated 2020-05-26

Title: Formula-Driven Table Generation
Description: Computes and displays complex tables of summary statistics. Output may be in LaTeX, HTML, plain text, or an R matrix for further processing.
Author: Duncan Murdoch
Maintainer: Duncan Murdoch <murdoch.duncan@gmail.com>

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Package devEMF updated to version 4.0 with previous version 3.8 dated 2020-06-29

Title: EMF Graphics Output Device
Description: Output graphics to EMF+/EMF.
Author: Philip Johnson
Maintainer: Philip Johnson <plfj@umd.edu>

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Package SwimmeR updated to version 0.4.1 with previous version 0.3.1 dated 2020-07-11

Title: Data Import, Cleaning, and Conversions for Swimming Results
Description: The goal for of the 'SwimmeR' package is to provide means of acquiring, and then analyzing, data from swimming (and diving) competitions. To that end 'SwimmeR' allows results to be read in from .html sources, like 'Hy-Tek' real time results pages, '.pdf' files, and now (on a development basis) '.hy3' files. Once read in, 'SwimmeR' can convert swimming times (performances) between the computationally useful format of seconds reported to the '100ths' place (e.g. 95.37), and the conventional reporting format (1:35.37) used in the swimming community. 'SwimmeR' can also score meets in a variety of formats with user defined point values, convert times between courses ('LCM', 'SCM', 'SCY') and draw single elimination brackets, as well as providing a suite of tools for working cleaning swimming data. This is a developmental package, not yet mature.
Author: Greg Pilgrim [aut, cre] (<https://orcid.org/0000-0001-7831-442X>), Caitlin Baldwin [ctb]
Maintainer: Greg Pilgrim <gpilgrim2670@gmail.com>

Diff between SwimmeR versions 0.3.1 dated 2020-07-11 and 0.4.1 dated 2020-08-28

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Package grobblR updated to version 0.1.1 with previous version 0.1.0 dated 2020-06-25

Title: Creating Flexible, Reproducible 'PDF' Reports
Description: A tool which allows users the ability to intuitively create flexible, reproducible portable document format reports comprised of aesthetically pleasing tables, images, plots and/or text.
Author: Calvin Floyd [aut, cre, cph]
Maintainer: Calvin Floyd <calvin.michael.floyd@gmail.com>

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Package skedastic updated to version 1.0.1 with previous version 1.0.0 dated 2020-08-11

Title: Heteroskedasticity Diagnostics for Linear Regression Models
Description: Implements numerous methods for detecting heteroskedasticity (sometimes called heteroscedasticity) in the classical linear regression model. These include a test based on Anscombe (1961) <https://projecteuclid.org/ euclid.bsmsp/1200512155>, Ramsey's (1969) BAMSET Test <doi:10.1111/j.2517-6161.1969.tb00796.x>, the tests of Bickel (1978) <doi:10.1214/aos/1176344124>, Breusch and Pagan (1979) <doi:10.2307/1911963> with and without the modification proposed by Koenker (1981) <doi:10.1016/0304-4076(81)90062-2>, Carapeto and Holt (2003) <doi:10.1080/0266476022000018475>, Cook and Weisberg (1983) <doi:10.1093/biomet/70.1.1> (including their graphical methods), Diblasi and Bowman (1997) <doi:10.1016/S0167-7152(96)00115-0>, Dufour, Khalaf, Bernard, and Genest (2004) <doi:10.1016/j.jeconom.2003.10.024>, Evans and King (1985) <doi:10.1016/0304-4076(85)90085-5> and Evans and King (1988) <doi:10.1016/0304-4076(88)90006-1>, Glejser (1969) <doi:10.1080/01621459.1969.10500976> as formulated by Mittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4), Godfrey and Orme (1999) <doi:10.1080/07474939908800438>, Goldfeld and Quandt (1965) <doi:10.1080/01621459.1965.10480811>, Harvey (1976) <doi:10.2307/1913974>, Honda (1989) <doi:10.1111/j.2517-6161.1989.tb01749.x>, Horn (1981) <doi:10.1080/03610928108828074>, Li and Yao (2019) <doi:10.1016/j.ecosta.2018.01.001> with and without the modification of Bai, Pan, and Yin (2016) <doi:10.1007/s11749-017-0575-x>, Rackauskas and Zuokas (2007) <doi:10.1007/s10986-007-0018-6>, Simonoff and Tsai (1994) <doi:10.2307/2986026> with and without the modification of Ferrari, Cysneiros, and Cribari-Neto (2004) <doi:10.1016/S0378-3758(03)00210-6>, Szroeter (1978) <doi:10.2307/1913831>, Verbyla (1993) <doi:10.1111/j.2517-6161.1993.tb01918.x>, White (1980) <doi:10.2307/1912934>, Wilcox and Keselman (2006) <doi:10.1080/10629360500107923>, and Zhou, Song, and Thompson (2015) <doi:10.1002/cjs.11252>. Besides these heteroskedasticity tests, there are supporting functions that compute the BLUS residuals of Theil (1965) <doi:10.1080/01621459.1965.10480851>, the conditional two-sided p-values of Kulinskaya (2008) <arXiv:0810.2124v1>, and probabilities for the nonparametric trend statistic of Lehmann (1975, ISBN: 0-816-24996-1). Homoskedasticity refers to the assumption of constant variance that is imposed on the model errors (disturbances); heteroskedasticity is the violation of this assumption.
Author: Thomas Farrar [aut, cre] (<https://orcid.org/0000-0003-0744-6972>), University of the Western Cape [cph]
Maintainer: Thomas Farrar <tjfarrar@alumni.uwaterloo.ca>

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Package pchc updated to version 0.2 with previous version 0.1 dated 2020-07-06

Title: Bayesian Network Learning with the PCHC Algorithm
Description: Bayesian network learning using the PCHC algorithm. PCHC stands for PC Hill-Climbing. It is a new hybrid algorithm that used PC to construct the skeleton of the BN and then utilizes the Hill-Climbing greedy search. The relevant paper has been submitted and is currently in revision.
Author: Michail Tsagris [aut, cre]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>

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Package IndTestPP updated to version 3.0 with previous version 1.0 dated 2016-08-01

Title: Tests of Independence and Analysis of Dependence Between Point Processes in Time
Description: It provides a general framework to analyse dependence between point processes in time. It includes parametric and non-parametric tests to study independence, and functions for generating and analysing different types of dependence.
Author: Ana C. Cebrian
Maintainer: Ana C. Cebrian <acebrian@unizar.es>

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Package DstarM updated to version 0.4.0 with previous version 0.3.0 dated 2018-05-18

Title: Analyze Two Choice Reaction Time Data with the D*M Method
Description: A collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.
Author: Don van den Bergh, Stijn Verdonck, Francis Tuerlinckx
Maintainer: Don van den Bergh <donvdbergh@hotmail.com>

Diff between DstarM versions 0.3.0 dated 2018-05-18 and 0.4.0 dated 2020-08-28

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Package BVSNLP updated to version 1.1.9 with previous version 1.1.8 dated 2019-06-12

Title: Bayesian Variable Selection in High Dimensional Settings using Nonlocal Priors
Description: Variable/Feature selection in high or ultra-high dimensional settings has gained a lot of attention recently specially in cancer genomic studies. This package provides a Bayesian approach to tackle this problem, where it exploits mixture of point masses at zero and nonlocal priors to improve the performance of variable selection and coefficient estimation. product moment (pMOM) and product inverse moment (piMOM) nonlocal priors are implemented and can be used for the analyses. This package performs variable selection for binary response and survival time response datasets which are widely used in biostatistic and bioinformatics community. Benefiting from parallel computing ability, it reports necessary outcomes of Bayesian variable selection such as Highest Posterior Probability Model (HPPM), Median Probability Model (MPM) and posterior inclusion probability for each of the covariates in the model. The option to use Bayesian Model Averaging (BMA) is also part of this package that can be exploited for predictive power measurements in real datasets.
Author: Amir Nikooienejad [aut, cre], Valen E. Johnson [ths]
Maintainer: Amir Nikooienejad <amir.nikooienejad@gmail.com>

Diff between BVSNLP versions 1.1.8 dated 2019-06-12 and 1.1.9 dated 2020-08-28

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Package prevR updated to version 3.4.1 with previous version 3.4.0 dated 2019-05-06

Title: Estimating Regional Trends of a Prevalence from a DHS and Similar Surveys
Description: Spatial estimation of a prevalence surface or a relative risks surface, using data from a Demographic and Health Survey (DHS) or an analog survey, see Larmarange et al. (2011) <doi:10.4000/cybergeo.24606>.
Author: Joseph Larmarange [aut, cre] (<https://orcid.org/0000-0001-7097-700X>)
Maintainer: Joseph Larmarange <joseph.larmarange@ird.fr>

Diff between prevR versions 3.4.0 dated 2019-05-06 and 3.4.1 dated 2020-08-28

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New package microclass with initial version 1.2
Encoding: UTF-8
Package: microclass
Type: Package
Title: Methods for Taxonomic Classification of Prokaryotes
Version: 1.2
Date: 2020-08-11
Author: Kristian Hovde Liland, Hilde Vinje, Lars Snipen
Maintainer: Lars Snipen <lars.snipen@nmbu.no>
Description: Functions for assigning 16S sequence data to a taxonomic level in the tree-of-life for prokaryotes.
License: GPL (>= 2)
Depends: R (>= 3.5.0), microseq, microcontax, dplyr, stringr, rlang
Imports: Rcpp (>= 0.11.1), RcppParallel, tibble
LinkingTo: Rcpp (>= 0.11.1), RcppEigen, RcppParallel
SystemRequirements: GNU make
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2020-08-11 14:29:47 UTC; larssn
Repository: CRAN
Date/Publication: 2020-08-28 12:20:08 UTC

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Package blocksdesign updated to version 4.4 with previous version 4.3 dated 2020-07-09

Title: Nested and Crossed Block Designs for Factorial and Unstructured Treatment Sets
Description: Constructs D-optimal or near D-optimal treatment and block designs for linear treatment models with crossed or nested block factors. The treatment design can be any arbitrary linear model defined by a treatment model formula and the block design can be any feasible combination of crossed or nested block factors. The block design factors are optimized sequentially and the levels of each successive block factor are optimized within the levels of each preceding block factor. Crossed block designs with non-singular interaction effects are optimized using a weighting scheme that allows for differential weighting of first and second-order block effects. Outputs include a table showing the allocation of treatments to blocks and tables showing the achieved D-efficiency factors for each block and treatment design.
Author: R. N. Edmondson.
Maintainer: Rodney Edmondson <rodney.edmondson@gmail.com>

Diff between blocksdesign versions 4.3 dated 2020-07-09 and 4.4 dated 2020-08-28

 DESCRIPTION                  |    8 -
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 inst/doc/design_Vignette.pdf |binary
 man/design.Rd                |   33 ++---
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More information about blocksdesign at CRAN
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Package arrow updated to version 1.0.1 with previous version 1.0.0 dated 2020-07-25

Title: Integration to 'Apache' 'Arrow'
Description: 'Apache' 'Arrow' <https://arrow.apache.org/> is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. This package provides an interface to the 'Arrow C++' library.
Author: Romain François [aut] (<https://orcid.org/0000-0002-2444-4226>), Jeroen Ooms [aut], Neal Richardson [aut, cre], Javier Luraschi [ctb], Jeffrey Wong [ctb], Apache Arrow [aut, cph]
Maintainer: Neal Richardson <neal@ursalabs.org>

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Package TTR updated to version 0.24.1 with previous version 0.24.0 dated 2020-08-10

Title: Technical Trading Rules
Description: A collection of over 50 technical indicators for creating technical trading rules. The package also provides fast implementations of common rolling-window functions, and several volatility calculations.
Author: Joshua Ulrich
Maintainer: Joshua Ulrich <josh.m.ulrich@gmail.com>

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Package TreeDist updated to version 1.2.0 with previous version 1.1.1 dated 2020-07-10

Title: Distances Between Phylogenetic Trees
Description: Implements measures of tree similarity, including information-based generalized Robinson-Foulds distances (Phylogenetic Information Distance, Clustering Information Distance, Matching Split Information Distance; Smith, 2020) <doi:10.1093/bioinformatics/btaa614>; Jaccard-Robinson-Foulds distances (Bocker et al. 2013) <doi:10.1007/978-3-642-40453-5_13>, including the Nye et al. (2006) metric <doi:10.1093/bioinformatics/bti720>; the Matching Split Distance (Bogdanowicz & Giaro 2012) <doi:10.1109/TCBB.2011.48>; Maximum Agreement Subtree distances; the Kendall-Colijn (2016) distance <doi:10.1093/molbev/msw124>, and the Nearest Neighbour Interchange (NNI) distance, approximated per Li et al. (1996) <doi:10.1007/3-540-61332-3_168>. Calculates the median of a set of trees under any distance metric.
Author: Martin R. Smith [aut, cre, cph, prg] (<https://orcid.org/0000-0001-5660-1727>), Roy Jonker [prg, cph], Yong Yang [ctb, cph], Yi Cao [ctb, cph]
Maintainer: Martin R. Smith <martin.smith@durham.ac.uk>

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New package survxai with initial version 0.2.2
Package: survxai
Title: Visualization of the Local and Global Survival Model Explanations
Version: 0.2.2
Authors@R: c( person("Aleksandra", "Grudziaz", email = "aleksandra.grudziaz@gmail.com", role = c("aut", "cre")), person("Alicja", "Gosiewska", email = "alicjagosiewska@gmail.com", role = c("aut")), person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut", "ths")) )
Description: Survival models may have very different structures. This package contains functions for creating a unified representation of a 'survival' models, which can be further processed by various survival explainers. Tools implemented in 'survxai' help to understand how input variables are used in the model and what impact do they have on the final model prediction. Currently, four explanation methods are implemented. We can divide them into two groups: local and global. Explanations of the methods can be found in Grudziaz et al.(2018) <doi:10.21105/joss.00961>.
License: GPL
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: breakDown, ggplot2, pec, scales, survival, survminer
Depends: prodlim
Suggests: CFC, covr, DALEX, knitr, markdown, randomForestSRC, rmarkdown, rms, testthat, tibble
VignetteBuilder: knitr
URL: https://mi2datalab.github.io/survxai/
BugReports: https://github.com/MI2DataLab/survxai/issues
NeedsCompilation: no
Packaged: 2020-08-27 13:21:22 UTC; user
Author: Aleksandra Grudziaz [aut, cre], Alicja Gosiewska [aut], Przemyslaw Biecek [aut, ths]
Maintainer: Aleksandra Grudziaz <aleksandra.grudziaz@gmail.com>
Repository: CRAN
Date/Publication: 2020-08-28 11:30:03 UTC

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Package stplanr updated to version 0.7.1 with previous version 0.7.0 dated 2020-08-27

Title: Sustainable Transport Planning
Description: Tools for transport planning with an emphasis on spatial transport data and non-motorized modes. Enables common transport planning tasks including: downloading and cleaning transport datasets; creating geographic "desire lines" from origin-destination (OD) data; route assignment, locally and via interfaces to routing services such as <https://cyclestreets.net/>; calculation of route segment attributes such as bearing and aggregate flow; and 'travel watershed' analysis. See Lovelace and Ellison (2018) <doi:10.32614/RJ-2018-053>.
Author: Robin Lovelace [aut, cre] (<https://orcid.org/0000-0001-5679-6536>), Richard Ellison [aut], Malcolm Morgan [aut] (<https://orcid.org/0000-0002-9488-9183>), Barry Rowlingson [ctb] (Author of overline), Nick Bearman [ctb] (Co-author of gclip), Nikolai Berkoff [ctb] (Co-author of line2route), Scott Chamberlain [rev] (Scott reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/10), Mark Padgham [ctb], Andrea Gilardi [ctb] (<https://orcid.org/0000-0002-9424-7439>)
Maintainer: Robin Lovelace <rob00x@gmail.com>

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Package rminer updated to version 1.4.6 with previous version 1.4.5 dated 2020-04-14

Title: Data Mining Classification and Regression Methods
Description: Facilitates the use of data mining algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.4.6 / 1.4.5 / 1.4.4 new automated machine learning (AutoML) and ensembles, via improved fit(), mining() and mparheuristic() functions, and new categorical preprocessing, via improved delevels() function; 1.4.3 new metrics (e.g., macro precision, explained variance), new "lssvm" model and improved mparheuristic() function; 1.4.2 new "NMAE" metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics; 1.2 - new input importance methods via improved Importance() function; 1.0 - first version.
Author: Paulo Cortez [aut, cre]
Maintainer: Paulo Cortez <pcortez@dsi.uminho.pt>

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Package mlr3proba updated to version 0.2.1 with previous version 0.2.0 dated 2020-07-25

Title: Probabilistic Supervised Learning for 'mlr3'
Description: Provides extensions for probabilistic supervised learning for 'mlr3'. This includes extending the regression task to probabilistic and interval regression, adding a survival task, and other specialized models, predictions, and measures.
Author: Raphael Sonabend [aut, cre] (<https://orcid.org/0000-0001-9225-4654>), Franz Kiraly [aut], Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Nurul Ain Toha [ctb], Andreas Bender [ctb] (<https://orcid.org/0000-0001-5628-8611>)
Maintainer: Raphael Sonabend <raphael.sonabend.15@ucl.ac.uk>

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Package CornerstoneR updated to version 2.0.2 with previous version 2.0.1 dated 2020-06-02

Title: Collection of Scripts for Interface Between 'Cornerstone' and 'R'
Description: Collection of generic 'R' scripts which enable you to use existing 'R' routines in 'Cornerstone'. The desktop application 'Cornerstone' (<https://www.camline.com/en/products/cornerstone/cornerstone-core.html>) is a data analysis software provided by 'camLine' that empowers engineering teams to find solutions even faster. The engineers incorporate intensified hands-on statistics into their projects. They benefit from an intuitive and uniquely designed graphical Workmap concept: you design experiments (DoE) and explore data, analyze dependencies, and find answers you can act upon, immediately, interactively, and without any programming. While 'Cornerstone's' interface to the statistical programming language 'R' has been available since version 6.0, the latest interface with 'R' is even much more efficient. 'Cornerstone' release 7.1.1 allows you to integrate user defined 'R' packages directly into the standard 'Cornerstone' GUI. Your engineering team stays in 'Cornerstone's' graphical working environment and can apply 'R' routines, immediately and without the need to deal with programming code. Additionally, your 'R' programming team develops corresponding 'R' packages detached from 'Cornerstone' in their favorite 'R' environment. Learn how to use 'R' packages in 'Cornerstone' 7.1.1 on 'camLineTV' YouTube channel (<https://www.youtube.com/watch?v=HEQHwq_laXU>) (available in German).
Author: Gerrith Djaja [aut, cre], Dirk Surmann [aut] (<https://orcid.org/0000-0003-0873-137X>)
Maintainer: Gerrith Djaja <gerrith.djaja@camline.com>

Diff between CornerstoneR versions 2.0.1 dated 2020-06-02 and 2.0.2 dated 2020-08-28

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Package bit64 updated to version 4.0.4 with previous version 4.0.2 dated 2020-07-30

Title: A S3 Class for Vectors of 64bit Integers
Description: Package 'bit64' provides serializable S3 atomic 64bit (signed) integers. These are useful for handling database keys and exact counting in +-2^63. WARNING: do not use them as replacement for 32bit integers, integer64 are not supported for subscripting by R-core and they have different semantics when combined with double, e.g. integer64 + double => integer64. Class integer64 can be used in vectors, matrices, arrays and data.frames. Methods are available for coercion from and to logicals, integers, doubles, characters and factors as well as many elementwise and summary functions. Many fast algorithmic operations such as 'match' and 'order' support inter- active data exploration and manipulation and optionally leverage caching.
Author: Jens Oehlschlägel [aut, cre], Leonardo Silvestri [ctb]
Maintainer: Jens Oehlschlägel <Jens.Oehlschlaegel@truecluster.com>

Diff between bit64 versions 4.0.2 dated 2020-07-30 and 4.0.4 dated 2020-08-28

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 12 files changed, 63 insertions(+), 34 deletions(-)

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Package BayesMallows updated to version 0.5.0 with previous version 0.4.4 dated 2020-08-07

Title: Bayesian Preference Learning with the Mallows Rank Model
Description: An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>; Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>). Both Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).
Author: Oystein Sorensen, Valeria Vitelli, Marta Crispino, Qinghua Liu
Maintainer: Oystein Sorensen <oystein.sorensen.1985@gmail.com>

Diff between BayesMallows versions 0.4.4 dated 2020-08-07 and 0.5.0 dated 2020-08-28

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New package amber with initial version 1.0.3
Package: amber
Type: Package
Title: Automated Model Benchmarking R Package
Version: 1.0.3
Author: Christian Seiler [cre, aut]
Maintainer: Christian Seiler <christian.seiler@canada.ca>
Description: Functions that quantify how well the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) reproduces land surface processes when compared against reference data. To summarize model performance across different statistical metrics, this package employs a skill score system that was originally developed by Collier et. al., (2018) <doi:10.1029/2018MS001354> .
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.4.0)
Imports: classInt, doParallel, foreach, Hmisc, latex2exp, ncdf4, parallel, raster, rgdal, rgeos, scico, sp, stats, utils, viridis, xtable
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2020-08-28 09:01:44 UTC; acrpcsr
Repository: CRAN
Date/Publication: 2020-08-28 11:20:02 UTC

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New package whSample with initial version 0.8.9
Package: whSample
Type: Package
Title: Utilities for Sampling
Version: 0.8.9
Depends: R (>= 3.5.0)
Authors@R: person("Paul", "West", role=c("aut", "cre"), email="fp8049@gmail.com")
Description: Interactive tools for generating random samples. Users select an .xlsx or .csv file with population data and are walked through selecting the sample type (Simple Random Sample or Stratified), the number of backups desired, and a "stratify_on" value (if desired). An .xlsx file is created with the sample and key metadata for reference. It is menu-driven and lets users pick an output directory. See vignettes for a detailed walk-through.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: data.table, magrittr, openxlsx, dplyr, purrr, tools, rChoiceDialogs, rJava
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-08-25 21:26:54 UTC; Paul
Author: Paul West [aut, cre]
Maintainer: Paul West <fp8049@gmail.com>
Repository: CRAN
Date/Publication: 2020-08-28 10:40:02 UTC

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New package staplr with initial version 3.1.0
Package: staplr
Type: Package
Title: A Toolkit for PDF Files
Version: 3.1.0
Depends: R (>= 3.4.0)
Authors@R: c( person("Priyanga Dilini", "Talagala", email="pritalagala@gmail.com", role= c("aut","cre")), person("Ogan", "Mancarci", email="ogan.mancarci@gmail.com", role='aut'), person("Daniel", "Padfield", email= "d.padfield@exeter.ac.uk", role ='aut'), person("Granville", "Matheson", email= "mathesong@gmail.com", role ='aut') )
Description: Provides function to manipulate PDF files: fill out PDF forms; merge multiple PDF files into one; remove selected pages from a file; rename multiple files in a directory; rotate entire pdf document; rotate selected pages of a pdf file; Select pages from a file; splits single input PDF document into individual pages; splits single input PDF document into parts from given points. 'staplr' requires Java 8 installed on your system.
SystemRequirements: Java 8 or higher
License: GPL-3
LazyData: true
RoxygenNote: 7.1.1
Imports: tcltk, stringr, assertthat, glue, XML, rJava
Suggests: lattice, testthat, pdftools
Encoding: UTF-8
BugReports: https://github.com/pridiltal/staplr/issues
NeedsCompilation: no
Packaged: 2020-08-24 23:12:55 UTC; priyangatalagala
Author: Priyanga Dilini Talagala [aut, cre], Ogan Mancarci [aut], Daniel Padfield [aut], Granville Matheson [aut]
Maintainer: Priyanga Dilini Talagala <pritalagala@gmail.com>
Repository: CRAN
Date/Publication: 2020-08-28 11:00:03 UTC

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New package portalr with initial version 0.3.5
Package: portalr
Title: Create Useful Summaries of the Portal Data
Version: 0.3.5
Authors@R: c(person(given = c("Glenda", "M."), family = "Yenni", role = c("aut", "cre"), email = "glenda@weecology.org", comment = c(ORCID = "0000-0001-6969-1848")), person(given = "Hao", family = "Ye", role = "aut", comment = c(ORCID = "0000-0002-8630-1458")), person(given = c("Erica", "M."), family = "Christensen", role = "aut", comment = c(ORCID = "0000-0002-5635-2502")), person(given = c("Juniper", "L."), family = "Simonis", role = "aut", comment = c(ORCID = "0000-0001-9798-0460")), person(given = c("Ellen", "K."), family = "Bledsoe", role = "aut", comment = c(ORCID = "0000-0002-3629-7235")), person(given = c("Renata", "M."), family = "Diaz", role = "aut", comment = c(ORCID = "0000-0003-0803-4734")), person(given = c("Shawn", "D."), family = "Taylor", role = "aut", comment = c(ORCID = "0000-0002-6178-6903")), person(given = c("Ethan", "P,"), family = "White", role = "aut", comment = c(ORCID = "0000-0001-6728-7745")), person(given = c("S.K.", "Morgan"), family = "Ernest", role = "aut", comment = c(ORCID = "0000-0002-6026-8530")), person("Weecology", role = "cph"))
Description: Download and generate summaries for the rodent, plant, ant, and weather data from the Portal Project. Portal is a long-term (and ongoing) experimental monitoring site in the Chihuahua desert. The raw data files can be found at <https://github.com/weecology/portaldata>.
License: MIT + file LICENSE
URL: https://weecology.github.io/portalr/, https://github.com/weecology/portalr
BugReports: https://github.com/weecology/portalr/issues
Depends: R (>= 3.2.3)
Imports: clipr, clisymbols, crayon, dplyr, forecast, gh (>= 1.1.0), httr, lubridate, lunar, magrittr, rlang, tidyr, tidyselect (>= 1.0.0), zoo
Suggests: covr, cowplot, ggplot2, httptest, knitr, pkgdown, rmarkdown, testthat, usethis
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-08-26 19:18:43 UTC; GlendaYenni
Author: Glenda M. Yenni [aut, cre] (<https://orcid.org/0000-0001-6969-1848>), Hao Ye [aut] (<https://orcid.org/0000-0002-8630-1458>), Erica M. Christensen [aut] (<https://orcid.org/0000-0002-5635-2502>), Juniper L. Simonis [aut] (<https://orcid.org/0000-0001-9798-0460>), Ellen K. Bledsoe [aut] (<https://orcid.org/0000-0002-3629-7235>), Renata M. Diaz [aut] (<https://orcid.org/0000-0003-0803-4734>), Shawn D. Taylor [aut] (<https://orcid.org/0000-0002-6178-6903>), Ethan P, White [aut] (<https://orcid.org/0000-0001-6728-7745>), S.K. Morgan Ernest [aut] (<https://orcid.org/0000-0002-6026-8530>), Weecology [cph]
Maintainer: Glenda M. Yenni <glenda@weecology.org>
Repository: CRAN
Date/Publication: 2020-08-28 11:00:07 UTC

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New package MLDAShiny2 with initial version 0.1.0
Package: MLDAShiny2
Type: Package
Title: Interactive Document for Working with Discriminant Analysis
Version: 0.1.0
Author: Kartikeya Bolar
Maintainer: Kartikeya Bolar <kartikeya.bolar@tapmi.edu.in>
Description: An interactive document on the topic of linear discriminant analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at <https://analyticmodels.shinyapps.io/DiscriminantAnalysisModelling/>.
License: GPL-2
Encoding: UTF-8
LazyData: TRUE
Depends: R (>= 4.0.0)
Imports: shiny,rmarkdown,klaR,MASS,dplyr,datasets,caret,DiscriMiner,e1071,rhandsontable
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-08-25 16:17:09 UTC; kartikeya.bolar
Repository: CRAN
Date/Publication: 2020-08-28 10:50:03 UTC

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Package hyfo updated to version 1.4.3 with previous version 1.4.2 dated 2020-04-04

Title: Hydrology and Climate Forecasting
Description: Focuses on data processing and visualization in hydrology and climate forecasting. Main function includes data extraction, data downscaling, data resampling, gap filler of precipitation, bias correction of forecasting data, flexible time series plot, and spatial map generation. It is a good pre- processing and post-processing tool for hydrological and hydraulic modellers.
Author: Yuanchao Xu [aut, cre]
Maintainer: Yuanchao Xu <xuyuanchao37@gmail.com>

Diff between hyfo versions 1.4.2 dated 2020-04-04 and 1.4.3 dated 2020-08-28

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New package haplo.stats with initial version 1.8.5
Package: haplo.stats
Version: 1.8.5
Date: 2020-8-27
Title: Statistical Analysis of Haplotypes with Traits and Covariates when Linkage Phase is Ambiguous
Author: Sinnwell JP, Schaid DJ
Maintainer: Jason P. Sinnwell <sinnwell.jason@mayo.edu>
Description: Routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em(), haplo.glm(), haplo.score(), and haplo.power(); all of which have detailed examples in the vignette.
License: GPL (>= 2)
Depends: R (>= 3.5.0), methods, stats, graphics, arsenal
Imports: rms
Suggests: R.rsp, testthat
VignetteBuilder: R.rsp
URL: https://cran.r-project.org/package=haplo.stats, https://www.mayo.edu/research/labs/statistical-genetics-genetic-epidemiology/software
NeedsCompilation: yes
Packaged: 2020-08-27 18:37:57 UTC; sinnwell
Repository: CRAN
Date/Publication: 2020-08-28 11:00:11 UTC

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Package gmvarkit updated to version 1.2.1 with previous version 1.2.0 dated 2020-08-24

Title: Estimate Gaussian Mixture Vector Autoregressive Model
Description: Unconstrained and constrained maximum likelihood estimation of structural and reduced form Gaussian mixture vector autoregressive (GMVAR) model, quantile residual tests, graphical diagnostics, simulations, forecasting, and estimation of generalized impulse response function. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>, Savi Virolainen (2020) <arXiv:2007.04713>.
Author: Savi Virolainen [aut, cre]
Maintainer: Savi Virolainen <savi.virolainen@helsinki.fi>

Diff between gmvarkit versions 1.2.0 dated 2020-08-24 and 1.2.1 dated 2020-08-28

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Package rsnps updated to version 0.4.0 with previous version 0.3.0 dated 2018-09-20

Title: Get 'SNP' ('Single-Nucleotide' 'Polymorphism') Data on the Web
Description: A programmatic interface to various 'SNP' 'datasets' on the web: 'OpenSNP' (<https://opensnp.org>), and 'NBCIs' 'dbSNP' database (<https://www.ncbi.nlm.nih.gov/projects/SNP/>). Functions are included for searching for 'NCBI'. For 'OpenSNP', functions are included for getting 'SNPs', and data for 'genotypes', 'phenotypes', annotations, and bulk downloads of data by user.
Author: Julia Gustavsen [aut, cre] (<https://orcid.org/0000-0002-4764-4802>), Sina Rüeger [aut] (<https://orcid.org/0000-0003-2848-9242>), Scott Chamberlain [aut] (<https://orcid.org/0000-0003-1444-9135>), Kevin Ushey [aut], Hao Zhu [aut]
Maintainer: Julia Gustavsen <j.gustavsen@gmail.com>

Diff between rsnps versions 0.3.0 dated 2018-09-20 and 0.4.0 dated 2020-08-28

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Package dimensionsR updated to version 0.0.2 with previous version 0.0.1 dated 2020-03-20

Title: Gathering Bibliographic Records from 'Digital Science Dimensions' Using 'DSL' API
Description: A set of tools to extract bibliographic content from 'Digital Science Dimensions' using 'DSL' API <https://www.dimensions.ai/dimensions-apis/>.
Author: Massimo Aria [aut, cre] (<https://orcid.org/0000-0002-8517-9411>)
Maintainer: Massimo Aria <massimo.aria@gmail.com>

Diff between dimensionsR versions 0.0.1 dated 2020-03-20 and 0.0.2 dated 2020-08-28

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Package COVID19 updated to version 2.3.0 with previous version 2.2.0 dated 2020-07-13

Title: R Interface to COVID-19 Data Hub
Description: Download COVID-19 data across governmental sources at national, regional, and city level, as described in Guidotti and Ardia (2020) <doi:10.21105/joss.02376>. Includes policy measures by 'Oxford COVID-19 Government Response Tracker' <https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker>. Provides a seamless integration with 'World Bank Open Data' <https://data.worldbank.org/>, 'Google Mobility Reports' <https://www.google.com/covid19/mobility/>, 'Apple Mobility Reports' <https://covid19.apple.com/mobility>.
Author: Emanuele Guidotti [aut, cre] (<https://orcid.org/0000-0002-8961-6623>), David Ardia [ctb] (<https://orcid.org/0000-0003-2823-782X>)
Maintainer: Emanuele Guidotti <emanuele.guidotti@unine.ch>

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Package ihs (with last version 1.0) was removed from CRAN

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

2015-02-25 1.0

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Package shapper updated to version 0.1.3 with previous version 0.1.2 dated 2019-08-02

Title: Wrapper of Python Library 'shap'
Description: Provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) <arXiv:1705.07874> The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package 'shapper' is a port of the Python library 'shap'.
Author: Szymon Maksymiuk [aut, cre], Alicja Gosiewska [aut], Przemyslaw Biecek [aut], Mateusz Staniak [ctb], Michal Burdukiewicz [ctb]
Maintainer: Szymon Maksymiuk <sz.maksymiuk@gmail.com>

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Package SpaDES.core updated to version 1.0.2 with previous version 1.0.1 dated 2020-05-15

Title: Core Utilities for Developing and Running Spatially Explicit Discrete Event Models
Description: Provides the core framework for a discrete event system (DES) to implement a complete data-to-decisions, reproducible workflow. The core DES components facilitate modularity, and easily enable the user to include additional functionality by running user-built modules. Includes conditional scheduling, restart after interruption, packaging of reusable modules, tools for developing arbitrary automated workflows, automated interweaving of modules of different temporal resolution, and tools for visualizing and understanding the DES project.
Author: Alex M Chubaty [aut, cre] (<https://orcid.org/0000-0001-7146-8135>), Eliot J B McIntire [aut] (<https://orcid.org/0000-0002-6914-8316>), Yong Luo [ctb], Steve Cumming [ctb], Ceres Barros [ctb] (<https://orcid.org/0000-0003-4036-977X>), Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources Canada [cph]
Maintainer: Alex M Chubaty <alex.chubaty@gmail.com>

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Package rags2ridges updated to version 2.2.3 with previous version 2.2.2 dated 2019-12-17

Title: Ridge Estimation of Precision Matrices from High-Dimensional Data
Description: Proper L2-penalized maximum likelihood estimators for precision matrices and supporting functions to employ these estimators in a graphical modeling setting. For details see van Wieringen & Peeters (2016) <doi:10.1016/j.csda.2016.05.012> and associated publications.
Author: Carel F.W. Peeters [cre, aut], Anders Ellern Bilgrau [aut], Wessel N. van Wieringen [aut]
Maintainer: Carel F.W. Peeters <cf.peeters@vumc.nl>

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Package pointblank updated to version 0.5.2 with previous version 0.5.1 dated 2020-08-27

Title: Validation of Local and Remote Data Tables
Description: Validate data in data frames, 'tibble' objects, and in database tables (e.g., 'PostgreSQL' and 'MySQL'). Validation pipelines can be made using easily-readable, consecutive validation steps. Upon execution of the validation plan, several reporting options are available. User-defined thresholds for failure rates allow for the determination of appropriate reporting actions.
Author: Richard Iannone [aut, cre] (<https://orcid.org/0000-0003-3925-190X>), Mauricio Vargas [aut] (<https://orcid.org/0000-0003-1017-7574>)
Maintainer: Richard Iannone <riannone@me.com>

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Package OpenMx updated to version 2.18.1 with previous version 2.17.4 dated 2020-06-07

Title: Extended Structural Equation Modelling
Description: Create structural equation models that can be manipulated programmatically. Models may be specified with matrices or paths (LISREL or RAM) Example models include confirmatory factor, multiple group, mixture distribution, categorical threshold, modern test theory, differential Fit functions include full information maximum likelihood, maximum likelihood, and weighted least squares. equations, state space, and many others. Support and advanced package binaries available at <http://openmx.ssri.psu.edu>. The software is described in Neale, Hunter, Pritikin, Zahery, Brick, Kirkpatrick, Estabrook, Bates, Maes, & Boker (2016) <doi:10.1007/s11336-014-9435-8>.
Author: Steven M. Boker [aut], Michael C. Neale [aut], Hermine H. Maes [aut], Michael J. Wilde [ctb], Michael Spiegel [aut], Timothy R. Brick [aut], Ryne Estabrook [aut], Timothy C. Bates [aut], Paras Mehta [ctb], Timo von Oertzen [ctb], Ross J. Gore [aut], Michael D. Hunter [aut], Daniel C. Hackett [ctb], Julian Karch [ctb], Andreas M. Brandmaier [ctb], Joshua N. Pritikin [aut, cre], Mahsa Zahery [aut], Robert M. Kirkpatrick [aut], Yang Wang [ctb], Ben Goodrich [ctb], Charles Driver [ctb], Massachusetts Institute of Technology [cph], S. G. Johnson [cph], Association for Computing Machinery [cph], Dieter Kraft [cph], Stefan Wilhelm [cph], Sarah Medland [cph], Carl F. Falk [cph], Matt Keller [cph], Manjunath B G [cph], The Regents of the University of California [cph], Lester Ingber [cph], Wong Shao Voon [cph], Juan Palacios [cph], Jiang Yang [cph], Gael Guennebaud [cph], Jitse Niesen [cph]
Maintainer: Joshua N. Pritikin <jpritikin@pobox.com>

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Package gausscov updated to version 0.0.5 with previous version 0.0.4 dated 2020-08-01

Title: The Gaussian Covariate Method for Variable Selection
Description: Given the standard linear model the traditional way of deciding whether to include the jth covariate is to apply the F-test to decide whether the corresponding beta coefficient is zero. The Gaussian covariate method is completely different. The question as to whether the beta coefficient is or is not zero is replaced by the question as to whether the covariate is better or worse than i.i.d. Gaussian noise. The P-value for the covariate is the probability that Gaussian noise is better. Surprisingly this can be given exactly and it is the same a the P-value for the classical model based on the F-distribution. The Gaussian covariate P-value is model free, it is the same for any data set. Using the idea it is possible to do covariate selection for a small number of covariates 25 by considering all subsets. Post selection inference causes no problems as the P-values hold whatever the data. The idea extends to stepwise regression again with exact probabilities. In the simplest version the only parameter is a specified cut-off P-value which can be interpreted as the probability of a false positive being included in the final selection. For more information see the website below and the accompanying papers: L. Davies and L. Duembgen, "Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities", 2020, <arXiv:1906.01990>. L. Davies, "Lasso, Knockoff and Gaussian covariates: A comparison", 2018, <arXiv:1807.09633>.
Author: Laurie Davies [aut, cre]
Maintainer: Laurie Davies <laurie.davies@uni-due.de>

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Package ezmmek updated to version 0.2.4 with previous version 0.2.1 dated 2019-12-20

Title: Easy Michaelis-Menten Enzyme Kinetics
Description: Serves as a platform for published fluorometric enzyme assay protocols. 'ezmmek' calibrates, calculates, and plots enzyme activities as they relate to the transformation of synthetic substrates. At present, 'ezmmek' implements two common protocols found in the literature, and is modular to accommodate additional protocols. Here, these protocols are referred to as the In-Sample Calibration (Hoppe, 1983; <doi:10.3354/meps011299>) and In-Buffer Calibration (German et al., 2011; <doi:10.1016/j.soilbio.2011.03.017>). protocols. By containing multiple protocols, 'ezmmek' aims to stimulate discussion about how to best optimize fluorometric enzyme assays. A standardized approach would make studies more comparable and reproducible.
Author: Christopher Cook [aut, cre], Andrew Steen [aut]
Maintainer: Christopher Cook <ccook62@vols.utk.edu>

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Package eplusr updated to version 0.13.0 with previous version 0.12.0 dated 2020-02-20

Title: A Toolkit for Using Whole Building Simulation Program 'EnergyPlus'
Description: A rich toolkit of using the whole building simulation program 'EnergyPlus'(<https://energyplus.net>), which enables programmatic navigation, modification of 'EnergyPlus' models and makes it less painful to do parametric simulations and analysis.
Author: Hongyuan Jia [aut, cre] (<https://orcid.org/0000-0002-0075-8183>)
Maintainer: Hongyuan Jia <hongyuan.jia@bears-berkeley.sg>

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Package ENMTools updated to version 1.0.1 with previous version 1.0 dated 2020-07-29

Title: Analysis of Niche Evolution using Niche and Distribution Models
Description: Description: Tools for constructing niche models and analyzing patterns of niche evolution. Acts as an interface for many popular modeling algorithms, and allows users to conduct Monte Carlo tests to address basic questions in evolutionary ecology and biogeography. Warren, D.L., R.E. Glor, and M. Turelli (2008) <doi:10.1111/j.1558-5646.2008.00482.x> Glor, R.E., and D.L. Warren (2011) <doi:10.1111/j.1558-5646.2010.01177.x> Warren, D.L., R.E. Glor, and M. Turelli (2010) <doi:10.1111/j.1600-0587.2009.06142.x> Cardillo, M., and D.L. Warren (2016) <doi:10.1111/geb.12455> D.L. Warren, L.J. Beaumont, R. Dinnage, and J.B. Baumgartner (2019) <doi:10.1111/ecog.03900>.
Author: Dan Warren [aut, cre] (<https://orcid.org/0000-0002-8747-2451>), Nick Matzke [ctb], Marcel Cardillo [ctb], John Baumgartner [ctb], Linda Beaumont [ctb], Nicholas Huron [ctb], Marianna Simoes [ctb], Teresa Iglesias [ctb], Russell Dinnage [aut] (<https://orcid.org/0000-0003-0846-2819>)
Maintainer: Dan Warren <dan.l.warren@gmail.com>

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Package EMSS updated to version 1.0.2 with previous version 1.0.1 dated 2020-05-31

Title: Some EM-Type Estimation Methods for the Heckman Selection Model
Description: Some EM-type algorithms to estimate parameters for the well-known Heckman selection model are provided in the package. Such algorithms are as follow: ECM(Expectation/Conditional Maximization), ECM(NR)(the Newton-Raphson method is adapted to the ECM) and ECME(Expectation/Conditional Maximization Either). Since the algorithms are based on the EM algorithm, they also have EM's main advantages, namely, stability and ease of implementation. Further details and explanations of the algorithms can be found in Zhao et al. (2020) <doi: 10.1016/j.csda.2020.106930>.
Author: Kexuan Yang <717260446@qq.com>, Sang Kyu Lee <leesa111@msu.edu>, Jun Zhao <zhaojun2021@hotmail.com>, and Hyoung-Moon Kim <hmk966a@gmail.com >
Maintainer: Sang Kyu Lee <leesa111@msu.edu>

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Package econet updated to version 0.1.91 with previous version 0.1.81 dated 2018-11-08

Title: Estimation of Parameter-Dependent Network Centrality Measures
Description: Provides methods for estimating parameter-dependent network centrality measures with linear-in-means models. Both non linear least squares and maximum likelihood estimators are implemented. The methods allow for both link and node heterogeneity in network effects, endogenous network formation and the presence of unconnected nodes. The routines also compare the explanatory power of parameter-dependent network centrality measures with those of standard measures of network centrality. Benefits and features of the 'econet' package are illustrated using data from Battaglini and Patacchini (2018) and Battaglini, Patacchini, and Leone Sciabolazza (2020). For additional details, see the vignette.
Author: Marco Battaglini [aut] (<https://orcid.org/0000-0001-9690-0721>), Valerio Leone Sciabolazza [aut, cre] (<https://orcid.org/0000-0003-2537-3084>), Eleonora Patacchini [aut] (<https://orcid.org/0000-0002-3510-2969>), Sida Peng [aut] (<https://orcid.org/0000-0002-2151-0523>)
Maintainer: Valerio Leone Sciabolazza <valerio.leonesciabolazza@uniparthenope.it>

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Package dynsurv updated to version 0.4-1 with previous version 0.3-7 dated 2019-08-27

Title: Dynamic Models for Survival Data
Description: Time-varying coefficient models for interval censored and right censored survival data including 1) Bayesian Cox model with time-independent, time-varying or dynamic coefficients for right censored and interval censored data studied by Sinha et al. (1999) <doi:10.1111/j.0006-341X.1999.00585.x> and Wang et al. (2013) <doi:10.1007/s10985-013-9246-8>, 2) Spline based time-varying coefficient Cox model for right censored data proposed by Perperoglou et al. (2006) <doi:10.1016/j.cmpb.2005.11.006>, and 3) Transformation model with time-varying coefficients for right censored data using estimating equations proposed by Peng and Huang (2007) <doi:10.1093/biomet/asm058>.
Author: Wenjie Wang [aut, cre] (<https://orcid.org/0000-0003-0363-3180>), Ming-Hui Chen [aut], Xiaojing Wang [aut], Jun Yan [aut] (<https://orcid.org/0000-0003-4401-7296>)
Maintainer: Wenjie Wang <wang@wwenjie.org>

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Package barsurf updated to version 0.6.1 with previous version 0.5.0 dated 2020-05-04

Title: Multivariate Function Visualization and Smooth Multiband Color Interpolation
Description: Combined contour/heatmap plots, 3d bar/surface plots, isosurface plots, triangular plots and 2d/3d vector fields. Builds on the colorspace package (Zeileis, A., et al. (2019) <arxiv.org/abs/1903.06490>), by supporting smooth multiband color interpolation, in sRGB, HSV and HCL color spaces. The default color functions for heatmaps, use HCL (Zeileis, A., et al. (2009) <doi:10.1016/j.csda.2008.11.033>) and support near-uniform perception.
Author: Abby Spurdle
Maintainer: Abby Spurdle <spurdle.a@gmail.com>

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Package Rcmdr updated to version 2.7-0 with previous version 2.6-2 dated 2020-01-22

Title: R Commander
Description: A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.
Author: John Fox [aut, cre], Milan Bouchet-Valat [aut], Liviu Andronic [ctb], Michael Ash [ctb], Theophilius Boye [ctb], Stefano Calza [ctb], Andy Chang [ctb], Vilmantas Gegzna [ctb], Philippe Grosjean [ctb], Richard Heiberger [ctb], Kosar Karimi Pour [ctb], G. Jay Kerns [ctb], Renaud Lancelot [ctb], Matthieu Lesnoff [ctb], Uwe Ligges [ctb], Samir Messad [ctb], Martin Maechler [ctb], Robert Muenchen [ctb], Duncan Murdoch [ctb], Erich Neuwirth [ctb], Dan Putler [ctb], Brian Ripley [ctb], Miroslav Ristic [ctb], Peter Wolf [ctb], Kevin Wright [ctb]
Maintainer: John Fox <jfox@mcmaster.ca>

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Package forecastHybrid updated to version 5.0.19 with previous version 5.0.18 dated 2020-04-02

Title: Convenient Functions for Ensemble Time Series Forecasts
Description: Convenient functions for ensemble forecasts in R combining approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(), thetaf(), nnetar(), stlm(), tbats(), and snaive() can be combined with equal weights, weights based on in-sample errors (introduced by Bates & Granger (1969) <doi:10.1057/jors.1969.103>), or cross-validated weights. Cross validation for time series data with user-supplied models and forecasting functions is also supported to evaluate model accuracy.
Author: David Shaub [aut, cre], Peter Ellis [aut]
Maintainer: David Shaub <davidshaub@gmx.com>

Diff between forecastHybrid versions 5.0.18 dated 2020-04-02 and 5.0.19 dated 2020-08-28

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Package fastmatrix updated to version 0.2-3 with previous version 0.2 dated 2020-08-14

Title: Fast Computation of some Matrices Useful in Statistics
Description: Small set of functions to fast computation of some matrices and operations useful in statistics.
Author: Felipe Osorio [aut, cre] (<https://orcid.org/0000-0002-4675-5201>), Alonso Ogueda [aut]
Maintainer: Felipe Osorio <felipe.osorios@usm.cl>

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Package ecodist updated to version 2.0.7 with previous version 2.0.5 dated 2020-04-07

Title: Dissimilarity-Based Functions for Ecological Analysis
Description: Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community data. The original package description is in Goslee and Urban (2007) <doi:10.18637/jss.v022.i07>, with further statistical detail in Goslee (2010) <doi:10.1007/s11258-009-9641-0>.
Author: Sarah Goslee [aut, cre], Dean Urban [aut]
Maintainer: Sarah Goslee <Sarah.Goslee@usda.gov>

Diff between ecodist versions 2.0.5 dated 2020-04-07 and 2.0.7 dated 2020-08-28

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Package jsmodule updated to version 1.1.1 with previous version 1.1.0 dated 2020-07-09

Title: 'RStudio' Addins and 'Shiny' Modules for Medical Research
Description: 'RStudio' addins and 'Shiny' modules for descriptive statistics, regression and survival analysis.
Author: Jinseob Kim [aut, cre] (<https://orcid.org/0000-0002-9403-605X>), Zarathu [cph, fnd]
Maintainer: Jinseob Kim <jinseob2kim@gmail.com>

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Package ibmAcousticR updated to version 0.2.1 with previous version 0.1.2 dated 2020-06-03

Title: Connect to Your 'IBM Acoustic' Data
Description: Authentication can be the most difficult part about working with a new API. 'ibmAcousticR' facilitates making a connection to the 'IBM Acoustic' email campaign management API and executing various queries. The 'IBM Acoustic' API documentation is available at <https://developer.ibm.com/customer-engagement/docs/>. This package is not supported by 'IBM'.
Author: Chris Umphlett [aut, cre], Avinash Panigrahi [aut]
Maintainer: Chris Umphlett <christopher.umphlett@gmail.com>

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Package SubTite updated to version 4.0.0 with previous version 3.0.2 dated 2020-07-02

Title: Subgroup Specific Optimal Dose Assignment
Description: Chooses subgroup specific optimal doses in a phase I dose finding clinical trial allowing for subgroup combination and simulates clinical trials under the subgroup specific time to event continual reassessment method. Chapple, A.G., Thall, P.F. (2018) <doi:10.1002/pst.1891>.
Author: Andrew Chapple
Maintainer: Andrew Chapple <achapp@lsuhsc.edu>

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Package qrandom (with last version 1.2.2) was removed from CRAN

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

2020-01-10 1.2.2
2019-12-11 1.2.1
2019-08-20 1.2
2019-02-14 1.1
2019-01-10 1.0

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