Fri, 16 Jul 2021

Package fishmethods updated to version 1.11-2 with previous version 1.11-1 dated 2019-12-13

Title: Fishery Science Methods and Models
Description: Functions for applying a wide range of fisheries stock assessment methods.
Author: Gary A. Nelson <gary.nelson@mass.gov>
Maintainer: Gary A. Nelson <gary.nelson@mass.gov>

Diff between fishmethods versions 1.11-1 dated 2019-12-13 and 1.11-2 dated 2021-07-16

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Package yum updated to version 0.1.0 with previous version 0.0.1 dated 2019-03-13

Title: Utilities to Extract and Process 'YAML' Fragments
Description: Provides a number of functions to facilitate extracting information in 'YAML' fragments from one or multiple files, optionally structuring the information in a 'data.tree'. 'YAML' (recursive acronym for "YAML ain't Markup Language") is a convention for specifying structured data in a format that is both machine- and human-readable. 'YAML' therefore lends itself well for embedding (meta)data in plain text files, such as Markdown files. This principle is implemented in 'yum' with minimal dependencies (i.e. only the 'yaml' packages, and the 'data.tree' package can be used to enable additional functionality).
Author: Gjalt-Jorn Peters [aut, cre]
Maintainer: Gjalt-Jorn Peters <gjalt-jorn@userfriendlyscience.com>

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Package modelObj updated to version 4.1 with previous version 4.0 dated 2019-02-26

Title: A Model Object Framework for Regression Analysis
Description: A utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions. In this way, a user can easily go from linear to non-linear models within the same package.
Author: Shannon T. Holloway
Maintainer: Shannon T. Holloway <sthollow@ncsu.edu>

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Package growfunctions updated to version 0.15 with previous version 0.14 dated 2018-03-31

Title: Bayesian Non-Parametric Dependent Models for Time-Indexed Functional Data
Description: Estimates a collection of time-indexed functions under either of Gaussian process (GP) or intrinsic Gaussian Markov random field (iGMRF) prior formulations where a Dirichlet process mixture allows sub-groupings of the functions to share the same covariance or precision parameters. The GP and iGMRF formulations both support any number of additive covariance or precision terms, respectively, expressing either or both of multiple trend and seasonality.
Author: Terrance Savitsky
Maintainer: Terrance Savitsky <tds151@gmail.com>

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Package bayesbr updated to version 0.0.1.0 with previous version 0.0.0.1 dated 2020-06-26

Title: Beta Regression on a Bayesian Model
Description: Applies the Beta regression model in the Bayesian statistical view with the possibility of adding a spatial effect in the parameters, the Beta regression is used when the response variable is a proportion variable, that is, it only accepts values between 0 and 1. The package 'bayesbr' uses 'rstan' package to build the Bayesian statistical models. The main function of the package receives as a parameter a form informing the independent variable and the co-variables of the model to be made, as output it returns a list with the results of the model. For more details see Ferrari and Cribari-Neto (2004) <doi:10.1080/0266476042000214501> and Hoffman and Gelman (2014) <arXiv:1111.4246>.
Author: Joao Melo [aut, cre], Vinicius Mayrink [aut]
Maintainer: Joao Melo <pjoao266@gmail.com>

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Package outliertree updated to version 1.7.3 with previous version 1.7.2 dated 2021-05-17

Title: Explainable Outlier Detection Through Decision Tree Conditioning
Description: Outlier detection method that flags suspicious values within observations, constrasting them against the normal values in a user-readable format, potentially describing conditions within the data that make a given outlier more rare. Full procedure is described in Cortes (2020) <arXiv:2001.00636>. Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera@gmail.com>

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Package RRphylo updated to version 2.5.7 with previous version 2.5.0 dated 2020-12-03

Title: Phylogenetic Ridge Regression Methods for Comparative Studies
Description: Functions for phylogenetic analysis (Castiglione et al, 2018 <doi:10.1111/2041-210X.12954>). The functions perform the estimation of phenotypic evolutionary rates, identification of phenotypic evolutionary rate shifts, quantification of direction and size of evolutionary change in multivariate traits, the computation of ontogenetic shape vectors and test for morphological convergence.
Author: Pasquale Raia, Silvia Castiglione, Carmela Serio, Alessandro Mondanaro, Marina Melchionna, Mirko Di Febbraro, Antonio Profico, Francesco Carotenuto
Maintainer: Pasquale Raia <pasquale.raia@unina.it>

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Package Polychrome updated to version 1.3.1 with previous version 1.2.6 dated 2020-11-11

Title: Qualitative Palettes with Many Colors
Description: Tools for creating, viewing, and assessing qualitative palettes with many (20-30 or more) colors. See Coombes and colleagues (2019) <doi:10.18637/jss.v090.c01>.
Author: Kevin R. Coombes, Guy Brock
Maintainer: Kevin R. Coombes <krc@silicovore.com>

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Package MOSS updated to version 0.2.0 with previous version 0.1.0 dated 2021-01-19

Title: Multi-Omic Integration via Sparse Singular Value Decomposition
Description: High dimensionality, noise and heterogeneity among samples and features challenge the omic integration task. Here we present an omic integration method based on sparse singular value decomposition (SVD) to deal with these limitations, by: a. obtaining the main axes of variation of the combined omics, b. imposing sparsity constraints at both subjects (rows) and features (columns) levels using Elastic Net type of shrinkage, and c. allowing both linear and non-linear projections (via t-Stochastic Neighbor Embedding) of the omic data to detect clusters in very convoluted data (Gonzalez-Reymundez & Vazquez, 2020) <doi:10.1038/s41598-020-65119-5>.
Author: Agustin Gonzalez-Reymundez [aut, cre, cph], Alexander Grueneberg [aut], Ana Vazquez [ctb, ths]
Maintainer: Agustin Gonzalez-Reymundez <agugonrey@gmail.com>

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Package HDSpatialScan updated to version 1.0.2 with previous version 1.0.1 dated 2021-05-27

Title: Multivariate and Functional Spatial Scan Statistics
Description: Allows to detect spatial clusters of abnormal values on multivariate or functional data. Martin KULLDORFF and Lan HUANG and Kevin KONTY (2009) <doi:10.1186/1476-072X-8-58>, Inkyung JUNG and Ho Jin CHO (2015) <doi:10.1186/s12942-015-0024-6>, Lionel CUCALA and Michael GENIN and Caroline LANIER and Florent OCCELLI (2017) <doi:10.1016/j.spasta.2017.06.001>, Lionel CUCALA and Michael GENIN and Florent OCCELLI and Julien SOULA (2019) <doi:10.1016/j.spasta.2018.10.002>, Zaineb SMIDA and Lionel CUCALA and Ali GANNOUN (2020) <https://hal.archives-ouvertes.fr/hal-02908496>, Camille FREVENT and Mohamed-Salem AHMED and Matthieu MARBAC and Michael GENIN (2021) <arXiv:2011.03482>, Camille FREVENT and Mohamed-Salem AHMED and Sophie DABO-NIANG and Michael GENIN (2021) <arXiv:2103.14401>.
Author: Camille FREVENT [aut, cre, cph], Mohamed-Salem AHMED [aut], Julien SOULA [aut], Zaineb SMIDA [aut], Lionel CUCALA [aut], Sophie DABO-NIANG [aut], Michaël GENIN [aut]
Maintainer: Camille FREVENT <camille.frevent@univ-lille.fr>

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Package ClassDiscovery updated to version 3.4.0 with previous version 3.3.13 dated 2020-11-11

Title: Classes and Methods for "Class Discovery" with Microarrays or Proteomics
Description: Defines the classes used for "class discovery" problems in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class discovery primarily consists of unsupervised clustering methods with attempts to assess their statistical significance.
Author: Kevin R. Coombes
Maintainer: Kevin R. Coombes <krc@silicovore.com>

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Package RcppArmadillo updated to version 0.10.6.0.0 with previous version 0.10.5.0.0 dated 2021-05-23

Title: 'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra Library
Description: 'Armadillo' is a templated C++ linear algebra library (by Conrad Sanderson) that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. The 'RcppArmadillo' package includes the header files from the templated 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Armadillo requires a C++11 compiler.
Author: Dirk Eddelbuettel, Romain Francois, Doug Bates and Binxiang Ni
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package netrankr updated to version 1.0.0 with previous version 0.3.0 dated 2020-09-09

Title: Analyzing Partial Rankings in Networks
Description: Implements methods for centrality related analyses of networks. While the package includes the possibility to build more than 20 indices, its main focus lies on index-free assessment of centrality via partial rankings obtained by neighborhood-inclusion or positional dominance. These partial rankings can be analyzed with different methods, including probabilistic methods like computing expected node ranks and relative rank probabilities (how likely is it that a node is more central than another?). The methodology is described in depth in the vignettes and in Schoch (2018) <doi:10.1016/j.socnet.2017.12.003>.
Author: David Schoch [aut, cre], Julian Müller [ctb]
Maintainer: David Schoch <david.schoch@manchester.ac.uk>

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Package clinUtils updated to version 0.0.3 with previous version 0.0.2 dated 2021-07-14

Title: General Utility Functions for Analysis of Clinical Data
Description: Utility functions to facilitate the import, the reporting and analysis of clinical data. Example datasets in 'SDTM' and 'ADaM' format, containing a subset of patients/domains from the 'CDISC Pilot 01 study' are also available as R datasets to demonstrate the package functionalities.
Author: Laure Cougnaud [aut], Michela Pasetto [aut, cre], Open Analytics [cph]
Maintainer: Michela Pasetto <michela.pasetto@openanalytics.eu>

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Package PhenotypeSimulator updated to version 0.3.4 with previous version 0.3.3 dated 2019-05-15

Title: Flexible Phenotype Simulation from Different Genetic and Noise Models
Description: Simulation is a critical part of method development and assessment in quantitative genetics. 'PhenotypeSimulator' allows for the flexible simulation of phenotypes under different models, including genetic variant and infinitesimal genetic effects (reflecting population structure) as well as non-genetic covariate effects, observational noise and additional correlation effects. The different phenotype components are combined into a final phenotype while controlling for the proportion of variance explained by each of the components. For each effect component, the number of variables, their distribution and the design of their effect across traits can be customised. For the simulation of the genetic effects, external genotype data from a number of standard software ('plink', 'hapgen2'/ 'impute2', 'genome', 'bimbam', simple text files) can be imported. The final simulated phenotypes and its components can be automatically saved into .rds or .csv files. In addition, they can be saved in formats compatible with commonly used genetic association software ('gemma', 'bimbam', 'plink', 'snptest', 'LiMMBo').
Author: Hannah Meyer [aut, cre] (<https://orcid.org/0000-0003-4564-0899>), Konrad Rudolph [ctb] (<https://orcid.org/0000-0002-9866-7051>)
Maintainer: Hannah Meyer <hannah.v.meyer@gmail.com>

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Package Mercator updated to version 1.1.1 with previous version 1.0.0 dated 2021-02-12

Title: Clustering and Visualizing Distance Matrices
Description: Defines the classes used to explore, cluster and visualize distance matrices, especially those arising from binary data. See Abrams and colleagues, 2021, <doi:10.1093/bioinformatics/btab037>.
Author: Kevin R. Coombes, Caitlin E. Coombes
Maintainer: Kevin R. Coombes <krc@silicovore.com>

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Package CoOL updated to version 1.0.3 with previous version 1.0.2 dated 2021-06-30

Title: Causes of Outcome Learning
Description: Implementing the computational phase of the Causes of Outcome Learning approach as described in Rieckmann, Dworzynski, Arras, Lapuschkin, Samek, Arah, Rod, Ekstrom. Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. medRxiv (2020) <doi:10.1101/2020.12.10.20225243>. The optional 'ggtree' package can be obtained through Bioconductor.
Author: Andreas Rieckmann [aut, cre], Piotr Dworzynski [aut], Leila Arras [ctb], Claus Thorn Ekstrom [aut]
Maintainer: Andreas Rieckmann <aric@sund.ku.dk>

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Package set6 updated to version 0.2.2 with previous version 0.2.1 dated 2021-02-17

Title: R6 Mathematical Sets Interface
Description: An object-oriented package for mathematical sets, upgrading the current gold-standard {sets}. Many forms of mathematical sets are implemented, including (countably finite) sets, tuples, intervals (countably infinite or uncountable), and fuzzy variants. Wrappers extend functionality by allowing symbolic representations of complex operations on sets, including unions, (cartesian) products, exponentiation, and differences (asymmetric and symmetric).
Author: Raphael Sonabend [aut, cre] (<https://orcid.org/0000-0001-9225-4654>), Franz Kiraly [aut]
Maintainer: Raphael Sonabend <raphaelsonabend@gmail.com>

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Package modeltime.ensemble updated to version 0.4.2 with previous version 0.4.1 dated 2021-05-31

Title: Ensemble Algorithms for Time Series Forecasting with Modeltime
Description: A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, weighted averaging, and stacking. These techniques are popular methods to improve forecast accuracy and stability. Refer to papers such as "Machine-Learning Models for Sales Time Series Forecasting" Pavlyshenko, B.M. (2019) <doi:10.3390>.
Author: Matt Dancho [aut, cre], Business Science [cph]
Maintainer: Matt Dancho <mdancho@business-science.io>

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Package joinet updated to version 0.0.9 with previous version 0.0.8 dated 2021-07-16

Title: Multivariate Elastic Net Regression
Description: Implements high-dimensional multivariate regression by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement. If required, install MRCE or remMap from GitHub (<https://github.com/cran/MRCE>, <https://github.com/cran/remMap>).
Author: Armin Rauschenberger [aut, cre]
Maintainer: Armin Rauschenberger <armin.rauschenberger@uni.lu>

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Package UCSCXenaShiny updated to version 1.1.0 with previous version 1.0.1 dated 2021-06-23

Title: Interactive Analysis of UCSC Xena Data
Description: Provides functions and a Shiny application for downloading, analyzing and visualizing datasets from UCSC Xena (<http://xena.ucsc.edu/>), which is a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.
Author: Shixiang Wang [aut, cre] (<https://orcid.org/0000-0001-9855-7357>), Yi Xiong [aut] (<https://orcid.org/0000-0002-4370-9824>), Longfei Zhao [aut] (<https://orcid.org/0000-0002-6277-0137>), Kai Gu [aut] (<https://orcid.org/0000-0002-0177-0774>), Yin Li [aut], Fei Zhao [aut]
Maintainer: Shixiang Wang <w_shixiang@163.com>

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Permanent link

Package bs4Dash updated to version 2.0.1 with previous version 2.0.0 dated 2021-05-26

Title: A 'Bootstrap 4' Version of 'shinydashboard'
Description: Make 'Bootstrap 4' Shiny dashboards. Use the full power of 'AdminLTE3', a dashboard template built on top of 'Bootstrap 4' <https://github.com/ColorlibHQ/AdminLTE>.
Author: David Granjon [aut, cre], RinteRface [cph], Almasaeed Studio [ctb, cph] (AdminLTE3 theme for Bootstrap 4), Winston Chang [ctb, cph] (Utils functions from shinydashboard), Thomas Park [ctb, cph] (Bootswatch Sketchy theme CSS)
Maintainer: David Granjon <dgranjon@ymail.com>

Diff between bs4Dash versions 2.0.0 dated 2021-05-26 and 2.0.1 dated 2021-07-16

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Package shinydashboardPlus updated to version 2.0.2 with previous version 2.0.1 dated 2021-04-26

Title: Add More 'AdminLTE2' Components to 'shinydashboard'
Description: Extend 'shinydashboard' with 'AdminLTE2' components. 'AdminLTE2' is a free 'Bootstrap 3' dashboard template available at <https://adminlte.io>. Customize boxes, add timelines and a lot more.
Author: David Granjon [aut, cre], RinteRface [cph], Almasaeed Studio [ctb, cph] (AdminLTE2 theme for Bootstrap 3), Guang Yang [ctb, cph] (ygdashboard original template), Winston Chang [ctb, cph] (Functions from shinydashboard), Victor Perrier [ctb] (improved the shinydashboardPlusGallery)
Maintainer: David Granjon <dgranjon@ymail.com>

Diff between shinydashboardPlus versions 2.0.1 dated 2021-04-26 and 2.0.2 dated 2021-07-16

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Package spatstat.core updated to version 2.3-0 with previous version 2.2-0 dated 2021-06-17

Title: Core Functionality of the 'spatstat' Family
Description: Functionality for data analysis and modelling of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
Author: Adrian Baddeley [aut, cre], Rolf Turner [aut], Ege Rubak [aut], Kasper Klitgaard Berthelsen [ctb], Achmad Choiruddin [ctb], Jean-Francois Coeurjolly [ctb], Ottmar Cronie [ctb], Tilman Davies [ctb], Chiara Fend [ctb], Julian Gilbey [ctb], Yongtao Guan [ctb], Ute Hahn [ctb], Kassel Hingee [ctb], Abdollah Jalilian [ctb], Marie-Colette van Lieshout [ctb], Greg McSwiggan [ctb], Tuomas Rajala [ctb], Suman Rakshit [ctb], Dominic Schuhmacher [ctb], Rasmus Plenge Waagepetersen [ctb], Hangsheng Wang [ctb]
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>

Diff between spatstat.core versions 2.2-0 dated 2021-06-17 and 2.3-0 dated 2021-07-16

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More information about spatstat.core at CRAN
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New package MNARclust with initial version 1.0.0
Package: MNARclust
Title: Clustering Data with Non-Ignorable Missingness using Semi-Parametric Mixture Models
Version: 1.0.0
Description: Clustering of data under a non-ignorable missingness mechanism. Clustering is achieved by a semi-parametric mixture model and missingness is managed by using the pattern-mixture approach. More details of the approach are available in Du Roy de Chaumaray et al. (2020) <arXiv:2009.07662>.
Authors@R: c(person(given="Marie", family="Du Roy de Chaumaray", role = "aut"), person(given="Matthieu", family="Marbac", role = c("aut", "cre", "cph"), email = "matthieu.marbac-lourdelle@ensai.fr"))
Maintainer: Matthieu Marbac <matthieu.marbac-lourdelle@ensai.fr>
License: GPL (>= 2)
Imports: Rcpp, parallel, sn, rmutil
LinkingTo: Rcpp, RcppArmadillo
ByteCompile: true
URL: https://arxiv.org/abs/2009.07662
Author: Marie Du Roy de Chaumaray [aut], Matthieu Marbac [aut, cre, cph]
Collate: algoCat.R algoCont.R algoMixed.R MNARclust.R RcppExports.R sampler.R tools.R
LazyData: true
LazyLoad: yes
Encoding: UTF-8
Depends: R (>= 3.5)
NeedsCompilation: yes
Packaged: 2021-07-15 19:04:30 UTC; matt
Repository: CRAN
Date/Publication: 2021-07-16 09:20:30 UTC

More information about MNARclust at CRAN
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New package EESPCA with initial version 0.3.0
Package: EESPCA
Title: Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)
Version: 0.3.0
Author: H. Robert Frost
Maintainer: H. Robert Frost <rob.frost@dartmouth.edu>
Description: Contains logic for computing sparse principal components via the EESPCA method, which is based on an approximation of the eigenvector/eigenvalue identity. Includes logic to support execution of the TPower and rifle sparse PCA methods, as well as logic to estimate the sparsity parameters used by EESPCA, TPower and rifle via cross-validation to minimize the out-of-sample reconstruction error. H. Robert Frost (2021) <arXiv:2006.01924>.
Depends: R (>= 3.6.0), rifle (>= 1.0.0), MASS, PMA
License: GPL (>= 2)
Copyright: Dartmouth College
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2021-07-15 17:20:42 UTC; robfrost
Repository: CRAN
Date/Publication: 2021-07-16 09:20:32 UTC

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Package deepredeff updated to version 0.1.1 with previous version 0.1.0 dated 2020-10-23

Title: Deep Learning Prediction of Effectors
Description: A tool that contains trained deep learning models for predicting effector proteins. 'deepredeff' has been trained to identify effector proteins using a set of known experimentally validated effectors from either bacteria, fungi, or oomycetes. Documentation is available via several vignettes, and the paper by Kristianingsih and MacLean (2020) <doi:10.1101/2020.07.08.193250>.
Author: Ruth Kristianingsih [aut, cre, cph] (<https://orcid.org/0000-0003-1873-6203>)
Maintainer: Ruth Kristianingsih <ruth.kristianingsih30@gmail.com>

Diff between deepredeff versions 0.1.0 dated 2020-10-23 and 0.1.1 dated 2021-07-16

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New package tsentiment with initial version 0.1.0
Package: tsentiment
Title: Fetching Tweet Data for Sentiment Analysis
Version: 0.1.0
Authors@R: c(person("Hakki","Sabah",email = "hakkisabah@hotmail.com",role = c("aut", "cre")))
Description: First of all need Twitter Developer Account, package has exporting image for result after using.
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://github.com/hakkisabah/tsentiment
BugReports: https://github.com/hakkisabah/tsentiment/issues
Depends: R (>= 4.0)
Imports: reshape2,haven,readr,grid,SnowballC,wordcloud,wordcloud2,webshot,RColorBrewer,ggplot2,tm,httr,jsonlite,syuzhet,tidyverse,tidytext,magrittr,dplyr,tibble,grDevices
NeedsCompilation: no
Packaged: 2021-07-15 13:04:47 UTC; hakki
Author: Hakki Sabah [aut, cre]
Maintainer: Hakki Sabah <hakkisabah@hotmail.com>
Repository: CRAN
Date/Publication: 2021-07-16 08:10:02 UTC

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Package SwimmeR updated to version 0.11.0 with previous version 0.10.0 dated 2021-06-02

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, 'ISL' results, and (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 0.10.0 dated 2021-06-02 and 0.11.0 dated 2021-07-16

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Package stringi updated to version 1.7.3 with previous version 1.7.2 dated 2021-07-14

Title: Character String Processing Facilities
Description: A multitude of character string/text/natural language processing tools: pattern searching (e.g., with 'Java'-like regular expressions or the 'Unicode' collation algorithm), random string generation, case mapping, string transliteration, concatenation, sorting, padding, wrapping, Unicode normalisation, date-time formatting and parsing, and many more. They are fast, consistent, convenient, and - thanks to 'ICU' (International Components for Unicode) - portable across all locales and platforms.
Author: Marek Gagolewski [aut, cre, cph] (<https://orcid.org/0000-0003-0637-6028>), Bartek Tartanus [ctb], and others (stringi source code); IBM, Unicode, Inc. and others (ICU4C source code, Unicode Character Database)
Maintainer: Marek Gagolewski <marek@gagolewski.com>

Diff between stringi versions 1.7.2 dated 2021-07-14 and 1.7.3 dated 2021-07-16

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New package SC.MEB with initial version 1.0
Package: SC.MEB
Title: Spatial Clustering with Hidden Markov Random Field using Empirical Bayes
Version: 1.0
Date: 2021-07-11
Authors@R: c( person("Yi", "Yang", email = "yygaosansiban@sina.com", role = c("aut", "cre")), person("Xingjie", "Shi", role = "aut"), person("Jin", "Liu", role = "aut"))
Description: Spatial clustering with hidden markov random field fitted via EM algorithm, details of which can be found in Yi Yang (2021) <doi:10.1101/2021.06.05.447181>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.
License: GPL-3
Depends: mclust, R (>= 2.10)
Imports: Rcpp (>= 1.0.6), SingleCellExperiment, purrr, Matrix,mvtnorm,GiRaF
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
Suggests: knitr, rmarkdown
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2021-07-15 14:55:09 UTC; yygao
Author: Yi Yang [aut, cre], Xingjie Shi [aut], Jin Liu [aut]
Maintainer: Yi Yang <yygaosansiban@sina.com>
Repository: CRAN
Date/Publication: 2021-07-16 08:50:02 UTC

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New package ReplicationSuccess with initial version 1.0.0
Package: ReplicationSuccess
Title: Design and Analysis of Replication Studies
Version: 1.0.0
Date: 2021-07-15
Authors@R: c(person(given = "Leonhard", family = "Held", role = c("aut"), email = "leonhard.held@uzh.ch", comment = c(ORCID = "0000-0002-8686-5325")), person(given = "Samuel", family = "Pawel", email = "samuel.pawel@uzh.ch", role = c("aut"), comment = c(ORCID = "0000-0003-2779-320X")), person(given = "Charlotte", family = "Micheloud", email = "charlotte.micheloud@uzh.ch", role = c("aut"), comment = c(ORCID = "0000-0002-4995-4505")), person(given = "Florian", family = "Gerber", email = "florian.gerber@uzh.ch", role = c("cre"), comment = c(ORCID = "0000-0001-8545-5263")))
Description: Provides utilities for the design and analysis of replication studies. Features both traditional methods based on statistical significance and more recent methods such as the sceptical p-value; Held L. (2020) <doi:10.1111/rssa.12493>. Also provides related methods including the harmonic mean chi-squared test; Held, L. (2020), <doi:10.1111/rssc.12410>, and intrinsic credibility; Held, L. (2019) <doi:10.1098/rsos.181534>. Contains datasets from four large-scale replication projects.
URL: https://florafauna.github.io/ReplicationSuccess/
BugReports: https://github.com/florafauna/ReplicationSuccess/issues/
Depends: R (>= 3.5)
VignetteBuilder: knitr
Imports: rootSolve
Suggests: knitr, roxygen2, testthat
License: GPL (>= 2)
LazyData: true
NeedsCompilation: no
Packaged: 2021-07-15 12:47:06 UTC; fl
Author: Leonhard Held [aut] (<https://orcid.org/0000-0002-8686-5325>), Samuel Pawel [aut] (<https://orcid.org/0000-0003-2779-320X>), Charlotte Micheloud [aut] (<https://orcid.org/0000-0002-4995-4505>), Florian Gerber [cre] (<https://orcid.org/0000-0001-8545-5263>)
Maintainer: Florian Gerber <florian.gerber@uzh.ch>
Repository: CRAN
Date/Publication: 2021-07-16 08:10:04 UTC

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New package mark with initial version 0.1.4
Package: mark
Title: Miscellaneous, Analytic R Kernels
Version: 0.1.4
Date: 2021-07-13
Authors@R: person(given = "Jordan Mark", family = "Barbone", role = c("aut", "cph", "cre"), email = "jmbarbone@gmail.com", comment = c(orcid = "0000-0001-9788-3628"))
Maintainer: Jordan Mark Barbone <jmbarbone@gmail.com>
Description: Miscellaneous functions and wrappers for development in other packages created, maintained by Jordan Mark Barbone.
License: MIT + file LICENSE
URL: https://github.com/jmbarbone/mark
BugReports: https://github.com/jmbarbone/mark/issues
Encoding: UTF-8
Depends: R (>= 3.6)
Imports: magrittr (>= 2.0.1), methods (>= 3.6), stats (>= 3.6), tibble (>= 3.0.4), tools (>= 3.6), utils (>= 3.6)
Suggests: bench (>= 1.1.1), bib2df (>= 1.1.1), crayon (>= 1.3.4), desc (>= 1.3.0), dplyr (>= 1.0.6), graphics (>= 4.0.3), knitr (>= 1.30), rcmdcheck (>= 1.3.3), stringi (>= 1.5.3), spelling (>= 2.2), testthat (>= 3.0.0), withr (>= 2.3.0)
Config/testthat/parallel: true
Language: en-US
NeedsCompilation: no
Packaged: 2021-07-13 17:15:00 UTC; jmbar
Author: Jordan Mark Barbone [aut, cph, cre] (0000-0001-9788-3628)
Repository: CRAN
Date/Publication: 2021-07-16 08:20:02 UTC

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Package GeoTcgaData updated to version 1.0.0 with previous version 0.2.5 dated 2021-06-06

Title: Processing various types of data on GEO and TCGA
Description: Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) provide us with a wealth of data, such as RNA-seq, DNA Methylation, and Copy number variation data. It's easy to download data from TCGA using the gdc tool, but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data.
Author: Erqiang Hu [aut, cre]
Maintainer: Erqiang Hu <13766876214@163.com>

Diff between GeoTcgaData versions 0.2.5 dated 2021-06-06 and 1.0.0 dated 2021-07-16

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New package CIMTx with initial version 0.3.0
Package: CIMTx
Title: Causal Inference for Multiple Treatments with a Binary Outcome
Version: 0.3.0
Authors@R: c( person("Liangyuan", "Hu", role = "aut", email = "Liangyuan.Hu@mountsinai.org"), person("Chenyang", "Gu", role = "aut", email = "chenyang_gu@alumni.brown.edu"), person("Michael", "Lopez", role = "aut", email = "Michael.Lopez@nfl.com"), person("Jiayi", "Ji", role = c("aut", "cre"), email = "Jiayi.Ji@mountsinai.org"))
Description: Different methods to conduct causal inference for multiple treatments with a binary outcome, including regression adjustment, vector matching, Bayesian additive regression trees, targeted maximum likelihood and inverse probability of treatment weighting using different generalized propensity score models such as multinomial logistic regression, generalized boosted models and super learner. For more details, see the paper by Liangyuan Hu (2020) <doi:10.1177/0962280220921909>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: false
Imports: nnet, BART, twang, arm, dplyr, Matching, magrittr, car, WeightIt, SuperLearner, tmle, tidyr, stats, class, gam, dbarts
NeedsCompilation: no
Packaged: 2021-07-15 23:33:06 UTC; jiayi
Author: Liangyuan Hu [aut], Chenyang Gu [aut], Michael Lopez [aut], Jiayi Ji [aut, cre]
Maintainer: Jiayi Ji <Jiayi.Ji@mountsinai.org>
Repository: CRAN
Date/Publication: 2021-07-16 08:20:11 UTC

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Package sparrpowR updated to version 0.2.3 with previous version 0.2.2 dated 2021-06-28

Title: Power Analysis to Detect Spatial Relative Risk Clusters
Description: Calculate the statistical power to detect clusters using kernel-based spatial relative risk functions that are estimated using the 'sparr' package. Details about the 'sparr' package methods can be found in the tutorial: Davies et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.
Author: Ian D. Buller [aut, cre, cph] (<https://orcid.org/0000-0001-9477-8582>), Derek W. Brown [aut, cph] (<https://orcid.org/0000-0001-8393-1713>), Mitchell J. Machiela [ctb] (<https://orcid.org/0000-0001-6538-9705>), Timothy A. Myers [ctb], NCI [cph, fnd]
Maintainer: Ian D. Buller <ian.buller@nih.gov>

Diff between sparrpowR versions 0.2.2 dated 2021-06-28 and 0.2.3 dated 2021-07-16

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Package secsse updated to version 2.1.7 with previous version 2.1.5 dated 2021-05-25

Title: Several Examined and Concealed States-Dependent Speciation and Extinction
Description: Simultaneously infers state-dependent diversification across two or more states of a single or multiple traits while accounting for the role of a possible concealed trait. See Herrera-Alsina et al. 2019 Systematic Biology 68: 317-328 <DOI:10.1093/sysbio/syy057>.
Author: Leonel Herrera Alsina [aut], Paul van Els [aut], Rampal S. Etienne [cre, aut] (<https://orcid.org/0000-0003-2142-7612>)
Maintainer: Rampal S. Etienne <r.s.etienne@rug.nl>

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Package ParallelLogger updated to version 2.0.2 with previous version 2.0.1 dated 2020-10-26

Title: Support for Parallel Computation, Logging, and Function Automation
Description: Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk, and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in parallel).
Author: Martijn Schuemie [aut, cre], Marc Suchard [aut], Observational Health Data Science and Informatics [cph]
Maintainer: Martijn Schuemie <schuemie@ohdsi.org>

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Package modeltime updated to version 0.7.0 with previous version 0.6.1 dated 2021-06-13

Title: The Tidymodels Extension for Time Series Modeling
Description: The time series forecasting framework for use with the 'tidymodels' ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" (<https://otexts.com/fpp2/>). Refer to "Prophet: forecasting at scale" (<https://research.fb.com/blog/2017/02/prophet-forecasting-at-scale/>.).
Author: Matt Dancho [aut, cre], Business Science [cph]
Maintainer: Matt Dancho <mdancho@business-science.io>

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Package MNLpred updated to version 0.0.8 with previous version 0.0.7 dated 2021-07-12

Title: Simulated Predicted Probabilities for Multinomial Logit Models
Description: Functions to easily return simulated predicted probabilities and first differences for multinomial logit models. It takes a specified scenario and a multinomial model to predict probabilities with a set of coefficients, drawn from a simulated sampling distribution. The simulated predictions allow for meaningful plots with means and confidence intervals. The methodological approach is based on the principles laid out by King, Tomz, and Wittenberg (2000) <doi:10.2307/2669316> and Hanmer and Ozan Kalkan (2016) <doi:10.1111/j.1540-5907.2012.00602.x>.
Author: Manuel Neumann [aut, cre] (<https://orcid.org/0000-0002-7953-3939>)
Maintainer: Manuel Neumann <manuel.neumann@mzes.uni-mannheim.de>

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More information about MNLpred at CRAN
Permanent link

New package mlr3tuningspaces with initial version 0.0.1
Package: mlr3tuningspaces
Title: Search Spaces for Hyperparameter Tuning
Version: 0.0.1
Authors@R: c(person(given = "Marc", family = "Becker", role = c("cre", "aut"), email = "marcbecker@posteo.de", comment = c(ORCID = "0000-0002-8115-0400")), person(given = "Michel", family = "Lang", role = "ctb", email = "michellang@gmail.com", comment = c(ORCID = "0000-0001-9754-0393")))
Description: Collection of search spaces for hyperparameter tuning. Includes various search spaces that can be directly applied on an `mlr3` learner. Additionally, meta information about the search space can be queried.
License: LGPL-3
Depends: mlr3tuning (>= 0.8.0), R (>= 3.1.0)
Imports: checkmate (>= 2.0.0), data.table (>= 1.14.0), mlr3 (>= 0.11.0), mlr3misc (>= 0.9.1), paradox (>= 0.7.1), R6 (>= 2.5.0)
Suggests: e1071 (>= 1.7-6), glmnet (>= 4.1-2), kknn (>= 1.3.1), mlr3learners (>= 0.4.5), ranger (>= 0.12.1), rpart (>= 4.1-15), testthat (>= 3.0.0), xgboost (>= 1.4.1.1)
Encoding: UTF-8
Collate: 'mlr_tuning_spaces.R' 'TuningSpace.R' 'bibentries.R' 'sugar.R' 'tuning_spaces_default.R' 'tuning_spaces_rbv2.R' 'zzz.R'
NeedsCompilation: no
Packaged: 2021-07-15 11:15:30 UTC; marc
Author: Marc Becker [cre, aut] (<https://orcid.org/0000-0002-8115-0400>), Michel Lang [ctb] (<https://orcid.org/0000-0001-9754-0393>)
Maintainer: Marc Becker <marcbecker@posteo.de>
Repository: CRAN
Date/Publication: 2021-07-16 08:00:02 UTC

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Package joinet updated to version 0.0.8 with previous version 0.0.7 dated 2021-03-09

Title: Multivariate Elastic Net Regression
Description: Implements high-dimensional multivariate regression by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement. If required, install MRCE or remMap from GitHub (<https://github.com/cran/MRCE>, <https://github.com/cran/remMap>).
Author: Armin Rauschenberger [aut, cre]
Maintainer: Armin Rauschenberger <armin.rauschenberger@uni.lu>

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Package grattan updated to version 1.9.0.9 with previous version 1.9.0.4 dated 2021-01-29

Title: Australian Tax Policy Analysis
Description: Utilities to cost and evaluate Australian tax policy, including fast projections of personal income tax collections, high-performance tax and transfer calculators, and an interface to common indices from the Australian Bureau of Statistics. Written to support Grattan Institute's Australian Perspectives program, and related projects. Access to the Australian Taxation Office's sample files of personal income tax returns is assumed.
Author: Hugh Parsonage [aut, cre], Tim Cameron [aut], Brendan Coates [aut], Matthew Katzen [aut], William Young [aut], Ittima Cherastidtham [dtc], W. Karsten [ctb], M. Enrique Garcia [ctb], Matt Cowgill [aut]
Maintainer: Hugh Parsonage <hugh.parsonage@gmail.com>

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New package isatabr with initial version 1.0.0
Package: isatabr
Date: 2021-07-15
Title: Implementation for the ISA Abstract Model
Version: 1.0.0
Description: ISA is a metadata framework to manage an increasingly diverse set of life science, environmental and biomedical experiments. In isatabr methods for reading, modifying and writing of files in the ISA-Tab format are implemented. It also contains methods for processing assay data.
Authors@R: c( person("Bart-Jan", "van Rossum", role = c("aut", "cre"), email = "bart-jan.vanrossum@wur.nl", comment = c(ORCID = "0000-0002-8673-2514")), person("Maikel", "Verouden", role = c("aut"), email = "maikel.verouden@wur.nl", comment = c(ORCID = "0000-0002-4893-3323")))
Encoding: UTF-8
Depends: methods
Suggests: tinytest, affy, Biobase, xcms, knitr, rmarkdown
License: GPL-3
URL: https://github.com/Biometris/isatabr/
BugReports: https://github.com/Biometris/isatabr/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-07-15 08:32:33 UTC; rossu027
Author: Bart-Jan van Rossum [aut, cre] (<https://orcid.org/0000-0002-8673-2514>), Maikel Verouden [aut] (<https://orcid.org/0000-0002-4893-3323>)
Maintainer: Bart-Jan van Rossum <bart-jan.vanrossum@wur.nl>
Repository: CRAN
Date/Publication: 2021-07-16 08:00:05 UTC

More information about isatabr at CRAN
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Package GMMAT updated to version 1.3.2 with previous version 1.3.1 dated 2020-09-23

Title: Generalized Linear Mixed Model Association Tests
Description: Perform association tests using generalized linear mixed models (GLMMs) in genome-wide association studies (GWAS) and sequencing association studies. First, GMMAT fits a GLMM with covariate adjustment and random effects to account for population structure and familial or cryptic relatedness. For GWAS, GMMAT performs score tests for each genetic variant as proposed in Chen et al. (2016) <DOI:10.1016/j.ajhg.2016.02.012>. For candidate gene studies, GMMAT can also perform Wald tests to get the effect size estimate for each genetic variant. For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT) as proposed in Chen et al. (2019) <DOI:10.1016/j.ajhg.2018.12.012>, including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets.
Author: Han Chen, Matthew P. Conomos, Duy T. Pham
Maintainer: Han Chen <Han.Chen.2@uth.tmc.edu>

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Package envi updated to version 0.1.9 with previous version 0.1.8 dated 2021-03-15

Title: Environmental Interpolation using Spatial Kernel Density Estimation
Description: Estimates an ecological niche using occurrence data, covariates, and kernel density-based estimation methods. For a single species with presence and absence data, the 'envi' package uses the spatial relative risk function that is estimated using the 'sparr' package. Details about the 'sparr' package methods can be found in the tutorial: Davies et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.
Author: Ian D. Buller [aut, cre, cph] (<https://orcid.org/0000-0001-9477-8582>), Lance A. Waller [ctb, ths] (<https://orcid.org/0000-0001-5002-8886>), Emory University [cph]
Maintainer: Ian D. Buller <ian.buller@alumni.emory.edu>

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Package do updated to version 1.9.0.0 with previous version 1.8.0.0 dated 2021-05-28

Title: Data Operator
Description: Flexibly convert data between long and wide format using just two functions: reshape_toLong() and reshape_toWide().
Author: Jing Zhang, Zhi Jin
Maintainer: Jing Zhang<zj391120@163.com>

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Package bikm1 updated to version 1.1.0 with previous version 1.0.0 dated 2020-01-28

Title: Co-Clustering Adjusted Rand Index and Bikm1 Procedure for Contingency and Binary Data-Sets
Description: Co-clustering of the rows and columns of a contingency or binary matrix, or double binary matrices and model selection for the number of row and column clusters. Three models are considered: the Poisson latent block model for contingency matrix, the binary latent block model for binary matrix and a new model we develop: the multiple latent block model for double binary matrices. A new procedure named bikm1 is implemented to investigate more efficiently the grid of numbers of clusters. Then, the studied model selection criteria are the integrated completed likelihood (ICL) and the Bayesian integrated likelihood (BIC). Finally, the co-clustering adjusted Rand index (CARI) to measure agreement between co-clustering partitions is implemented. Robert Valerie, Vasseur Yann, Brault Vincent (2021) <doi:10.1007/s00357-020-09379-w>.
Author: Valerie Robert [aut, cre]
Maintainer: Valerie Robert <valerie.robert.math@gmail.com>

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Package ausplotsR updated to version 1.2.6 with previous version 1.2.5 dated 2021-07-15

Title: TERN AusPlots Analysis Package
Description: Extraction, preparation, visualisation and analysis of TERN AusPlots ecosystem monitoring data. Direct access to plot-based data on vegetation and soils across Australia, including physical sample barcode numbers. Simple function calls extract the data and merge them into species occurrence matrices for downstream analysis, or calculate things like basal area and fractional cover. TERN AusPlots is a national field plot-based ecosystem surveillance monitoring method and dataset for Australia. The data have been collected across a national network of plots and transects by the Terrestrial Ecosystem Research Network (TERN - <https://www.tern.org.au>), an Australian Government NCRIS-enabled project, and its Ecosystem Surveillance platform (<https://www.tern.org.au/tern-observatory/tern-ecosystem-surveillance/>).
Author: Greg Guerin [aut, cre], Tom Saleeba [aut], Samantha Munroe [aut], Bernardo Blanco-Martin [aut], Irene Martín-Forés [aut], Andrew Tokmakoff [aut]
Maintainer: Greg Guerin <ggueri01@gmail.com>

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Package archive updated to version 1.0.2 with previous version 1.0.1 dated 2021-07-09

Title: Multi-Format Archive and Compression Support
Description: Bindings to 'libarchive' <http://www.libarchive.org> the Multi-format archive and compression library. Offers R connections and direct extraction for many archive formats including 'tar', 'ZIP', '7-zip', 'RAR', 'CAB' and compression formats including 'gzip', 'bzip2', 'compress', 'lzma' and 'xz'.
Author: Jim Hester [aut, cre] (<https://orcid.org/0000-0002-2739-7082>), Ondrej Holy [cph] (archive_write_add_filter implementation), RStudio [cph, fnd]
Maintainer: Jim Hester <jim.hester@rstudio.com>

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Package SignacX updated to version 2.2.3 with previous version 2.2.2 dated 2021-06-24

Title: Cell Type Identification and Discovery from Single Cell Gene Expression Data
Description: An implementation of neural networks trained with flow-sorted gene expression data to classify cellular phenotypes in single cell RNA-sequencing data. See Chamberlain M et al. (2021) <doi:10.1101/2021.02.01.429207> for more details.
Author: Mathew Chamberlain [aut, cre], Virginia Savova [aut], Richa Hanamsagar [aut], Frank Nestle [aut], Emanuele de Rinaldis [aut], Sanofi US [fnd]
Maintainer: Mathew Chamberlain <chamberlainphd@gmail.com>

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Package qgcomp updated to version 2.8.0 with previous version 2.7.0 dated 2021-01-26

Title: Quantile G-Computation
Description: G-computation for a set of time-fixed exposures with quantile-based basis functions, possibly under linearity and homogeneity assumptions. This approach estimates a regression line corresponding to the expected change in the outcome (on the link basis) given a simultaneous increase in the quantile-based category for all exposures. Works with continuous, binary, and right-censored time-to-event outcomes. Reference: Alexander P. Keil, Jessie P. Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and Alexandra J. White (2019) A quantile-based g-computation approach to addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.
Author: Alexander Keil [aut, cre]
Maintainer: Alexander Keil <akeil@unc.edu>

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Package ncar updated to version 0.4.4 with previous version 0.4.3 dated 2021-06-10

Title: Noncompartmental Analysis for Pharmacokinetic Report
Description: Conduct a noncompartmental analysis with industrial strength. Some features are 1) CDISC SDTM terms 2) Automatic or manual slope selection 3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method 4) Interval(partial) AUCs with 'linear' or 'log' interpolation method 5) Produce pdf, rtf, text report files. * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).
Author: Kyun-Seop Bae [aut]
Maintainer: Kyun-Seop Bae <k@acr.kr>

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Package AgroR updated to version 1.2.2 with previous version 1.2.1 dated 2021-06-24

Title: Experimental Statistics and Graphics for Agricultural Sciences
Description: Performs the analysis of completely randomized experimental designs (CRD), randomized blocks (RBD) and Latin square (LSD), experiments in double and triple factorial scheme (in CRD and RBD), experiments in subdivided plot scheme (in CRD and RBD), subdivided and joint analysis of experiments in CRD and RBD, linear regression analysis, test for two samples. The package performs analysis of variance, ANOVA assumptions and multiple comparison test of means or regression, according to Pimentel-Gomes (2009, ISBN: 978-85-7133-055-9), nonparametric test (Conover, 1999, ISBN: 0471160687), test for two samples, joint analysis of experiments according to Ferreira (2018, ISBN: 978-85-7269-566-4), Anova of aligned ranks (Wobbrock, JO, Findlater, L., Gergle, D., Higgins , JJ (2011), <doi: 10.1145/1978942.1978963>) and generalized linear model (glm) for binomial and Poisson family in CRD and RBD (Carvalho, FJ (2019), <doi: 10.14393/ufu.te.2019.1244>). It can also be used to obtain descriptive measures and graphics, in addition to correlations and creative graphics used in agricultural sciences (Agronomy, Zootechnics, Food Science and related areas).
Author: Gabriel Danilo Shimizu [aut, cre] (<https://orcid.org/0000-0001-8524-508X>), Rodrigo Yudi Palhaci Marubayashi [aut, ctb] (<https://orcid.org/0000-0003-2778-8654>), Leandro Simoes Azeredo Goncalves [aut, ctb] (<https://orcid.org/0000-0001-9700-9375>)
Maintainer: Gabriel Danilo Shimizu <shimizu@uel.br>

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