Fri, 23 Oct 2020

Package ICON updated to version 0.4.0 with previous version 0.3.0 dated 2020-08-29

Title: Provides Easy Access to Complex Systems Datasets
Description: Provides easy-to-use and easy-to-access datasets from the Index of COmplex Networks (ICON) database available on the University of Colorado website. All datasets can be loaded with a single function call and new datasets are being slowly added from ICON at <https://icon.colorado.edu>. Specific ICON datasets of interest can be requested at the BugReports URL <https://github.com/rrrlw/ICON/issues>.
Author: Raoul Wadhwa [aut, cre] (<https://orcid.org/0000-0003-0503-9580>), Jacob Scott [aut] (<https://orcid.org/0000-0003-2971-7673>)
Maintainer: Raoul Wadhwa <raoulwadhwa@gmail.com>

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Package VC2copula updated to version 0.1.1 with previous version 0.1.0 dated 2019-12-15

Title: Extend the 'copula' Package with Families and Models from 'VineCopula'
Description: Provides new classes for (rotated) BB1, BB6, BB7, BB8, and Tawn copulas, extends the existing Gumbel and Clayton families with rotations, and allows to set up a vine copula model using the 'copula' API. Corresponding objects from the 'VineCopula' API can easily be converted.
Author: Thomas Nagler [aut, cre], Benedikt Graeler [ctb]
Maintainer: Thomas Nagler <mail@tnagler.com>

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Package knitrdata updated to version 0.5.2 with previous version 0.5.1 dated 2020-10-22

Title: Data Language Engine for 'knitr' / 'rmarkdown'
Description: Implements a data language engine for incorporating data directly in 'rmarkdown' documents so that they can be made completely standalone.
Author: David M. Kaplan [aut, cre, cph] (<https://orcid.org/0000-0001-6087-359X>, dmkaplan2000)
Maintainer: David M. Kaplan <dmkaplan2000@gmail.com>

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Package RcppSpdlog updated to version 0.0.3 with previous version 0.0.2 dated 2020-09-20

Title: 'spdlog' C++ Header Library for Logging
Description: The mature and widely-used C++ logging library 'spdlog' provides many desirable features. This package bundles these header files for easy use by R packages via a simple 'LinkingTo:' inclusion.
Author: Dirk Eddelbuettel
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package rGEDI updated to version 0.1.9 with previous version 0.1.8 dated 2020-07-15

Title: NASA's Global Ecosystem Dynamics Investigation (GEDI) Data Visualization and Processing
Description: Set of tools for downloading, reading, visualizing and processing GEDI Level1B, Level2A and Level2B data.
Author: Carlos Alberto Silva [aut, cre, cph], Caio Hamamura [aut, cph], Ruben Valbuena [aut, ctb], Steven Hancock [aut, ctb], Adrian Cardil [aut, ctb], Eben North Broadbent [aut, ctb], Danilo Roberti Alves de Almeida [aut, ctb], Celso H. L. Silva Junior [aut, ctb], Carine Klauberg [aut, ctb], Burton Garbow [cph] (Is the author of the MINPACK-1 Least Squares Fitting Library), Kenneth Hillstrom [cph] (Is the author of the MINPACK-1 Least Squares Fitting Library), Jorge More [cph] (Is the author of the MINPACK-1 Least Squares Fitting Library), Craig Markwardt [cph] (Is the author of the enhanced MINPACK-1 Least Squares Fitting Library)
Maintainer: Carlos Alberto Silva <carlos_engflorestal@outlook.com>

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Package plotdap updated to version 0.0.8 with previous version 0.0.7 dated 2020-03-20

Title: Easily Visualize Data from 'ERDDAP' Servers via the 'rerddap' Package
Description: Easily visualize and animate 'tabledap' and 'griddap' objects obtained via the 'rerddap' package in a simple one-line command, using either base graphics or 'ggplot2' graphics. 'plotdap' handles extracting and reshaping the data, map projections and continental outlines. Optionally the data can be animated through time using the 'gganmiate' package.
Author: Carson Sievert [aut], Roy Mendelssohn [aut, ctb, cre]
Maintainer: Roy Mendelssohn <roy.mendelssohn@noaa.gov>

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Package graphicalVAR updated to version 0.2.4 with previous version 0.2.3 dated 2020-04-09

Title: Graphical VAR for Experience Sampling Data
Description: Estimates within and between time point interactions in experience sampling data, using the Graphical vector autoregression model in combination with regularization. See also Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.
Author: Sacha Epskamp
Maintainer: Sacha Epskamp <mail@sachaepskamp.com>

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Package fun updated to version 0.3 with previous version 0.2 dated 2018-12-05

Title: Use R for Fun
Description: This is a collection of R games and other funny stuff, such as the classic Mine sweeper and sliding puzzles.
Author: Yihui Xie [aut, cre] (<https://orcid.org/0000-0003-0645-5666>), Yixuan Qiu [aut], Taiyun Wei [aut]
Maintainer: Yihui Xie <xie@yihui.name>

Diff between fun versions 0.2 dated 2018-12-05 and 0.3 dated 2020-10-23

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New package MPGE with initial version 1.0.0
Package: MPGE
Type: Package
Title: A Two-Step Approach to Testing Overall Effect of Gene-Environment Interaction for Multiple Phenotypes
Version: 1.0.0
Date: 2020-10-14
Authors@R: c(person("Arunabha", "Majumdar", email = "statgen.arunabha@gmail.com", role = c("aut", "cre")), person("Tanushree", "Haldar", email = "tanushree.haldar@gmail.com", role = "aut"))
Description: Interaction between a genetic variant (e.g., a single nucleotide polymorphism) and an environmental variable (e.g., physical activity) can have a shared effect on multiple phenotypes (e.g., blood lipids). We implement a two-step method to test for an overall interaction effect on multiple phenotypes. In first step, the method tests for an overall marginal genetic association between the genetic variant and the multivariate phenotype. The genetic variants which show an evidence of marginal overall genetic effect in the first step are prioritized while testing for an overall gene-environment interaction effect in the second step. Methodology is available from: A Majumdar, KS Burch, S Sankararaman, B Pasaniuc, WJ Gauderman, JS Witte (2020) <doi:10.1101/2020.07.06.190256>.
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
URL: https://github.com/ArunabhaCodes/MPGE
BugReports: https://github.com/ArunabhaCodes/MPGE/issues
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
Imports: car, purrr, stats, utils
NeedsCompilation: no
Packaged: 2020-10-14 20:45:02 UTC; arunmacpro13
Author: Arunabha Majumdar [aut, cre], Tanushree Haldar [aut]
Maintainer: Arunabha Majumdar <statgen.arunabha@gmail.com>
Repository: CRAN
Date/Publication: 2020-10-23 15:40:07 UTC

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Package mlrMBO updated to version 1.1.5 with previous version 1.1.4 dated 2020-02-28

Title: Bayesian Optimization and Model-Based Optimization of Expensive Black-Box Functions
Description: Flexible and comprehensive R toolbox for model-based optimization ('MBO'), also known as Bayesian optimization. It implements the Efficient Global Optimization Algorithm and is designed for both single- and multi- objective optimization with mixed continuous, categorical and conditional parameters. The machine learning toolbox 'mlr' provide dozens of regression learners to model the performance of the target algorithm with respect to the parameter settings. It provides many different infill criteria to guide the search process. Additional features include multi-point batch proposal, parallel execution as well as visualization and sophisticated logging mechanisms, which is especially useful for teaching and understanding of algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that single components can be easily replaced or adapted by the user for specific use cases.
Author: Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Jakob Richter [aut, cre] (<https://orcid.org/0000-0003-4481-5554>), Jakob Bossek [aut] (<https://orcid.org/0000-0002-4121-4668>), Daniel Horn [aut], Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Janek Thomas [aut] (<https://orcid.org/0000-0003-4511-6245>)
Maintainer: Jakob Richter <code@jakob-r.de>

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New package EnvExpInd with initial version 0.1.0
Package: EnvExpInd
Type: Package
Title: Environmental Exposure on the Individual Level
Imports: gstat,RCurl,dplyr,stringi,sp,maptools,zoo
Version: 0.1.0
Depends: R(>= 3.5.0)
Authors@R: person("Bing","Zhang", role = c("aut", "cre"), email = "zhangb269@mail2.sysu.edu.cn")
Description: Tools for the assessment of the environmental exposure. The package provides three methods (nearest monitoring site, inverse distance weighted as described in Li Wu (2017) <doi:10.1016/j.envint.2016.11.013>,and ordinary kriging) to calculate the environmental exposure (e.g. air pollution) on the individual level.
URL: https://github.com/Spatial-R/EnvExpInd
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-10-15 00:26:49 UTC; spatialr
Author: Bing Zhang [aut, cre]
Maintainer: Bing Zhang <zhangb269@mail2.sysu.edu.cn>
Repository: CRAN
Date/Publication: 2020-10-23 15:50:02 UTC

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Package findInFiles updated to version 0.1.1 with previous version 0.1.0 dated 2020-10-22

Title: Find Pattern in Files
Description: Creates a HTML widget which displays the results of searching for a pattern in files in a given folder. The results can be viewed in the 'RStudio' viewer pane, included in a 'R Markdown' document or in a 'Shiny' app.
Author: Stéphane Laurent [aut, cre], Rob Burns [ctb, cph] ('ansi-to-html' library)
Maintainer: Stéphane Laurent <laurent_step@outlook.fr>

Diff between findInFiles versions 0.1.0 dated 2020-10-22 and 0.1.1 dated 2020-10-23

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Package epitweetr updated to version 0.1.24 with previous version 0.1.20 dated 2020-10-01

Title: Early Detection of Public Health Threats from Twitter Data
Description: It allows you to automatically monitor trends of tweets by time, place and topic aiming at detecting public health threats early through the detection of signals (e.g. an unusual increase in the number of tweets). It was designed to focus on infectious diseases, and it can be extended to all hazards or other fields of study by modifying the topics and keywords.
Author: Francisco Orchard [aut, ctr] (<https://orcid.org/0000-0001-5793-3301>, Author of the package and original code), Laura Espinosa [aut, cre, fnd] (<https://orcid.org/0000-0003-0748-9657>, Project manager, author of the design and concept of the package, and package maintainer), Ariana Wijermans [ctb] (Contributor to the design and concept of the package), Thomas Mollet [ctb, fnd] (Business owner of the project, and contributor to the design and concept of the package), Adrian Prodan [ctb], Thomas Czernichow [ctb], Maria Prieto Gonzalez [ctb], Esther Kissling [ctb], Michael Höhle [ctb]
Maintainer: Laura Espinosa <laura.espinosa@ecdc.europa.eu>

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Package corto updated to version 1.1.3 with previous version 1.1.2 dated 2020-10-13

Title: Inference of Gene Regulatory Networks
Description: We present 'corto' (Correlation Tool), a simple package to infer gene regulatory networks and visualize master regulators from gene expression data using DPI (Data Processing Inequality) and bootstrapping to recover edges. An initial step is performed to calculate all significant edges between a list of source nodes (centroids) and target genes. Then all triplets containing two centroids and one target are tested in a DPI step which removes edges. A bootstrapping process then calculates the robustness of the network, eventually re-adding edges previously removed by DPI. The algorithm has been optimized to run outside a computing cluster, using a fast correlation implementation. The package finally provides functions to calculate network enrichment analysis from RNA-Seq and ATAC-Seq signatures as described in the article by Giorgi lab (2020) <doi:10.1093/bioinformatics/btaa223>.
Author: Federico M. Giorgi [aut, cre], Daniele Mercatelli [ctb], Gonzalo Lopez-Garcia [ctb]
Maintainer: Federico M. Giorgi <federico.giorgi@gmail.com>

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Package prospectr updated to version 0.2.1 with previous version 0.2.0 dated 2020-03-14

Title: Miscellaneous Functions for Processing and Sample Selection of Spectroscopic Data
Description: Functions to preprocess spectroscopic data and conduct (representative) sample selection/calibration sampling.
Author: Antoine Stevens [aut, cre] (<https://orcid.org/0000-0002-1588-7519>), Leonardo Ramirez-Lopez [aut, cre] (<https://orcid.org/0000-0002-5369-5120>), Guillaume Hans [ctb] (<https://orcid.org/0000-0002-6503-5760>)
Maintainer: Leonardo Ramirez-Lopez <ramirez.lopez.leo@gmail.com>

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Package opentripplanner updated to version 0.3.0 with previous version 0.2.3.0 dated 2020-06-09

Title: Setup and connect to 'OpenTripPlanner'
Description: Setup and connect to 'OpenTripPlanner' (OTP) <http://www.opentripplanner.org/>. OTP is an open source platform for multi-modal and multi-agency journey planning written in 'Java'. The package allows you to manage a local version or connect to remote OTP server. This package has been peer-reviewed by rOpenSci (v. 0.2.0.0).
Author: Malcolm Morgan [aut, cre] (<https://orcid.org/0000-0002-9488-9183>), Marcus Young [aut] (<https://orcid.org/0000-0003-4627-1116>), Robin Lovelace [aut] (<https://orcid.org/0000-0001-5679-6536>), Layik Hama [ctb] (<https://orcid.org/0000-0003-1912-4890>)
Maintainer: Malcolm Morgan <m.morgan1@leeds.ac.uk>

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Package lmerTest updated to version 3.1-3 with previous version 3.1-2 dated 2020-04-08

Title: Tests in Linear Mixed Effects Models
Description: Provides p-values in type I, II or III anova and summary tables for lmer model fits (cf. lme4) via Satterthwaite's degrees of freedom method. A Kenward-Roger method is also available via the pbkrtest package. Model selection methods include step, drop1 and anova-like tables for random effects (ranova). Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed effects are also available.
Author: Alexandra Kuznetsova [aut], Per Bruun Brockhoff [aut, ths], Rune Haubo Bojesen Christensen [aut, cre], Sofie Pødenphant Jensen [ctb]
Maintainer: Rune Haubo Bojesen Christensen <Rune.Haubo@gmail.com>

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Package RPEIF updated to version 1.1.0 with previous version 1.0.8 dated 2020-04-23

Title: Computation and Plots of Influence Functions for Risk and Performance Measures
Description: Computes the influence functions time series of the returns for the risk and performance measures as mentioned in Chen and Martin (2018) <https://www.ssrn.com/abstract=3085672>, as well as in Zhang et al. (2019) <https://www.ssrn.com/abstract=3415903>. Also evaluates estimators influence functions at a set of parameter values and plots them to display the shapes of the influence functions.
Author: Anthony Christidis <anthony.christidis@stat.ubc.ca>, Shengyu Zhang <syzhang@uw.edu>, Douglas Martin <doug@amath.washington.edu>
Maintainer: Anthony Christidis <anthony.christidis@stat.ubc.ca>

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New package scoringTools with initial version 0.1.0
Package: scoringTools
Type: Package
Title: Credit Scoring Tools
Version: 0.1.0
Date: 2020-10-06
Authors@R: c(person("Adrien", "Ehrhardt", email = "adrien.ehrhardt@centraliens-lille.org", role = c("aut", "cre")))
Maintainer: Adrien Ehrhardt <adrien.ehrhardt@centraliens-lille.org>
Description: Grouping essential tools for credit scoring. These statistical tools may be useful for other use-cases as well but were primarily designed for it. First, there are Reject Inference methods (Ehrhardt et al. (2017) <arXiv:1903.10855>). Second, we build upon the already CRAN-available package 'discretization' to automate discretization of continuous features.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: discretization, sqldf, magrittr, dplyr, methods
Suggests: MASS, rpart, Rmixmod, mvtnorm, ggplot2, speedglm, knitr, rmarkdown, plotly, pROC, shiny, testthat, covr
URL: https://adimajo.github.io/scoringTools/
BugReports: https://github.com/adimajo/scoringTools/issues
VignetteBuilder: knitr
Collate: 'allClasses.R' 'allMethods.R' 'augmentation.R' 'check_consistency.R' 'chi2.R' 'chiM.R' 'cut.dataset.R' 'data.R' 'discretize.cutp.R' 'echi2.R' 'fuzzyAugmentation.R' 'get_cutp.R' 'mdlp.R' 'modChi2.R' 'model_f.R' 'normalizedGini.R' 'parcelling.R' 'reclassification.R' 'runDemo.R' 'scoring.R' 'topdown.R' 'twins.R'
NeedsCompilation: no
Packaged: 2020-10-14 14:20:14 UTC; adimajo
Author: Adrien Ehrhardt [aut, cre]
Repository: CRAN
Date/Publication: 2020-10-23 10:30:02 UTC

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Package PowerTOST updated to version 1.5-1 with previous version 1.5-0 dated 2020-08-05

Title: Power and Sample Size for (Bio)Equivalence Studies
Description: Contains functions to calculate power and sample size for various study designs used in bioequivalence studies. Use known.designs() to see the designs supported. Power and sample size can be obtained based on different methods, amongst them prominently the TOST procedure (two one-sided t-tests). See README and NEWS for further information.
Author: Detlew Labes [aut, cre] (<https://orcid.org/0000-0003-2169-426X>), Helmut Schütz [aut] (<https://orcid.org/0000-0002-1167-7880>), Benjamin Lang [aut]
Maintainer: Detlew Labes <DetlewLabes@gmx.de>

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Package glca updated to version 1.1.0 with previous version 1.0.2 dated 2020-10-20

Title: An R Package for Estimating a Group-Effect in Latent Class Analysis
Description: Fits latent class analysis (LCA) including group variable and covariates. The group variable can be handled either by multilevel LCA described in Vermunt (2003) <DOI:10.1111/j.0081-1750.2003.t01-1-00131.x> or standard LCA at each level of group variable. The covariates can be incorporated in the form of logistic regression (Bandeen-Roche et al. (1997) <DOI:10.1080/01621459.1997.10473658>).
Author: Youngsun Kim [aut, cre], Hwan Chung [aut]
Maintainer: Youngsun Kim <kim0sun@korea.ac.kr>

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New package deepredeff with initial version 0.1.0
Package: deepredeff
Title: Deep Learning Prediction of Effectors
Version: 0.1.0
Authors@R: person(given = "Ruth", family = "Kristianingsih", role = c("aut", "cre", "cph"), email = "ruth.kristianingsih30@gmail.com", comment = c(ORCID = "0000-0003-1873-6203"))
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>.
License: MIT + file LICENSE
URL: https://github.com/ruthkr/deepredeff/
BugReports: https://github.com/ruthkr/deepredeff/issues/
Depends: R (>= 2.10)
Imports: Biostrings, dplyr, ggplot2, ggthemes, keras, magrittr, purrr, reticulate, rlang, seqinr, tensorflow
Suggests: covr, kableExtra, knitr, rmarkdown, stringr, testthat
VignetteBuilder: knitr
biocViews:
Config/reticulate: list( packages = list( list(package = "tensorflow", version = "2.0.0", pip = FALSE) ) )
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-10-14 15:23:40 UTC; ruth
Author: Ruth Kristianingsih [aut, cre, cph] (<https://orcid.org/0000-0003-1873-6203>)
Maintainer: Ruth Kristianingsih <ruth.kristianingsih30@gmail.com>
Repository: CRAN
Date/Publication: 2020-10-23 10:30:06 UTC

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Package scam updated to version 1.2-8 with previous version 1.2-7 dated 2020-09-24

Title: Shape Constrained Additive Models
Description: Routines for generalized additive modelling under shape constraints on the component functions of the linear predictor (Pya and Wood, 2015) <doi:10.1007/s11222-013-9448-7>. Models can contain multiple shape constrained (univariate and/or bivariate) and unconstrained terms. The routines of gam() in package 'mgcv' are used for setting up the model matrix, printing and plotting the results. Penalized likelihood maximization based on Newton-Raphson method is used to fit a model with multiple smoothing parameter selection by GCV or UBRE/AIC.
Author: Natalya Pya <nat.pya@gmail.com>
Maintainer: Natalya Pya <nat.pya@gmail.com>

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Package lme4 updated to version 1.1-25 with previous version 1.1-23 dated 2020-04-07

Title: Linear Mixed-Effects Models using 'Eigen' and S4
Description: Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".
Author: Douglas Bates [aut] (<https://orcid.org/0000-0001-8316-9503>), Martin Maechler [aut] (<https://orcid.org/0000-0002-8685-9910>), Ben Bolker [aut, cre] (<https://orcid.org/0000-0002-2127-0443>), Steven Walker [aut] (<https://orcid.org/0000-0002-4394-9078>), Rune Haubo Bojesen Christensen [ctb] (<https://orcid.org/0000-0002-4494-3399>), Henrik Singmann [ctb] (<https://orcid.org/0000-0002-4842-3657>), Bin Dai [ctb], Fabian Scheipl [ctb] (<https://orcid.org/0000-0001-8172-3603>), Gabor Grothendieck [ctb], Peter Green [ctb] (<https://orcid.org/0000-0002-0238-9852>), John Fox [ctb], Alexander Bauer [ctb], Pavel N. Krivitsky [ctb, cph] (<https://orcid.org/0000-0002-9101-3362>, shared copyright on simulate.formula)
Maintainer: Ben Bolker <bbolker+lme4@gmail.com>

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Package fastJT (with last version 1.0.5) was removed from CRAN

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

2019-07-18 1.0.5
2017-08-14 1.0.4
2017-05-11 1.0.3
2017-02-20 1.0.2
2017-02-06 1.0.1
2017-01-27 1.0

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Package dbmss (with last version 2.7-1) was removed from CRAN

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

2020-01-07 2.7-1
2019-03-25 2.7-0
2018-03-19 2.6-3
2017-11-28 2.5-1
2017-03-26 2.4-0
2016-09-18 2.3-0
2016-06-30 2.2-5
2015-12-10 2.2-4
2015-10-01 2.2.3
2015-09-17 2.2.2
2015-07-14 2.2.1
2015-03-11 2.2.0
2014-11-01 2.1.2
2014-05-13 2.1.1
2014-03-19 2.1.0
2013-11-19 2.0.5
2013-05-29 2.0.3
2012-12-17 1.2.4
2012-11-15 1.2.3

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Package jtGWAS (with last version 1.5.1) was removed from CRAN

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

2017-08-14 1.5.1
2017-02-20 1.5
2016-07-07 1.4
2016-06-15 1.3
2016-06-14 1.2
2016-06-13 1.1

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Package VGAM updated to version 1.1-4 with previous version 1.1-3 dated 2020-04-28

Title: Vector Generalized Linear and Additive Models
Description: An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (150+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Hauck-Donner effect detection is implemented. Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes.
Author: Thomas Yee [aut, cre], Cleve Moler [ctb] (author of several LINPACK routines)
Maintainer: Thomas Yee <t.yee@auckland.ac.nz>

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 VGAM-1.1-4/VGAM/man/alteredvglm.Rd           |   49 
 VGAM-1.1-4/VGAM/man/anovavglm.Rd             |   24 
 VGAM-1.1-4/VGAM/man/backPain.Rd              |    2 
 VGAM-1.1-4/VGAM/man/biclaytoncop.Rd          |   21 
 VGAM-1.1-4/VGAM/man/biclaytoncopUC.Rd        |    4 
 VGAM-1.1-4/VGAM/man/binomialff.Rd            |    2 
 VGAM-1.1-4/VGAM/man/binormalcop.Rd           |   18 
 VGAM-1.1-4/VGAM/man/bistudentt.Rd            |    6 
 VGAM-1.1-4/VGAM/man/bistudenttUC.Rd          |   17 
 VGAM-1.1-4/VGAM/man/cao.Rd                   |    3 
 VGAM-1.1-4/VGAM/man/cratio.Rd                |    2 
 VGAM-1.1-4/VGAM/man/cumulative.Rd            |    2 
 VGAM-1.1-4/VGAM/man/dgaitplot.Rd             |only
 VGAM-1.1-4/VGAM/man/diffzetaUC.Rd            |    2 
 VGAM-1.1-4/VGAM/man/gaitbinomUC.Rd           |only
 VGAM-1.1-4/VGAM/man/gaitlog.Rd               |only
 VGAM-1.1-4/VGAM/man/gaitlogUC.Rd             |  114 
 VGAM-1.1-4/VGAM/man/gaitnbinomUC.Rd          |only
 VGAM-1.1-4/VGAM/man/gaitpoisUC.Rd            |  194 
 VGAM-1.1-4/VGAM/man/gaitpoisson.Rd           |only
 VGAM-1.1-4/VGAM/man/gaitzeta.Rd              |only
 VGAM-1.1-4/VGAM/man/gaitzetaUC.Rd            |  112 
 VGAM-1.1-4/VGAM/man/gengammaUC.Rd            |    2 
 VGAM-1.1-4/VGAM/man/genpois0UC.Rd            |only
 VGAM-1.1-4/VGAM/man/genpois1UC.Rd            |only
 VGAM-1.1-4/VGAM/man/genpoisson0.Rd           |only
 VGAM-1.1-4/VGAM/man/genpoisson1.Rd           |only
 VGAM-1.1-4/VGAM/man/genpoisson2.Rd           |only
 VGAM-1.1-4/VGAM/man/gompertzUC.Rd            |    2 
 VGAM-1.1-4/VGAM/man/hdeff.Rd                 |   51 
 VGAM-1.1-4/VGAM/man/inv.gaussianff.Rd        |    4 
 VGAM-1.1-4/VGAM/man/logUC.Rd                 |    9 
 VGAM-1.1-4/VGAM/man/logff.Rd                 |    9 
 VGAM-1.1-4/VGAM/man/lrt.stat.Rd              |    8 
 VGAM-1.1-4/VGAM/man/lrtest.Rd                |   11 
 VGAM-1.1-4/VGAM/man/multilogitlink.Rd        |    6 
 VGAM-1.1-4/VGAM/man/multinomial.Rd           |    4 
 VGAM-1.1-4/VGAM/man/negbinomial.Rd           |   12 
 VGAM-1.1-4/VGAM/man/notdocumentedyet.Rd      |   22 
 VGAM-1.1-4/VGAM/man/plotdgait.Rd             |only
 VGAM-1.1-4/VGAM/man/plotrcim0.Rd             |    5 
 VGAM-1.1-4/VGAM/man/poissonff.Rd             |   13 
 VGAM-1.1-4/VGAM/man/posbernoulli.t.Rd        |    2 
 VGAM-1.1-4/VGAM/man/posbinomial.Rd           |    5 
 VGAM-1.1-4/VGAM/man/posnegbinomial.Rd        |   25 
 VGAM-1.1-4/VGAM/man/pospoisson.Rd            |   18 
 VGAM-1.1-4/VGAM/man/propodds.Rd              |    2 
 VGAM-1.1-4/VGAM/man/rayleighUC.Rd            |    2 
 VGAM-1.1-4/VGAM/man/round2.Rd                |only
 VGAM-1.1-4/VGAM/man/score.stat.Rd            |   50 
 VGAM-1.1-4/VGAM/man/specialsvglm.Rd          |   15 
 VGAM-1.1-4/VGAM/man/sratio.Rd                |    2 
 VGAM-1.1-4/VGAM/man/step4vglm.Rd             |   14 
 VGAM-1.1-4/VGAM/man/summarypvgam.Rd          |    2 
 VGAM-1.1-4/VGAM/man/summaryvglm.Rd           |   10 
 VGAM-1.1-4/VGAM/man/tobit.Rd                 |    6 
 VGAM-1.1-4/VGAM/man/undocumented-methods.Rd  |   16 
 VGAM-1.1-4/VGAM/man/vgam.Rd                  |    9 
 VGAM-1.1-4/VGAM/man/vglm.Rd                  |    2 
 VGAM-1.1-4/VGAM/man/vglmff-class.Rd          |    4 
 VGAM-1.1-4/VGAM/man/wald.stat.Rd             |   96 
 VGAM-1.1-4/VGAM/man/zabinomUC.Rd             |    4 
 VGAM-1.1-4/VGAM/man/zanegbinUC.Rd            |   12 
 VGAM-1.1-4/VGAM/man/zanegbinomial.Rd         |    9 
 VGAM-1.1-4/VGAM/man/zapoisson.Rd             |   21 
 VGAM-1.1-4/VGAM/man/zeta.Rd                  |    6 
 VGAM-1.1-4/VGAM/man/zetaUC.Rd                |    6 
 VGAM-1.1-4/VGAM/man/zetaff.Rd                |   11 
 VGAM-1.1-4/VGAM/man/zibinomUC.Rd             |    4 
 VGAM-1.1-4/VGAM/man/zibinomial.Rd            |    5 
 VGAM-1.1-4/VGAM/man/zipoisUC.Rd              |    6 
 VGAM-1.1-4/VGAM/man/zipoisson.Rd             |    3 
 151 files changed, 11396 insertions(+), 12890 deletions(-)

More information about VGAM at CRAN
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