Wed, 18 Nov 2020

Package mlr3proba updated to version 0.2.5 with previous version 0.2.4 dated 2020-11-11

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. mlr3extralearners is available from <https://github.com/mlr-org/mlr3extralearners>.
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>

Diff between mlr3proba versions 0.2.4 dated 2020-11-11 and 0.2.5 dated 2020-11-18

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Package missSBM updated to version 0.3.0 with previous version 0.2.1 dated 2019-09-16

Title: Handling Missing Data in Stochastic Block Models
Description: When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 due to missing information between node pairs), it is possible to account for the underlying process that generates those NAs. 'missSBM' adjusts the popular stochastic block model from network data sampled under various missing data conditions, as described in Tabouy, Barbillon and Chiquet (2019) <doi:10.1080/01621459.2018.1562934>.
Author: Julien Chiquet [aut, cre] (<https://orcid.org/0000-0002-3629-3429>), Pierre Barbillon [aut] (<https://orcid.org/0000-0002-7766-7693>), Timothée Tabouy [aut], großBM team [ctb]
Maintainer: Julien Chiquet <julien.chiquet@inrae.fr>

Diff between missSBM versions 0.2.1 dated 2019-09-16 and 0.3.0 dated 2020-11-18

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Package memisc updated to version 0.99.27.3 with previous version 0.99.27.2 dated 2020-11-02

Title: Management of Survey Data and Presentation of Analysis Results
Description: An infrastructure for the management of survey data including value labels, definable missing values, recoding of variables, production of code books, and import of (subsets of) 'SPSS' and 'Stata' files is provided. Further, the package allows to produce tables and data frames of arbitrary descriptive statistics and (almost) publication-ready tables of regression model estimates, which can be exported to 'LaTeX' and HTML.
Author: Martin Elff (with contributions from Christopher N. Lawrence, Dave Atkins, Jason W. Morgan, Achim Zeileis)
Maintainer: Martin Elff <memisc@elff.eu>

Diff between memisc versions 0.99.27.2 dated 2020-11-02 and 0.99.27.3 dated 2020-11-18

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New package lightgbm with initial version 3.1.0
Package: lightgbm
Type: Package
Title: Light Gradient Boosting Machine
Version: 3.1.0
Date: 2020-11-15
Authors@R: c( person("Guolin", "Ke", email = "guolin.ke@microsoft.com", role = c("aut", "cre")), person("Damien", "Soukhavong", email = "damien.soukhavong@skema.edu", role = c("aut")), person("James", "Lamb", email="jaylamb20@gmail.com", role = c("aut")), person("Qi", "Meng", role = c("aut")), person("Thomas", "Finley", role = c("aut")), person("Taifeng", "Wang", role = c("aut")), person("Wei", "Chen", role = c("aut")), person("Weidong", "Ma", role = c("aut")), person("Qiwei", "Ye", role = c("aut")), person("Tie-Yan", "Liu", role = c("aut")), person("Yachen", "Yan", role = c("ctb")), person("Microsoft Corporation", role = c("cph")), person("Dropbox, Inc.", role = c("cph")), person("Jay", "Loden", role = c("cph")), person("Dave", "Daeschler", role = c("cph")), person("Giampaolo", "Rodola", role = c("cph")), person("IBM Corporation", role = c("ctb")) )
Description: Tree based algorithms can be improved by introducing boosting frameworks. 'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>. This package offers an R interface to work with it. It is designed to be distributed and efficient with the following advantages: 1. Faster training speed and higher efficiency. 2. Lower memory usage. 3. Better accuracy. 4. Parallel learning supported. 5. Capable of handling large-scale data. In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions. Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines.
Encoding: UTF-8
License: MIT + file LICENSE
URL: https://github.com/Microsoft/LightGBM
BugReports: https://github.com/Microsoft/LightGBM/issues
NeedsCompilation: yes
Biarch: true
Suggests: testthat
Depends: R (>= 3.5), R6 (>= 2.0)
Imports: data.table (>= 1.9.6), graphics, jsonlite (>= 1.0), Matrix (>= 1.1-0), methods, utils
SystemRequirements: C++11
RoxygenNote: 7.1.1
Packaged: 2020-11-16 02:40:16 UTC; jlamb
Author: Guolin Ke [aut, cre], Damien Soukhavong [aut], James Lamb [aut], Qi Meng [aut], Thomas Finley [aut], Taifeng Wang [aut], Wei Chen [aut], Weidong Ma [aut], Qiwei Ye [aut], Tie-Yan Liu [aut], Yachen Yan [ctb], Microsoft Corporation [cph], Dropbox, Inc. [cph], Jay Loden [cph], Dave Daeschler [cph], Giampaolo Rodola [cph], IBM Corporation [ctb]
Maintainer: Guolin Ke <guolin.ke@microsoft.com>
Repository: CRAN
Date/Publication: 2020-11-18 23:20:11 UTC

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Package landmap updated to version 0.0.6 with previous version 0.0.5 dated 2020-11-11

Title: Automated Spatial Prediction using Ensemble Machine Learning
Description: Functions and tools for spatial interpolation and/or prediction of environmental variables (points to grids) based on using Ensemble Machine Learning with geographical distances. Package also provides access to Global Environmental Layers (<https://www.OpenLandMap.org>) produced by the OpenGeoHub.org foundation and collaborators. Some functions have been migrated and adopted from the Global Soil Information Facilities package.
Author: Tomislav Hengl [aut, cre]
Maintainer: Tomislav Hengl <tom.hengl@opengeohub.org>

Diff between landmap versions 0.0.5 dated 2020-11-11 and 0.0.6 dated 2020-11-18

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Package IMIFA updated to version 2.1.4 with previous version 2.1.3 dated 2020-05-12

Title: Infinite Mixtures of Infinite Factor Analysers and Related Models
Description: Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.
Author: Keefe Murphy [aut, cre] (<https://orcid.org/0000-0002-7709-3159>), Cinzia Viroli [ctb], Isobel Claire Gormley [ctb]
Maintainer: Keefe Murphy <keefe.murphy@mu.ie>

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Package fcaR updated to version 1.0.6 with previous version 1.0.4 dated 2020-07-07

Title: Formal Concept Analysis
Description: Provides tools to perform fuzzy formal concept analysis, presented in Wille (1982) <doi:10.1007/978-3-642-01815-2_23> and in Ganter and Obiedkov (2016) <doi:10.1007/978-3-662-49291-8>. It provides functions to load and save a formal context, extract its concept lattice and implications. In addition, one can use the implications to compute semantic closures of fuzzy sets and, thus, build recommendation systems.
Author: Domingo Lopez Rodriguez [aut, cre] (<https://orcid.org/0000-0002-0172-1585>), Angel Mora [aut], Jesus Dominguez [aut], Ana Villalon [aut], Ian Johnson [ctb]
Maintainer: Domingo Lopez Rodriguez <dominlopez@uma.es>

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Package safedata updated to version 1.0.9 with previous version 1.0.8 dated 2020-10-09

Title: Interface to Data from the SAFE Project
Description: The SAFE Project (<https://www.safeproject.net/>) is a large scale ecological experiment in Malaysian Borneo that explores the impact of habitat fragmentation and conversion on ecosystem function and services. Data collected at the SAFE Project is made available under a common format through the Zenodo data repository and this package makes it easy to discover and load that data into R.
Author: Andy Aldersley [aut], David Orme [aut, cre] (<https://orcid.org/0000-0002-7005-1394>)
Maintainer: David Orme <d.orme@imperial.ac.uk>

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Package robCompositions updated to version 2.3.0 with previous version 2.2.1 dated 2020-02-11

Title: Compositional Data Analysis
Description: Methods for analysis of compositional data including robust methods (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), count zeros (<doi:10.1177/1471082X14535524>), methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>), contingency (<doi:10.1080/03610926.2013.824980>) and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram.
Author: Matthias Templ [aut, cre] (<https://orcid.org/0000-0002-8638-5276>), Karel Hron [aut] (<https://orcid.org/0000-0002-1847-6598>), Peter Filzmoser [aut] (<https://orcid.org/0000-0002-8014-4682>), Kamila Facevicova [ctb], Petra Kynclova [ctb], Jan Walach [ctb], Veronika Pintar [ctb], Jiajia Chen [ctb], Dominika Miksova [ctb], Bernhard Meindl [ctb], Alessandra Menafoglio [ctb] (<https://orcid.org/0000-0003-0682-6412>), Alessia Di Blasi [ctb], Federico Pavone [ctb], Gianluca Zeni [ctb]
Maintainer: Matthias Templ <matthias.templ@gmail.com>

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Package eoffice updated to version 0.2.1 with previous version 0.2.0 dated 2020-10-22

Title: Export or Graph and Tables to 'Microsoft' Office and Import Figures and Tables
Description: Provides wrap functions to export and import graphics and data frames in R to 'microsoft' office. And This package also provide write out figures with lots of different formats. Since people may work on the platform without GUI support, the package also provide function to easily write out figures to lots of different type of formats. Now this package provide function to extract colors from all types of figures and pdf files.
Author: Kai Guo [aut, cre]
Maintainer: Kai Guo <guokai8@gmail.com>

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Package cmocean updated to version 0.3-1 with previous version 0.2 dated 2019-05-06

Title: Beautiful Colour Maps for Oceanography
Description: Perceptually uniform palettes for commonly used variables in oceanography as functions taking an integer and producing character vectors of colours. See Thyng, K.M., Greene, C.A., Hetland, R.D., Zimmerle, H.M. and S.F. DiMarco (2016) <doi:10.5670/oceanog.2016.66> for the guidelines adhered to when creating the palettes.
Author: Kristen Thyng [aut], Clark Richards [ctb], Ilja Kocken [ctb], Ivan Krylov [cre]
Maintainer: Ivan Krylov <krylov.r00t@gmail.com>

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Package trending updated to version 0.0.2 with previous version 0.0.1.0 dated 2020-10-19

Title: Model Temporal Trends
Description: Provides a coherent interface to multiple modelling tools for fitting trends along with a standardised approach for generating confidence and prediction intervals.
Author: Dirk Schumacher [aut], Thibaut Jombart [aut], Tim Taylor [ctb, cre] (<https://orcid.org/0000-0002-8587-7113>)
Maintainer: Tim Taylor <tim.taylor@hiddenelephants.co.uk>

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Package RISCA updated to version 0.9 with previous version 0.8.2 dated 2020-04-04

Title: Causal Inference and Prediction in Cohort-Based Analyses
Description: We propose numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events (Le Borgne, 2016, <doi:10.1002/sim.6777>), competing events (Trebern-Launay, 2018, <doi: 10.1007/s10654-017-0322-3>), and multi-state data (Gillaizeau, 2018, <doi: 10.1002/sim.7550>). For multistate data, semi-Markov model with interval censoring (Foucher, 2008, <doi: 10.1177/0962280208093889>) may be considered and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables (Gillaizeau, 2017, <doi: 10.1177/0962280215586456>). For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders (Le Borgne, 2018, <doi: 10.1177/0962280217702416>). Finally, several functions are available to assess time-dependent ROC curves (Combescure, 2017, <doi: 10.1177/0962280212464542>) or survival curves (Combescure, 2014, <doi: 10.1002/sim.6111>) from aggregated data.
Author: Yohann Foucher [aut, cre] (<https://orcid.org/0000-0003-0330-7457>), Florent Le Borgne [aut], Etienne Dantan [aut], Florence Gillaizeau [aut], Arthur Chatton [aut], Christophe Combescure [aut]
Maintainer: Yohann Foucher <Yohann.Foucher@univ-nantes.fr>

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Package EValue updated to version 4.1.0 with previous version 3.0.0 dated 2020-06-16

Title: Sensitivity Analyses for Unmeasured Confounding and Other Biases in Observational Studies and Meta-Analyses
Description: Conducts sensitivity analyses for unmeasured confounding,selection bias, and measurement error (individually or in combination) in individual studies. Also conducts sensitivity analyses for unmeasured confounding in meta-analyses.
Author: Maya B. Mathur [cre, aut], Louisa H. Smith [aut], Peng Ding [aut], Tyler J. VanderWeele [aut]
Maintainer: Maya B. Mathur <mmathur@stanford.edu>

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Package dm updated to version 0.1.9 with previous version 0.1.7 dated 2020-09-02

Title: Relational Data Models
Description: Provides tools for working with multiple related tables, stored as data frames or in a relational database. Multiple tables (data and metadata) are stored in a compound object, which can then be manipulated with a pipe-friendly syntax.
Author: Tobias Schieferdecker [aut], Kirill Müller [aut, cre] (<https://orcid.org/0000-0002-1416-3412>), Darko Bergant [aut], Katharina Brunner [ctb], James Wondrasek [ctb], energie360° AG [fnd], cynkra GmbH [fnd, cph]
Maintainer: Kirill Müller <krlmlr+r@mailbox.org>

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Package rasciidoc updated to version 2.2.1 with previous version 2.2.0 dated 2020-09-22

Title: Create Reports Using R and 'asciidoc'
Description: Inspired by Karl Broman`s reader on using 'knitr' with 'asciidoc' (<http://kbroman.org/knitr_knutshell/pages/asciidoc.html>), this is merely a wrapper to 'knitr' and 'asciidoc'.
Author: Andreas Dominik Cullmann [aut, cre]
Maintainer: Andreas Dominik Cullmann <fvafrcu@mailbox.org>

Diff between rasciidoc versions 2.2.0 dated 2020-09-22 and 2.2.1 dated 2020-11-18

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Package bootComb updated to version 1.0.1 with previous version 1.0.0 dated 2020-09-07

Title: Combine Parameter Estimates via Parametric Bootstrap
Description: Propagate uncertainty from several estimates when combining these estimates via a function. This is done by using the parametric bootstrap to simulate values from the distribution of each estimate to build up an empirical distribution of the combined parameter. Finally either the percentile method is used or the highest density interval is chosen to derive a confidence interval for the combined parameter with the desired coverage. References: Davison and Hinkley (1997,ISBN:0-521-57471-4) for the parametric bootstrap and percentile method, Gelman et al. (2014,ISBN:978-1-4398-4095-5) for the highest density interval, Stockdale et al. (2020)<doi:10.1016/j.jhep.2020.04.008> for an example of combining conditional prevalences.
Author: Marc Henrion [aut, cre] (<https://orcid.org/0000-0003-1242-839X>)
Maintainer: Marc Henrion <mhenrion@mlw.mw>

Diff between bootComb versions 1.0.0 dated 2020-09-07 and 1.0.1 dated 2020-11-18

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Package apexcharter updated to version 0.1.8 with previous version 0.1.7 dated 2020-10-16

Title: Create Interactive Chart with the JavaScript 'ApexCharts' Library
Description: Provides an 'htmlwidgets' interface to 'apexcharts.js'. 'Apexcharts' is a modern JavaScript charting library to build interactive charts and visualizations with simple API. 'Apexcharts' examples and documentation are available here: <https://apexcharts.com/>.
Author: Victor Perrier [aut, cre], Fanny Meyer [aut], Juned Chhipa [cph] (apexcharts.js library), Mike Bostock [cph] (d3.format library)
Maintainer: Victor Perrier <victor.perrier@dreamrs.fr>

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Package rbibutils updated to version 2.0 with previous version 1.4 dated 2020-11-06

Title: Convert Between Bibliography Formats
Description: Converts between a number of bibliography formats, including 'BibTeX', 'BibLaTeX' and 'Bibentry'. Includes a port of the 'bibutils' utilities by Chris Putnam <https://sourceforge.net/projects/bibutils/>. Supports all bibliography formats and character encodings implemented in 'bibutils'.
Author: Georgi N. Boshnakov [aut, cre] (R port, modifications to bibutils' C code), Chris Putman [aut] (src/*, author of the bibutils libraries, https://sourceforge.net/projects/bibutils/), Richard Mathar [ctb] (src/addsout.c), Johannes Wilm [ctb] (src/biblatexin.c, src/bltypes.c)
Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk>

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Package AggregateR updated to version 0.1.0 with previous version 0.0.2 dated 2015-11-23

Title: Aggregate Numeric, Date and Categorical Variables
Description: Convenience functions for aggregating a data frame or data table. Currently mean, sum and variance are supported. For Date variables, the recency and duration are supported. There is also support for dummy variables in predictive contexts. Code has been completely re-written in data.table for computational speed.
Author: Matthias Bogaert, Michel Ballings, Dirk Van den Poel
Maintainer: Matthias Bogaert <matthias.bogaert@ugent.be>

Diff between AggregateR versions 0.0.2 dated 2015-11-23 and 0.1.0 dated 2020-11-18

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Package squashinformr updated to version 0.1.5 with previous version 0.1.4 dated 2020-10-25

Title: Politely Web Scrape Data from SquashInfo
Description: Scrape SquashInfo <http://www.squashinfo.com/> for data on the Professional Squash Association World Tour and other squash events. 'squashinformr' functions scrape, parse, and clean data associated with players, tournaments, and rankings.
Author: Hayden MacDonald [aut, cre]
Maintainer: Hayden MacDonald <hayden.macdonald.8778@gmail.com>

Diff between squashinformr versions 0.1.4 dated 2020-10-25 and 0.1.5 dated 2020-11-18

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Package spinifex updated to version 0.2.7 with previous version 0.2.6 dated 2020-11-09

Title: Manual Tours, Manual Control of Dynamic Projections of Numeric Multivariate Data
Description: Data visualization tours animates linear projection of multivariate data as its basis (ie. orientation) changes. The 'spinifex' packages generates paths for manual tours by manipulating the contribution of a single variable at a time ['Cook' & 'Buja' (1997) <doi:10.2307/1390747>]. Other types of tours, such as grand (random walk) and guided (optimizing some objective function) are available in the 'tourr' package ['Wickham' 'et' 'al.' (2011) <doi:10.18637/jss.v040.i02>]. 'spinifex' builds on 'tourr' and can render tours with 'gganimate' and 'plotly' graphics, and allows for exporting as an .html widget and as an .gif, respectively. This work is fully discussed at ['Spyrison' & 'Cook' (2020) <doi:10.32614/RJ-2020-027>.
Author: Nicholas Spyrison [aut, cre], Dianne Cook [aut, ths]
Maintainer: Nicholas Spyrison <spyrison@gmail.com>

Diff between spinifex versions 0.2.6 dated 2020-11-09 and 0.2.7 dated 2020-11-18

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New package GWnnegPCA with initial version 0.0.4
Package: GWnnegPCA
Type: Package
Title: Geographically Weighted Non-Negative Principal Components Analysis
Version: 0.0.4
Authors@R: c(person(given = "Narumasa", family = "Tsutsumida", role = c("aut", "cre"), email = "rsnaru.jp@gmail.com", comment = c(ORCID = "0000-0002-6333-0301")))
Description: Implements a geographically weighted non-negative principal components analysis, which consists of the fusion of geographically weighted and sparse non-negative principal components analyses <doi:10.17608/k6.auckland.9850826.v1>.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Language: en-US
Depends: R (>= 3.5.0)
Imports: sp, sf, pracma, geodist, nsprcomp, methods, spData
SystemRequirements: C++11, GDAL (>= 2.0.1), GEOS (>= 3.4.0), PROJ (>= 4.8.0)
NeedsCompilation: no
Packaged: 2020-11-17 13:49:02 UTC; nt
Author: Narumasa Tsutsumida [aut, cre] (<https://orcid.org/0000-0002-6333-0301>)
Maintainer: Narumasa Tsutsumida <rsnaru.jp@gmail.com>
Repository: CRAN
Date/Publication: 2020-11-18 14:50:06 UTC

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Package survivalmodels updated to version 0.1.4 with previous version 0.1.3 dated 2020-11-11

Title: Models for Survival Analysis
Description: Implementations of classical and machine learning models for survival analysis, including deep neural networks via 'keras' and 'tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk, survival probabilities, or survival distributions with 'distr6' <https://CRAN.R-project.org/package=distr6>. Models are either implemented from 'Python' via 'reticulate' <https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using 'Rcpp' <https://CRAN.R-project.org/package=Rcpp>. Novel machine learning survival models wil be included in the package in near-future updates. Neural networks are implemented from the 'Python' package 'pycox' <https://github.com/havakv/pycox> and are detailed by Kvamme et al. (2019) <https://jmlr.org/papers/v20/18-424.html>. The 'Akritas' estimator is defined in Akritas (1994) <doi:10.1214/aos/1176325630>. 'DNNSurv' is defined in Zhao and Feng (2020) <arXiv:1908.02337>.
Author: Raphael Sonabend [aut, cre] (<https://orcid.org/0000-0001-9225-4654>)
Maintainer: Raphael Sonabend <raphael.sonabend.15@ucl.ac.uk>

Diff between survivalmodels versions 0.1.3 dated 2020-11-11 and 0.1.4 dated 2020-11-18

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New package tsBSS with initial version 0.5.6
Package: tsBSS
Type: Package
Title: Blind Source Separation and Supervised Dimension Reduction for Time Series
Version: 0.5.6
Date: 2020-11-17
Author: Markus Matilainen, Christophe Croux, Jari Miettinen, Klaus Nordhausen, Hannu Oja, Sara Taskinen, Joni Virta
Maintainer: Markus Matilainen <markus.matilainen@outlook.com>
Depends: ICtest (>= 0.3-2), JADE (>= 2.0-2)
Imports: Rcpp (>= 0.11.0), forecast, boot, parallel, xts, zoo
LinkingTo: Rcpp, RcppArmadillo
Suggests: stochvol, MTS, tsbox, dr
Description: Different estimators are provided to solve the blind source separation problem for multivariate time series with stochastic volatility (Matilainen, Nordhausen and Oja (2015) <doi:10.1016/j.spl.2015.04.033>; Matilainen, Miettinen, Nordhausen, Oja and Taskinen (2017) <doi:10.17713/ajs.v46i3-4.671>) and supervised dimension reduction problem for multivariate time series (Matilainen, Croux, Nordhausen and Oja (2017) <doi:10.1016/j.ecosta.2017.04.002>). Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace.
License: GPL (>= 2)
LazyData: true
NeedsCompilation: yes
Packaged: 2020-11-17 16:07:35 UTC; manmat
Repository: CRAN
Date/Publication: 2020-11-18 13:40:03 UTC

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New package RNGforGPD with initial version 1.1.0
Package: RNGforGPD
Type: Package
Title: Random Number Generation for Generalized Poisson Distribution
Version: 1.1.0
Date: 2020-11-17
Author: Hesen Li, Ruizhe Chen, Hai Nguyen, Yu-Che Chung, Ran Gao, Hakan Demirtas
Maintainer: Ruizhe Chen <rchen18@uic.edu>
Description: Generation of univariate and multivariate data that follow the generalized Poisson distribution. The details of the univariate part are explained in Demirtas (2017) <doi: 10.1080/03610918.2014.968725>, and the multivariate part is an extension of the correlated Poisson data generation routine that was introduced in Yahav and Shmueli (2012) <doi: 10.1002/asmb.901>.
License: GPL-2 | GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Depends: R (>= 3.5.0)
Imports: corpcor, mvtnorm, Matrix
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-11-17 20:23:09 UTC; hesenli
Repository: CRAN
Date/Publication: 2020-11-18 13:10:03 UTC

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New package pssmooth with initial version 1.0.3
Package: pssmooth
Type: Package
Title: Flexible and Efficient Evaluation of Principal Surrogates/Treatment Effect Modifiers
Version: 1.0.3
Authors@R: person("Michal", "Juraska", email = "mjuraska@fredhutch.org", role = c("aut", "cre"))
BugReports: https://github.com/mjuraska/pssmooth/issues
Description: Implements estimation and testing procedures for evaluating an intermediate biomarker response as a principal surrogate of a clinical response to treatment (i.e., principal stratification effect modification analysis), as described in Juraska M, Huang Y, and Gilbert PB (2020), Inference on treatment effect modification by biomarker response in a three-phase sampling design, Biostatistics, 21(3): 545-560 <doi:10.1093/biostatistics/kxy074>. The methods avoid the restrictive 'placebo structural risk' modeling assumption common to past methods and further improve robustness by the use of nonparametric kernel smoothing for biomarker density estimation. A randomized controlled two-group clinical efficacy trial is assumed with an ordered categorical or continuous univariate biomarker response measured at a fixed timepoint post-randomization and with a univariate baseline surrogate measure allowed to be observed in only a subset of trial participants with an observed biomarker response (see the flexible three-phase sampling design in the paper for details). Bootstrap-based procedures are available for pointwise and simultaneous confidence intervals and testing of four relevant hypotheses. Summary and plotting functions are provided for estimation results.
License: GPL-2
URL: https://github.com/mjuraska/pssmooth
Encoding: UTF-8
LazyData: true
Imports: graphics, stats, osDesign, np, chngpt, MASS
Suggests: knitr, rmarkdown
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-11-18 01:01:21 UTC; mjuraska
Author: Michal Juraska [aut, cre]
Maintainer: Michal Juraska <mjuraska@fredhutch.org>
Repository: CRAN
Date/Publication: 2020-11-18 13:20:03 UTC

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Package LHD updated to version 1.3.1 with previous version 1.3.0 dated 2020-11-09

Title: Latin Hypercube Designs (LHDs)
Description: Contains different algorithms and construction methods for efficient Latin hypercube designs (LHDs) with flexible sizes. Our package is comprehensive since it is capable of generating maximin distance LHDs, maximum projection LHDs, and orthogonal and nearly orthogonal LHDs. Documentations for all the algorithms and construction methods include informative explanation along with corresponding references. This package is particularly useful in the area of Design and Analysis of Experiments (DAE). More specifically, design of computer experiments.
Author: Hongzhi Wang, Qian Xiao, Abhyuday Mandal
Maintainer: Hongzhi Wang <hw34508@uga.edu>

Diff between LHD versions 1.3.0 dated 2020-11-09 and 1.3.1 dated 2020-11-18

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Package tm updated to version 0.7-8 with previous version 0.7-7 dated 2019-12-12

Title: Text Mining Package
Description: A framework for text mining applications within R.
Author: Ingo Feinerer [aut, cre] (<https://orcid.org/0000-0001-7656-8338>), Kurt Hornik [aut] (<https://orcid.org/0000-0003-4198-9911>), Artifex Software, Inc. [ctb, cph] (pdf_info.ps taken from GPL Ghostscript)
Maintainer: Ingo Feinerer <feinerer@logic.at>

Diff between tm versions 0.7-7 dated 2019-12-12 and 0.7-8 dated 2020-11-18

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New package rbenvo with initial version 1.0.5
Package: rbenvo
Type: Package
Title: Built Environment Objects
Version: 1.0.5
Authors@R: person(given = "Adam", family = "Peterson", email ="atpvyc@umich.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-7071-7873"))
Description: Provides S3 class objects and methods for built environment data to ease the use of working with these data and facilitate other packages that make use of this data structure.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: ggplot2, dplyr, magrittr, tidyr, purrr, lme4, Matrix, forcats, sf, lubridate, rlang, stringr,
RoxygenNote: 7.1.1
Depends: R (>= 2.10)
Suggests: ggmap, knitr, glue, rmarkdown, testthat (>= 2.1.0), tidygraph, lwgeom
VignetteBuilder: knitr
URL: https://github.com/apeterson91/rbenvo
NeedsCompilation: no
Packaged: 2020-11-13 15:39:17 UTC; Adam
Author: Adam Peterson [aut, cre] (<https://orcid.org/0000-0001-7071-7873>)
Maintainer: Adam Peterson <atpvyc@umich.edu>
Repository: CRAN
Date/Publication: 2020-11-18 10:40:02 UTC

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New package PoSI with initial version 1.1
Package: PoSI
Type: Package
Title: Valid Post-Selection Inference for Linear LS Regression
Version: 1.1
Date: 2020-11-04
Authors@R: c(person("Andreas", "Buja", role = "aut"), person("Kai", "Zhang", role = "aut"), person("Wan", "Zhang", role = "cre", email = "wanz63@live.unc.edu"))
Description: In linear LS regression, calculate for a given design matrix the multiplier K of coefficient standard errors such that the confidence intervals [b - K*SE(b), b + K*SE(b)] have a guaranteed coverage probability for all coefficient estimates b in any submodels after performing arbitrary model selection.
Suggests: MASS
License: GPL-3
NeedsCompilation: no
Repository: CRAN
Packaged: 2020-11-12 16:31:59 UTC; zhangwan
Author: Andreas Buja [aut], Kai Zhang [aut], Wan Zhang [cre]
Maintainer: Wan Zhang <wanz63@live.unc.edu>
Date/Publication: 2020-11-18 10:30:07 UTC

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New package mully with initial version 2.1.26
Package: mully
Type: Package
Title: Create, Modify and Visualize Multi-Layered Networks
Version: 2.1.26
Author: Zaynab Hammoud
Maintainer: Zaynab Hammoud <zaynabhassanhammoud@gmail.com>
Description: Allows the user to create graph with multiple layers. The user can also modify the layers, the nodes, and the edges. The graph can also be visualized. Zaynab Hammoud and Frank Kramer (2018) <doi:10.3390/genes9110519>. More about multilayered graphs and their usage can be found in our review paper: Zaynab Hammoud and Frank Kramer (2020) <doi:10.1186/s41044-020-00046-0>.
URL: https://github.com/frankkramer-lab/mully
BugReports: https://github.com/frankkramer-lab/mully/issues
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: igraph, rgl, randomcoloR, shape
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-11-13 19:07:34 UTC; Administrator
Repository: CRAN
Date/Publication: 2020-11-18 10:50:02 UTC

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New package latrend with initial version 1.0.1
Package: latrend
Type: Package
Version: 1.0.1
Date: 2020-11-12
Title: Framework for Clustering Longitudinal Data
Description: A framework for clustering longitudinal datasets in a standardized way. Provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. The interface enables users to compare results between methods, and to implement and evaluate new methods with ease.
Maintainer: Niek Den Teuling <niek.den.teuling@philips.com>
Authors@R: c(person(given = "Niek", family = "Den Teuling", role = c("aut", "cre"), email = "niek.den.teuling@philips.com", comment = c(ORCID = "https://orcid.org/0000-0003-1026-5080")), person(given = "Steffen", family = "Pauws", role = "ctb", email = "s.c.pauws@uvt.nl"), person(given = "Edwin", family = "van den Heuvel", role = "ctb", email = "e.r.v.d.heuvel@tue.nl"), person(given = "Copyright © 2020 Koninklijke Philips N.V.", role = "cph"))
URL: https://github.com/philips-software/latrend
BugReports: https://github.com/philips-software/latrend/issues
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.6.0)
Imports: stats (>= 3.6.0), methods (>= 3.6.0), R.utils (>= 2.9.0), assertthat (>= 0.2.1), foreach (>= 1.4.7), data.table (>= 1.12.0), longitudinalData (>= 2.4.1), magrittr (>= 1.5), plyr (>= 1.8.4), ggplot2 (>= 2.2.1), matrixStats (>= 0.55.0), stackoverflow (>= 0.3.0), clusterCrit (>= 1.2.8), mclustcomp (>= 0.3.1), scales (>= 1.0.0), caret (>= 6.0), lme4 (>= 1.1), mclust (>= 5.4.5)
Suggests: testthat (>= 3.0.0), roxygen2 (>= 7.1.0), knitr (>= 1.24), rmarkdown (>= 1.18), kml (>= 2.4.1), lcmm (>= 1.7.8), longclust (>= 1.2.3), mixtools (>= 1.1.0), flexmix (>= 2.3), fda (>= 2.4.8), funFEM (>= 1.1), igraph (>= 1.2.4), crimCV (>= 0.9.6), akmedoids (>= 0.1.5), dtwclust (>= 5.5.6), mixAK (>= 5.3), psych (>= 1.9.12.31), qqplotr (>= 0.0.4)
RoxygenNote: 7.1.1
VignetteBuilder: knitr
Collate: 'assert.R' 'compute.R' 'data.R' 'formula.R' 'generics.R' 'latrend.R' 'make.R' 'matrix.R' 'method.R' 'methodMatrix.R' 'methodAKMedoids.R' 'methodCrimCV.R' 'methodCustom.R' 'methodDtwclust.R' 'methodFlexmix.R' 'methodFlexmixGBTM.R' 'methodFunFEM.R' 'plot.R' 'model.R' 'modelCustom.R' 'methodTwoStep.R' 'methodGCKM.R' 'methodKML.R' 'methodLMKM.R' 'methodLcmmGMM.R' 'methodLcmmGBTM.R' 'methodLongclust.R' 'methodMclustLLPA.R' 'methodMixAK_GLMM.R' 'methodMixTVEM.R' 'methodMixtoolsGMM.R' 'methodMixtoolsNPRM.R' 'methodRandom.R' 'methodStratify.R' 'methods.R' 'metrics.R' 'model-summary.R' 'model-transform.R' 'modelApprox.R' 'modelCrimCV.R' 'modelDtwclust.R' 'modelFlexmix.R' 'modelFunFEM.R' 'modelKML.R' 'modelLMKM.R' 'modelLcmmGMM.R' 'modelLcmmGBTM.R' 'modelLongclust.R' 'modelMclustLLPA.R' 'modelMixAK_GLMM.R' 'modelMixAK_GLMMlist.R' 'modelMixTVEM.R' 'modelMixtoolsGMM.R' 'modelMixtoolsRM.R' 'modelPartition.R' 'modelStratify.R' 'modelTwoStep.R' 'modelWeightedPartition.R' 'models.R' 'random.R' 'verbose.R' 'zzz.R'
NeedsCompilation: no
Packaged: 2020-11-12 16:47:42 UTC; 310083353
Author: Niek Den Teuling [aut, cre] (<https://orcid.org/0000-0003-1026-5080>), Steffen Pauws [ctb], Edwin van den Heuvel [ctb], Copyright © 2020 Koninklijke Philips N.V. [cph]
Repository: CRAN
Date/Publication: 2020-11-18 10:10:02 UTC

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New package kibior with initial version 0.1.0
Type: Package
Date: 2020-11-05
Package: kibior
Version: 0.1.0
Encoding: UTF-8
VignetteBuilder: knitr
RoxygenNote: 7.1.1
URL: https://github.com/regisoc/kibior
BugReports: https://github.com/regisoc/kibior/issues
Title: A Simple Data Management and Sharing Tool
biocViews: DataImport, DataRepresentation, ThirdPartyClient
Authors@R: c( person(given = "Régis", family = "Ongaro-Carcy", role = c("aut", "cre", "ctb"), email = "regis.ongaro-carcy2@crchudequebec.ulaval.ca", comment = c(ORCID = "0000-0002-0091-8810")), person(given = "Arnaud", family = "Droit", role = c("fnd"), email = "arnaud.droit@crchudequebec.ulaval.ca"))
Description: An interface to store, retrieve, search, join and share datasets, based on Elasticsearch (ES) API. As a decentralized, FAIR and collaborative search engine and database effort, it proposes a simple push/pull/search mechanism only based on ES, a tool which can be deployed on nearly any hardware. It is a high-level R-ES binding to ease data usage using 'elastic' package (S. Chamberlain (2020)) <https://docs.ropensci.org/elastic/>, extends joins from 'dplyr' package (H. Wickham et al. (2020)) <https://dplyr.tidyverse.org/> and integrates specific biological format importation with Bioconductor packages such as 'rtracklayer' (M. Lawrence and al. (2009) <doi:10.1093/bioinformatics/btp328>) <http://bioconductor.org/packages/rtracklayer>, 'Biostrings' (H. Pagès and al. (2020) <doi:10.18129/B9.bioc.Biostrings>) <http://bioconductor.org/packages/Biostrings>, and 'Rsamtools' (M. Morgan and al. (2020) <doi:10.18129/B9.bioc.Rsamtools>) <http://bioconductor.org/packages/Rsamtools>, but also a long list of more common ones with 'rio' (C-h. Chan and al. (2018)) <https://cran.r-project.org/package=rio>.
Depends: R (>= 4.0)
Imports: R6 (>= 2.5.0), data.table (>= 1.13.2), stringr (>= 1.4.0), purrr (>= 0.3.4), jsonlite (>= 1.7.1), rio (>= 0.5.16), tibble (>= 3.0.4), dplyr (>= 1.0.2), magrittr (>= 1.5), tidyr (>= 1.1.2), elastic (>= 1.1.0), Biostrings, Rsamtools, rtracklayer
Suggests: ggplot2 (>= 3.3.2), readr (>= 1.4.0), xml2 (>= 1.3.2), yaml (>= 2.2.1), testthat (>= 3.0.0), rmarkdown (>= 2.5), knitr (>= 1.30)
License: GPL-2
LazyData: true
NeedsCompilation: no
Packaged: 2020-11-13 14:56:47 UTC; root
Author: Régis Ongaro-Carcy [aut, cre, ctb] (<https://orcid.org/0000-0002-0091-8810>), Arnaud Droit [fnd]
Maintainer: Régis Ongaro-Carcy <regis.ongaro-carcy2@crchudequebec.ulaval.ca>
Repository: CRAN
Date/Publication: 2020-11-18 10:30:03 UTC

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New package gpbStat with initial version 0.3.0
Package: gpbStat
Type: Package
Title: Comprehensive Statistical Analysis of Plant Breeding Experiments
Version: 0.3.0
Authors@R: c( person( c("Nandan", "Patil") , email = "tryanother609@gmail.com" , role = c("cre", "aut") , comment = c(ORCID = "0000-0003-0100-9931") ) , person( c("Lakshmi", "R.", "Gangavati") , email = "lakshmurg8@gmail.com" , role = c("aut", "ctb") ) )
Maintainer: Nandan Patil <tryanother609@gmail.com>
Note: Department of Genetics and Plant Breeding, University of Agricultural Sciecnes, Dharwad.
Description: Performs statistical data analysis of various Plant Breeding experiments. Contains functions for Line by Tester analysis as per Arunachalam, V.(1974) <http://repository.ias.ac.in/89299/>.
License: GPL-2
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.0)
Date: 2020-11-11
URL: https://github.com/nandp1/gpbStat/
BugReports: https://github.com/nandp1/gpbStat/issues
RoxygenNote: 7.1.1
Suggests: testthat, knitr, rmarkdown, reshape2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-11-12 18:56:03 UTC; ACER
Author: Nandan Patil [cre, aut] (<https://orcid.org/0000-0003-0100-9931>), Lakshmi R. Gangavati [aut, ctb]
Repository: CRAN
Date/Publication: 2020-11-18 10:20:03 UTC

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New package DSMolgenisArmadillo with initial version 1.3.4
Package: DSMolgenisArmadillo
Type: Package
Version: 1.3.4
Title: 'DataSHIELD' Client for 'MOLGENIS Armadillo'
Description: 'DataSHIELD' is an infrastructure and series of R packages that enables the remote and 'non-disclosive' analysis of sensitive research data. This package is the 'DataSHIELD' interface implementation to analyze data shared on a 'MOLGENIS Armadillo' server. 'MOLGENIS Armadillo' is a light-weight 'DataSHIELD' server using a file store and an 'RServe' server.
Authors@R: c(person(given = "Sido", family = "Haakma", role = c("aut"), email = "s.haakma@rug.nl", comment = c(ORCID = "0000-0003-1278-9144")), person(given = "Tommy", family = "de Boer", role = c("aut"), email = "t.de.boer01@umcg.nl", comment = c(ORCID = "0000-0002-4492-7225")), person(given = "Fleur", family = "Kelpin", role = c("aut", "cre"), email = "f.kelpin@umcg.nl", comment = c(ORCID = "0000-0003-1527-5130")), person(given = "MOLGENIS org", role = c("cph", "fnd"), email = "molgenis-support@umcg.nl"))
Maintainer: Fleur Kelpin <f.kelpin@umcg.nl>
Depends: R (>= 3.6), DSI (>= 1.1.0), MolgenisAuth
Imports: methods, httr, stringr, utils, base64enc, urltools
Suggests: dsBaseClient (>= 6.0), knitr, mockery, testthat (>= 2.1.0), tibble, rmarkdown, metafor
License: LGPL (>= 2.1)
URL: https://github.com/molgenis/molgenis-r-datashield/, https://molgenis.github.io/molgenis-r-datashield/
BugReports: https://github.com/molgenis/molgenis-r-datashield/issues/
RoxygenNote: 7.1.1
VignetteBuilder: knitr
Encoding: UTF-8
Collate: 'ArmadilloDriver.R' 'ArmadilloConnection.R' 'ArmadilloOAuth.R' 'ArmadilloResult.R' 'DSMolgenisArmadillo-package.R' 'utils.R'
Additional_repositories: https://cran.datashield.org/
NeedsCompilation: no
Packaged: 2020-11-12 17:00:12 UTC; root
Author: Sido Haakma [aut] (<https://orcid.org/0000-0003-1278-9144>), Tommy de Boer [aut] (<https://orcid.org/0000-0002-4492-7225>), Fleur Kelpin [aut, cre] (<https://orcid.org/0000-0003-1527-5130>), MOLGENIS org [cph, fnd]
Repository: CRAN
Date/Publication: 2020-11-18 11:00:02 UTC

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New package driveR with initial version 0.1.1
Package: driveR
Title: Prioritizing Cancer Driver Genes Using Genomics Data
Version: 0.1.1
Authors@R: person(given = "Ege", family = "Ulgen", role = c("aut", "cre", "cph"), email = "egeulgen@gmail.com", comment = c(ORCID = "0000-0003-2090-3621"))
Maintainer: Ege Ulgen <egeulgen@gmail.com>
Description: Cancer genomes contain large numbers of somatic alterations but few genes drive tumor development. Identifying molecular cancer driver genes is critical for precision oncology. Most of current approaches either identify driver genes based on mutational recurrence or using estimated scores predicting the functional consequences of mutations. 'driveR' is a tool for personalized or batch analysis of genomic data for driver gene prioritization by combining genomic information and prior biological knowledge. As features, 'driveR' uses coding impact metaprediction scores, non-coding impact scores, somatic copy number alteration scores, hotspot gene/double-hit gene condition, 'phenolyzer' gene scores and memberships to cancer-related KEGG pathways. It uses these features to estimate cancer-type-specific probability for each gene of being a cancer driver using the related task of a multi-task learning classification model. The method is described in detail in Ulgen E, Sezerman U. 2020. driveR: A Novel Method for Prioritizing Cancer Driver Genes Using Somatic Genomics Data. bioRxiv <doi:10.1101/2020.11.10.376707>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
URL: https://egeulgen.github.io/driveR/, https://github.com/egeulgen/driveR/
BugReports: https://github.com/egeulgen/driveR/issues
biocViews:
Imports: caret, randomForest, Homo.sapiens, GenomicRanges, GenomeInfoDb, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, S4Vectors, org.Hs.eg.db, rlang
Depends: R (>= 4.0)
Suggests: testthat, covr, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-11-12 14:02:39 UTC; egeulgen
Author: Ege Ulgen [aut, cre, cph] (<https://orcid.org/0000-0003-2090-3621>)
Repository: CRAN
Date/Publication: 2020-11-18 10:10:08 UTC

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New package bayesGAM with initial version 0.0.1
Package: bayesGAM
Title: Fit Multivariate Response Generalized Additive Models using Hamiltonian Monte Carlo
Version: 0.0.1
Authors@R: c(person(given = "Samuel", family = "Thomas", role = c("cre", "aut"), email = "samthoma@iu.edu"), person(given = "Wanzhu", family = "Tu", role = "ctb", email = "wtu1@iu.edu"), person(given = "Trustees of Columbia University (R/rstanMethods.R)", role = "cph"))
Description: The 'bayesGAM' package is designed to provide a user friendly option to fit univariate and multivariate response Generalized Additive Models (GAM) using Hamiltonian Monte Carlo (HMC) with few technical burdens. The functions in this package use 'rstan' (Stan Development Team 2020) to call 'Stan' routines that run the HMC simulations. The 'Stan' code for these models is already pre-compiled for the user. The programming formulation for models in 'bayesGAM' is designed to be familiar to analysts who fit statistical models in 'R'. Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., ... & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of statistical software, 76(1). Stan Development Team. 2018. RStan: the R interface to Stan. R package version 2.17.3. <https://mc-stan.org/> Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418. Betancourt, Michael, and Mark Girolami. "Hamiltonian Monte Carlo for hierarchical models." Current trends in Bayesian methodology with applications 79.30 (2015): 2-4. Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" <arXiv:2006.16194>, Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174. Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric regression (No. 12). Cambridge university press. ISBN: 978-0521785167.
Maintainer: Samuel Thomas <samthoma@iu.edu>
Depends: R (>= 3.6)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Biarch: true
Imports: bayesplot, boot, cluster, corrplot, ggplot2, graphics, gridExtra, loo, methods, mlbench, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), rstantools (>= 2.1.0.9000), SemiPar, stats, geometry, MASS
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0)
Suggests: testthat
SystemRequirements: GNU make
NeedsCompilation: yes
Packaged: 2020-11-13 04:06:31 UTC; samuelthomas
Author: Samuel Thomas [cre, aut], Wanzhu Tu [ctb], Trustees of Columbia University (R/rstanMethods.R) [cph]
Repository: CRAN
Date/Publication: 2020-11-18 10:20:06 UTC

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New package potential with initial version 0.1.0
Package: potential
Title: Implementation of the Potential Model
Version: 0.1.0
Authors@R: c( person("Timothée", "Giraud", email = "timothee.giraud@cnrs.fr", role = c("cre","aut"), comment = c(ORCID = "0000-0002-1932-3323")), person("Hadrien", "Commenges", email = "hadrien.commenges@univ-paris1.fr", role = c("aut")))
Description: Provides functions to compute the potential model as defined by Stewart (1941) <doi:10.1126/science.93.2404.89>. Several options are available to customize the model, such as the possibility to fine-tune the distance friction functions or to use custom distance matrices. Some computations are parallelized to improve their efficiency.
Depends: R (>= 3.5.0)
License: GPL-3
LazyData: true
Imports: sf, graphics, methods, isoband, parallel, doParallel, foreach
Suggests: covr, lwgeom, cartography, knitr, tinytest, rmarkdown
SystemRequirements: GDAL (>= 2.0.1), GEOS (>= 3.4.0), PROJ.4 (>= 4.8.0)
URL: https://github.com/riatelab/potential
BugReports: https://github.com/riatelab/potential/issues
Encoding: UTF-8
RoxygenNote: 7.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-11-12 10:44:28 UTC; tim
Author: Timothée Giraud [cre, aut] (<https://orcid.org/0000-0002-1932-3323>), Hadrien Commenges [aut]
Maintainer: Timothée Giraud <timothee.giraud@cnrs.fr>
Repository: CRAN
Date/Publication: 2020-11-18 09:40:02 UTC

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New package netmhc2pan with initial version 1.3.1
Package: netmhc2pan
Title: Interface to 'NetMHCIIpan'
Version: 1.3.1
Authors@R: c( person("Richèl J.C.", "Bilderbeek", email = "richel@richelbilderbeek.nl", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1107-7049")), person("Byungkuk", "Min", role = "ctb"))
Maintainer: Richèl J.C. Bilderbeek <richel@richelbilderbeek.nl>
Description: The field of immunology benefits from software that can predict which peptide sequences trigger an immune response. 'NetMHCIIpan' is a such a tool: it predicts the binding strength of a short peptide to a Major Histocompatibility Complex class II (MHC-II) molecule. 'NetMHCIIpan' can be used from a web server at <https://services.healthtech.dtu.dk/service.php?NetMHCIIpan-3.2/> or from the command-line, using a local installation. This package allows to call 'NetMHCIIpan' from R.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: assertive, devtools, dplyr, rappdirs, readr, seqinr, stringr, testit, tibble
Suggests: knitr, rmarkdown, testthat, spelling
VignetteBuilder: knitr
RoxygenNote: 7.1.1
Language: en-US
URL: https://github.com/richelbilderbeek/netmhc2pan/
BugReports: https://github.com/richelbilderbeek/netmhc2pan/issues
SystemRequirements: NetMHC2pan (https://services.healthtech.dtu.dk/service.php?NetMHCIIpan-3.2)
NeedsCompilation: no
Packaged: 2020-11-12 09:13:56 UTC; richel
Author: Richèl J.C. Bilderbeek [aut, cre] (<https://orcid.org/0000-0003-1107-7049>), Byungkuk Min [ctb]
Repository: CRAN
Date/Publication: 2020-11-18 09:40:05 UTC

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New package mixComp with initial version 0.1-1
Package: mixComp
Version: 0.1-1
Title: Estimation of the Order of Mixture Distributions
Description: Methods for estimating the order of a mixture model. The approaches considered are based on the following papers (the extended list of references is available in the vignette): 1. Dacunha-Castelle, Didier, and Elisabeth Gassiat. The estimation of the order of a mixture model. Bernoulli 3, no. 3 (1997): 279-299. <https://projecteuclid.org/download/pdf_1/euclid.bj/1177334456>. 2. Woo, Mi-Ja, and T. N. Sriram. Robust estimation of mixture complexity. Journal of the American Statistical Association 101, no. 476 (2006): 1475-1486. <doi:10.1198/016214506000000555>. 3. Woo, Mi-Ja, and T. N. Sriram. Robust estimation of mixture complexity for count data. Computational statistics & data analysis 51, no. 9 (2007): 4379-4392. <doi:10.1016/j.csda.2006.06.006>. 4. Umashanger, T., and T. N. Sriram. L2E estimation of mixture complexity for count data. Computational statistics & data analysis 53, no. 12 (2009): 4243-4254. <doi:10.1016/j.csda.2009.05.013>. 5. Karlis, Dimitris, and Evdokia Xekalaki. On testing for the number of components in a mixed Poisson model. Annals of the Institute of Statistical Mathematics 51, no. 1 (1999): 149-162. <doi:10.1023/A:1003839420071>. 6. Cutler, Adele, and Olga I. Cordero-Brana. Minimum Hellinger Distance Estimation for Finite Mixture Models. Journal of the American Statistical Association 91, no. 436 (1996): 1716-1723. <doi:10.2307/2291601>.
Authors@R: c(person(given = "Anja", family = "Weigel", role = "aut", email = "anja.weigel@yahoo.de"), person(given = "Yulia", family = "Kulagina", role = c("aut", "cre"), email = "yulia.kulagina@stat.math.ethz.ch"), person(given = "Fadoua", family = "Balabdaoui", role = c("aut", "ths"), email = "fadoua.balabdaoui@stat.math.ethz.ch"), person(given = "Lilian", family = "Mueller", role = "ctb", email = "lilian.mueller@gmail.com"), person(given = "Martin", family = "Maechler", role = "ctb", comment = c("package 'nor1mix' as model", ORCID = "0000-0002-8685-9910")) )
Imports: cluster, boot, expm, matrixcalc, Rsolnp, kdensity
Suggests: knitr, rmarkdown
License: GPL-3
VignetteBuilder: knitr
Packaged: 2020-10-22 11:02:01 UTC; julia
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Author: Anja Weigel [aut], Yulia Kulagina [aut, cre], Fadoua Balabdaoui [aut, ths], Lilian Mueller [ctb], Martin Maechler [ctb] (package 'nor1mix' as model, <https://orcid.org/0000-0002-8685-9910>)
Maintainer: Yulia Kulagina <yulia.kulagina@stat.math.ethz.ch>
Repository: CRAN
Date/Publication: 2020-11-18 10:00:02 UTC

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New package gamlss.mx with initial version 6.0-0
Package: gamlss.mx
Type: Package
Title: Fitting Mixture Distributions with GAMLSS
Version: 6.0-0
Date: 2020-12-05
Author: Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>, Bob Rigby <r.rigby@londonmet.ac.uk>
Maintainer: Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>
LazyLoad: yes
Depends: R (>= 2.2.1), gamlss.dist, gamlss, nnet, stats, graphics
Suggests: MASS
Description: The main purpose of this package is to allow fitting of mixture distributions with generalised additive models for location scale and shape models see Chapter 7 of Stasinopoulos et al. (2017) <doi:10.1201/b21973-4>.
License: GPL-2 | GPL-3
URL: https://www.gamlss.com/
NeedsCompilation: no
Packaged: 2020-11-12 08:51:49 UTC; dimitriosstasinopoulos
Repository: CRAN
Date/Publication: 2020-11-18 09:30:02 UTC

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Package FCSlib updated to version 1.3.0 with previous version 1.2.1 dated 2020-09-20

Title: A Collection of Fluorescence Fluctuation Spectroscopy Analysis Methods
Description: This is a package for fluorescence fluctuation spectroscopy data analysis methods such as spFCS, FCCS, scanning-FCS, pCF, N&B and pCOMB, among others. In addition, several data detrending tools are provided. For an extensive user's guide for the use of FCSlib, please navigate to (<https://github.com/FCSlib/FCSlib/tree/master/Documentation>). Sample data can be found at (<https://github.com/FCSlib/FCSlib/tree/master/Sample%20Data>). The original paper where this package is presented can be found at (<doi:10.1093/bioinformatics/btaa876>).
Author: Raúl Pinto Cámara, Adán Guerrero, Alejandro Linares, José Damián Martínez Reyes, Haydee Hernández.
Maintainer: Raúl Pinto Cámara <support.fcslib@mail.ibt.unam.mx>

Diff between FCSlib versions 1.2.1 dated 2020-09-20 and 1.3.0 dated 2020-11-18

 FCSlib-1.2.1/FCSlib/R/boxcarDetrend.R        |only
 FCSlib-1.2.1/FCSlib/R/expDetrend.R           |only
 FCSlib-1.2.1/FCSlib/R/polyDetrend.R          |only
 FCSlib-1.2.1/FCSlib/man/boxcarDetrend.Rd     |only
 FCSlib-1.2.1/FCSlib/man/expDetrend.Rd        |only
 FCSlib-1.2.1/FCSlib/man/polyDetrend.Rd       |only
 FCSlib-1.3.0/FCSlib/DESCRIPTION              |   11 ++++++-----
 FCSlib-1.3.0/FCSlib/MD5                      |   12 ++++--------
 FCSlib-1.3.0/FCSlib/NAMESPACE                |    4 +---
 FCSlib-1.3.0/FCSlib/R/detrendTimeSeries.R    |only
 FCSlib-1.3.0/FCSlib/man/detrendTimeSeries.Rd |only
 11 files changed, 11 insertions(+), 16 deletions(-)

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New package Cronbach with initial version 0.1
Package: Cronbach
Type: Package
Title: Cronbach's Alpha
Version: 0.1
URL:
Date: 2020-11-12
Authors@R: c(person("Michail", "Tsagris", role = c("aut", "cre"), email = "mtsagris@uoc.gr"), person("Constantinos", "Frangos", role = "aut", email = "kfragkos@outlook.com"), person("Christos", "Frangos", role = "ctb", email = "cfragos@teiath.gr"))
Author: Michail Tsagris [aut, cre], Constantinos Frangos [aut], Christos Frangos [ctb]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>
Depends: R (>= 3.6.0)
Imports: boot, stats
Description: Cronbach's alpha and various formulas for confidence intervals. The relevant paper is Tsagris M., Frangos C.C. and Frangos C.C. (2013). "Confidence intervals for Cronbach's reliability coefficient". Recent Techniques in Educational Science, 14-16 May, Athens, Greece.
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2020-11-12 07:08:24 UTC; user
Repository: CRAN
Date/Publication: 2020-11-18 09:10:02 UTC

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New package CatDataAnalysis with initial version 0.1-3
Package: CatDataAnalysis
Version: 0.1-3
Date: 2020-11-11
Title: Datasetsfor Categorical Data Analysis by Agresti
Author: Alan Agesti <aa@stat.ufl.edu>
Maintainer: Charles J. Geyer <charlie@stat.umn.edu>
Depends: R (>= 2.14.0)
Description: Datasets used in the book "Categorical Data Analysis" by Agresti (2012, ISBN:978-0-470-46363-5) but not printed in the book. Datasets and help pages were automatically produced from the source <http://www.stat.ufl.edu/~aa/cda/data.html> by the R script foo.R, which can be found in the GitHub repository.
License: GPL (>= 2)
URL: https://github.com/cjgeyer/CatDataAnalysis
NeedsCompilation: no
Packaged: 2020-11-12 03:04:35 UTC; geyer
Repository: CRAN
Date/Publication: 2020-11-18 09:10:05 UTC

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New package tmhmm with initial version 1.3
Package: tmhmm
Type: Package
Title: Interface to TMHMM
Version: 1.3
Author: Richel J.C. Bilderbeek
Maintainer: Richel J.C. Bilderbeek <richel@richelbilderbeek.nl>
Description: Predicting the topology of membrane proteins is part of the field of computational biology. 'TMHMM' is a program to predict transmembrane helices in proteins. 'TMHMM' can be called from a website and/or locally using Bash. This package allows to install and call TMHMM from R and parse its results.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: ggplot2, rappdirs, stringr, tibble
Suggests: curl, testthat (>= 2.1.0), knitr, rmarkdown
URL: https://github.com/richelbilderbeek/tmhmm/
BugReports: https://github.com/richelbilderbeek/tmhmm/issues
RoxygenNote: 7.1.1
VignetteBuilder: knitr
SystemRequirements: TMHMM (https://services.healthtech.dtu.dk/service.php?TMHMM-2.0)
NeedsCompilation: no
Packaged: 2020-11-11 18:35:13 UTC; richel
Repository: CRAN
Date/Publication: 2020-11-18 08:50:02 UTC

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New package RstoxData with initial version 1.0.16
Package: RstoxData
Version: 1.0.16
Date: 2020-11-11
Title: Tools to Read and Manipulate Fisheries Data
Authors@R: c( person(given = "Ibrahim", family = "Umar", role = c("cre", "aut"), email = "ibrahim.umar@hi.no"), person(given = "Sindre", family = "Vatnehol", role = "aut"), person(given = "Arne Johannes", family = "Holmin", role = "aut"), person(given = "Edvin", family = "Fuglebakk", role = "aut"), person(given = "Espen", family = "Johnsen", role = "aut"), person(given = "Norwegian Institute of Marine Research", role = c("cph", "fnd")))
Depends: R (>= 3.6)
Description: Set of tools to read and manipulate various data formats for fisheries. Mainly catered towards scientific trawl survey sampling ('biotic') data, acoustic trawl data, and commercial fishing catch ('landings') data. Among the supported data formats are the data products from the Norwegian Institute Marine Research ('IMR') and the International Council for the Exploration of the Sea (ICES).
URL: https://github.com/StoXProject/RstoxData
BugReports: https://github.com/StoXProject/RstoxData/issues
License: LGPL-3
LazyData: true
Encoding: UTF-8
Imports: data.table (>= 1.12.6), Rcpp (>= 1.0.0), xml2 (>= 1.2.2), readr (>= 1.3.1)
Suggests: testthat
LinkingTo: Rcpp
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2020-11-11 22:22:25 UTC; baim
Author: Ibrahim Umar [cre, aut], Sindre Vatnehol [aut], Arne Johannes Holmin [aut], Edvin Fuglebakk [aut], Espen Johnsen [aut], Norwegian Institute of Marine Research [cph, fnd]
Maintainer: Ibrahim Umar <ibrahim.umar@hi.no>
Repository: CRAN
Date/Publication: 2020-11-18 08:50:15 UTC

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New package quadrupen with initial version 0.2-8
Package: quadrupen
Type: Package
Title: Sparsity by Worst-Case Quadratic Penalties
Version: 0.2-8
Date: 2020-11-04
Authors@R: c( person("Julien", "Chiquet", role = c("aut", "cre"), email = "julien.chiquet@inrae.fr", comment = c(ORCID = "0000-0002-3629-3429")) )
Description: Fits classical sparse regression models with efficient active set algorithms by solving quadratic problems as described by Grandvalet, Chiquet and Ambroise (2017) <arXiv:1210.2077>. Also provides a few methods for model selection purpose (cross-validation, stability selection).
License: GPL (>= 3)
Depends: Rcpp, ggplot2, Matrix
Imports: reshape2, methods, scales, grid, parallel
Suggests: testthat, spelling, lars, elasticnet, glmnet
LinkingTo: Rcpp, RcppArmadillo
Packaged: 2020-11-11 17:20:26 UTC; jchiquet
NeedsCompilation: yes
Maintainer: Julien Chiquet <julien.chiquet@inrae.fr>
Encoding: UTF-8
RoxygenNote: 7.1.1
Language: en-US
Author: Julien Chiquet [aut, cre] (<https://orcid.org/0000-0002-3629-3429>)
Repository: CRAN
Date/Publication: 2020-11-18 09:00:03 UTC

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New package irt with initial version 0.1.3
Package: irt
Type: Package
Title: Item Response Theory and Computerized Adaptive Testing Functions
Version: 0.1.3
Authors@R: c(person(given = "Emre", family = "Gonulates", role = c("aut", "cre"), email = "egonulates@gmail.com", comment = c(ORCID="0000-0002-3834-3266")))
Maintainer: Emre Gonulates <egonulates@gmail.com>
Description: A collection of Item Response Theory (IRT) and Computerized Adaptive Testing (CAT) functions that are used in psychometrics.
License: GPL-3
Depends: methods
LinkingTo: Rcpp
Imports: Rcpp (>= 1.0.4), ggplot2, parallel
NeedsCompilation: yes
RoxygenNote: 7.1.1
Encoding: UTF-8
LazyData: true
Collate: 'Item-class.R' 'Item-class-methods.R' 'Itempool-class.R' 'Itempool-class-methods.R' 'RcppExports.R' 'Testlet-class-methods.R' 'ability_estimation.R' 'bilog.R' 'cat_sim.R' 'cat_sim_helper_functions.R' 'dif.R' 'info.R' 'ipd.R' 'irt.R' 'item_analysis.R' 'item_fit.R' 'misc.R' 'person_fit.R' 'plot_IRT.R' 'prob.R' 'resp_lik.R' 'resp_loglik.R' 'rsss.R' 'sim_resp.R'
Packaged: 2020-11-17 03:27:10 UTC; gonul
Author: Emre Gonulates [aut, cre] (<https://orcid.org/0000-0002-3834-3266>)
Repository: CRAN
Date/Publication: 2020-11-18 08:50:05 UTC

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New package getDTeval with initial version 0.0.1
Package: getDTeval
Title: Translating Coding Statements using get() and eval() for Improved Run-Time Coding Efficiency
Version: 0.0.1
Authors@R: c( person("David", "Shilane", , "david.shilane@columbia.edu", c("aut")), person("Mayur","Bansal", , "mb4511@columbia.edu", c("ctb","cre")), person("Anderson", "Nelson", , "an2908@columbia.edu", c("ctb")), person("Caffrey", "Lee", , "cl3802@columbia.edu", c("ctb")), person("Zichen", "Huang", , "zh2380@columbia.edu", c("ctb")) )
Depends: R (>= 3.6.0)
Description: The getDTeval() function facilitates the translation of the original coding statement to an optimized form for improved runtime efficiency without compromising on the programmatic coding design. The function can either provide a translation of the coding statement, directly evaluate the translation to return a coding result, or provide both of these outputs.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown, dplyr, testthat (>= 2.1.0), covr, formulaic, devtools
Imports: stats, data.table, microbenchmark, utils
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-11-12 02:37:05 UTC; mayurbansal
Author: David Shilane [aut], Mayur Bansal [ctb, cre], Anderson Nelson [ctb], Caffrey Lee [ctb], Zichen Huang [ctb]
Maintainer: Mayur Bansal <mb4511@columbia.edu>
Repository: CRAN
Date/Publication: 2020-11-18 09:00:06 UTC

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New package evolqg with initial version 0.2-8
Package: evolqg
Type: Package
Title: Tools for Evolutionary Quantitative Genetics
Version: 0.2-8
Date: 2020-11-14
Author: Ana Paula Assis, Diogo Melo, Edgar Zanella, Fabio Andrade Machado, Guilherme Garcia
Maintainer: Diogo Melo <diogro@usp.br>
Description: Provides functions for covariance matrix comparisons, estimation of repeatabilities in measurements and matrices, and general evolutionary quantitative genetics tools.
Depends: R (>= 3.6.0), plyr (>= 1.7.1)
Imports: Matrix, reshape2, ggplot2, vegan, ape, expm, matrixcalc, mvtnorm, coda, igraph, MCMCpack, graphics, grDevices, methods, stats, utils
Suggests: dplyr, testthat, foreach, magrittr, grid, gridExtra, doParallel, PHENIX, cowplot
LinkingTo: Rcpp, RcppArmadillo
License: MIT + file LICENSE
BugReports: https://github.com/lem-usp/evolqg/issues
Encoding: UTF-8
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2020-11-14 23:01:19 UTC; diogro
Repository: CRAN
Date/Publication: 2020-11-18 08:50:10 UTC

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Package BET updated to version 0.2.0 with previous version 0.1.0 dated 2020-10-19

Title: Binary Expansion Testing
Description: Nonparametric detection of nonuniformity and dependence with Binary Expansion Testing (BET). See Kai Zhang (2019) BET on Independence, Journal of the American Statistical Association, 114:528, 1620-1637, <DOI:10.1080/01621459.2018.1537921> and Zhigen Zhao, Michael Baiocchi, Kai Zhang. SorBET: A Fast and Powerful Algorithm to Test Dependence of Variables.
Author: Wan Zhang [aut, cre], Zhigen Zhao [aut], Michael Baiocchi [aut], Kai Zhang [aut]
Maintainer: Wan Zhang <wanz63@live.unc.edu>

Diff between BET versions 0.1.0 dated 2020-10-19 and 0.2.0 dated 2020-11-18

 BET-0.1.0/BET/R/BET_package.R     |only
 BET-0.1.0/BET/src/bitwise.cpp     |only
 BET-0.2.0/BET/DESCRIPTION         |    6 +--
 BET-0.2.0/BET/MD5                 |   29 +++++++-------
 BET-0.2.0/BET/NAMESPACE           |    2 -
 BET-0.2.0/BET/R/RcppExports.R     |    8 ++--
 BET-0.2.0/BET/R/plot.R            |   75 +++++++++++++++-----------------------
 BET-0.2.0/BET/R/star-data.R       |   32 ++++++++--------
 BET-0.2.0/BET/man/BET.Rd          |   15 ++++++-
 BET-0.2.0/BET/man/BET_package.Rd  |   42 ++++++++++-----------
 BET-0.2.0/BET/man/BETs.Rd         |   15 ++++++-
 BET-0.2.0/BET/man/bet.plot.Rd     |    6 ++-
 BET-0.2.0/BET/man/star.Rd         |    2 -
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Package Bayesrel updated to version 0.7.0.2 with previous version 0.7.0 dated 2020-11-11

Title: Bayesian Reliability Estimation
Description: Functionality for the most common single test reliability estimates: Coefficient alpha, 'Guttman's' lambda-2/-4/-6, the Greatest lower bound and coefficient omega. The Bayesian estimates are provided with credible intervals. The frequentist estimates are provided with bootstrapped confidence intervals The method for the Bayesian estimates, except for omega, is sampling from the posterior inverse 'Wishart' for the covariance matrix based measures (see 'Murphy', 2007, <https://www.seas.harvard.edu/courses/cs281/papers/murphy-2007.pdf>. In the case of omega it is 'Gibbs' Sampling from the joint conditional distributions of a single factor model ('Lee', 2007, <doi:10.1002/9780470024737>). The glb method uses adjusted code from the 'Rcsdp' package by 'Hector Corrada Bravo', <https://CRAN.R-project.org/package=Rcsdp>. This process applies a slightly adjusted solving algorithm from the 'CSDP' library by 'Brian Borchers' <https://github.com/coin-or/Csdp/wiki>, <doi.org/10.1080/10556789908805765>, but is wrapped in 'RcppArmadillo'. Guttman's Lambda-4 is from 'Benton' (2015) <doi:10.1007/978-3-319-07503-7_19>. The principal factor analysis for a version of frequentist omega is from 'Schlegel' (2017) <https://www.r-bloggers.com/2017/03/iterated-principal-factor-method-of-factor-analysis-with-r/>. The analytic confidence interval of alpha is from 'Bonett' and 'Wright' (2015) <doi:10.1002/job.1960>.
Author: Julius M. Pfadt [aut, cre], Don van den Bergh [aut], Joris Goosen [aut]
Maintainer: Julius M. Pfadt <julius.pfadt@gmail.com>

Diff between Bayesrel versions 0.7.0 dated 2020-11-11 and 0.7.0.2 dated 2020-11-18

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New package ADMUR with initial version 1.0.0
Package: ADMUR
Version: 1.0.0
Date: 2020-11-05
Title: Ancient Demographic Modelling Using Radiocarbon
Authors@R: person("Adrian", "Timpson", email = "a.timpson@ucl.ac.uk", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-0292-8729"))
Maintainer: Adrian Timpson <a.timpson@ucl.ac.uk>
Depends: R (>= 3.2.0)
Imports: scales, stats, zoo
Suggests: DEoptimR, knitr, rmarkdown
Description: Provides tools to directly model underlying population dynamics using date datasets (radiocarbon and other) with a Continuous Piecewise Linear (CPL) model framework, and model comparison framework using BIC. Package also calibrates 14C samples, generates Summed Probability Distributions (SPD), and performs SPD simulation analysis to generate a Goodness-of-fit test for the best selected model.
License: GPL-3
URL: https://github.com/UCL/ADMUR
Encoding: UTF-8
VignetteBuilder: knitr
LazyData: true
NeedsCompilation: no
Packaged: 2020-11-11 22:05:37 UTC; adrian
Author: Adrian Timpson [aut, cre] (<https://orcid.org/0000-0003-0292-8729>)
Repository: CRAN
Date/Publication: 2020-11-18 08:50:25 UTC

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Package MEDseq (with last version 1.1.1) was removed from CRAN

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

2020-05-12 1.1.1
2020-03-31 1.1.0
2019-12-10 1.0.1
2019-08-24 1.0.0

Permanent link
Package spant updated to version 1.8.0 with previous version 1.7.0 dated 2020-07-09

Title: MR Spectroscopy Analysis Tools
Description: Tools for reading, visualising and processing Magnetic Resonance Spectroscopy data.
Author: Martin Wilson [cre, aut], Yong Wang [ctb], John Muschelli [ctb]
Maintainer: Martin Wilson <martin@pipegrep.co.uk>

Diff between spant versions 1.7.0 dated 2020-07-09 and 1.8.0 dated 2020-11-18

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More information about spant at CRAN
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