Tue, 18 Jan 2022

Package PoolTestR updated to version 0.1.2 with previous version 0.1.1 dated 2021-02-15

Title: Prevalence and Regression for Pool-Tested (Group-Tested) Data
Description: An easy-to-use tool for working with presence/absence tests on 'pooled' or 'grouped' samples. The primary application is for estimating prevalence of a marker in a population based on the results of tests on pooled specimens. This sampling method is often employed in surveillance of rare conditions in humans or animals (e.g. molecular xenomonitoring). The package was initially conceived as an R-based alternative to the molecular xenomonitoring software, 'PoolScreen' <https://sites.uab.edu/statgenetics/software/>. However, it goes further, allowing for estimates of prevalence to be adjusted for hierarchical sampling frames, and perform flexible mixed-effect regression analyses (McLure et al. Environmental Modelling and Software. <DOI:10.1016/j.envsoft.2021.105158>). The package is currently in early stages, however more features are planned or in the works: e.g. adjustments for imperfect test specificity/sensitivity, functions for helping with optimal experimental design, and functions for spatial modelling.
Author: Angus McLure [aut, cre] (<https://orcid.org/0000-0003-2551-3059>)
Maintainer: Angus McLure <angus.mclure@anu.edu.au>

Diff between PoolTestR versions 0.1.1 dated 2021-02-15 and 0.1.2 dated 2022-01-18

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Package Pade updated to version 1.0.4 with previous version 1.0.3 dated 2020-11-10

Title: Padé Approximant Coefficients
Description: Given a vector of Taylor series coefficients of sufficient length as input, the function returns the numerator and denominator coefficients for the Padé approximant of appropriate order (Baker, 1975) <ISBN:9780120748556>.
Author: Avraham Adler [aut, cph, cre] (<https://orcid.org/0000-0002-3039-0703>)
Maintainer: Avraham Adler <Avraham.Adler@gmail.com>

Diff between Pade versions 1.0.3 dated 2020-11-10 and 1.0.4 dated 2022-01-18

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Package lamW updated to version 2.1.1 with previous version 2.1.0 dated 2021-05-21

Title: Lambert-W Function
Description: Implements both real-valued branches of the Lambert-W function (Corless et al, 1996) <doi:10.1007/BF02124750> without the need for installing the entire GSL.
Author: Avraham Adler [aut, cph, cre] (<https://orcid.org/0000-0002-3039-0703>)
Maintainer: Avraham Adler <Avraham.Adler@gmail.com>

Diff between lamW versions 2.1.0 dated 2021-05-21 and 2.1.1 dated 2022-01-18

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Package iNZightMR updated to version 2.2.6 with previous version 2.2.5 dated 2020-05-05

Title: Tools for Exploring Multiple Response Data
Description: Interaction and analysis of multiple response data, along with other tools for analysing these types of data including missing value analysis and calculation of standard errors for a range of covariance matrix results (proportions, multinomial, independent samples, and multiple response).
Author: Tom Elliott [aut, cre] (<https://orcid.org/0000-0002-7815-6318>), Junjie Zeng [aut] (Original author), Simon Potter [aut]
Maintainer: Tom Elliott <tom.elliott@auckland.ac.nz>

Diff between iNZightMR versions 2.2.5 dated 2020-05-05 and 2.2.6 dated 2022-01-18

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Package iNZightTools updated to version 1.12.2 with previous version 1.12.1 dated 2021-11-04

Title: Tools for 'iNZight'
Description: Provides a collection of wrapper functions for common variable and dataset manipulation workflows primarily used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Additionally, many of the functions return the 'tidyverse' code used to obtain the result in an effort to bridge the gap between GUI and coding.
Author: Tom Elliott [aut, cre] (<https://orcid.org/0000-0002-7815-6318>), Daniel Barnett [aut], Owen Jin [aut], Yiwen He [aut], Christoph Knopf [ctb], Akshay Gupta [ctb], Lushi Cai [ctb]
Maintainer: Tom Elliott <tom.elliott@auckland.ac.nz>

Diff between iNZightTools versions 1.12.1 dated 2021-11-04 and 1.12.2 dated 2022-01-18

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New package braQCA with initial version 1.2.1.18
Package: braQCA
Title: Bootstrapped Robustness Assessment for Qualitative Comparative Analysis
Version: 1.2.1.18
Description: Test the robustness of a user's Qualitative Comparative Analysis solutions to randomness, using the bootstrapped assessment: baQCA(). This package also includes a function that provides recommendations for improving solutions to reach typical significance levels: brQCA(). Data included come from McVeigh et al. (2014) <doi:10.1177/0003122414534065>.
Depends: R (>= 3.2.3)
Imports: QCA, dplyr, bootstrap
License: GPL-3
LazyData: true
NeedsCompilation: no
Packaged: 2022-01-18 17:39:16 UTC; burrelvannjr
Author: C. Ben Gibson [aut] (<https://orcid.org/0000-0003-2561-9574>), Burrel Vann Jr [aut, cre] (<https://orcid.org/0000-0003-3066-5815>)
Maintainer: Burrel Vann Jr <bvannjr@sdsu.edu>
Repository: CRAN
Date/Publication: 2022-01-18 21:02:42 UTC

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Package rclipboard updated to version 0.1.4 with previous version 0.1.3 dated 2021-01-13

Title: Shiny/R Wrapper for 'clipboard.js'
Description: Leverages the functionality of 'clipboard.js', a JavaScript library for HMTL5-based copy to clipboard from web pages (see <https://clipboardjs.com> for more information), and provides a reactive copy-to-clipboard UI button component, called 'rclipButton', and a a reactive copy-to-clipboard UI link component, called 'rclipLink', for 'shiny' R applications.
Author: Sebastien Bihorel
Maintainer: Sebastien Bihorel <sb.pmlab@gmail.com>

Diff between rclipboard versions 0.1.3 dated 2021-01-13 and 0.1.4 dated 2022-01-18

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New package NetDA with initial version 0.1.0
Package: NetDA
Title: Network-Based Discriminant Analysis Subject to Multi-Label Classes
Version: 0.1.0
Author: Li-Pang Chen
Maintainer: Li-Pang Chen <lchen723@nccu.edu.tw>
Description: Implementation of discriminant analysis with network structures in predictors accommodated to do classification and prediction.
License: GPL-2
Encoding: UTF-8
Imports: glasso
NeedsCompilation: yes
LazyData: true
Packaged: 2022-01-18 08:40:34 UTC; Li-Pang Chen
Repository: CRAN
Date/Publication: 2022-01-18 20:32:46 UTC

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New package namedropR with initial version 2.1.1
Package: namedropR
Title: Create Visual Citations to Include in Presentations
Version: 2.1.1
Description: Provides 'visual citations' containing the metadata of a scientific paper and a 'QR' code. A 'visual citation' is a banner containing title, authors, journal and year of a publication. This package can create such banners based on 'BibTeX' and 'BibLaTeX' references and includes a QR code pointing to the 'DOI'. The resulting HTML object or PNG image can be included in a presentation to point the audience to good resources for further reading. Styling is possible via predefined designs or via custom 'CSS'. This package is not intended as replacement for proper reference manager packages, but a tool to enrich scientific presentation slides.
License: MIT + file LICENSE
Imports: htmltools, qrcode, here, lubridate, webshot, bib2df, dplyr
Encoding: UTF-8
BugReports: https://github.com/nucleic-acid/namedropR/issues
URL: https://github.com/nucleic-acid/namedropR
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-01-17 21:40:18 UTC; christian
Author: Christian A. Gebhard [aut, cre] (<https://orcid.org/0000-0002-8792-565X>)
Maintainer: Christian A. Gebhard <christian.gebhard@posteo.de>
Repository: CRAN
Date/Publication: 2022-01-18 20:32:43 UTC

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New package LongDat with initial version 1.0.0
Package: LongDat
Title: A Tool for 'Confounder'-Sensitive Longitudinal Analysis on 'Multi-omics' Data
Version: 1.0.0
Description: This tool takes longitudinal dataset as input and analyzes if there is significant change of the features over time (a proxy for treatments), while detects and controls for 'confounders' simultaneously. 'LongDat' is able to take in several data types as input, including count, proportion, binary, ordinal and continuous data. The output table contains p values, effect sizes and 'confounders' of each feature, making the downstream analysis easy.
License: GPL-2
Encoding: UTF-8
Language: en-US
LazyData: true
URL: https://github.com/CCY-dev/LongDat
BugReports: https://github.com/CCY-dev/LongDat/issues
Depends: R (>= 4.0.0)
Imports: lme4, reshape2, glmmTMB, emmeans, bestNormalize, MASS, ggplot2, stringr, magrittr, tibble, dplyr, graphics, utils, stats, rlang, car, rstatix, effsize, tidyr, patchwork
Suggests: rmarkdown, knitr, tidyverse, kableExtra
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-01-18 11:33:05 UTC; Jessica
Author: Chia-Yu Chen [aut, cre] (<https://orcid.org/0000-0003-1765-7132>), Sofia Forslund [ctb] (<https://orcid.org/0000-0003-4285-6993>)
Maintainer: Chia-Yu Chen <Chia-Yu.Chen@mdc-berlin.de>
Repository: CRAN
Date/Publication: 2022-01-18 20:42:44 UTC

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New package Bayesiantreg with initial version 1.0
Package: Bayesiantreg
Title: Bayesian t Regression for Modeling Mean and Scale Parameters
License: GPL (>= 2)
Version: 1.0
Date: 2022-01-18
Author: Margarita Marin and Edilberto Cepeda-Cuervo.
Maintainer: Margarita Marin <mmarinj@unal.edu.co>
Depends: R (>= 4.1.0)
Imports: MASS (>= 7.3), Matrix (>= 1.2), mvtnorm (>= 1.1)
Description: Performs Bayesian t Regression where mean and scale parameters are modeling by lineal regression structures, and the degrees of freedom parameters are estimated.
Encoding: latin1
Packaged: 2022-01-18 15:08:52 UTC; usuario
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2022-01-18 20:42:47 UTC

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Package RandomFields updated to version 3.3.14 with previous version 3.3.13 dated 2021-11-02

Title: Simulation and Analysis of Random Fields
Description: Methods for the inference on and the simulation of Gaussian fields are provided. Furthermore, methods for the simulation of extreme value random fields are provided. Main geostatistical parts are based among others on the books by Christian Lantuejoul <doi:10.1007/978-3-662-04808-5>.
Author: Martin Schlather [aut, cre], Alexander Malinowski [aut], Marco Oesting [aut], Daphne Boecker [aut], Kirstin Strokorb [aut], Sebastian Engelke [aut], Johannes Martini [aut], Felix Ballani [aut], Olga Moreva [aut], Jonas Auel[ctr], Peter Menck [ctr], Sebastian Gross [ctr], Ulrike Ober [ctb], Paulo Ribeiro [ctb], Brian D. Ripley [ctb], Richard Singleton [ctb], Ben Pfaff [ctb], R Core Team [ctb]
Maintainer: Martin Schlather <schlather@math.uni-mannheim.de>

Diff between RandomFields versions 3.3.13 dated 2021-11-02 and 3.3.14 dated 2022-01-18

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Package gmvarkit updated to version 2.0.2 with previous version 2.0.1 dated 2021-12-14

Title: Estimate Gaussian and Student's t Mixture Vector Autoregressive Models
Description: Unconstrained and constrained maximum likelihood estimation of structural and reduced form Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t mixture vector autoregressive models, quantile residual tests, graphical diagnostics, simulations, forecasting, and estimation of generalized impulse response function and generalized forecast error variance decomposition. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>, Savi Virolainen (2021) <arXiv:2007.04713>, Savi Virolainen (2021) <arXiv:2109.13648>.
Author: Savi Virolainen [aut, cre]
Maintainer: Savi Virolainen <savi.virolainen@helsinki.fi>

Diff between gmvarkit versions 2.0.1 dated 2021-12-14 and 2.0.2 dated 2022-01-18

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Package uGMAR updated to version 3.4.1 with previous version 3.4.0 dated 2021-09-02

Title: Estimate Univariate Gaussian and Student's t Mixture Autoregressive Models
Description: Maximum likelihood estimation of univariate Gaussian Mixture Autoregressive (GMAR), Student's t Mixture Autoregressive (StMAR), and Gaussian and Student's t Mixture Autoregressive (G-StMAR) models, quantile residual tests, graphical diagnostics, forecast and simulate from GMAR, StMAR and G-StMAR processes. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2015) <doi:10.1111/jtsa.12108>, Mika Meitz, Daniel Preve, Pentti Saikkonen (2021) <doi:10.1080/03610926.2021.1916531>, Savi Virolainen (2021) <doi:10.1515/snde-2020-0060>.
Author: Savi Virolainen [aut, cre]
Maintainer: Savi Virolainen <savi.virolainen@helsinki.fi>

Diff between uGMAR versions 3.4.0 dated 2021-09-02 and 3.4.1 dated 2022-01-18

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Package CBPS updated to version 0.23 with previous version 0.22 dated 2021-03-29

Title: Covariate Balancing Propensity Score
Description: Implements the covariate balancing propensity score (CBPS) proposed by Imai and Ratkovic (2014) <DOI:10.1111/rssb.12027>. The propensity score is estimated such that it maximizes the resulting covariate balance as well as the prediction of treatment assignment. The method, therefore, avoids an iteration between model fitting and balance checking. The package also implements optimal CBPS from Fan et al. (in-press) <DOI:10.1080/07350015.2021.2002159>, several extensions of the CBPS beyond the cross-sectional, binary treatment setting. They include the CBPS for longitudinal settings so that it can be used in conjunction with marginal structural models from Imai and Ratkovic (2015) <DOI:10.1080/01621459.2014.956872>, treatments with three- and four-valued treatment variables, continuous-valued treatments from Fong, Hazlett, and Imai (2018) <DOI:10.1214/17-AOAS1101>, propensity score estimation with a large number of covariates from Ning, Peng, and Imai (2020) <DOI:10.1093/biomet/asaa020>, and the situation with multiple distinct binary treatments administered simultaneously. In the future it will be extended to other settings including the generalization of experimental and instrumental variable estimates.
Author: Christian Fong [aut, cre], Marc Ratkovic [aut], Kosuke Imai [aut], Chad Hazlett [ctb], Xiaolin Yang [ctb], Sida Peng [ctb], Inbeom Lee [ctb]
Maintainer: Christian Fong <cjfong@umich.edu>

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Package smcfcs updated to version 1.6.1 with previous version 1.6.0 dated 2021-06-17

Title: Multiple Imputation of Covariates by Substantive Model Compatible Fully Conditional Specification
Description: Implements multiple imputation of missing covariates by Substantive Model Compatible Fully Conditional Specification. This is a modification of the popular FCS/chained equations multiple imputation approach, and allows imputation of missing covariate values from models which are compatible with the user specified substantive model.
Author: Jonathan Bartlett [aut, cre], Ruth Keogh [aut], Edouard F. Bonneville [aut], Claus Thorn Ekstrøm [ctb]
Maintainer: Jonathan Bartlett <j.w.bartlett@bath.ac.uk>

Diff between smcfcs versions 1.6.0 dated 2021-06-17 and 1.6.1 dated 2022-01-18

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Package rugarch updated to version 1.4-6 with previous version 1.4-4 dated 2020-07-16

Title: Univariate GARCH Models
Description: ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.
Author: Alexios Galanos [aut, cre], Tobias Kley [ctb]
Maintainer: Alexios Galanos <alexios@4dscape.com>

Diff between rugarch versions 1.4-4 dated 2020-07-16 and 1.4-6 dated 2022-01-18

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Package PracTools updated to version 1.2.5 with previous version 1.2.4 dated 2021-12-20

Title: Tools for Designing and Weighting Survey Samples
Description: Functions and datasets to support Valliant, Dever, and Kreuter, Practical Tools for Designing and Weighting Survey Samples (2nd edition, 2018). Contains functions for sample size calculation for survey samples using stratified or clustered one-, two-, and three-stage sample designs. Other functions compute variance components for multistage designs and sample sizes in two-phase designs. A number of example data sets are included.
Author: Richard Valliant, Jill A. Dever, Frauke Kreuter
Maintainer: Richard Valliant <valliant@umich.edu>

Diff between PracTools versions 1.2.4 dated 2021-12-20 and 1.2.5 dated 2022-01-18

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Package mcboost updated to version 0.4.0 with previous version 0.3.3.0 dated 2021-08-03

Title: Multi-Calibration Boosting
Description: Implements 'Multi-Calibration Boosting' (2018) <https://proceedings.mlr.press/v80/hebert-johnson18a.html> and 'Multi-Accuracy Boosting' (2019) <arXiv:1805.12317> for the multi-calibration of a machine learning model's prediction. 'MCBoost' updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups. Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are. This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations.
Author: Florian Pfisterer [cre, aut] (<https://orcid.org/0000-0001-8867-762X>), Susanne Dandl [ctb] (<https://orcid.org/0000-0003-4324-4163>), Christoph Kern [ctb] (<https://orcid.org/0000-0001-7363-4299>), Carolin Becker [ctb], Bernd Bischl [ctb] (<https://orcid.org/0000-0001-6002-6980>)
Maintainer: Florian Pfisterer <pfistererf@googlemail.com>

Diff between mcboost versions 0.3.3.0 dated 2021-08-03 and 0.4.0 dated 2022-01-18

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Package bcf updated to version 1.3.1 with previous version 1.3 dated 2019-08-28

Title: Causal Inference for a Binary Treatment and Continuous Outcome using Bayesian Causal Forests
Description: Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2017) <arXiv:1706.09523> for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) <arXiv:1309.1906>.
Author: P. Richard Hahn, Jared S. Murray, and Carlos Carvalho
Maintainer: Jared S. Murray <jared.murray@mccombs.utexas.edu>

Diff between bcf versions 1.3 dated 2019-08-28 and 1.3.1 dated 2022-01-18

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Package tidycharts updated to version 0.1.3 with previous version 0.1.2 dated 2021-08-23

Title: Generate Tidy Charts Inspired by 'IBCS'
Description: There is a wide range of R packages created for data visualization, but still, there was no simple and easily accessible way to create clean and transparent charts - up to now. The 'tidycharts' package enables the user to generate charts compliant with International Business Communication Standards ('IBCS'). It means unified bar widths, colors, chart sizes, etc. Creating homogeneous reports has never been that easy! Additionally, users can apply semantic notation to indicate different data scenarios (plan, budget, forecast). What's more, it is possible to customize the charts by creating a personal color pallet with the possibility of switching to default options after the experiments. We wanted the package to be helpful in writing reports, so we also made joining charts in a one, clear image possible. All charts are generated in SVG format and can be shown in the 'RStudio' viewer pane or exported to HTML output of 'knitr'/'markdown'.
Author: Przemysław Biecek [aut] (<https://orcid.org/0000-0001-8423-1823>), Piotr Piątyszek [aut], Kinga Ułasik [aut], Bartosz Sawicki [aut, cre]
Maintainer: Bartosz Sawicki <sawicki.bartosz@interia.pl>

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More information about tidycharts at CRAN
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Package secr updated to version 4.5.1 with previous version 4.5.0 dated 2022-01-06

Title: Spatially Explicit Capture-Recapture
Description: Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.
Author: Murray Efford [aut, cre] (<https://orcid.org/0000-0001-5231-5184>)
Maintainer: Murray Efford <murray.efford@otago.ac.nz>

Diff between secr versions 4.5.0 dated 2022-01-06 and 4.5.1 dated 2022-01-18

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Package RandomFieldsUtils updated to version 1.1.0 with previous version 1.0.11 dated 2021-11-20

Title: Utilities for the Simulation and Analysis of Random Fields and Genetic Data
Description: Various utilities are provided that might be used in spatial statistics and elsewhere. It delivers a method for solving linear equations that checks the sparsity of the matrix before any algorithm is used.
Author: Martin Schlather [aut, cre], Alexander FreudenBerg [aut], Reinhard Furrer [ctb], Martin Kroll [ctb], Brian D. Ripley [ctb], John W. Ratcliff et al. (cph)
Maintainer: Martin Schlather <schlather@math.uni-mannheim.de>

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Package N2R updated to version 1.0.1 with previous version 1.0.0 dated 2021-11-19

Title: Fast and Scalable Approximate k-Nearest Neighbor Search Methods using 'N2' Library
Description: Implements methods to perform fast approximate K-nearest neighbor search on input matrix. Algorithm based on the 'N2' implementation of an approximate nearest neighbor search using hierarchical Navigable Small World (NSW) graphs. The original algorithm is described in "Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs", Y. Malkov and D. Yashunin, <doi:10.1109/TPAMI.2018.2889473>, <arXiv:1603.09320>.
Author: Peter Kharchenko [aut], Viktor Petukhov [aut], Dirk Eddelbuettel [ctb], Evan Biederstedt [cre, aut]
Maintainer: Evan Biederstedt <evan.biederstedt@gmail.com>

Diff between N2R versions 1.0.0 dated 2021-11-19 and 1.0.1 dated 2022-01-18

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Package MANOVA.RM updated to version 0.5.3 with previous version 0.5.2 dated 2021-09-17

Title: Resampling-Based Analysis of Multivariate Data and Repeated Measures Designs
Description: Implemented are various tests for semi-parametric repeated measures and general MANOVA designs that do neither assume multivariate normality nor covariance homogeneity, i.e., the procedures are applicable for a wide range of general multivariate factorial designs. In addition to asymptotic inference methods, novel bootstrap and permutation approaches are implemented as well. These provide more accurate results in case of small to moderate sample sizes. Furthermore, post-hoc comparisons are provided for the multivariate analyses. Friedrich, S., Konietschke, F. and Pauly, M. (2019) <doi:10.32614/RJ-2019-051>.
Author: Sarah Friedrich, Frank Konietschke, Markus Pauly
Maintainer: Sarah Friedrich <sarah.friedrich@math.uni-augsburg.de>

Diff between MANOVA.RM versions 0.5.2 dated 2021-09-17 and 0.5.3 dated 2022-01-18

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Package logistf updated to version 1.24.1 with previous version 1.24 dated 2020-09-16

Title: Firth's Bias-Reduced Logistic Regression
Description: Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal solution to the problem of separation in logistic regression, see Heinze and Schemper (2002) <doi:10.1002/sim.1047>. If needed, the bias reduction can be turned off such that ordinary maximum likelihood logistic regression is obtained. Two new modifications of Firth's method, FLIC and FLAC, lead to unbiased predictions and are now available in the package as well, see Puhr et al (2017) <doi:10.1002/sim.7273>.
Author: Georg Heinze [aut, cre], Meinhard Ploner [aut], Daniela Dunkler [ctb], Harry Southworth [ctb], Lena Jiricka [aut]
Maintainer: Georg Heinze <georg.heinze@meduniwien.ac.at>

Diff between logistf versions 1.24 dated 2020-09-16 and 1.24.1 dated 2022-01-18

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Package clubSandwich updated to version 0.5.5 with previous version 0.5.4 dated 2022-01-08

Title: Cluster-Robust (Sandwich) Variance Estimators with Small-Sample Corrections
Description: Provides several cluster-robust variance estimators (i.e., sandwich estimators) for ordinary and weighted least squares linear regression models, including the bias-reduced linearization estimator introduced by Bell and McCaffrey (2002) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2002002/article/9058-eng.pdf> and developed further by Pustejovsky and Tipton (2017) <DOI:10.1080/07350015.2016.1247004>. The package includes functions for estimating the variance- covariance matrix and for testing single- and multiple- contrast hypotheses based on Wald test statistics. Tests of single regression coefficients use Satterthwaite or saddle-point corrections. Tests of multiple- contrast hypotheses use an approximation to Hotelling's T-squared distribution. Methods are provided for a variety of fitted models, including lm() and mlm objects, glm(), ivreg() (from package 'AER'), plm() (from package 'plm'), gls() and lme() (from 'nlme'), lmer() (from `lme4`), robu() (from 'robumeta'), and rma.uni() and rma.mv() (from 'metafor').
Author: James Pustejovsky [aut, cre] (<https://orcid.org/0000-0003-0591-9465>)
Maintainer: James Pustejovsky <jepusto@gmail.com>

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Package Biostatistics updated to version 1.0.4 with previous version 1.0.3 dated 2021-09-02

Title: Statistics Tutorials for Biologists
Description: Tutorials for statistics, aimed at biological scientists. Subjects range from basic descriptive statistics through to complex linear modelling. The tutorials include text, videos, interactive coding exercises and multiple choice quizzes. The package also includes 19 datasets which are used in the tutorials.
Author: Rob Knell
Maintainer: Rob Knell <r.knell@qmul.ac.uk>

Diff between Biostatistics versions 1.0.3 dated 2021-09-02 and 1.0.4 dated 2022-01-18

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Package LogConcDEAD updated to version 1.6-5 with previous version 1.6-4 dated 2020-12-02

Title: Log-Concave Density Estimation in Arbitrary Dimensions
Description: Software for computing a log-concave (maximum likelihood) estimator for i.i.d. data in any number of dimensions. For a detailed description of the method see Cule, Samworth and Stewart (2010, Journal of Royal Statistical Society Series B, <doi:10.1111/j.1467-9868.2010.00753.x>).
Author: Madeleine Cule, Robert Gramacy, Richard Samworth, Yining Chen
Maintainer: Yining Chen <Y.Chen101@lse.ac.uk>

Diff between LogConcDEAD versions 1.6-4 dated 2020-12-02 and 1.6-5 dated 2022-01-18

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Package SimVitD updated to version 1.0.1 with previous version 1.0 dated 2021-01-05

Title: Simulation Tools for Planning Vitamin D Studies
Description: Simulation tools for planning Vitamin D studies. Individual vitamin D status profiles are simulated, modelling population heterogeneity in trial arms. Exposures to infectious agents are generated, with infection depending on vitamin D status.
Author: Rebecca Mangan [aut], Jason Wyse [aut, cre], Lina Zgaga [aut]
Maintainer: Jason Wyse <wyseja@tcd.ie>

Diff between SimVitD versions 1.0 dated 2021-01-05 and 1.0.1 dated 2022-01-18

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Package populR updated to version 0.1.4 with previous version 0.1.3 dated 2021-12-19

Title: Population Down-Scaling
Description: Areal interpolation of population data obtained by census surveys. Given a set of source zone polygons such as census tracts or city blocks alongside with population counts and a target zone of incogruent yet superimposed polygon features (such as individual buildings) populR transforms population counts from the former to the latter using Areal Weighted and Volume Weighted Interpolation methods.
Author: Marios Batsaris
Maintainer: Marios Batsaris <m.batsaris@aegean.gr>

Diff between populR versions 0.1.3 dated 2021-12-19 and 0.1.4 dated 2022-01-18

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Package multinma updated to version 0.4.0 with previous version 0.3.0 dated 2021-03-18

Title: Bayesian Network Meta-Analysis of Individual and Aggregate Data
Description: Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a Bayesian framework using 'Stan'.
Author: David M. Phillippo [aut, cre] (<https://orcid.org/0000-0003-2672-7841>)
Maintainer: David M. Phillippo <david.phillippo@bristol.ac.uk>

Diff between multinma versions 0.3.0 dated 2021-03-18 and 0.4.0 dated 2022-01-18

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Package amt updated to version 0.1.6 with previous version 0.1.5 dated 2021-12-03

Title: Animal Movement Tools
Description: Manage and analyze animal movement data. The functionality of 'amt' includes methods to calculate home ranges, track statistics (e.g. step lengths, speed, or turning angles), prepare data for fitting habitat selection analyses, and simulation of space-use from fitted step-selection functions.
Author: Johannes Signer [aut, cre], Brian Smith [ctb], Bjoern Reineking [ctb], Ulrike Schlaegel [ctb], John Fieberg [ctb], Josh O'Brien [ctb], Bernardo Niebuhr [ctb], Scott LaPoint [dtc]
Maintainer: Johannes Signer <jsigner@gwdg.de>

Diff between amt versions 0.1.5 dated 2021-12-03 and 0.1.6 dated 2022-01-18

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Package varTestnlme updated to version 1.3.0 with previous version 1.2.0 dated 2021-12-07

Title: Variance Components Testing for Linear and Nonlinear Mixed Effects Models
Description: An implementation of the Likelihood ratio Test (LRT) for testing that, in a (non)linear mixed effects model, the variances of a subset of the random effects are equal to zero. There is no restriction on the subset of variances that can be tested: for example, it is possible to test that all the variances are equal to zero. Note that the implemented test is asymptotic. This package should be used on model fits from packages 'nlme', 'lmer', and 'saemix'. Charlotte Baey, Paul-Henry Cournède and Estelle Kuhn (2019) <doi:10.1016/j.csda.2019.01.014>.
Author: Charlotte Baey [aut, cre] (<https://orcid.org/0000-0002-1413-1058>), Estelle Kuhn [aut]
Maintainer: Charlotte Baey <charlotte.baey@univ-lille.fr>

Diff between varTestnlme versions 1.2.0 dated 2021-12-07 and 1.3.0 dated 2022-01-18

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Package bhmbasket updated to version 0.9.4 with previous version 0.9.3 dated 2021-10-11

Title: Bayesian Hierarchical Models for Basket Trials
Description: Provides functions for the evaluation of basket trial designs with binary endpoints. Operating characteristics of a basket trial design are assessed by simulating trial data according to scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and assessing decision probabilities on stratum and trial-level based on Go / No-go decision making. The package is build for high flexibility regarding decision rules, number of interim analyses, number of strata, and recruitment. The BHMs proposed by Berry et al. (2013) <doi:10.1177/1740774513497539> and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>, as well as a model that combines both approaches are implemented. Functions are provided to implement Bayesian decision rules as for example proposed by Fisch et al. (2015) <doi:10.1177/2168479014533970>. In addition, posterior point estimates (mean/median) and credible intervals for response rates and some model parameters can be calculated. For simulated trial data, bias and mean squared errors of posterior point estimates for response rates can be provided.
Author: Stephan Wojciekowski [aut, cre]
Maintainer: Stephan Wojciekowski <stephan.wojciekowski@boehringer-ingelheim.com>

Diff between bhmbasket versions 0.9.3 dated 2021-10-11 and 0.9.4 dated 2022-01-18

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Package soilassessment updated to version 0.2.2 with previous version 0.2.1 dated 2020-08-30

Title: Assessment Models for Agriculture Soil Conditions and Crop Suitability
Description: Soil assessment builds information for improved decision in soil management. It analyzes soil conditions with regard to agriculture crop suitability requirements [such as those given by FAO <https://www.fao.org/land-water/databases-and-software/crop-information/en/>] soil fertility classes, soil erosion models and soil salinity classification. Suitability requirements are for crops grouped into cereal crops, nuts, legumes, fruits, vegetables, industrial crops, and root crops.
Author: Christian Thine Omuto
Maintainer: Christian Thine Omuto <thineomuto@yahoo.com>

Diff between soilassessment versions 0.2.1 dated 2020-08-30 and 0.2.2 dated 2022-01-18

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New package nsm3data with initial version 0.1
Package: nsm3data
Title: Datasets to Accompany Hollander, Wolfe, and Chicken NSM3
Version: 0.1
Description: Designed to add datasets which are used in the Nonparametric Statistical Methods textbook, 3rd edition.
Depends: R (>= 3.5.0)
Suggests: NSM3
LazyData: true
License: GPL-2
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2022-01-17 21:06:05 UTC; kim3
Author: Grant Schneider [aut], Eric Chicken [aut], Rachel Becvarik [aut], Sejin Kim [aut, cre]
Maintainer: Sejin Kim <kim3@kenyon.edu>
Repository: CRAN
Date/Publication: 2022-01-18 08:32:43 UTC

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New package MSclassifR with initial version 0.1.0
Package: MSclassifR
Title: Automated Classification of Mass Spectra
Version: 0.1.0
Maintainer: Alexandre Godmer <alexandre.godmer@aphp.fr>
Description: Functions to classify mass spectra in known categories, and to determine discriminant mass-over-charge values. It includes easy-to-use functions for pre-processing mass spectra, functions to determine discriminant mass-over-charge values (m/z) from a library of mass spectra corresponding to different categories, and functions to predict the category (species, phenotypes, etc.) associated to a mass spectrum from a list of selected mass-over-charge values.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Depends: R (>= 4.0)
Imports: e1071, MALDIquant, MALDIrppa, MALDIquantForeign, mixOmics, caret, reshape2, ggplot2, nnet, dplyr, fuzzyjoin, VSURF, metap, xgboost, glmnet,
Suggests: knitr, rmarkdown,
NeedsCompilation: yes
Packaged: 2022-01-17 10:48:48 UTC; alexa
Author: Alexandre Godmer [aut, cre], Quentin Giai Gianetto [aut]
Repository: CRAN
Date/Publication: 2022-01-18 08:12:46 UTC

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New package lmls with initial version 0.1.0
Package: lmls
Title: Gaussian Location-Scale Regression
Version: 0.1.0
Description: The Gaussian location-scale regression model is a multi-predictor model with explanatory variables for the mean (= location) and the standard deviation (= scale) of a response variable. This package implements maximum likelihood and Markov chain Monte Carlo (MCMC) inference (using algorithms from Girolami and Calderhead (2011) <doi:10.1111/j.1467-9868.2010.00765.x> and Nesterov (2009) <doi:10.1007/s10107-007-0149-x>), a parametric bootstrap algorithm, and diagnostic plots for the model class.
License: MIT + file LICENSE
URL: https://hriebl.github.io/lmls/
Depends: R (>= 3.5.0)
Imports: generics (>= 0.1.0)
Suggests: bookdown, coda, covr, ggplot2, knitr, mgcv, mvtnorm, numDeriv, patchwork, rmarkdown, testthat (>= 3.0.0)
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-01-17 21:33:25 UTC; hannes
Author: Hannes Riebl [aut, cre]
Maintainer: Hannes Riebl <hriebl@uni-goettingen.de>
Repository: CRAN
Date/Publication: 2022-01-18 08:32:47 UTC

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New package dynatop with initial version 0.2.1
Package: dynatop
Title: An Implementation of Dynamic TOPMODEL Hydrological Model in R
Version: 0.2.1
Description: An R implementation and enhancement of the Dynamic TOPMODEL semi-distributed hydrological model originally proposed by Beven and Freer (2001) <doi:10.1002/hyp.252>. The 'dynatop' package implements code for simulating models which can be created using the 'dynatopGIS' package.
License: GPL-2
Encoding: UTF-8
LazyData: true
ByteCompile: true
Imports: R6, zoo, xts, Rcpp
LinkingTo: Rcpp
Depends: R (>= 4.0.0)
BugReports: https://github.com/waternumbers/dynatop/issues
URL: https://waternumbers.github.io/dynatop/, https://github.com/waternumbers/dynatop
Suggests: raster, rgdal, knitr, rmarkdown, covr, bookdown, pkgdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2022-01-12 16:05:08 UTC; smithpj1
Author: Paul Smith [aut, cre] (<https://orcid.org/0000-0002-0034-3412>), Peter Metcalfe [aut]
Maintainer: Paul Smith <paul@waternumbers.co.uk>
Repository: CRAN
Date/Publication: 2022-01-18 08:02:42 UTC

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New package dsos with initial version 0.1.0
Package: dsos
Title: Dataset Shift with Outlier Scores
Version: 0.1.0
Description: Test for no adverse shift in two-sample comparison when we have a training set, the reference distribution, and a test set. The approach is flexible and relies on a robust and powerful test statistic, the weighted AUC. Technical details are in Kamulete, V. M. (2021) <arXiv:1908.04000>. Modern notions of outlyingness such as trust scores and prediction uncertainty can be used as the underlying scores for example.
License: GPL (>= 3)
URL: https://github.com/vathymut/dsos
BugReports: https://github.com/vathymut/dsos/issues
Imports: data.table (>= 1.14.0), ggplot2 (>= 3.3.3), isotree (>= 0.2.7), ranger (>= 0.12.1), scales (>= 1.1.1), simctest (>= 2.6), stats (>= 3.6.1), WeightedROC (>= 2020.1.31)
Suggests: fdrtool (>= 1.2.16), knitr (>= 1.33), rmarkdown (>= 2.7), testthat (>= 3.0.2)
VignetteBuilder: knitr
Encoding: UTF-8
Language: en-US
NeedsCompilation: no
Packaged: 2022-01-17 21:14:01 UTC; vathy
Author: Vathy M. Kamulete [aut, cre] (<https://orcid.org/0000-0002-4451-3743>), Royal Bank of Canada (RBC) [cph] (Research supported by RBC)
Maintainer: Vathy M. Kamulete <vathymut@gmail.com>
Repository: CRAN
Date/Publication: 2022-01-18 08:32:51 UTC

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New package DisaggregateTS with initial version 1.0
Package: DisaggregateTS
Title: High-Dimensional Temporal Disaggregation
Version: 1.0
Description: First - Generates (potentially high-dimensional) high-frequency and low-frequency series for simulation studies in temporal disaggregation; Second - a toolkit utilizing temporal disaggregation and benchmarking techniques with a low-dimensional matrix of indicator series previously proposed in Dagum and Cholette (2006, ISBN:978-0-387-35439-2) ; and Third - novel techniques proposed by Mosley, Gibberd and Eckley (2021) <arXiv:2108.05783> for disaggregating low-frequency series in the presence of high-dimensional indicator matrices.
Imports: Rdpack, zoo, lars, Matrix, withr
RdMacros: Rdpack
License: GPL (>= 3)
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2022-01-17 12:11:25 UTC; mosleyl
Author: Luke Mosley [aut, cre], Kaveh S. Nobari [aut] (<https://orcid.org/0000-0002-4053-0781>)
Maintainer: Luke Mosley <l.mosley@lancaster.ac.uk>
Repository: CRAN
Date/Publication: 2022-01-18 08:12:48 UTC

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Package bruceR updated to version 0.8.3 with previous version 0.8.2 dated 2021-12-12

Title: Broadly Useful Convenient and Efficient R Functions
Description: Broadly useful convenient and efficient R functions that bring users concise and elegant R data analyses. This package includes easy-to-use functions for (1) basic R programming (e.g., set working directory to the path of currently opened file, import/export data from/to files with any format, print strings with rich formats and colors); (2) multivariate computation (e.g., compute scale sums/means/... with reverse scoring); (3) reliability analyses and factor analyses; (4) descriptive statistics and correlation analyses; (5) t-test, multi-factor analysis of variance (ANOVA), simple-effect analysis, and post-hoc multiple comparison; (6) tidy report of statistical models (to R Console and Microsoft Word); (7) mediation and moderation analyses (PROCESS); and (8) additional toolbox for statistics and graphics.
Author: Han-Wu-Shuang Bao [aut, cre]
Maintainer: Han-Wu-Shuang Bao <baohws@foxmail.com>

Diff between bruceR versions 0.8.2 dated 2021-12-12 and 0.8.3 dated 2022-01-18

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New package treefit with initial version 1.0.2
Package: treefit
Title: The First Software for Quantitative Trajectory Inference
Version: 1.0.2
Description: Perform two types of analysis: 1) checking the goodness-of-fit of tree models to your single-cell gene expression data; and 2) deciding which tree best fits your data.
License: GPL (>= 3)
URL: https://hayamizu-lab.github.io/treefit-r/, https://github.com/hayamizu-lab/treefit-r/
BugReports: https://github.com/hayamizu-lab/treefit-r/issues
Encoding: UTF-8
Imports: ggplot2, igraph, patchwork, pracma
Suggests: Seurat, gridExtra, knitr, plotly, qpdf, rmarkdown, testthat
VignetteBuilder: knitr
Language: en-US
NeedsCompilation: no
Packaged: 2022-01-17 07:25:23 UTC; kou
Author: Momoko Hayamizu [aut] (<https://orcid.org/0000-0001-8825-6331>), Kouhei Sutou [aut, cre] (<https://orcid.org/0000-0002-5528-5109>), Ryohei Suzuki [aut] (<https://orcid.org/0000-0002-1528-665X>), Hiromi Ishii [aut] (<https://orcid.org/0000-0002-7752-1782>)
Maintainer: Kouhei Sutou <kou@clear-code.com>
Repository: CRAN
Date/Publication: 2022-01-18 07:50:02 UTC

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New package spfilteR with initial version 1.1.2
Package: spfilteR
Title: Semiparametric Spatial Filtering with Eigenvectors in (Generalized) Linear Models
Version: 1.1.2
Description: Tools to decompose (transformed) spatial connectivity matrices and perform supervised or unsupervised semiparametric spatial filtering in a regression framework. The package supports unsupervised spatial filtering in standard linear as well as some generalized linear regression models.
License: GPL-3
URL: https://github.com/sjuhl/spfilteR
BugReports: https://github.com/sjuhl/spfilteR/issues
Depends: R (>= 3.5.0)
Imports: stats
Suggests: testthat, knitr, rmarkdown
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-01-15 11:53:06 UTC; SJuhl
Author: Sebastian Juhl [aut, cre] (<https://orcid.org/0000-0002-7123-5398>)
Maintainer: Sebastian Juhl <sebastian.juhl@t-online.de>
Repository: CRAN
Date/Publication: 2022-01-18 07:50:05 UTC

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Package rasterbc updated to version 1.0.1 with previous version 1.0.0 dated 2021-12-16

Title: Access Forest Ecology Layers for British Columbia in 2001-2018
Description: R-based access to a large set of data variables relevant to forest ecology in British Columbia (BC), Canada. Layers are in raster format at 100m resolution in the BC Albers projection, hosted at the Federated Research Data Repository (FRDR) with <doi:10.20383/101.0283>. The collection includes: elevation; biogeoclimatic zone; wildfire; cutblocks; forest attributes from Hansen et al. (2013) <doi:10.1139/cjfr-2013-0401> and Beaudoin et al. (2017) <doi:10.1139/cjfr-2017-0184>; and rasterized Forest Insect and Disease Survey (FIDS) maps for a number of insect pest species, all covering the period 2001-2018. Users supply a polygon or point location in the province of BC, and 'rasterbc' will download the overlapping raster tiles hosted at FRDR, merging them as needed and returning the result in R as a 'SpatRaster' object. Metadata associated with these layers, and code for downloading them from their original sources can be found in the 'github' repository <https://github.com/deankoch/rasterbc_src>.
Author: Dean Koch [aut, cre] (<https://orcid.org/0000-0002-8849-859X>)
Maintainer: Dean Koch <dkoch@ualberta.ca>

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Package psychmeta updated to version 2.6.2 with previous version 2.6.1 dated 2022-01-03

Title: Psychometric Meta-Analysis Toolkit
Description: Tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more. Bugs can be reported to <https://github.com/psychmeta/psychmeta/issues> or <issues@psychmeta.com>.
Author: Jeffrey A. Dahlke [aut, cre], Brenton M. Wiernik [aut], Wesley Gardiner [ctb] (Unit tests), Michael T. Brannick [ctb] (Testing), Jack Kostal [ctb] (Code for reshape_mat2dat function), Sean Potter [ctb] (Testing; Code for cumulative and leave1out plots), John Sakaluk [ctb] (Code for funnel and forest plots), Yuejia (Mandy) Teng [ctb] (Testing)
Maintainer: Jeffrey A. Dahlke <jdahlke@humrro.org>

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Package marginaleffects updated to version 0.3.2 with previous version 0.3.1 dated 2022-01-09

Title: Marginal Effects, Marginal Means, Predictions, and Contrasts
Description: Compute, summarize, and plot marginal effects, adjusted predictions, contrasts, and marginal means for a wide variety of models.
Author: Vincent Arel-Bundock [aut, cre, cph] (<https://orcid.org/0000-0003-2042-7063>)
Maintainer: Vincent Arel-Bundock <vincent.arel-bundock@umontreal.ca>

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Package GaSP updated to version 1.0.1 with previous version 1.0.0 dated 2021-06-07

Title: Train and Apply a Gaussian Stochastic Process Model
Description: Train a Gaussian stochastic process model of an unknown function, possibly observed with error, via maximum likelihood or MAP estimation, run model diagnostics, and make predictions, following Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989) "Design and Analysis of Computer Experiments", Statistical Science, <doi:10.1214/ss/1177012413>. Perform sensitivity analysis and visualize low-order effects, following Schonlau, M. and Welch, W.J. (2006), "Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization", <doi:10.1007/0-387-28014-6_14>.
Author: William J. Welch [aut, cre, cph] (<https://orcid.org/0000-0002-4575-3124>), Yilin Yang [aut] (<https://orcid.org/0000-0003-0885-6017>)
Maintainer: William J. Welch <will@stat.ubc.ca>

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New package GFD with initial version 0.3.3
Package: GFD
Title: Tests for General Factorial Designs
Version: 0.3.3
Date: 2022-01-17
Author: Sarah Friedrich, Frank Konietschke, Markus Pauly, Marc Ditzhaus, Philipp Steinhauer
Maintainer: Sarah Friedrich <sarah.friedrich@math.uni-augsburg.de>
Depends: R (>= 3.3)
Description: Implemented are the Wald-type statistic, a permuted version thereof as well as the ANOVA-type statistic for general factorial designs, even with non-normal error terms and/or heteroscedastic variances, for crossed designs with an arbitrary number of factors and nested designs with up to three factors. Friedrich et al. (2017) <doi:10.18637/jss.v079.c01>.
License: GPL-2 | GPL-3
Imports: plyr (>= 1.8.3), MASS (>= 7.3-43), Matrix (>= 1.2-2), magic (>= 1.5-6), plotrix (>= 3.5-12), methods, shiny (>= 1.4), shinyjs, shinyWidgets, shinythemes, tippy
LazyData: TRUE
Suggests: RGtk2 (>= 2.20.31), knitr, rmarkdown, HSAUR
VignetteBuilder: knitr, rmarkdown
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2022-01-17 17:01:09 UTC; sfriedrich
Repository: CRAN
Date/Publication: 2022-01-18 07:52:51 UTC

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Package driveR updated to version 0.3.0 with previous version 0.2.1 dated 2020-11-25

Title: Prioritizing Cancer Driver Genes Using Genomics Data
Description: Cancer genomes contain large numbers of somatic alterations but few genes drive tumor development. Identifying 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 OU. 2021. driveR: driveR: a novel method for prioritizing cancer driver genes using somatic genomics data. BMC Bioinformatics <doi:10.1186/s12859-021-04203-7>.
Author: Ege Ulgen [aut, cre, cph] (<https://orcid.org/0000-0003-2090-3621>)
Maintainer: Ege Ulgen <egeulgen@gmail.com>

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