Updates in r-universehttps://r-universe.devPackage updated in r-universecranlike-server 0.11.6https://github.com/r-universe.png?size=400Updates in r-universehttps://r-universe.devSun, 22 May 2022 09:07:20 GMT[certe-medical-epidemiology] certeapi 0.1.3m.berends@certe.nl (Matthijs S. Berends)A Certe R Package for providing an
application programming interface (API) using 'plumber'.
This package is part of the 'certedata' universe.https://github.com/r-universe/certe-medical-epidemiology/actions/runs/2366064972Sun, 22 May 2022 09:07:20 GMTcerteapi0.1.3successhttps://certe-medical-epidemiology.r-universe.devhttps://github.com/certe-medical-epidemiology/certeapi[saiemgilani] hoopR 1.7.0saiem.gilani@gmail.com (Saiem Gilani)A utility to quickly obtain clean and tidy men's
basketball play by play data. Provides functions to access
live play by play and box score data from ESPN<https://www.espn.com> with shot locations
when available. It is also a full NBA Stats API<https://www.nba.com/stats/> wrapper.
It is also a scraping and aggregating interface for Ken Pomeroy's
men's college basketball statistics website<https://kenpom.com>. It provides users with an
active subscription the capability to scrape the website tables and
analyze the data for themselves.https://github.com/r-universe/saiemgilani/actions/runs/2365918618Sun, 22 May 2022 08:20:37 GMThoopR1.7.0successhttps://saiemgilani.r-universe.devhttps://github.com/saiemgilani/hoopR[openpharma] visR 0.2.0.9003bailliem@gmail.com (Mark Baillie)To enable fit-for-purpose, reusable clinical and medical
research focused visualizations and tables with sensible defaults and
based on graphical principles as described in: "Vandemeulebroecke et
al. (2018)" <doi:10.1002/pst.1912>, "Vandemeulebroecke et al. (2019)"
<doi:10.1002/psp4.12455>, and "Morris et al. (2019)"
<doi:10.1136/bmjopen-2019-030215>.https://github.com/r-universe/openpharma/actions/runs/2365771908Sun, 22 May 2022 07:20:16 GMTvisR0.2.0.9003successhttps://openpharma.r-universe.devhttps://github.com/openpharma/visrConsort_flow_diagram.RmdConsort_flow_diagram.htmlCreating consort flow diagram with visR2021-06-02 13:34:102022-04-23 08:19:47Styling_KM_plots.RmdStyling_KM_plots.htmlStyling survival plots2021-06-02 13:34:102022-04-23 08:19:47Time_to_event_analysis.RmdTime_to_event_analysis.htmlSurvival Analysis with visR2021-03-18 16:36:222022-05-08 10:35:37CDISC_ADaM.RmdCDISC_ADaM.htmlSurvival Analysis with visR using CDISC ADaM Time-To-Event Analysis Dataset (ADTTE)2021-03-24 21:06:152022-04-23 08:19:47[saiemgilani] wehoop 1.5.0saiem.gilani@gmail.com (Saiem Gilani)A utility for working with women's basketball data. A scraping and aggregating interface for the WNBA Stats API <https://www.espn.com> and ESPN's <https://www.espn.com> women's college basketball and WNBA statistics. It provides users with the capability to access the game play-by-plays, box scores, standings and results to analyze the data for themselves.https://github.com/r-universe/saiemgilani/actions/runs/2365770097Sun, 22 May 2022 07:06:21 GMTwehoop1.5.0successhttps://saiemgilani.r-universe.devhttps://github.com/saiemgilani/wehoop[jimhester] lintr 2.0.1.9000james.f.hester@gmail.com (Jim Hester)Checks adherence to a given style, syntax errors and possible semantic issues.
Supports on the fly checking of R code edited with 'RStudio IDE', 'Emacs', 'Vim', 'Sublime Text',
'Atom' and 'Visual Studio Code'.https://github.com/r-universe/jimhester/actions/runs/2365625523Sun, 22 May 2022 06:46:56 GMTlintr2.0.1.9000successhttps://jimhester.r-universe.devhttps://github.com/jimhester/lintrcreating_linters.Rmdcreating_linters.htmlCreating new linters2014-11-04 18:52:462022-05-20 21:25:31using_lintr.Rmdusing_lintr.htmlUsing lintr2021-01-30 20:50:532022-05-16 20:38:36[r-lib] lintr 2.0.1.9000james.f.hester@gmail.com (Jim Hester)Checks adherence to a given style, syntax errors and possible semantic issues.
Supports on the fly checking of R code edited with 'RStudio IDE', 'Emacs', 'Vim', 'Sublime Text',
'Atom' and 'Visual Studio Code'.https://github.com/r-universe/r-lib/actions/runs/2365632916Sun, 22 May 2022 06:46:56 GMTlintr2.0.1.9000successhttps://r-lib.r-universe.devhttps://github.com/r-lib/lintrcreating_linters.Rmdcreating_linters.htmlCreating new linters2014-11-04 18:52:462022-05-20 21:25:31using_lintr.Rmdusing_lintr.htmlUsing lintr2021-01-30 20:50:532022-05-16 20:38:36[inbo] lintr 2.0.1.9000james.f.hester@gmail.com (Jim Hester)Checks adherence to a given style, syntax errors and possible semantic issues.
Supports on the fly checking of R code edited with 'RStudio IDE', 'Emacs', 'Vim', 'Sublime Text',
'Atom' and 'Visual Studio Code'.https://github.com/r-universe/inbo/actions/runs/2365621482Sun, 22 May 2022 06:46:56 GMTlintr2.0.1.9000successhttps://inbo.r-universe.devhttps://github.com/r-lib/lintrcreating_linters.Rmdcreating_linters.htmlCreating new linters2014-11-04 18:52:462022-05-20 21:25:31using_lintr.Rmdusing_lintr.htmlUsing lintr2021-01-30 20:50:532022-05-16 20:38:36[spatstat] spatstat.core 2.4-4.010Adrian.Baddeley@curtin.edu.au (Adrian Baddeley)Functionality for data analysis and modelling of
spatial data, mainly spatial point patterns,
in the 'spatstat' family of packages.
(Excludes analysis of spatial data on a linear network,
which is covered by the separate package 'spatstat.linnet'.)
Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods.
A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above.
Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.https://github.com/r-universe/spatstat/actions/runs/2365617590Sun, 22 May 2022 06:26:28 GMTspatstat.core2.4-4.010successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.core[skranz] xsynthdid 0.1.0sebastian.kranz@uni-ulm.de (Sebastian Kranz)Simple function to adjust for covariates in synthdid. See vignete for details.https://github.com/r-universe/skranz/actions/runs/2365617188Sun, 22 May 2022 06:15:43 GMTxsynthdid0.1.0successhttps://skranz.r-universe.devhttps://github.com/skranz/xsynthdid[skranz] RStudioStataConsole 0.1.0sebastian.kranz@uni-ulm.de (Sebastian Kranz)If you like to use Stata interactively on a web server (or in a docker container),
you could access the Stata console version using a Terminal in an RStudio Server session in
the browser. This small packages has three short addins that simplify this process.
It is assumed that the Stata is installed and the `stata` command is on the path.https://github.com/r-universe/skranz/actions/runs/2365617130Sun, 22 May 2022 06:14:33 GMTRStudioStataConsole0.1.0successhttps://skranz.r-universe.devhttps://github.com/skranz/RStudioStataConsole[ropensci] mctq 0.2.0.9000danvartan@gmail.com (Daniel Vartanian)A complete toolkit to process the Munich ChronoType
Questionnaire (MCTQ) for its three versions (standard, micro, and shift).
MCTQ is a quantitative and validated tool to assess chronotypes using
peoples' sleep behavior, originally presented by Till Roenneberg, Anna
Wirz-Justice, and Martha Merrow (2003, <doi:10.1177/0748730402239679>).https://github.com/r-universe/ropensci/actions/runs/2365494426Sun, 22 May 2022 05:59:53 GMTmctq0.2.0.9000successhttps://ropensci.r-universe.devhttps://github.com/ropensci/mctqmctq.Rmdmctq.htmlIntroduction to mctq2020-12-31 03:31:042022-05-08 15:09:47missing-sections.Rmdmissing-sections.htmlMissing sections2021-01-26 02:34:222022-05-08 15:09:47sjl-computation.Rmdsjl-computation.htmlSocial jetlag computation2021-10-10 05:03:012022-05-08 15:09:47time-span-objects.Rmdtime-span-objects.htmlWhy Duration and not Period?2021-10-10 05:03:012021-12-04 05:27:29[r-forge] GNE 0.99-4dutangc@gmail.com (Christophe Dutang)Provide functions to compute standard and generalized Nash Equilibria.
Optimization methods are available nonsmooth reformulation, fixed-point formulation,
minimization problem and constrained-equation reformulation.
See e.g. Kanzow and Facchinei (2010), <doi:10.1007/s10479-009-0653-x>.https://github.com/r-universe/r-forge/actions/runs/2365629222Sun, 22 May 2022 05:59:07 GMTGNE0.99-4successhttps://r-forge.r-universe.devhttps://github.com/r-forge/optimizerGNE-howto.RnwGNE-howto.pdfUser guide for the GNE package2012-07-26 07:09:252022-05-22 05:59:07[r-forge] minqa 1.2.1katharine.mullen@nist.gov (Katharine M. Mullen)Derivative-free optimization by quadratic approximation based
on an interface to Fortran implementations by M. J. D. Powell.https://github.com/r-universe/r-forge/actions/runs/2365629721Sun, 22 May 2022 05:59:07 GMTminqa1.2.1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/optimizer[r-forge] numDeriv 2020.2-1pgilbert.ttv9z@ncf.ca (Paul Gilbert)Methods for calculating (usually) accurate
numerical first and second order derivatives. Accurate calculations
are done using 'Richardson''s' extrapolation or, when applicable, a
complex step derivative is available. A simple difference
method is also provided. Simple difference is (usually) less accurate
but is much quicker than 'Richardson''s' extrapolation and provides a
useful cross-check.
Methods are provided for real scalar and vector valued functions.https://github.com/r-universe/r-forge/actions/runs/2365630284Sun, 22 May 2022 05:59:07 GMTnumDeriv2020.2-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/optimizerGuide.StexGuide.pdfnumDeriv Guide2011-11-17 01:59:242012-08-14 17:15:09[r-forge] GPArotation 2015.7-1pgilbert.ttv9z@ncf.ca (Paul Gilbert)Gradient Projection Algorithm Rotation for Factor Analysis. See '?GPArotation.Intro' for more details.https://github.com/r-universe/r-forge/actions/runs/2365629274Sun, 22 May 2022 05:59:07 GMTGPArotation2015.7-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/optimizerGuide.StexGuide.pdfgpa Guide2011-11-17 01:59:242012-08-14 17:15:09[pik-piam] magpie4 1.129.0bodirsky@pik-potsdam.de (Benjamin Leon Bodirsky)Common output routines for extracting results from the MAgPIE framework (versions 4.x).https://github.com/r-universe/pik-piam/actions/runs/2365352674Sun, 22 May 2022 05:02:11 GMTmagpie41.129.0successhttps://pik-piam.r-universe.devhttps://github.com/pik-piam/magpie4[cynkra] dm 0.2.8.9002krlmlr+r@mailbox.org (Kirill Müller)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.https://github.com/r-universe/cynkra/actions/runs/2365358937Sun, 22 May 2022 04:16:23 GMTdm0.2.8.9002successhttps://cynkra.r-universe.devhttps://github.com/cynkra/dmdm.Rmddm.htmlFirst read: Getting started with dm2019-04-17 16:01:022021-05-27 03:08:56howto-dm-copy.Rmdhowto-dm-copy.htmlHow to: Copy data to and from a database2020-11-17 07:35:022021-09-29 14:13:20howto-dm-db.Rmdhowto-dm-db.htmlHow to: Create a dm object from a database2020-05-24 10:07:372021-10-01 19:58:00howto-dm-df.Rmdhowto-dm-df.htmlHow to: Create a dm object from data frames2020-05-24 10:07:372021-08-18 02:44:36howto-dm-rows.Rmdhowto-dm-rows.htmlHow to: Insert, update or remove rows in a database2020-11-17 07:35:022021-05-27 03:08:56howto-dm-theory.Rmdhowto-dm-theory.htmlHow to: Understand relational data models2020-05-24 10:07:372021-05-03 12:32:06tech-dm-draw.Rmdtech-dm-draw.htmlTechincal: Visualizing dm objects2020-05-24 10:07:372021-04-25 02:15:08tech-dm-class.Rmdtech-dm-class.htmlTechnical: Class dm and basic operations2020-05-24 10:07:372022-05-22 04:01:18tech-dm-filter.Rmdtech-dm-filter.htmlTechnical: Filtering in relational data models2020-05-24 10:07:372021-05-05 03:00:24tech-dm-naming.Rmdtech-dm-naming.htmlTechnical: Function naming logic2020-05-24 10:07:372021-04-25 17:28:27tech-dm-join.Rmdtech-dm-join.htmlTechnical: Joining in relational data models2020-05-24 10:07:372022-05-22 04:01:18tech-dm-cdm.Rmdtech-dm-cdm.htmlTechnical: Migration guide: 'cdm' -> 'dm'2020-05-24 10:07:372021-04-25 02:15:08tech-dm-low-level.Rmdtech-dm-low-level.htmlTechnical: Model verification - keys, constraints and normalization2020-05-24 10:07:372021-04-25 02:15:08tech-dm-zoom.Rmdtech-dm-zoom.htmlTechnical: Zooming and manipulating tables2020-05-24 10:07:372021-10-15 12:14:43[ajlyons] wrkshputils 0.2.5andlyons@ucanr.edu (Andy Lyons)Utilties for planning and conducting virtual R workshops.https://github.com/r-universe/ajlyons/actions/runs/2365118558Sun, 22 May 2022 03:11:19 GMTwrkshputils0.2.5successhttps://ajlyons.r-universe.devhttps://github.com/ucanr-igis/wrkshputils[azure] AzureStor 3.6.1.9000hongooi73@gmail.com (Hong Ooi)Manage storage in Microsoft's 'Azure' cloud: <https://azure.microsoft.com/en-us/product-categories/storage/>. On the admin side, 'AzureStor' includes features to create, modify and delete storage accounts. On the client side, it includes an interface to blob storage, file storage, and 'Azure Data Lake Storage Gen2': upload and download files and blobs; list containers and files/blobs; create containers; and so on. Authenticated access to storage is supported, via either a shared access key or a shared access signature (SAS). Part of the 'AzureR' family of packages.https://github.com/r-universe/azure/actions/runs/2365118285Sun, 22 May 2022 03:03:08 GMTAzureStor3.6.1.9000successhttps://azure.r-universe.devhttps://github.com/azure/azurestoraad.rmdaad.htmlAAD authentication setup2021-03-08 08:57:102021-03-13 17:24:00generics.Rmdgenerics.htmlAzureStor generics2019-02-15 10:07:512019-10-22 04:11:16intro.rmdintro.htmlIntroduction to AzureStor2018-11-06 18:13:432021-01-13 21:38:23[spatstat] spatstat.linnet 2.3-2.015Adrian.Baddeley@curtin.edu.au (Adrian Baddeley)Defines types of spatial data on a linear network
and provides functionality for geometrical operations,
data analysis and modelling of data on a linear network,
in the 'spatstat' family of packages.
Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks.
Data types defined on a network include point patterns, pixel images, functions, and tessellations.
Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood.
Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
Random point patterns on a network can be generated using a variety of models.https://github.com/r-universe/spatstat/actions/runs/2364890396Sun, 22 May 2022 00:24:39 GMTspatstat.linnet2.3-2.015successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.linnet[spatstat] spatstat.random 2.2-0.005Adrian.Baddeley@curtin.edu.au (Adrian Baddeley)Functionality for random generation of spatial data in the 'spatstat' family of packages.
Generates random spatial patterns of points according to many simple rules (complete spatial randomness,
Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns
(thinning, random shift, jittering), simulated realisations of random point processes
(simple sequential inhibition, Matern inhibition models, Matern cluster process,
Neyman-Scott cluster processes, log-Gaussian Cox processes, product shot noise cluster processes)
and simulation of Gibbs point processes (Metropolis-Hastings birth-death-shift algorithm,
alternating Gibbs sampler). Also generates random spatial patterns of line segments,
random tessellations, and random images (random noise, random mosaics).
Excludes random generation on a linear network,
which is covered by the separate package 'spatstat.linnet'.https://github.com/r-universe/spatstat/actions/runs/2364890494Sun, 22 May 2022 00:23:14 GMTspatstat.random2.2-0.005successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.random[spatstat] spatstat.geom 2.4-0.021Adrian.Baddeley@curtin.edu.au (Adrian Baddeley)Defines spatial data types and supports geometrical operations
on them. Data types include point patterns, windows (domains),
pixel images, line segment patterns, tessellations and hyperframes.
Capabilities include creation and manipulation of data
(using command line or graphical interaction),
plotting, geometrical operations (rotation, shift, rescale,
affine transformation), convex hull, discretisation and
pixellation, Dirichlet tessellation, Delaunay triangulation,
pairwise distances, nearest-neighbour distances,
distance transform, morphological operations
(erosion, dilation, closing, opening), quadrat counting,
geometrical measurement, geometrical covariance,
colour maps, calculus on spatial domains,
Gaussian blur, level sets of images, transects of images,
intersections between objects, minimum distance matching.
(Excludes spatial data on a network, which are supported by
the package 'spatstat.linnet'.)https://github.com/r-universe/spatstat/actions/runs/2364890299Sun, 22 May 2022 00:04:49 GMTspatstat.geom2.4-0.021successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.geom[r-lib] devtools 2.4.3.9000jenny@rstudio.com (Jennifer Bryan)Collection of package development tools.https://github.com/r-universe/r-lib/actions/runs/2364743395Sat, 21 May 2022 23:23:50 GMTdevtools2.4.3.9000successhttps://r-lib.r-universe.devhttps://github.com/r-lib/devtoolsdependencies.Rmddependencies.htmlDevtools dependencies2015-09-02 22:20:042021-11-18 19:02:47[dmurdoch] rgl 0.108.43murdoch.duncan@gmail.com (Duncan Murdoch)Provides medium to high level functions for 3D interactive graphics, including
functions modelled on base graphics (plot3d(), etc.) as well as functions for
constructing representations of geometric objects (cube3d(), etc.). Output
may be on screen using OpenGL, or to various standard 3D file formats including
WebGL, PLY, OBJ, STL as well as 2D image formats, including PNG, Postscript, SVG, PGF.https://github.com/r-universe/dmurdoch/actions/runs/2364420212Sat, 21 May 2022 21:48:30 GMTrgl0.108.43successhttps://dmurdoch.r-universe.devhttps://github.com/dmurdoch/rgltransparency.Rmdtransparency.htmlA Note on Transparency2021-02-02 18:06:092021-04-11 14:05:13rgl.Rmdrgl.htmlrgl Overview2014-11-23 22:35:482022-04-17 15:04:17WebGL.RmdWebGL.htmlUser Interaction in WebGL (updated)2015-01-31 19:31:512021-10-08 12:24:57pkgdown.Rmdpkgdown.htmlUsing RGL in pkgdown web sites2021-04-11 14:05:132021-12-01 12:09:23[jl5000] tidyged.utils 0.4.0.9000jalendrum@gmail.com (Jamie Lendrum)Various utilities to manage and clean family tree GEDCOM files using tidy dataframes.https://github.com/r-universe/jl5000/actions/runs/2364427970Sat, 21 May 2022 21:33:25 GMTtidyged.utils0.4.0.9000successhttps://jl5000.r-universe.devhttps://github.com/jl5000/tidyged.utilsautomation.Rmdautomation.htmlAutomating data entry2021-04-17 21:44:322022-05-01 19:42:45organising.Rmdorganising.htmlOrganisation functions2021-04-17 21:44:322022-05-01 19:42:45redaction.Rmdredaction.htmlRedaction of sensitive data2021-04-17 21:44:322022-05-01 19:42:45reducing_bloat.Rmdreducing_bloat.htmlReducing file bloat2021-04-17 21:44:322022-05-01 19:42:45merging_splitting.Rmdmerging_splitting.htmlSplitting and Merging GEDCOM files2021-02-28 11:27:022022-05-01 19:42:45[r-forge] ClassDiscovery 3.4.3krc@silicovore.com (Kevin R. Coombes)Defines the classes used for "class discovery" problems
in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
discovery primarily consists of unsupervised clustering methods with
attempts to assess their statistical significance.https://github.com/r-universe/r-forge/actions/runs/2364903136Sat, 21 May 2022 21:23:34 GMTClassDiscovery3.4.3successhttps://r-forge.r-universe.devhttps://github.com/r-forge/oompaoompa-cd.Rnwoompa-cd.pdfOOMPA ClassDiscovery2014-02-10 15:51:102014-02-10 15:51:10maha-test.Rnwmaha-test.pdfOOMPA Mahalanobis Distance2014-02-10 15:51:102014-02-10 15:51:10[r-forge] TailRank 3.2.2krc@silicovore.com (Kevin R. Coombes)Implements the tail-rank statistic for selecting biomarkers
from a microarray data set, an efficient nonparametric test focused
on the distributional tails. See
<https://gitlab.com/krcoombes/coombeslab/-/blob/master/doc/papers/tolstoy-new.pdf>.https://github.com/r-universe/r-forge/actions/runs/2364905926Sat, 21 May 2022 21:23:34 GMTTailRank3.2.2successhttps://r-forge.r-universe.devhttps://github.com/r-forge/oompabetabinomial.Rnwbetabinomial.pdfBeta-Binomial Distribution2014-05-09 13:11:102016-05-06 12:50:35tailrank.Rnwtailrank.pdfTailRank2014-02-10 15:51:102016-05-06 12:50:35[r-forge] Polychrome 1.5.1krc@silicovore.com (Kevin R. Coombes)Tools for creating, viewing, and assessing qualitative
palettes with many (20-30 or more) colors. See Coombes and colleagues
(2019) <doi:10.18637/jss.v090.c01>.https://github.com/r-universe/r-forge/actions/runs/2364904759Sat, 21 May 2022 21:23:34 GMTPolychrome1.5.1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/oompacolor-deficits.Rmdcolor-deficits.htmlColor Deficits2017-11-19 14:58:032018-04-11 13:09:15creatingPalettes.RmdcreatingPalettes.htmlCreating Palettes with Polychrome2017-04-27 19:43:022018-05-17 12:51:39polychrome.Rmdpolychrome.htmlPolychrome2016-05-11 13:07:592018-05-17 12:51:39testgg.Rmdtestgg.htmlUsing Polychrome With ggplot2020-11-10 15:35:472020-11-10 15:35:47[r-forge] PreProcess 3.1.7krc@silicovore.com (Kevin R. Coombes)Provides classes to pre-process microarray gene
expression data as part of the OOMPA collection of packages
described at <http://oompa.r-forge.r-project.org/>.https://github.com/r-universe/r-forge/actions/runs/2364904940Sat, 21 May 2022 21:23:34 GMTPreProcess3.1.7successhttps://r-forge.r-universe.devhttps://github.com/r-forge/oompaoompa-prep.Rnwoompa-prep.pdfOOMPA PreProcessing2014-02-10 15:51:102017-04-25 13:56:14[r-forge] ClassComparison 3.2.0krc@silicovore.com (Kevin R. Coombes)Defines the classes used for "class comparison" problems
in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
comparison includes tests for differential expression; see Simon's
book for details on typical problem types.https://github.com/r-universe/r-forge/actions/runs/2364902991Sat, 21 May 2022 21:23:34 GMTClassComparison3.2.0successhttps://r-forge.r-universe.devhttps://github.com/r-forge/oompaoompa-cc.Rnwoompa-cc.pdfOOMPA ClassComparison2014-02-10 15:51:102014-05-05 19:51:49[r-forge] oompaBase 3.2.9krc@silicovore.com (Kevin R. Coombes)Provides the class unions that must be
preloaded in order for the basic tools in the OOMPA (Object-Oriented
Microarray and Proteomics Analysis) project to be defined and loaded.
It also includes vectorized operations for row-by-row means,
variances, and t-tests. Finally, it provides new color schemes.
Details on the packages in the OOMPA project can be found at
<http://oompa.r-forge.r-project.org/>.https://github.com/r-universe/r-forge/actions/runs/2364904342Sat, 21 May 2022 21:23:34 GMToompaBase3.2.9successhttps://r-forge.r-universe.devhttps://github.com/r-forge/oompaoompa.Rnwoompa.pdfOOMPA2014-02-10 15:51:102019-08-19 17:48:24[r-forge] oompaData 3.1.2krc@silicovore.com (Kevin R. Coombes)This is a data-only package to provide example data for
other packages that are part of the "Object-Oriented Microrray and
Proteomics Analysis" suite of packages. These are described in more
detail at the package URL.https://github.com/r-universe/r-forge/actions/runs/2364904515Sat, 21 May 2022 21:23:34 GMToompaData3.1.2successhttps://r-forge.r-universe.devhttps://github.com/r-forge/oompa[mhahsler] stream 1.5-1.1mhahsler@lyle.smu.edu (Michael Hahsler)A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893 and NIH R21HG005912. Hahsler et al (2017) <doi:10.18637/jss.v076.i14>.https://github.com/r-universe/mhahsler/actions/runs/2364414077Sat, 21 May 2022 21:17:21 GMTstream1.5-1.1successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/streamstream_extension.Rnwstream_extension.pdfExtending the stream Framework2015-12-06 02:45:552020-12-01 20:10:30stream.Rnwstream.pdfIntroduction to stream2015-12-06 02:45:552022-05-19 23:33:25[lorenzwalthert] precommit 0.3.0.9000lorenz.walthert@icloud.com (Lorenz Walthert)Useful git hooks for R building on top of the multi-language
framework 'pre-commit' for hook management. This package provides git
hooks for common tasks like formatting files with 'styler' or spell
checking as well as wrapper functions to access the 'pre-commit'
executable.https://github.com/r-universe/lorenzwalthert/actions/runs/2364282998Sat, 21 May 2022 20:47:22 GMTprecommit0.3.0.9000successhttps://lorenzwalthert.r-universe.devhttps://github.com/lorenzwalthert/precommitavailable-hooks.Rmdavailable-hooks.htmlavailable-hooks2019-10-20 21:12:322022-05-20 11:28:47ci.Rmdci.htmlContinuous Integration2021-11-26 21:37:212022-05-20 11:28:47FAQ.RmdFAQ.htmlFAQ2019-12-03 07:14:272022-05-20 11:28:47hook-order.Rmdhook-order.htmlhook-order2020-05-05 08:23:512022-05-20 11:28:47precommit.Rmdprecommit.htmlprecommit2022-03-13 15:01:362022-05-20 11:28:47testing.Rmdtesting.htmltesting2021-05-05 12:30:582022-05-20 11:28:47why-use-hooks.Rmdwhy-use-hooks.htmlwhy-use-hooks2019-10-21 21:01:402022-05-20 11:28:47FAILURE: [fastverse] arrow 8.0.0.9000neal@ursalabs.org (Neal Richardson)https://github.com/r-universe/fastverse/actions/runs/2365296976Sat, 21 May 2022 20:38:26 GMTarrow8.0.0.9000https://github.com/apache/arrow[ropensci] deposits 0.0.2.056mark.padgham@email.com (Mark Padgham)A universal client for depositing and accessing research data
anywhere. Currently supported services are zenodo and figshare.https://github.com/r-universe/ropensci/actions/runs/2364282688Sat, 21 May 2022 20:34:44 GMTdeposits0.0.2.056successhttps://ropensci.r-universe.devhttps://github.com/ropenscilabs/depositsentities.Rmdentities.htmlMetadata and Data Entities2022-02-17 06:46:102022-05-16 09:01:38deposits.Rmddeposits.htmlThe deposits package2022-02-25 05:54:522022-02-25 23:29:54[certe-medical-epidemiology] certestats 1.5.0m.berends@certe.nl (Matthijs S. Berends)A Certe R Package for applying statistical modelling
(such as creating machine learning models), QC rules and distribution
analysis.
This package is part of the 'certedata' universe.https://github.com/r-universe/certe-medical-epidemiology/actions/runs/2364135480Sat, 21 May 2022 19:40:12 GMTcertestats1.5.0successhttps://certe-medical-epidemiology.r-universe.devhttps://github.com/certe-medical-epidemiology/certestats[gadenbuie] epoxy 0.0.2.9000garrick@adenbuie.com (Garrick Aden-Buie)Extra strength 'glue' for data-driven templating. String
interpolation for 'R Markdown' documents and 'Shiny' apps, built on
the 'glue' and 'whisker' packages.https://github.com/r-universe/gadenbuie/actions/runs/2363987149Sat, 21 May 2022 19:00:24 GMTepoxy0.0.2.9000successhttps://gadenbuie.r-universe.devhttps://github.com/gadenbuie/epoxyepoxy.Rmdepoxy.htmlepoxy2022-04-26 14:59:432022-04-26 14:59:43inline-reporting.Rmdinline-reporting.htmlInline Reporting2022-04-24 00:36:052022-04-25 01:22:36[modeloriented] DALEX 2.4.2przemyslaw.biecek@gmail.com (Przemyslaw Biecek)Any unverified black box model is the path to failure. Opaqueness leads to distrust.
Distrust leads to ignoration. Ignoration leads to rejection.
DALEX package xrays any model and helps to explore and explain its behaviour.
Machine Learning (ML) models are widely used and have various applications in classification
or regression. Models created with boosting, bagging, stacking or similar techniques are often
used due to their high performance. But such black-box models usually lack direct interpretability.
DALEX package contains various methods that help to understand the link between input variables
and model output. Implemented methods help to explore the model on the level of a single instance
as well as a level of the whole dataset.
All model explainers are model agnostic and can be compared across different models.
DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration.
Find more details in (Biecek 2018) <arXiv:1806.08915>.https://github.com/r-universe/modeloriented/actions/runs/2363983884Sat, 21 May 2022 18:46:23 GMTDALEX2.4.2successhttps://modeloriented.r-universe.devhttps://github.com/modeloriented/dalex[rossellhayes] nombre 0.4.0.9000alexander@rossellhayes.com (Alexander Rossell Hayes)Converts numeric vectors to character vectors of English
number names. Provides conversion to cardinals, ordinals, numerators,
and denominators. Supports negative and non-integer numbers.https://github.com/r-universe/rossellhayes/actions/runs/2363981548Sat, 21 May 2022 18:37:14 GMTnombre0.4.0.9000successhttps://rossellhayes.r-universe.devhttps://github.com/rossellhayes/nombre[nflverse] nflreadr 1.2.0.11tan@tanho.ca (Tan Ho)A minimal package for downloading data from 'GitHub'
repositories of the 'nflverse' project.https://github.com/r-universe/nflverse/actions/runs/2363989234Sat, 21 May 2022 18:26:50 GMTnflreadr1.2.0.11successhttps://nflverse.r-universe.devhttps://github.com/nflverse/nflreadrdictionary_combine.Rmddictionary_combine.htmlData Dictionary - Combine2022-01-01 19:55:392022-01-01 19:55:39dictionary_contracts.Rmddictionary_contracts.htmlData Dictionary - Contracts2022-04-26 12:01:002022-04-26 12:01:00dictionary_depth_charts.Rmddictionary_depth_charts.htmlData Dictionary - Depth Charts2022-01-01 19:55:392022-01-01 19:55:39dictionary_draft_picks.Rmddictionary_draft_picks.htmlData Dictionary - Draft Picks2021-08-06 19:15:462021-08-09 15:04:04dictionary_espn_qbr.Rmddictionary_espn_qbr.htmlData Dictionary - ESPN QBR2021-12-31 16:44:132021-12-31 16:44:13dictionary_ff_opportunity.Rmddictionary_ff_opportunity.htmlData Dictionary - FF Opportunity2022-01-27 16:27:252022-01-27 16:27:25dictionary_ff_playerids.Rmddictionary_ff_playerids.htmlData Dictionary - FF Player IDs2021-07-29 15:38:562021-08-09 15:04:04dictionary_ff_rankings.Rmddictionary_ff_rankings.htmlData Dictionary - FF Rankings2021-08-08 01:43:152021-08-09 15:04:04dictionary_injuries.Rmddictionary_injuries.htmlData Dictionary - Injuries2022-01-01 19:55:392022-01-01 19:55:39dictionary_nextgen_stats.Rmddictionary_nextgen_stats.htmlData Dictionary - Next Gen Stats2021-08-01 19:46:222021-10-05 17:12:07dictionary_pbp.Rmddictionary_pbp.htmlData Dictionary - PBP2021-07-27 13:17:162021-07-27 13:17:16dictionary_pfr_passing.Rmddictionary_pfr_passing.htmlData Dictionary - PFR Passing2021-08-06 19:15:462021-10-05 17:12:07dictionary_player_stats.Rmddictionary_player_stats.htmlData Dictionary - Player Stats2021-07-27 13:17:162021-12-29 16:09:29dictionary_rosters.Rmddictionary_rosters.htmlData Dictionary - Rosters2021-07-30 13:51:372021-12-29 16:09:29dictionary_schedules.Rmddictionary_schedules.htmlData Dictionary - Schedules2021-07-30 13:51:372021-12-29 16:09:29dictionary_snap_counts.Rmddictionary_snap_counts.htmlData Dictionary - Snap Counts2021-08-06 19:15:462021-08-09 15:04:04dictionary_trades.Rmddictionary_trades.htmlData Dictionary - Trades2022-01-01 19:55:392022-01-01 19:55:39exporting_nflreadr.Rmdexporting_nflreadr.htmlUsing nflreadr in packages2021-07-27 13:17:162021-08-16 15:57:37[enchufa2] RcppXPtrUtils 0.1.2iucar@fedoraproject.org (Iñaki Ucar)Provides the means to compile user-supplied C++ functions with
'Rcpp' and retrieve an 'XPtr' that can be passed to other C++ components.https://github.com/r-universe/enchufa2/actions/runs/2363834872Sat, 21 May 2022 17:48:56 GMTRcppXPtrUtils0.1.2successhttps://enchufa2.r-universe.devhttps://github.com/Enchufa2/RcppXPtrUtils[enchufa2] units 0.8-1edzer.pebesma@uni-muenster.de (Edzer Pebesma)Support for measurement units in R vectors, matrices
and arrays: automatic propagation, conversion, derivation
and simplification of units; raising errors in case of unit
incompatibility. Compatible with the POSIXct, Date and difftime
classes. Uses the UNIDATA udunits library and unit database for
unit compatibility checking and conversion.
Documentation about 'units' is provided in the paper by Pebesma, Mailund &
Hiebert (2016, <doi:10.32614/RJ-2016-061>), included in this package as a
vignette; see 'citation("units")' for details.https://github.com/r-universe/enchufa2/actions/runs/2363834935Sat, 21 May 2022 16:46:49 GMTunits0.8-1successhttps://enchufa2.r-universe.devhttps://github.com/r-quantities/unitsmeasurement_units_in_R.Rmdmeasurement_units_in_R.htmlMeasurement units in R2017-03-02 00:17:322022-02-03 11:04:34units.Rmdunits.htmlUnits of Measurement for R Vectors: an Introduction2016-06-08 13:27:132022-02-03 11:04:34[r-quantities] units 0.8-1edzer.pebesma@uni-muenster.de (Edzer Pebesma)Support for measurement units in R vectors, matrices
and arrays: automatic propagation, conversion, derivation
and simplification of units; raising errors in case of unit
incompatibility. Compatible with the POSIXct, Date and difftime
classes. Uses the UNIDATA udunits library and unit database for
unit compatibility checking and conversion.
Documentation about 'units' is provided in the paper by Pebesma, Mailund &
Hiebert (2016, <doi:10.32614/RJ-2016-061>), included in this package as a
vignette; see 'citation("units")' for details.https://github.com/r-universe/r-quantities/actions/runs/2363701835Sat, 21 May 2022 16:46:49 GMTunits0.8-1successhttps://r-quantities.r-universe.devhttps://github.com/r-quantities/unitsmeasurement_units_in_R.Rmdmeasurement_units_in_R.htmlMeasurement units in R2017-03-02 00:17:322022-02-03 11:04:34units.Rmdunits.htmlUnits of Measurement for R Vectors: an Introduction2016-06-08 13:27:132022-02-03 11:04:34[kelly-sovacool] mikropml 1.3.0sovacool@umich.edu (Kelly Sovacool)An interface to build machine learning models for
classification and regression problems. 'mikropml' implements the ML
pipeline described by Topçuoğlu et al. (2020)
<doi:10.1128/mBio.00434-20> with reasonable default options for data
preprocessing, hyperparameter tuning, cross-validation, testing, model
evaluation, and interpretation steps. See the website
<https://www.schlosslab.org/mikropml/> for more information,
documentation, and examples.https://github.com/r-universe/kelly-sovacool/actions/runs/2363707887Sat, 21 May 2022 16:19:06 GMTmikropml1.3.0successhttps://kelly-sovacool.r-universe.devhttps://github.com/SchlossLab/mikropmlintroduction.Rmdintroduction.htmlIntroduction to mikropml2020-07-01 21:51:502022-05-21 16:19:06paper.Rmdpaper.htmlmikropml paper2020-10-15 15:51:342022-05-21 16:19:06[mlr-org] mlr3spatiotempcv 1.0.1.9000patrick.schratz@gmail.com (Patrick Schratz)Extends the mlr3 ML framework with spatio-temporal resampling
methods to account for the presence of spatiotemporal autocorrelation
(STAC) in predictor variables. STAC may cause highly biased
performance estimates in cross-validation if ignored.https://github.com/r-universe/mlr-org/actions/runs/2363562275Sat, 21 May 2022 15:49:27 GMTmlr3spatiotempcv1.0.1.9000successhttps://mlr-org.r-universe.devhttps://github.com/mlr-org/mlr3spatiotempcvmlr3spatiotempcv.Rmdmlr3spatiotempcv.htmlGetting Started2020-01-15 08:19:302022-03-03 14:41:51spatiotemp-viz.Rmdspatiotemp-viz.htmlSpatiotemporal Visualization2020-07-30 16:28:092021-08-26 09:15:45[openbiox] UCSCXenaShiny 1.1.8w_shixiang@163.com (Shixiang Wang)Provides functions and a Shiny application for downloading,
analyzing and visualizing datasets from UCSC Xena
(<http://xena.ucsc.edu/>), which is a collection of UCSC-hosted public
databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.https://github.com/r-universe/openbiox/actions/runs/2363571120Sat, 21 May 2022 15:12:49 GMTUCSCXenaShiny1.1.8successhttps://openbiox.r-universe.devhttps://github.com/openbiox/ucscxenashinyapi.Rmdapi.htmlAccessible Functions2021-03-26 10:34:592021-04-26 09:04:11[shixiangwang] UCSCXenaShiny 1.1.8w_shixiang@163.com (Shixiang Wang)Provides functions and a Shiny application for downloading,
analyzing and visualizing datasets from UCSC Xena
(<http://xena.ucsc.edu/>), which is a collection of UCSC-hosted public
databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.https://github.com/r-universe/shixiangwang/actions/runs/2363563246Sat, 21 May 2022 15:12:49 GMTUCSCXenaShiny1.1.8successhttps://shixiangwang.r-universe.devhttps://github.com/openbiox/UCSCXenaShinyapi.Rmdapi.htmlAccessible Functions2021-03-26 10:34:592021-04-26 09:04:11[ropensci] osmextract 0.4.0.9000andrea.gilardi@unimib.it (Andrea Gilardi)Match, download, convert and import Open Street Map data extracts
obtained from several providers.https://github.com/r-universe/ropensci/actions/runs/2363274511Sat, 21 May 2022 14:02:43 GMTosmextract0.4.0.9000successhttps://ropensci.r-universe.devhttps://github.com/ropensci/osmextractproviders.Rmdproviders.htmlAdd new OpenStreetMap providers2020-07-10 11:02:192021-01-27 17:50:50providers_comparisons.Rmdproviders_comparisons.htmlComparing the supported OSM providers2020-09-30 08:11:522021-04-07 12:39:04osmextract.Rmdosmextract.htmlIntroducing osmextract2020-07-31 09:31:462022-03-07 10:22:25[fastverse] tidytable 0.7.2.9mark.t.fairbanks@gmail.com (Mark Fairbanks)A tidy interface to 'data.table' that is 'rlang' compatible,
giving users the speed of 'data.table' with the clean syntax of the tidyverse.https://github.com/r-universe/fastverse/actions/runs/2363265324Sat, 21 May 2022 14:01:46 GMTtidytable0.7.2.9successhttps://fastverse.r-universe.devhttps://github.com/markfairbanks/tidytable