Updates in r-universehttps://r-universe.devPackage updated in r-universecranlike-server 0.13.0https://github.com/r-universe.png?size=400Updates in r-universehttps://r-universe.devSun, 04 Dec 2022 00:30:39 GMT[jessecambon] tidygeocoder 1.0.5.9000jesse.cambon@gmail.com (Jesse Cambon)An intuitive interface for getting data from geocoding services.https://github.com/r-universe/jessecambon/actions/runs/3611048737Sun, 04 Dec 2022 00:30:39 GMTtidygeocoder1.0.5.9000successhttps://jessecambon.r-universe.devhttps://github.com/jessecambon/tidygeocodertidygeocoder.Rmdtidygeocoder.htmlGetting Started2019-10-15 03:07:162022-08-13 14:27:42[dbdahl] fangs 0.2.2dahl@stat.byu.edu (David B. Dahl)A neighborhood-based, greedy search algorithm is performed to estimate a feature allocation by minimizing the expected loss based on posterior samples from the feature allocation distribution. The method is currently under peer review but an earlier draft is available in Dahl, Johnson, and Andros (2022+) <doi:10.48550/arXiv.2207.13824>.https://github.com/r-universe/dbdahl/actions/runs/3610897534Sun, 04 Dec 2022 00:24:03 GMTfangs0.2.2successhttps://dbdahl.r-universe.devhttps://github.com/dbdahl/fangs[pharmaverse] rlistings 0.1.1.9011gabembecker@gmail.com (Gabriel Becker)Listings are often part of the submission of clinical trial
data in regulatory settings. We provide a framework for the specific
formatting features often used when displaying large datasets in that
context.https://github.com/r-universe/pharmaverse/actions/runs/3610900865Sun, 04 Dec 2022 00:23:11 GMTrlistings0.1.1.9011successhttps://pharmaverse.r-universe.devhttps://github.com/insightsengineering/rlistingsrlistings.Rmdrlistings.htmlGetting Started2022-10-31 20:42:122022-11-14 22:21:00[dbdahl] salso 0.3.27dahl@stat.byu.edu (David B. Dahl)The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, Müller (2022) <doi:10.1080/10618600.2022.2069779>.https://github.com/r-universe/dbdahl/actions/runs/3610897422Sun, 04 Dec 2022 00:18:28 GMTsalso0.3.27successhttps://dbdahl.r-universe.devhttps://github.com/dbdahl/salso[glin] reactable 0.3.0.9000glin@glin.io (Greg Lin)Interactive data tables for R, based on the 'React Table'
JavaScript library. Provides an HTML widget that can be used in 'R Markdown'
or 'Quarto' documents, 'Shiny' applications, or viewed from an R console.https://github.com/r-universe/glin/actions/runs/3610896436Sat, 03 Dec 2022 23:51:51 GMTreactable0.3.0.9000successhttps://glin.r-universe.devhttps://github.com/glin/reactable[freguglia] mrf2d 1.0victorfreguglia@gmail.com (Victor Freguglia)Model fitting, sampling and visualization
for the (Hidden) Markov Random Field model with pairwise interactions and
general interaction structure from
Freguglia, Garcia & Bicas (2020) <doi:10.1002/env.2613>,
which has many popular models used in 2-dimensional lattices
as particular cases, like the Ising Model and Potts Model.
A complete manuscript describing the package is available in
Freguglia & Garcia (2022) <doi:10.18637/jss.v101.i08>.https://github.com/r-universe/freguglia/actions/runs/3610896275Sat, 03 Dec 2022 23:45:47 GMTmrf2d1.0successhttps://freguglia.r-universe.devhttps://github.com/freguglia/mrf2dmrf2d-family.Rmdmrf2d-family.htmlParameter restriction families in mrf2d2020-04-30 00:45:582020-06-02 17:57:45three-dimensions-on-mrf2d.Rmdthree-dimensions-on-mrf2d.htmlWorking with 3d lattices in mrf2d2020-10-27 20:35:592020-10-29 18:23:13[bryandmartin] corncob 0.3.1bmartin6@uw.edu (Bryan D Martin)Statistical modeling for correlated count data using the beta-binomial distribution, described in Martin et al. (2020) <doi:10.1214/19-AOAS1283>. It allows for both mean and overdispersion covariates.https://github.com/r-universe/bryandmartin/actions/runs/3610897447Sat, 03 Dec 2022 23:45:11 GMTcorncob0.3.1successhttps://bryandmartin.r-universe.devhttps://github.com/bryandmartin/corncobcorncob-intro.pdf.asiscorncob-intro.pdfIntroduction to corncob2021-03-09 22:01:552022-12-03 23:45:11[ipeagit] r5r 0.7.90000rafa.pereira.br@gmail.com (Rafael H. M. Pereira)Rapid realistic routing on multimodal transport networks
(walk, bike, public transport and car) using 'R5', the Rapid Realistic
Routing on Real-world and Reimagined networks engine
<https://github.com/conveyal/r5>. The package allows users to generate
detailed routing analysis or calculate travel time matrices using
seamless parallel computing on top of the R5 Java machine. While R5
is developed by Conveyal, the package r5r is independently developed
by a team at the Institute for Applied Economic Research (Ipea) with
contributions from collaborators. Apart from the documentation in this
package, users will find additional information on R5 documentation at
<https://docs.conveyal.com/>. Although we try to keep new releases of
r5r in synchrony with R5, the development of R5 follows Conveyal's
independent update process. Hence, users should confirm the R5 version
implied by the Conveyal user manual (see
<https://docs.conveyal.com/changelog>) corresponds with the R5 version
that r5r depends on.https://github.com/r-universe/ipeagit/actions/runs/3610896748Sat, 03 Dec 2022 23:27:33 GMTr5r0.7.90000failurehttps://ipeagit.r-universe.devhttps://github.com/ipeaGIT/r5rfare_structure.Rmdfare_structure.htmlAccounting for monetary costs2022-05-18 22:24:172022-12-03 22:17:39pareto_frontier.Rmdpareto_frontier.htmlAnalyzing the trade-offs between travel time and monetary cost2022-05-11 18:17:202022-12-03 23:27:33calculating_accessibility.Rmdcalculating_accessibility.htmlCalculating and visualizing Accessibility2021-02-26 19:28:032022-12-03 17:55:19calculating_isochrones.Rmdcalculating_isochrones.htmlCalculating and visualizing Isochrones2021-02-01 12:46:092022-12-03 17:55:19travel_time_matrix.Rmdtravel_time_matrix.htmlCalculating travel time matrices2022-05-18 22:24:172022-12-03 23:27:33r5r.Rmdr5r.htmlIntro to r5r: Rapid Realistic Routing with R5 in R2022-05-11 19:47:372022-12-03 17:55:19detailed_itineraries.Rmddetailed_itineraries.htmlPlanning routes with detailed_itineraries()2022-05-19 02:56:182022-12-03 17:55:19time_window.Rmdtime_window.htmlUsing the time_window parameter2022-05-11 15:54:062022-12-03 23:27:33[sebkrantz] collapse 1.9.0.9000sebastian.krantz@graduateinstitute.ch (Sebastian Krantz)A C/C++ based package for advanced data transformation and
statistical computing in R that is extremely fast, class-agnostic,
and programmer friendly through a flexible and parsimonious syntax.
It is well integrated with base R, 'dplyr' / (grouped) 'tibble',
'data.table', 'sf', 'plm' (panel-series and data frames), and
non-destructively handles other matrix or data frame based classes (like
'ts', 'xts' / 'zoo', 'tsibble', ...)
--- Key Features: ---
(1) Advanced statistical programming: A full set of fast statistical functions
supporting grouped and weighted computations on vectors, matrices and
data frames. Fast and programmable grouping, ordering, unique values/rows,
factor generation and interactions. Fast and flexible functions for data
manipulation, data object conversions, and memory efficient R programming.
(2) Advanced aggregation: Fast and easy multi-data-type, multi-function, weighted
and parallelized data aggregation.
(3) Advanced transformations: Fast row/column arithmetic, (grouped) replacing
and sweeping out of statistics (by reference), (grouped, weighted) scaling/standardizing,
(higher-dimensional) between (averaging) and (quasi-)within (demeaning) transformations,
linear prediction, model fitting and testing exclusion restrictions.
(4) Advanced time-computations: Fast and flexible indexed time series and panel data classes.
Fast (sequences of) lags/leads, and (lagged/leaded, iterated, quasi-, log-)
differences and (compounded) growth rates on (irregular) time series and panels.
Multivariate auto-, partial- and cross-correlation functions for panel data.
Panel data to (ts-)array conversions.
(5) List processing: Recursive list search, splitting,
extraction/subsetting, apply, and generalized row-binding / unlisting to data frame.
(6) Advanced data exploration: Fast (grouped, weighted, panel-decomposed)
summary statistics and descriptive tools.https://github.com/r-universe/sebkrantz/actions/runs/3610603842Sat, 03 Dec 2022 22:56:26 GMTcollapse1.9.0.9000successhttps://sebkrantz.r-universe.devhttps://github.com/sebkrantz/collapsecollapse_and_dplyr.Rmdcollapse_and_dplyr.htmlcollapse and dplyr: Fast (Weighted) Aggregations and Transformations in a Piped Workflow2020-03-12 06:25:552021-06-27 22:02:38collapse_and_data.table.Rmdcollapse_and_data.table.htmlcollapse and dplyr: Harmony and High Performance2020-01-06 17:41:142021-07-01 22:23:30collapse_and_plm.Rmdcollapse_and_plm.htmlcollapse and plm: Fast Transformation and Exploration of Panel Data2020-03-12 06:25:552022-10-07 00:23:28collapse_and_sf.Rmdcollapse_and_sf.htmlcollapse and sf: Fast Manipulation of Simple Features Data Frames2021-06-27 22:02:382022-01-21 13:50:31collapse_documentation.Rmdcollapse_documentation.htmlcollapse Documentation and Resources2021-03-01 22:09:462022-10-07 00:54:52collapse_intro.Rmdcollapse_intro.htmlIntroduction to collapse: Advanced and Fast Data Transformation in R2020-01-06 17:41:142022-01-14 10:54:53[fastverse] collapse 1.9.0.9000sebastian.krantz@graduateinstitute.ch (Sebastian Krantz)A C/C++ based package for advanced data transformation and
statistical computing in R that is extremely fast, class-agnostic,
and programmer friendly through a flexible and parsimonious syntax.
It is well integrated with base R, 'dplyr' / (grouped) 'tibble',
'data.table', 'sf', 'plm' (panel-series and data frames), and
non-destructively handles other matrix or data frame based classes (like
'ts', 'xts' / 'zoo', 'tsibble', ...)
--- Key Features: ---
(1) Advanced statistical programming: A full set of fast statistical functions
supporting grouped and weighted computations on vectors, matrices and
data frames. Fast and programmable grouping, ordering, unique values/rows,
factor generation and interactions. Fast and flexible functions for data
manipulation, data object conversions, and memory efficient R programming.
(2) Advanced aggregation: Fast and easy multi-data-type, multi-function, weighted
and parallelized data aggregation.
(3) Advanced transformations: Fast row/column arithmetic, (grouped) replacing
and sweeping out of statistics (by reference), (grouped, weighted) scaling/standardizing,
(higher-dimensional) between (averaging) and (quasi-)within (demeaning) transformations,
linear prediction, model fitting and testing exclusion restrictions.
(4) Advanced time-computations: Fast and flexible indexed time series and panel data classes.
Fast (sequences of) lags/leads, and (lagged/leaded, iterated, quasi-, log-)
differences and (compounded) growth rates on (irregular) time series and panels.
Multivariate auto-, partial- and cross-correlation functions for panel data.
Panel data to (ts-)array conversions.
(5) List processing: Recursive list search, splitting,
extraction/subsetting, apply, and generalized row-binding / unlisting to data frame.
(6) Advanced data exploration: Fast (grouped, weighted, panel-decomposed)
summary statistics and descriptive tools.https://github.com/r-universe/fastverse/actions/runs/3610603144Sat, 03 Dec 2022 22:56:26 GMTcollapse1.9.0.9000successhttps://fastverse.r-universe.devhttps://github.com/SebKrantz/collapsecollapse_and_dplyr.Rmdcollapse_and_dplyr.htmlcollapse and dplyr: Fast (Weighted) Aggregations and Transformations in a Piped Workflow2020-03-12 06:25:552021-06-27 22:02:38collapse_and_data.table.Rmdcollapse_and_data.table.htmlcollapse and dplyr: Harmony and High Performance2020-01-06 17:41:142021-07-01 22:23:30collapse_and_plm.Rmdcollapse_and_plm.htmlcollapse and plm: Fast Transformation and Exploration of Panel Data2020-03-12 06:25:552022-10-07 00:23:28collapse_and_sf.Rmdcollapse_and_sf.htmlcollapse and sf: Fast Manipulation of Simple Features Data Frames2021-06-27 22:02:382022-01-21 13:50:31collapse_documentation.Rmdcollapse_documentation.htmlcollapse Documentation and Resources2021-03-01 22:09:462022-10-07 00:54:52collapse_intro.Rmdcollapse_intro.htmlIntroduction to collapse: Advanced and Fast Data Transformation in R2020-01-06 17:41:142022-01-14 10:54:53[eth-mds] ricu 0.5.4r@nbenn.ch (Nicolas Bennett)Focused on (but not exclusive to) data sets hosted on PhysioNet
(<https://physionet.org>), 'ricu' provides utilities for download, setup
and access of intensive care unit (ICU) data sets. In addition to
functions for running arbitrary queries against available data sets, a
system for defining clinical concepts and encoding their representations
in tabular ICU data is presented.https://github.com/r-universe/eth-mds/actions/runs/3610602624Sat, 03 Dec 2022 22:53:58 GMTricu0.5.4successhttps://eth-mds.r-universe.devhttps://github.com/eth-mds/ricujss.Rmdjss.htmlAccessing ICU data with R (Bennett & Plečko, JSS 2021)2020-06-03 14:47:402022-07-12 07:11:27ricu.Rmdricu.htmlQuick start guide2020-06-02 14:30:112021-08-05 07:14:02uom.Rmduom.htmlUnits of measurement2020-05-05 13:34:232021-08-05 07:14:02[lindbrook] packageRank 0.7.2.9008lindbrook@gmail.com (lindbrook)Compute and visualize the cross-sectional and longitudinal number
and rank percentile of package downloads from RStudio's CRAN mirror.https://github.com/r-universe/lindbrook/actions/runs/3610622725Sat, 03 Dec 2022 22:51:07 GMTpackageRank0.7.2.9008successhttps://lindbrook.r-universe.devhttps://github.com/lindbrook/packagerank[pachterlab] Voyager 1.0.5dlu2@caltech.edu (Lambda Moses)SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic
exploratory spatial data analysis (ESDA) methods for SFE. This first version
supports univariate global spatial ESDA methods such as Moran's I,
permutation testing for Moran's I, and correlograms. The Voyager package also implements
plotting functions to plot SFE data and ESDA results. Multivariate ESDA
and univariate local metrics will be added in later versions.https://github.com/r-universe/pachterlab/actions/runs/3610602621Sat, 03 Dec 2022 22:50:36 GMTVoyager1.0.5successhttps://pachterlab.r-universe.devhttps://github.com/pachterlab/voyageroverview.Rmdoverview.htmlFunctionality overview2022-07-07 06:52:042022-11-01 03:59:16[collinerickson] GauPro 0.2.6.9000collinberickson@gmail.com (Collin Erickson)Fits a Gaussian process model to data. Gaussian processes
are commonly used in computer experiments to fit an interpolating model.
The model is stored as an 'R6' object and can be easily updated with new
data. There are options to run in parallel, and 'Rcpp'
has been used to speed up calculations.
For more info about Gaussian process software, see Erickson et al. (2018)
<doi:10.1016/j.ejor.2017.10.002>.https://github.com/r-universe/collinerickson/actions/runs/3610603082Sat, 03 Dec 2022 22:43:12 GMTGauPro0.2.6.9000successhttps://collinerickson.r-universe.devhttps://github.com/collinerickson/gauproGauPro.RmdGauPro.htmlA Guide to the GauPro R package2017-03-26 04:08:472017-08-24 04:00:49derivatives.Rmdderivatives.htmlDerivatives for estimating Gaussian process parameters2016-10-20 03:08:452022-11-10 05:06:20IntroductionToGPs.RmdIntroductionToGPs.htmlIntroduction to Gaussian Processes2017-05-27 16:36:112017-10-04 20:04:25CrossValidationErrorCorrection.RmdCrossValidationErrorCorrection.htmlLeave-one-out cross-validation and error correction2017-06-05 02:39:032017-09-15 15:31:23surface_derivatives.Rmdsurface_derivatives.htmlSpatial derivatives of Gaussian process models2016-11-17 22:55:202022-11-10 05:06:20[dbdahl] caviarpd 0.3.2dahl@stat.byu.edu (David B. Dahl)Cluster analysis is performed using pairwise distance information and a random partition distribution. The method is
implemented for two random partition distributions. It draws samples and then obtains and plots clustering estimates.
An implementation of a selection algorithm is provided for the mass parameter of the partition distribution. Since
pairwise distances are the principal input to this procedure, it is most comparable to the hierarchical and k-medoids
clustering methods. The method is Dahl, Andros, Carter (2022+) <doi:10.1002/sam.11602>.https://github.com/r-universe/dbdahl/actions/runs/3610602950Sat, 03 Dec 2022 22:29:22 GMTcaviarpd0.3.2successhttps://dbdahl.r-universe.devhttps://github.com/dbdahl/caviarpd[ropensci] phylocomr 0.3.3sanchez.reyes.luna@gmail.com (Luna Luisa Sanchez Reyes)Interface to 'Phylocom' (<https://phylodiversity.net/phylocom/>),
a library for analysis of 'phylogenetic' community structure and
character evolution. Includes low level methods for interacting with
the three executables, as well as higher level interfaces for methods
like 'aot', 'ecovolve', 'bladj', 'phylomatic', and more.https://github.com/r-universe/ropensci/actions/runs/3610410681Sat, 03 Dec 2022 21:32:04 GMTphylocomr0.3.3successhttps://ropensci.r-universe.devhttps://github.com/ropensci/phylocomrphylocomr.Rmdphylocomr.htmlIntroduction to the phylocomr package2019-07-23 18:44:552022-12-02 22:32:45[ncss-tech] soilDB 2.7.6andrew.g.brown@usda.gov (Andrew Brown)A collection of functions for reading data from USDA-NCSS soil databases.https://github.com/r-universe/ncss-tech/actions/runs/3610410283Sat, 03 Dec 2022 21:28:26 GMTsoilDB2.7.6successhttps://ncss-tech.r-universe.devhttps://github.com/ncss-tech/soildb[idahoagstats] soilDB 2.7.6andrew.g.brown@usda.gov (Andrew Brown)A collection of functions for reading data from USDA-NCSS soil databases.https://github.com/r-universe/idahoagstats/actions/runs/3610410547Sat, 03 Dec 2022 21:28:26 GMTsoilDB2.7.6successhttps://idahoagstats.r-universe.devhttps://github.com/ncss-tech/soilDB[ethanbass] ggtukey 0.1.0ethanbass@gmail.com (Ethan Bass)Provides a simple interface to visualize paired comparisons in 'ggplot2'
by adding compact letter displays (i.e. Tukey letters).https://github.com/r-universe/ethanbass/actions/runs/3610413075Sat, 03 Dec 2022 21:13:47 GMTggtukey0.1.0successhttps://ethanbass.r-universe.devhttps://github.com/ethanbass/ggtukeyggtukey.Rmdggtukey.htmlggtukey2022-11-21 05:26:142022-12-03 21:13:45[revbayes] RevGadgets 1.1.0ctribble09@gmail.com (Carrie Tribble)Processes and visualizes the output of complex phylogenetic analyses from the 'RevBayes' phylogenetic graphical modeling software.https://github.com/r-universe/revbayes/actions/runs/3611204956Sat, 03 Dec 2022 21:13:40 GMTRevGadgets1.1.0successhttps://revbayes.r-universe.devhttps://github.com/revbayes/revgadgets[econabhishek] datagovindia 1.0.5abhishek.arora1996@gmail.com (Abhishek Arora)This wrapper allows the user to communicate with more than
80,000 API posted on data.gov.in - open data
platform of the government of India <https:data.gov.in/ogpl_apis>.
It also allows the user to search for the API required through the universe
of the API with a better interface than the one the official website provides.
Once a user has the ID by using the API discovery functionalities,
it allows one to converse with the API using a consistent format across all
available API.https://github.com/r-universe/econabhishek/actions/runs/3610413268Sat, 03 Dec 2022 21:04:13 GMTdatagovindia1.0.5successhttps://econabhishek.r-universe.devhttps://github.com/econabhishek/datagovindiadatagovindia_vignette.Rmddatagovindia_vignette.htmlGetting Started with datagovindia2021-04-04 18:05:322022-02-26 18:19:44[s0521] guiplot 0.3.03212418315@qq.com (Fu Yongchao)Create a user-friendly plotting GUI for 'R'.
In addition, one purpose of creating the 'R' package is to facilitate third-party software to call 'R' for drawing, for example, 'Phoenix WinNonlin' software calls 'R' to draw the drug concentration versus time curve.https://github.com/r-universe/s0521/actions/runs/3610237299Sat, 03 Dec 2022 20:52:38 GMTguiplot0.3.0successhttps://s0521.r-universe.devhttps://github.com/s0521/guiplot[kkholst] mets 1.3.1klaus@holst.it (Klaus K. Holst)Implementation of various statistical models for multivariate
event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate
cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random
effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>.
Also contains two-stage binomial modelling that can do pairwise odds-ratio
dependence modelling based marginal logistic regression models. This is an
alternative to the alternating logistic regression approach (ALR).https://github.com/r-universe/kkholst/actions/runs/3610221646Sat, 03 Dec 2022 20:12:55 GMTmets1.3.1successhttps://kkholst.r-universe.devhttps://github.com/kkholst/metstime-to-event-family-studies-arev.Rmdtime-to-event-family-studies-arev.htmlA practical guide to Human Genetics with Lifetime Data2021-08-25 12:57:552022-10-01 11:38:30binomial-twin.Rmdbinomial-twin.htmlAnalysis of bivariate binomial data: Twin analysis2020-05-27 10:05:422022-10-01 11:38:30binomial-family.Rmdbinomial-family.htmlAnalysis of multivariate binomial data: family analysis2020-05-27 10:05:422022-08-08 07:01:41twostage-survival.Rmdtwostage-survival.htmlAnalysis of multivariate survival data2020-05-29 09:23:382022-10-01 11:38:30binreg-ate.Rmdbinreg-ate.htmlAverage treatment effect (ATE) for Competing risks and binary outcomes2021-08-25 12:57:552022-10-01 11:38:30binreg.Rmdbinreg.htmlBinomial Regression2021-03-07 20:05:322021-09-05 17:06:03cifreg.Rmdcifreg.htmlCumulative Incidence Regression2020-08-11 07:36:542022-08-08 07:01:41interval-discrete-survival.Rmdinterval-discrete-survival.htmlDiscrete Interval Censored Survival Models2021-04-28 11:09:572022-08-08 07:01:41basic-dutils.Rmdbasic-dutils.htmldUtility data-frame manipulations2020-05-25 18:35:352021-09-05 17:06:03haplo-discrete-ttp.Rmdhaplo-discrete-ttp.htmlHaplotype Discrete Survival Models2020-08-27 14:29:232021-10-27 12:24:20marginal-cox.Rmdmarginal-cox.htmlMarginal modelling of clustered survival data2020-05-25 09:08:552022-10-01 11:38:30mediation-survival.Rmdmediation-survival.htmlMediation Analysis for survival data2022-07-05 08:13:382022-09-03 10:58:03recurrent-events.Rmdrecurrent-events.htmlRecurrent events2020-05-25 10:33:392022-11-18 14:02:13quantitative-twin.Rmdquantitative-twin.htmlTwin models2020-05-29 07:38:432022-10-01 11:38:30[kkholst] targeted 0.3klaus@holst.it (Klaus K. Holst)Various methods for targeted and semiparametric inference including
augmented inverse probability weighted estimators for missing data and
causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>)
and estimators for risk differences and relative risks (Richardson et al. (2017)
<doi:10.1080/01621459.2016.1192546>).https://github.com/r-universe/kkholst/actions/runs/3610221787Sat, 03 Dec 2022 20:10:47 GMTtargeted0.3successhttps://kkholst.r-universe.devhttps://github.com/kkholst/targetedate.Rmdate.htmlAverage Treatment Effects2021-10-21 22:35:382022-08-03 12:33:21riskregression.Rmdriskregression.htmlEstimating a relative risk or risk difference with a binary exposure2020-04-15 20:51:472022-12-03 18:17:10[benjaminrich] linpk 1.1.2mail@benjaminrich.net (Benjamin Rich)Generate concentration-time profiles from linear pharmacokinetic
(PK) systems, possibly with first-order absorption or zero-order infusion,
possibly with one or more peripheral compartments, and possibly under
steady-state conditions. Single or multiple doses may be specified. Secondary
(derived) PK parameters (e.g. Cmax, Ctrough, AUC, Tmax, half-life, etc.) are
computed.https://github.com/r-universe/benjaminrich/actions/runs/3610028345Sat, 03 Dec 2022 19:52:20 GMTlinpk1.1.2successhttps://benjaminrich.r-universe.devhttps://github.com/benjaminrich/linpklinpk-intro.Rmdlinpk-intro.htmlSimulating Pharmacokinetic Concentration-Time Profiles With the linpk Package2017-10-31 19:17:572022-12-03 19:34:45[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/3610029048Sat, 03 Dec 2022 19:42:08 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 00:17:322022-12-03 09:56:13units.Rmdunits.htmlUnits of Measurement for R Vectors: an Introduction2016-06-08 13:27:132022-02-03 11:04:34[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/3610029133Sat, 03 Dec 2022 19:42:08 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 00:17:322022-12-03 09:56:13units.Rmdunits.htmlUnits of Measurement for R Vectors: an Introduction2016-06-08 13:27:132022-02-03 11:04:34[dfriend21] quadtree 0.1.10dafriend.R@gmail.com (Derek Friend)Provides functionality for working with raster-like quadtrees
(also called “region quadtrees”), which allow for variable-sized
cells. The package allows for flexibility in the quadtree creation
process. Several functions defining how to split and aggregate cells
are provided, and custom functions can be written for both of these
processes. In addition, quadtrees can be created using other quadtrees
as “templates”, so that the new quadtree's structure is identical to
the template quadtree. The package also includes functionality for
modifying quadtrees, querying values, saving quadtrees to a file, and
calculating least-cost paths using the quadtree as a resistance
surface.https://github.com/r-universe/dfriend21/actions/runs/3610132580Sat, 03 Dec 2022 19:25:53 GMTquadtree0.1.10successhttps://dfriend21.r-universe.devhttps://github.com/dfriend21/quadtreequadtree-code.Rmdquadtree-code.htmlquadtree-code2021-09-08 00:05:502021-09-15 22:44:44quadtree-creation.Rmdquadtree-creation.htmlquadtree-creation2021-09-01 21:43:412021-11-09 18:54:28quadtree-lcp.Rmdquadtree-lcp.htmlquadtree-lcp2021-09-02 23:39:212022-01-14 00:14:54quadtree-usage.Rmdquadtree-usage.htmlquadtree-usage2021-09-07 23:08:542021-12-03 19:35:01[polkas] cat2cat 0.4.5.9000nasinski.maciej@gmail.com (Maciej Nasinski)Unifying of an inconsistently coded categorical variable between two different time points in accordance with a mapping table.
The main rule is to replicate the observation if it could be assign to a few categories.
Then using simple frequencies or modern statistical methods to approximate probabilities of being assign to each of them.
This novel procedure was invented and implemented in the paper by (Nasinski, Majchrowska and Broniatowska (2020) <doi:10.24425/cejeme.2020.134747>).https://github.com/r-universe/polkas/actions/runs/3610027912Sat, 03 Dec 2022 19:13:20 GMTcat2cat0.4.5.9000successhttps://polkas.r-universe.devhttps://github.com/polkas/cat2catcat2cat.Rmdcat2cat.htmlGet Started2022-03-10 18:40:162022-12-03 19:13:20[kingaa] pomp 4.4.2.1kingaa@umich.edu (Aaron A. King)Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.https://github.com/r-universe/kingaa/actions/runs/3610034182Sat, 03 Dec 2022 19:09:38 GMTpomp4.4.2.1successhttps://kingaa.r-universe.devhttps://github.com/kingaa/pomp[apache] arrow 10.0.0.20221202neal@ursalabs.org (Neal Richardson)'Apache' 'Arrow' <https://arrow.apache.org/> is a cross-language
development platform for in-memory data. It specifies a standardized
language-independent columnar memory format for flat and hierarchical data,
organized for efficient analytic operations on modern hardware. This
package provides an interface to the 'Arrow C++' library.https://github.com/r-universe/apache/actions/runs/3610033209Sat, 03 Dec 2022 19:05:04 GMTarrow10.0.0.20221202successhttps://apache.r-universe.devhttps://github.com/apache/arrow[fastverse] arrow 10.0.0.20221202neal@ursalabs.org (Neal Richardson)'Apache' 'Arrow' <https://arrow.apache.org/> is a cross-language
development platform for in-memory data. It specifies a standardized
language-independent columnar memory format for flat and hierarchical data,
organized for efficient analytic operations on modern hardware. This
package provides an interface to the 'Arrow C++' library.https://github.com/r-universe/fastverse/actions/runs/3610026955Sat, 03 Dec 2022 19:05:04 GMTarrow10.0.0.20221202successhttps://fastverse.r-universe.devhttps://github.com/apache/arrow[idahoagstats] mappoly 0.3.3.0mmollin@ncsu.edu (Marcelo Mollinari)Construction of genetic maps in autopolyploid full-sib populations.
Uses pairwise recombination fraction estimation as the first
source of information to sequentially position allelic variants
in specific homologous chromosomes. For situations where pairwise
analysis has limited power, the algorithm relies on the multilocus
likelihood obtained through a hidden Markov model (HMM).
For more detail, please see Mollinari and Garcia (2019)
<doi:10.1534/g3.119.400378> and Mollinari et al. (2020)
<doi:10.1534/g3.119.400620>.https://github.com/r-universe/idahoagstats/actions/runs/3609834373Sat, 03 Dec 2022 18:21:04 GMTmappoly0.3.3.0successhttps://idahoagstats.r-universe.devhttps://github.com/mmollina/MAPpolymappoly_startguide.Rmdmappoly_startguide.htmlmappoly_intro2022-05-23 23:52:302022-11-21 06:46:02[mmollina] mappoly 0.3.3.0mmollin@ncsu.edu (Marcelo Mollinari)Construction of genetic maps in autopolyploid full-sib populations.
Uses pairwise recombination fraction estimation as the first
source of information to sequentially position allelic variants
in specific homologous chromosomes. For situations where pairwise
analysis has limited power, the algorithm relies on the multilocus
likelihood obtained through a hidden Markov model (HMM).
For more detail, please see Mollinari and Garcia (2019)
<doi:10.1534/g3.119.400378> and Mollinari et al. (2020)
<doi:10.1534/g3.119.400620>.https://github.com/r-universe/mmollina/actions/runs/3609838314Sat, 03 Dec 2022 18:21:04 GMTmappoly0.3.3.0successhttps://mmollina.r-universe.devhttps://github.com/mmollina/mappolymappoly_startguide.Rmdmappoly_startguide.htmlmappoly_intro2022-05-23 23:52:302022-11-21 06:46:02[polyploids] mappoly 0.3.3.0mmollin@ncsu.edu (Marcelo Mollinari)Construction of genetic maps in autopolyploid full-sib populations.
Uses pairwise recombination fraction estimation as the first
source of information to sequentially position allelic variants
in specific homologous chromosomes. For situations where pairwise
analysis has limited power, the algorithm relies on the multilocus
likelihood obtained through a hidden Markov model (HMM).
For more detail, please see Mollinari and Garcia (2019)
<doi:10.1534/g3.119.400378> and Mollinari et al. (2020)
<doi:10.1534/g3.119.400620>.https://github.com/r-universe/polyploids/actions/runs/3609838336Sat, 03 Dec 2022 18:21:04 GMTmappoly0.3.3.0successhttps://polyploids.r-universe.devhttps://github.com/mmollina/MAPpolymappoly_startguide.Rmdmappoly_startguide.htmlmappoly_intro2022-05-23 23:52:302022-11-21 06:46:02[fastverse] duckdb 0.6.0hannes@cwi.nl (Hannes Mühleisen)The DuckDB project is an embedded analytical data
management system with support for the Structured Query Language (SQL). This package includes all of
DuckDB and a R Database Interface (DBI) connector.https://github.com/r-universe/fastverse/actions/runs/3609833017Sat, 03 Dec 2022 18:20:10 GMTduckdb0.6.0successhttps://fastverse.r-universe.devhttps://github.com/duckdb/duckdb[duckdb] duckdb 0.6.0hannes@cwi.nl (Hannes Mühleisen)The DuckDB project is an embedded analytical data
management system with support for the Structured Query Language (SQL). This package includes all of
DuckDB and a R Database Interface (DBI) connector.https://github.com/r-universe/duckdb/actions/runs/3609832985Sat, 03 Dec 2022 18:20:10 GMTduckdb0.6.0successhttps://duckdb.r-universe.devhttps://github.com/duckdb/duckdb[r-lib] pak 0.3.1.9000csardi.gabor@gmail.com (Gábor Csárdi)The goal of 'pak' is to make package installation faster and
more reliable. In particular, it performs all HTTP operations in parallel,
so metadata resolution and package downloads are fast. Metadata and package
files are cached on the local disk as well. 'pak' has a dependency solver,
so it finds version conflicts before performing the installation. This
version of 'pak' supports CRAN, 'Bioconductor' and 'GitHub' packages as well.https://github.com/r-universe/r-lib/actions/runs/3610222447Sat, 03 Dec 2022 18:14:57 GMTpak0.3.1.9000successhttps://r-lib.r-universe.devhttps://github.com/r-lib/pak[ropensci] postdoc 1.0.9000jeroen@berkeley.edu (Jeroen Ooms)Generates simple and beautiful one-page HTML reference manuals
with package documentation. Math rendering and syntax highlighting are done
server-side in R such that no JavaScript libraries are needed in the
browser, which makes the documentation portable and fast to load.https://github.com/r-universe/ropensci/actions/runs/3609833070Sat, 03 Dec 2022 18:04:58 GMTpostdoc1.0.9000successhttps://ropensci.r-universe.devhttps://github.com/ropensci/postdoc[shaelebrown] TDApplied 2.0.0shaelebrown@gmail.com (Shael Brown)Topological data analysis is a powerful tool for finding non-linear global structure
in whole datasets. 'TDApplied' aims to bridge topological data analysis with data, statistical
and machine learning practitioners so that more analyses may benefit from the
power of topological data analysis. The main tool of topological data analysis is
persistent homology, which computes a shape descriptor of a dataset, called
a persistence diagram. There are five goals of this package: (1) convert persistence diagrams
computed using the two main R packages for topological data analysis into a data frame,
(2) implement fast versions of both distance and kernel calculations
for pairs of persistence diagrams, (3) provide methods for machine learning
and inference for persistence diagrams which scale well, (4) deliver a fast implementation
of persistent homology via a python interface, and (5) contribute tools for the interpretation of
persistence diagrams.https://github.com/r-universe/shaelebrown/actions/runs/3609645239Sat, 03 Dec 2022 18:03:26 GMTTDApplied2.0.0successhttps://shaelebrown.r-universe.devhttps://github.com/shaelebrown/tdappliedML_and_Inference.RmdML_and_Inference.htmlinference2022-03-30 19:44:472022-11-08 16:07:57[kassambara] ggpubr 0.5.0.999alboukadel.kassambara@gmail.com (Alboukadel Kassambara)The 'ggplot2' package is excellent and flexible for elegant data
visualization in R. However the default generated plots requires some formatting
before we can send them for publication. Furthermore, to customize a 'ggplot',
the syntax is opaque and this raises the level of difficulty for researchers
with no advanced R programming skills. 'ggpubr' provides some easy-to-use
functions for creating and customizing 'ggplot2'- based publication ready plots.https://github.com/r-universe/kassambara/actions/runs/3609644273Sat, 03 Dec 2022 17:56:26 GMTggpubr0.5.0.999successhttps://kassambara.r-universe.devhttps://github.com/kassambara/ggpubrFAILURE: [cjvanlissa] pema 0.1.2c.j.vanlissa@tilburguniversity.edu (Caspar J van Lissa)https://github.com/r-universe/cjvanlissa/actions/runs/3609642303Sat, 03 Dec 2022 17:47:11 GMTpema0.1.2https://github.com/cjvanlissa/pema[rvlenth] emmeans 1.8.2-090007russell-lenth@uiowa.edu (Russell V. Lenth)Obtain estimated marginal means (EMMs) for many linear, generalized
linear, and mixed models. Compute contrasts or linear functions of EMMs,
trends, and comparisons of slopes. Plots and other displays.
Least-squares means are discussed, and the term "estimated marginal means"
is suggested, in Searle, Speed, and Milliken (1980) Population marginal means
in the linear model: An alternative to least squares means, The American
Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.https://github.com/r-universe/rvlenth/actions/runs/3609633845Sat, 03 Dec 2022 17:46:20 GMTemmeans1.8.2-090007successhttps://rvlenth.r-universe.devhttps://github.com/rvlenth/emmeansbasics.Rmdbasics.htmlBasics of EMMs2017-09-30 17:31:292022-12-01 22:32:35comparisons.Rmdcomparisons.htmlComparisons and contrasts2017-09-30 17:31:292022-12-01 22:32:35confidence-intervals.Rmdconfidence-intervals.htmlConfidence intervals and tests2017-09-30 17:31:292022-06-17 22:00:09xplanations.Rmdxplanations.htmlExplanations supplement2020-05-15 16:43:032022-12-01 22:32:35FAQs.RmdFAQs.htmlFAQs for emmeans2017-09-30 17:31:292022-05-16 01:34:23xtending.Rmdxtending.htmlFor developers: Extending emmeans2018-04-18 00:58:382020-12-07 21:35:31vignette-topics.Rmdvignette-topics.htmlIndex of vignette topics2018-04-07 16:04:432022-12-03 17:46:20interactions.Rmdinteractions.htmlInteraction analysis in emmeans2017-09-30 17:31:292022-12-01 22:32:35models.Rmdmodels.htmlModels supported by emmeans2017-09-30 17:31:292022-10-13 20:25:34predictions.Rmdpredictions.htmlPrediction in emmeans2019-06-06 03:41:312022-12-01 22:32:35re-engineering-clds.rmdre-engineering-clds.htmlRe-engineering CLDs2022-10-17 17:48:432022-10-27 23:02:50sophisticated.Rmdsophisticated.htmlSophisticated models in emmeans2017-10-12 01:34:522022-12-03 17:46:20transformations.Rmdtransformations.htmlTransformations and link functions2017-09-30 17:31:292022-12-01 22:32:35utilities.Rmdutilities.htmlUtilities and options2017-10-02 20:49:292022-07-26 00:30:51messy-data.Rmdmessy-data.htmlWorking with messy data2017-09-30 17:31:292022-12-01 22:32:35[tanho63] aoc.elf 0.0.10tan@tanho.ca (Tan Ho)Helpers for downloading Advent of Code answers. Eventually will help submit also?https://github.com/r-universe/tanho63/actions/runs/3609631696Sat, 03 Dec 2022 17:25:21 GMTaoc.elf0.0.10successhttps://tanho63.r-universe.devhttps://github.com/tanho63/aoc.elf[bimsbbioinfo] deconvR 1.4.3irembgunduz@gmail.com (Irem B. Gündüz)This package provides a collection of functions designed for
analyzing deconvolution of the bulk sample(s) using an atlas of reference
omic signature profiles and a user-selected model. Users are given the
option to create or extend a reference atlas and,also simulate the desired
size of the bulk signature profile of the reference cell types.The package
includes the cell-type-specific methylation atlas and, Illumina Epic B5
probe ids that can be used in deconvolution. Additionally,we included
BSmeth2Probe, to make mapping WGBS data to their probe IDs easier.https://github.com/r-universe/bimsbbioinfo/actions/runs/3609628568Sat, 03 Dec 2022 17:16:32 GMTdeconvR1.4.3failurehttps://bimsbbioinfo.r-universe.devhttps://github.com/bimsbbioinfo/deconvr[jinghuazhao] tdthap 1.2jinghuazhao@hotmail.com (Jing Hua Zhao)Functions and examples are provided for Transmission/disequilibrium tests
for extended marker haplotypes, as in
Clayton, D. and Jones, H. (1999) "Transmission/disequilibrium tests
for extended marker haplotypes". Amer. J. Hum. Genet., 65:1161-1169,
<doi:10.1086/302566>.https://github.com/r-universe/jinghuazhao/actions/runs/3609628657Sat, 03 Dec 2022 17:13:22 GMTtdthap1.2successhttps://jinghuazhao.r-universe.devhttps://github.com/jinghuazhao/rtdthap-paper.Rnwtdthap-paper.pdfDisequilibrium Tests for Extended Marker Haplotypes2018-01-26 12:35:082018-06-29 21:56:45[jinghuazhao] gap 1.3-1jinghuazhao@hotmail.com (Jing Hua Zhao)As first reported [Zhao, J. H. 2007. "gap: Genetic Analysis Package". J Stat Soft 23(8):1-18.
<doi:10.18637/jss.v023.i08>], it is designed as an integrated package for genetic data
analysis of both population and family data. Currently, it contains functions for
sample size calculations of both population-based and family-based designs, probability
of familial disease aggregation, kinship calculation, statistics in linkage analysis,
and association analysis involving genetic markers including haplotype analysis with or
without environmental covariates. Over years, the package has been developed in-between
many projects hence also in line with the name (gap).https://github.com/r-universe/jinghuazhao/actions/runs/3609628361Sat, 03 Dec 2022 17:13:22 GMTgap1.3-1successhttps://jinghuazhao.r-universe.devhttps://github.com/jinghuazhao/rgap.Rmdgap.htmlgap2021-05-28 20:05:022022-09-12 11:54:33jss.Rnwjss.pdfgap: genetic analysis package2018-01-23 16:28:492021-05-29 12:26:15shinygap.Rmdshinygap.htmlShiny for Genetic Analysis Package (gap) Designs2021-06-07 08:14:442021-06-27 20:44:20[jinghuazhao] lmm 1.3jinghuazhao@hotmail.com (Jing hua Zhao)It implements Expectation/Conditional Maximization Either (ECME)
and rapidly converging algorithms as well as
Bayesian inference for linear mixed models,
which is described in Schafer, J.L. (1998)
"Some improved procedures for linear mixed models".
Dept. of Statistics, The Pennsylvania State University.https://github.com/r-universe/jinghuazhao/actions/runs/3609628545Sat, 03 Dec 2022 17:13:22 GMTlmm1.3successhttps://jinghuazhao.r-universe.devhttps://github.com/jinghuazhao/rlmm-tr.Rnwlmm-tr.pdfSome improved procedures for linear mixed models2018-01-26 12:35:032018-06-29 21:34:46[jinghuazhao] gap.datasets 0.0.6jinghuazhao@hotmail.com (Jing Hua Zhao)Datasets associated with the 'gap' package. Currently,
it includes an example data for regional association
plot (CDKN), an example data for a genomewide association
meta-analysis (OPG), data in studies of Parkinson's diease (PD),
ALHD2 markers and alcoholism (aldh2), APOE/APOC1 markers
and Schizophrenia (apoeapoc), cystic fibrosis (cf), a
Olink/INF panel (inf1), Manhattan plots with (hr1420, mhtdata)
and without (w4) gene annotations.https://github.com/r-universe/jinghuazhao/actions/runs/3609628461Sat, 03 Dec 2022 17:13:22 GMTgap.datasets0.0.6successhttps://jinghuazhao.r-universe.devhttps://github.com/jinghuazhao/r[myles-lewis] nestedcv 0.4.4myles.lewis@qmul.ac.uk (Myles Lewis)Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) <doi:10.1111/j.2517-6161.1977.tb01603.x>. Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) <doi:10.1214/17-EJS1337SI>.https://github.com/r-universe/myles-lewis/actions/runs/3609628366Sat, 03 Dec 2022 17:06:48 GMTnestedcv0.4.4successhttps://myles-lewis.r-universe.devhttps://github.com/myles-lewis/nestedcvnestedcv.Rmdnestedcv.htmlnestedcv2022-03-14 17:02:132022-12-02 17:28:18