pkg_search() starts a new search query, or shows the details of the
previous query, if called without arguments.
ps() is an alias to pkg_search().
more() retrieves that next page of results for the previous query.
Search query string. If this argument is missing or
NULL, then the results of the last query are printed, in
short and long formats, in turns for successive
pkg_search() calls. If this argument is missing, then all
other arguments are ignored.
Default formatting of the results. short only outputs the name and title of the packages, long also prints the author, last version, full description and URLs. Note that this only affects the default printing, and you can still inspect the full results, even if you specify short here.
Where to start listing the results, for pagination.
The number of results to list.
Object to summarize.
Additional arguments, ignored currently.
Object to print.
A data frame with columns:
score: Score of the hit. See Section Scoring for some details.
package: Package name.
version: Latest package version.
title: Package title.
description: Short package description.
date: Time stamp of the last release.
maintainer_name: Name of the package maintainer.
maintainer_email: Email address of the package maintainer.
revdeps: Number of (strong and weak) reverse dependencies of the
package.
downloads_last_month: Raw number of package downloads last month,
from the RStudio CRAN mirror.
license: Package license.
url: Package URL(s).
bugreports: URL of issue tracker, or email address for bug reports.
Note that the search needs a working Internet connection.
# Example
ps("survival")
#> - "survival" --------------------------------- 1217 packages in 0.014 seconds -
#> # package version by @ title
#> 1 100 survival 3.8.3 Terry M Therneau 23d Survival Analysis
#> 2 10 survminer 0.5.0 Alboukadel Kassambara 2M Drawing Survival...
#> 3 10 flexsurv 2.3.2 Christopher Jackson 5M Flexible Paramet...
#> 4 6 rstpm2 1.6.6.1 Mark Clements 19d Smooth Survival ...
#> 5 6 rpart 4.1.24 Beth Atkinson 3d Recursive Partit...
#> 6 5 randomForestSRC 3.3.1 Udaya B. Kogalur 6M Fast Unified Ran...
#> 7 5 pec 2023.4.12 Thomas A. Gerds 2y Prediction Error...
#> 8 5 relsurv 2.2.9 Damjan Manevski 2y Relative Survival
#> 9 4 timereg 2.0.6 Thomas Scheike 4M Flexible Regress...
#> 10 4 muhaz 1.2.6.4 David Winsemius 4y Hazard Function ...
# Pagination
ps("networks")
#> - "networks" --------------------------------- 1016 packages in 0.008 seconds -
#> # package version by @ title
#> 1 100 igraph 2.1.3 Kirill Müller 2d Network Analysis and Vi...
#> 2 54 network 1.19.0 Carter T. Butts 1M Classes for Relational ...
#> 3 39 nnet 7.3.20 Brian Ripley 8d Feed-Forward Neural Net...
#> 4 37 DiagrammeR 1.0.11 Richard Iannone 1y Graph/Network Visualiza...
#> 5 33 RCurl 1.98.1.16 CRAN Team 6M General Network (HTTP/F...
#> 6 29 ggraph 2.2.1 Thomas Lin Pedersen 10M An Implementation of Gr...
#> 7 26 visNetwork 2.1.2 Benoit Thieurmel 2y Network Visualization u...
#> 8 26 sna 2.8 Carter T. Butts 4M Tools for Social Networ...
#> 9 21 snow 0.4.4 Luke Tierney 3y Simple Network of Works...
#> 10 19 neuralnet 1.44.2 Marvin N. Wright 6y Training of Neural Netw...
more()
#> - "networks" --------------------------------- 1016 packages in 0.009 seconds -
#> # package version by @ title
#> 11 17 torch 0.13.0 Daniel Falbel 8M Tensors and Neural Ne...
#> 12 15 networkD3 0.4 Christopher Gandrud 8y D3 JavaScript Network...
#> 13 15 ergm 4.7.5 Pavel N. Krivitsky 2M Fit, Simulate and Dia...
#> 14 14 bnlearn 5.0.2 Marco Scutari 2d Bayesian Network Stru...
#> 15 13 spatstat.linnet 3.2.3 Adrian Baddeley 2M Linear Networks Funct...
#> 16 13 intergraph 2.0.4 Michał Bojanowski 1y Coercion Routines for...
#> 17 12 WGCNA 1.73 Peter Langfelder 4M Weighted Correlation ...
#> 18 12 diagram 1.6.5 Karline Soetaert 4y Functions for Visuali...
#> 19 11 RSNNS 0.4.17 Christoph Bergmeir 1y Neural Networks using...
#> 20 11 networkDynamic 0.11.5 Skye Bender-deMoll 2M Dynamic Extensions fo...
# Details
ps("visualization")
#> - "visualization" ---------------------------- 1909 packages in 0.008 seconds -
#> # package version by @ title
#> 1 100 ggplot2 3.5.1 Thomas Lin Pedersen 9M Create Elegant Data Visua...
#> 2 60 scales 1.3.0 Thomas Lin Pedersen 1y Scale Functions for Visua...
#> 3 55 igraph 2.1.3 Kirill Müller 2d Network Analysis and Visu...
#> 4 30 rgl 1.3.16 Duncan Murdoch 3d 3D Visualization Using Op...
#> 5 28 vdiffr 1.0.8 Lionel Henry 2M Visual Regression Testing...
#> 6 20 DiagrammeR 1.0.11 Richard Iannone 1y Graph/Network Visualization
#> 7 17 corrplot 0.95 Taiyun Wei 3M Visualization of a Correl...
#> 8 16 circlize 0.4.16 Zuguang Gu 11M Circular Visualization
#> 9 14 visNetwork 2.1.2 Benoit Thieurmel 2y Network Visualization usi...
#> 10 14 ROCR 1.0.11 Felix G.M. Ernst 5y Visualizing the Performan...
ps()
#> - "visualization" ---------------------------- 1909 packages in 0.008 seconds -
#>
#> 1 ggplot2 @ 3.5.1 Thomas Lin Pedersen, 9 months ago
#> -----------------
#> # Create Elegant Data Visualisations Using the Grammar of Graphics
#> A system for 'declaratively' creating graphics, based on "The Grammar
#> of Graphics". You provide the data, tell 'ggplot2' how to map
#> variables to aesthetics, what graphical primitives to use, and it
#> takes care of the details.
#> https://ggplot2.tidyverse.org
#> https://github.com/tidyverse/ggplot2
#>
#> 2 scales @ 1.3.0 Thomas Lin Pedersen, about a year ago
#> ----------------
#> # Scale Functions for Visualization
#> Graphical scales map data to aesthetics, and provide methods for
#> automatically determining breaks and labels for axes and legends.
#> https://scales.r-lib.org
#> https://github.com/r-lib/scales
#>
#> 3 igraph @ 2.1.3 Kirill Müller, 2 days ago
#> ----------------
#> # Network Analysis and Visualization
#> Routines for simple graphs and network analysis. It can handle large
#> graphs very well and provides functions for generating random and
#> regular graphs, graph visualization, centrality methods and much
#> more.
#> https://r.igraph.org/
#> https://igraph.org/
#> https://igraph.discourse.group/
#>
#> 4 rgl @ 1.3.16 Duncan Murdoch, 3 days ago
#> --------------
#> # 3D Visualization Using OpenGL
#> 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/dmurdoch/rgl
#> https://dmurdoch.github.io/rgl/
#>
#> 5 vdiffr @ 1.0.8 Lionel Henry, 2 months ago
#> ----------------
#> # Visual Regression Testing and Graphical Diffing
#> An extension to the 'testthat' package that makes it easy to add
#> graphical unit tests. It provides a Shiny application to manage the
#> test cases.
#> https://vdiffr.r-lib.org/
#> https://github.com/r-lib/vdiffr
#>
#> 6 DiagrammeR @ 1.0.11 Richard Iannone, about a year ago
#> ---------------------
#> # Graph/Network Visualization
#> Build graph/network structures using functions for stepwise addition
#> and deletion of nodes and edges. Work with data available in tables
#> for bulk addition of nodes, edges, and associated metadata. Use graph
#> selections and traversals to apply changes to specific nodes or
#> edges. A wide selection of graph algorithms allow for the analysis of
#> graphs. Visualize the graphs and take advantage of any aesthetic
#> properties assigned to nodes and edges.
#> https://rich-iannone.github.io/DiagrammeR/
#> https://github.com/rich-iannone/DiagrammeR
#>
#> 7 corrplot @ 0.95 Taiyun Wei, 3 months ago
#> -----------------
#> # Visualization of a Correlation Matrix
#> Provides a visual exploratory tool on correlation matrix that
#> supports automatic variable reordering to help detect hidden patterns
#> among variables.
#> https://github.com/taiyun/corrplot
#>
#> 8 circlize @ 0.4.16 Zuguang Gu, 11 months ago
#> -------------------
#> # Circular Visualization
#> Circular layout is an efficient way for the visualization of huge
#> amounts of information. Here this package provides an implementation
#> of circular layout generation in R as well as an enhancement of
#> available software. The flexibility of the package is based on the
#> usage of low-level graphics functions such that self-defined
#> high-level graphics can be easily implemented by users for specific
#> purposes. Together with the seamless connection between the powerful
#> computational and visual environment in R, it gives users more
#> convenience and freedom to design figures for better understanding
#> complex patterns behind multiple dimensional data. The package is
#> described in Gu et al. 2014 <doi:10.1093/bioinformatics/btu393>.
#> https://github.com/jokergoo/circlize
#> https://jokergoo.github.io/circlize_book/book/
#>
#> 9 visNetwork @ 2.1.2 Benoit Thieurmel, 2 years ago
#> --------------------
#> # Network Visualization using 'vis.js' Library
#> Provides an R interface to the 'vis.js' JavaScript charting library.
#> It allows an interactive visualization of networks.
#> https://datastorm-open.github.io/visNetwork/
#>
#> 10 ROCR @ 1.0.11 Felix G.M. Ernst, 5 years ago
#> ----------------
#> # Visualizing the Performance of Scoring Classifiers
#> ROC graphs, sensitivity/specificity curves, lift charts, and
#> precision/recall plots are popular examples of trade-off
#> visualizations for specific pairs of performance measures. ROCR is a
#> flexible tool for creating cutoff-parameterized 2D performance curves
#> by freely combining two from over 25 performance measures (new
#> performance measures can be added using a standard interface). Curves
#> from different cross-validation or bootstrapping runs can be averaged
#> by different methods, and standard deviations, standard errors or box
#> plots can be used to visualize the variability across the runs. The
#> parameterization can be visualized by printing cutoff values at the
#> corresponding curve positions, or by coloring the curve according to
#> cutoff. All components of a performance plot can be quickly adjusted
#> using a flexible parameter dispatching mechanism. Despite its
#> flexibility, ROCR is easy to use, with only three commands and
#> reasonable default values for all optional parameters.
#> http://ipa-tys.github.io/ROCR/
# See the underlying data frame
ps("ropensci")
#> - "ropensci" ---------------------------------- 279 packages in 0.011 seconds -
#> # package version by @ title
#> 1 100 vcr 1.6.0 Scott Chamberlain 6M Record 'HTTP' Cal...
#> 2 78 RSelenium 1.7.9 Ju Yeong Kim 2y R Bindings for 'S...
#> 3 72 webmockr 1.0.0 Scott Chamberlain 6M Stubbing and Sett...
#> 4 66 tracerer 2.2.3 Richèl J.C. Bilderbeek 1y Tracer from R
#> 5 56 charlatan 0.6.1 Roel M. Hogervorst 3M Make Fake Data
#> 6 55 fastMatMR 1.2.5 Rohit Goswami 1y High-Performance ...
#> 7 54 rfisheries 0.2 Karthik Ram 9y 'Programmatic Int...
#> 8 53 googleLanguageR 0.3.0 Mark Edmondson 5y Call Google's 'Na...
#> 9 53 mcbette 1.15.3 Richèl J.C. Bilderbeek 5M Model Comparison ...
#> 10 52 beastier 2.5.2 Richèl J.C. Bilderbeek 3M Call 'BEAST2'
ps()[]
#> # A data frame: 10 × 14
#> score package version title description date maintainer_name
#> <dbl> <chr> <pckg_> <chr> <chr> <dttm> <chr>
#> 1 714. vcr 1.6.0 "Rec… "Record te… 2024-07-23 20:40:02 Scott Chamberl…
#> 2 560. RSelenium 1.7.9 "R B… "Provides … 2022-09-02 07:10:11 Ju Yeong Kim
#> 3 513. webmockr 1.0.0 "Stu… "Stubbing … 2024-07-23 04:50:02 Scott Chamberl…
#> 4 471. tracerer 2.2.3 "Tra… "'BEAST2' … 2023-09-27 10:30:02 Richèl J.C. Bi…
#> 5 397. charlatan 0.6.1 "Mak… "Make fake… 2024-10-17 04:20:02 Roel M. Hogerv…
#> 6 395. fastMatMR 1.2.5 "Hig… "An interf… 2023-11-03 21:00:06 Rohit Goswami
#> 7 387. rfisheri… 0.2 "'Pr… "A program… 2016-02-19 08:50:03 Karthik Ram
#> 8 380. googleLa… 0.3.0 "Cal… "Call 'Goo… 2020-04-19 12:40:02 Mark Edmondson
#> 9 379. mcbette 1.15.3 "Mod… "'BEAST2' … 2024-08-19 07:40:06 Richèl J.C. Bi…
#> 10 374. beastier 2.5.2 "Cal… "'BEAST2' … 2024-10-06 14:00:02 Richèl J.C. Bi…
#> # ℹ 7 more variables: maintainer_email <chr>, revdeps <int>,
#> # downloads_last_month <int>, license <chr>, url <chr>, bugreports <chr>,
#> # package_data <I<list>>