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" --------------------------------- 1332 packages in 0.057 seconds -
#> # package version by @ title
#> 1 100 survival 3.8.3 Terry M Therneau 1y Survival Analysis
#> 2 11 survminer 0.5.1 Alboukadel Kassambara 3M Drawing Survival...
#> 3 10 flexsurv 2.3.2 Christopher Jackson 1y Flexible Paramet...
#> 4 5 rpart 4.1.24 Beth Atkinson 11M Recursive Partit...
#> 5 5 timereg 2.0.7 Thomas Scheike 3M Flexible Regress...
#> 6 5 pec 2025.6.24 Thomas A. Gerds 4M Prediction Error...
#> 7 5 relsurv 2.3.3 Damjan Manevski 3M Relative Survival
#> 8 4 riskRegression 2025.9.17 Thomas Alexander Gerds 2M Risk Regression ...
#> 9 4 muhaz 1.2.6.4 David Winsemius 5y Hazard Function ...
#> 10 4 censored 0.3.3 Hannah Frick 9M 'parsnip' Engine...
# Pagination
ps("networks")
#> - "networks" --------------------------------- 1093 packages in 0.056 seconds -
#> # package version by @ title
#> 1 100 network 1.19.0 Carter T. Butts 1y Classes for Relational ...
#> 2 78 igraph 2.2.1 Kirill Müller 22d Network Analysis and Vi...
#> 3 73 nnet 7.3.20 Brian Ripley 11M Feed-Forward Neural Net...
#> 4 69 DiagrammeR 1.0.11 Richard Iannone 2y Graph/Network Visualiza...
#> 5 58 RCurl 1.98.1.17 CRAN Team 8M General Network (HTTP/F...
#> 6 55 ggraph 2.2.2 Thomas Lin Pedersen 3M An Implementation of Gr...
#> 7 51 visNetwork 2.1.4 Benoit Thieurmel 3M Network Visualization u...
#> 8 46 sna 2.8 Carter T. Butts 1y Tools for Social Networ...
#> 9 39 snow 0.4.4 Luke Tierney 4y Simple Network of Works...
#> 10 34 neuralnet 1.44.2 Marvin N. Wright 7y Training of Neural Netw...
more()
#> - "networks" --------------------------------- 1093 packages in 0.031 seconds -
#> # package version by @ title
#> 11 28 networkD3 0.4.1 Christopher Gandrud 7M D3 JavaScript Network...
#> 12 26 ergm 4.10.1 Pavel N. Krivitsky 3M Fit, Simulate and Dia...
#> 13 26 bnlearn 5.1 Marco Scutari 3M Bayesian Network Stru...
#> 14 24 spatstat.linnet 3.3.2 Adrian Baddeley 2M Linear Networks Funct...
#> 15 23 intergraph 2.0.4 Michał Bojanowski 2y Coercion Routines for...
#> 16 23 WGCNA 1.73 Peter Langfelder 1y Weighted Correlation ...
#> 17 22 diagram 1.6.5 Karline Soetaert 5y Functions for Visuali...
#> 18 21 ggnetwork 0.5.14 François Briatte 2M Geometries to Plot Ne...
#> 19 21 netmeta 3.2.0 Guido Schwarzer 7M Network Meta-Analysis...
#> 20 20 sfnetworks 0.6.5 Lucas van der Meer 1y Tidy Geospatial Networks
# Details
ps("visualization")
#> - "visualization" ----------------------------- 2219 packages in 0.05 seconds -
#> # package version by @ title
#> 1 100 scales 1.4.0 Thomas Lin Pedersen 7M Scale Functions for Visua...
#> 2 46 vdiffr 1.0.8 Lionel Henry 1y Visual Regression Testing...
#> 3 43 ggplot2 4.0.1 Thomas Lin Pedersen 4d Create Elegant Data Visua...
#> 4 37 igraph 2.2.1 Kirill Müller 22d Network Analysis and Visu...
#> 5 35 rgl 1.3.31 Duncan Murdoch 4d 3D Visualization Using Op...
#> 6 33 DiagrammeR 1.0.11 Richard Iannone 2y Graph/Network Visualization
#> 7 28 corrplot 0.95 Taiyun Wei 1y Visualization of a Correl...
#> 8 27 circlize 0.4.16 Zuguang Gu 2y Circular Visualization
#> 9 24 visNetwork 2.1.4 Benoit Thieurmel 3M Network Visualization usi...
#> 10 22 ROCR 1.0.11 Felix G.M. Ernst 6y Visualizing the Performan...
ps()
#> - "visualization" ----------------------------- 2219 packages in 0.05 seconds -
#>
#> 1 scales @ 1.4.0 Thomas Lin Pedersen, 7 months 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
#>
#> 2 vdiffr @ 1.0.8 Lionel Henry, about a year 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
#>
#> 3 ggplot2 @ 4.0.1 Thomas Lin Pedersen, 4 days 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
#>
#> 4 igraph @ 2.2.1 Kirill Müller, 22 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/
#>
#> 5 rgl @ 1.3.31 Duncan Murdoch, 4 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/
#>
#> 6 DiagrammeR @ 1.0.11 Richard Iannone, 2 years 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, about a year 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, 2 years 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 @ 2.1.4 Benoit Thieurmel, 3 months 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, 6 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" ---------------------------------- 300 packages in 0.053 seconds -
#> # package version by @ title
#> 1 100 vcr 2.0.0 Scott Chamberlain 4M Record 'HTTP' Calls to...
#> 2 75 webmockr 2.2.0 Scott Chamberlain 4M Stubbing and Setting E...
#> 3 73 RSelenium 1.7.9 Ju Yeong Kim 3y R Bindings for 'Seleni...
#> 4 69 tracerer 2.2.3 Richèl J.C. Bilderbeek 2y Tracer from R
#> 5 57 fastMatMR 1.2.5 Rohit Goswami 2y High-Performance Matri...
#> 6 55 rfisheries 0.2 Karthik Ram 10y 'Programmatic Interfac...
#> 7 55 mcbette 1.15.3 Richèl J.C. Bilderbeek 1y Model Comparison Using...
#> 8 55 beastier 2.5.2 Richèl J.C. Bilderbeek 1y Call 'BEAST2'
#> 9 54 visdat 0.6.0 Nicholas Tierney 3y Preliminary Visualisat...
#> 10 50 charlatan 0.6.1 Roel M. Hogervorst 1y Make Fake Data
ps()[]
#> # A data frame: 10 × 14
#> score package version title description date maintainer_name
#> <dbl> <chr> <pckg_> <chr> <chr> <dttm> <chr>
#> 1 700. vcr 2.0.0 Reco… "Record te… 2025-07-23 11:24:48 Scott Chamberl…
#> 2 523. webmockr 2.2.0 Stub… "Stubbing … 2025-07-21 04:00:02 Scott Chamberl…
#> 3 511. RSelenium 1.7.9 R Bi… "Provides … 2022-09-02 07:10:11 Ju Yeong Kim
#> 4 481. tracerer 2.2.3 Trac… "'BEAST2' … 2023-09-27 10:30:02 Richèl J.C. Bi…
#> 5 396. fastMatMR 1.2.5 High… "An interf… 2023-11-03 21:00:06 Rohit Goswami
#> 6 387. rfisheri… 0.2 'Pro… "A program… 2016-02-19 08:50:03 Karthik Ram
#> 7 385. mcbette 1.15.3 Mode… "'BEAST2' … 2024-08-19 07:40:06 Richèl J.C. Bi…
#> 8 385. beastier 2.5.2 Call… "'BEAST2' … 2024-10-06 14:00:02 Richèl J.C. Bi…
#> 9 379. visdat 0.6.0 Prel… "Create pr… 2023-02-02 02:10:02 Nicholas Tiern…
#> 10 352. charlatan 0.6.1 Make… "Make fake… 2024-10-17 04:20:02 Roel M. Hogerv…
#> # ℹ 7 more variables: maintainer_email <chr>, revdeps <int>,
#> # downloads_last_month <int>, license <chr>, url <chr>, bugreports <chr>,
#> # package_data <I<list>>