Package: rnndescent 0.2.0.9000

rnndescent: Nearest Neighbor Descent Method for Approximate Nearest Neighbors

The Nearest Neighbor Descent method for finding approximate nearest neighbors by Dong and co-workers (2010) <doi:10.1145/1963405.1963487>. Based on the 'Python' package 'PyNNDescent' <https://github.com/lmcinnes/pynndescent>.

Authors:James Melville [aut, cre, cph], Vitalie Spinu [ctb], Ralf Stubner [ctb]

rnndescent_0.2.0.9000.tar.gz
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rnndescent_0.2.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rnndescent/json (API)

# Install 'rnndescent' in R:
install.packages('rnndescent', repos = c('https://jlmelville.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jlmelville/rnndescent/issues

Pkgdown/docs site:https://jlmelville.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

approximate-nearest-neighbor-searchcpp

9.25 score 17 stars 3 packages 104 scripts 3.6k downloads 17 exports 6 dependencies

Last updated from:90b3778a3c. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK211
linux-devel-x86_64OK178
source / vignettesOK278
linux-release-arm64OK214
linux-release-x86_64OK192
macos-release-arm64OK135
macos-release-x86_64OK266
macos-oldrel-arm64OK152
macos-oldrel-x86_64OK253
windows-develOK206
windows-releaseOK193
windows-oldrelOK234
wasm-releaseOK202

Exports:brute_force_knnbrute_force_knn_querygraph_knn_queryk_occurmerge_knnneighbor_overlapnnd_knnprepare_search_graphrandom_knnrandom_knn_queryrnnd_buildrnnd_knnrnnd_queryrpf_buildrpf_filterrpf_knnrpf_knn_query

Dependencies:BHdqrnglatticeMatrixRcppsitmo

Querying Data
Brute Force | Random Projection Forests | Graph Search | n_threads | epsilon | init | Neighbor Graph Input | Neighbor Indices Only | Forest initialization | Preparing the Search Graph | Diversification Probability | Degree Pruning | References
Rendered fromquerying-data.Rmdusingknitr::rmarkdown

Last update: 2026-05-30
Started: 2023-11-18

Metrics
Specialized Binary Metrics | References
Rendered frommetrics.Rmdusingknitr::rmarkdown

Last update: 2025-12-24
Started: 2023-11-19

Hubness
Comparing Low- and High-Dimensional Nearest Neighbors | Detecting Hubness | k-occurrence in the 2D case | k-occurrence in the 1000D case | k-occurrence as a diagnostic of NND failure | Detecting Poorly Predicted Neighbors | Detecting Problems Early | Improving accuracy | Weight candidates by degree | Use More Neighbors | Use More Candidates | Decrease the convergence tolerance | Merging Multiple Independent Results | Using a Search Graph | Conclusions | References
Rendered fromhubness.Rmdusingknitr::rmarkdown

Last update: 2023-12-30
Started: 2023-11-15

Random Partition Forests
Building a Space-Partitioning Tree | Building a Random Partition Tree | From Trees to Forests | Build a Forest | Finding Nearest Neighbors | A Small Optimization for the k-Nearest Neighbors | Margin | Filtering a Forest | References
Rendered fromrandom-partition-forests.Rmdusingknitr::rmarkdown

Last update: 2023-11-30
Started: 2023-11-16

rnndescent
Find the k-nearest neighbors | The Neighbor Graph Format | Build an Index | Querying Data | Parallelism | Available Metrics | Supported Data Types | Parameters | References
Rendered fromrnndescent.Rmdusingknitr::rmarkdown

Last update: 2023-11-30
Started: 2021-08-09

Brute Force Search
Rendered frombrute-force.Rmdusingknitr::rmarkdown

Last update: 2023-11-20
Started: 2023-11-19

Nearest Neighbor Descent
Local Join | Other Heuristics | PyNNDescent Modifications | Example | Troubleshooting | Querying New Data | References
Rendered fromnearest-neighbor-descent.Rmdusingknitr::rmarkdown

Last update: 2023-11-18
Started: 2023-11-18