# umap v0.2.2.0

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## Uniform Manifold Approximation and Projection

Uniform manifold approximation and projection is a technique
for dimension reduction. The algorithm was described by McInnes and
Healy (2018) in <arXiv:1802.03426>. This package provides an interface
for two implementations. One is written from scratch, including components
for nearest-neighbor search and for embedding. The second implementation
is a wrapper for 'python' package 'umap-learn' (requires separate
installation, see vignette for more details).

## Functions in umap

Name | Description | |

identity.coo | Construct an identity matrix | |

detect.umap.learn | adjust config depending on umap-learn version | |

predict.umap | project data points onto an existing umap embedding | |

print.umap.knn | Display summary of knn.info | |

optimize_epoch | run one epoch of the umap optimization | |

reduce.coo | Remove some entires in a coo matrix where values are zero | |

clip | Force (clip) a value into a finite range | |

check.learn.available | check whether python module is available, abort if not | |

dManhattan | compute Manhattan distance between two vectors | |

dEuclidean | compute Euclidean distance between two vectors | |

find.ab.params | Estimate a/b parameters | |

knn.from.dist | get information about k nearest neighbors from a distance object or from a matrix with distances | |

make.initial.spectator.embedding | Create an initial embedding for a set of spectators | |

dCenteredPearson | compute pearson correlation distance between two vectors | |

make.epochs.per.sample | Compute a value to capture how often each item contributes to layout optimization | |

dCosine | compute cosine dissimilarity between two vectors | |

make.initial.embedding | Create an initial embedding for a graph | |

knn.info | Compute knn information | |

mdManhattan | compute Manhattan distances | |

message.w.date | Send a message() with a prefix with a data | |

clip4 | perform a compound transformation on a vector, including clipping | |

umap.learn | Create a umap embedding using python package umap-learn | |

mdCosine | compute cosine distances | |

concomp.coo | Count the number of connected components in a coo graph | |

knn.from.data | get information about approximate k nearest neighbors from a data matrix | |

naive.optimize.embedding | modify an existing embedding | |

make.random.embedding | Make an initial embedding with random coordinates | |

knn.from.data.reps | Repeat knn.from.data multiple times, pick the best neighbors | |

make.spectral.embedding | Create a spectral embedding for a connectivity graph | |

naive.simplicial.set.embedding | create an embedding of graph into a low-dimensional space | |

mdEuclidean | compute Euclidean distances | |

umap.learn.predict | predict embedding of new data given an existing umap object | |

umap.prep.input | Prep primary input as a data matrix | |

mdCenteredPearson | compute pearson correlation distances | |

stop.coo | Stop execution with a custom message | |

umap.small | Create an embedding object compatible with package umap for very small inputs | |

multiply.coo | Multiply two coo objects element-wise | |

umap.naive | Create a umap embedding | |

umap.naive.predict | predict embedding of new data given an existing umap object | |

subset.coo | Subset a coo | |

set.global.seed | set .Random.seed to a pre-saved value | |

print.umap | Display a summary of a umap object | |

print.umap.config | Display contents of a umap configuration | |

smooth.knn.dist | compute a "smooth" distance to the kth neighbor and approximate first neighbor | |

umap.check.config | Validator functions for umap settings | |

umap.defaults | Default configuration for umap | |

umap.error | stop execution with a custom error message | |

umap.check.config.class | Validator for config class component | |

umap.warning | create a warning message | |

naive.fuzzy.simplicial.set | create a simplicial set from a distance object | |

spectator.knn.info | compute knn information for spectators relative to data | |

spectral.eigenvectors | get a set of k eigenvectors for the laplacian of x | |

t.coo | Transpose a coo matrix | |

umap | Computes a manifold approximation and projection | |

coo | Create a coo representation of a square matrix | |

check.coo | Check class for coo | |

check.compatible.coo | Check that two coo objects are compatible for addition, multiplication | |

add.coo | Add two coo objects element-wise | |

coo2mat | Convert from coo object into conventional matrix | |

center.embedding | Adjust a matrix so that each column is centered around zero | |

laplacian.coo | Construct a normalized Laplacian for a graph | |

make.coo | Helper to construct coo objects | |

get.global.seed | lookup .Random.seed in global environment | |

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## Vignettes of umap

Name | ||

umap.Rmd | ||

umap_learn.Rmd | ||

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## Last month downloads

## Details

License | MIT + file LICENSE |

URL | https://github.com/tkonopka/umap |

LinkingTo | Rcpp |

LazyData | true |

VignetteBuilder | knitr |

RoxygenNote | 6.1.1 |

NeedsCompilation | yes |

Packaged | 2019-05-13 05:38:11 UTC; tkonopka |

Repository | CRAN |

Date/Publication | 2019-05-13 06:20:03 UTC |

suggests | knitr , rmarkdown , testthat |

imports | methods , Rcpp (>= 0.12.6) , reticulate , RSpectra , stats |

depends | R (>= 3.1.2) |

Contributors |

#### Include our badge in your README

```
[![Rdoc](http://www.rdocumentation.org/badges/version/umap)](http://www.rdocumentation.org/packages/umap)
```