R-universe search: exports:kNN

Showing 8 of total 8 results (show query)

mhahsler

dbscan:Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms

A fast reimplementation of several density-based algorithms of the DBSCAN family. Includes the clustering algorithms DBSCAN (density-based spatial clustering of applications with noise) and HDBSCAN (hierarchical DBSCAN), the ordering algorithm OPTICS (ordering points to identify the clustering structure), shared nearest neighbor clustering, and the outlier detection algorithms LOF (local outlier factor) and GLOSH (global-local outlier score from hierarchies). The implementations use the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. An R interface to fast kNN and fixed-radius NN search is also provided. Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.

Maintained by Michael Hahsler. Last updated 2 months ago.

clustering dbscan density-based-clustering hdbscan lof optics cpp

324 stars 15.60 score 1.6k scripts 85 dependents

statistikat

VIM:Visualization and Imputation of Missing Values

New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.

Maintained by Matthias Templ. Last updated 8 months ago.

hotdeck imputation-methods model-predictions visualization cpp

85 stars 14.44 score 2.6k scripts 19 dependents

bioc

xcms:LC-MS and GC-MS Data Analysis

Framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. Preprocesses data for high-throughput, untargeted analyte profiling.

Maintained by Steffen Neumann. Last updated 19 days ago.

immunooncology massspectrometry metabolomics bioconductor feature-detection mass-spectrometry peak-detection cpp

196 stars 14.31 score 984 scripts 11 dependents

matloff

regtools:Regression and Classification Tools

Tools for linear, nonlinear and nonparametric regression and classification. Novel graphical methods for assessment of parametric models using nonparametric methods. One vs. All and All vs. All multiclass classification, optional class probabilities adjustment. Nonparametric regression (k-NN) for general dimension, local-linear option. Nonlinear regression with Eickert-White method for dealing with heteroscedasticity. Utilities for converting time series to rectangular form. Utilities for conversion between factors and indicator variables. Some code related to "Statistical Regression and Classification: from Linear Models to Machine Learning", N. Matloff, 2017, CRC, ISBN 9781498710916.

Maintained by Norm Matloff. Last updated 2 months ago.

127 stars 9.39 score 48 scripts 3 dependents

ltorgo

DMwR2:Functions and Data for the Second Edition of "Data Mining with R"

Functions and data accompanying the second edition of the book "Data Mining with R, learning with case studies" by Luis Torgo, published by CRC Press.

Maintained by Luis Torgo. Last updated 8 years ago.

27 stars 7.64 score 380 scripts 2 dependents

rezamoammadi

liver:"Eating the Liver of Data Science"

Provides a suite of helper functions and a collection of datasets used in the book <https://uncovering-data-science.netlify.app>. It is designed to make data science techniques accessible to individuals with minimal coding experience. Inspired by an ancient Persian idiom, the package likens this learning process to "eating the liver of data science," symbolizing deep and immersive engagement with the field.

Maintained by Reza Mohammadi. Last updated 15 days ago.

4.13 score 67 scripts

elvanceyhan

nnspat:Nearest Neighbor Methods for Spatial Patterns

Contains the functions for testing the spatial patterns (of segregation, spatial symmetry, association, disease clustering, species correspondence and reflexivity) based on nearest neighbor relations, especially using contingency tables such as nearest neighbor contingency tables (Ceyhan (2010) <doi:10.1007/s10651-008-0104-x> and Ceyhan (2017) <doi:10.1016/j.jkss.2016.10.002> and references therein), nearest neighbor symmetry contingency tables (Ceyhan (2014) <doi:10.1155/2014/698296>), species correspondence contingency tables and reflexivity contingency tables (Ceyhan (2018) <doi:10.2436/20.8080.02.72>) for two (or higher) dimensional data. Also contains functions for generating patterns of segregation, association, uniformity in a multi-class setting (Ceyhan (2014) <doi:10.1007/s00477-013-0824-9>), and various non-random labeling patterns for disease clustering in two dimensional cases (Ceyhan (2014) <doi:10.1002/sim.6053>), and for visualization of all these patterns for the two dimensional data. The tests are usually (asymptotic) normal z-tests and chi-square tests.

Maintained by Elvan Ceyhan. Last updated 3 years ago.

2.90 score 16 scripts

aalborg-intelligence

aai:Functions, apps, exercises and other R related stuff used in "AI - Aalborg Intelligence"

Functions, apps, exercises and other R related stuff used in "AI - Aalborg Intelligence" The project (2020 - 2026) is supported by the Novo Nordisk Foundation to develop teaching material to be used in the Danish highschools to strengthen the understanding of AI while explaining how basic maths is used in the some popular AI methods.

Maintained by Ege Rubak. Last updated 2 years ago.

1 stars 1.70 score 1 scripts