R-universe search: exports:euclidean

Showing 8 of total 8 results (show query)

branchlab

metasnf:Meta Clustering with Similarity Network Fusion

Framework to facilitate patient subtyping with similarity network fusion and meta clustering. The similarity network fusion (SNF) algorithm was introduced by Wang et al. (2014) in <doi:10.1038/nmeth.2810>. SNF is a data integration approach that can transform high-dimensional and diverse data types into a single similarity network suitable for clustering with minimal loss of information from each initial data source. The meta clustering approach was introduced by Caruana et al. (2006) in <doi:10.1109/ICDM.2006.103>. Meta clustering involves generating a wide range of cluster solutions by adjusting clustering hyperparameters, then clustering the solutions themselves into a manageable number of qualitatively similar solutions, and finally characterizing representative solutions to find ones that are best for the user's specific context. This package provides a framework to easily transform multi-modal data into a wide range of similarity network fusion-derived cluster solutions as well as to visualize, characterize, and validate those solutions. Core package functionality includes easy customization of distance metrics, clustering algorithms, and SNF hyperparameters to generate diverse clustering solutions; calculation and plotting of associations between features, between patients, and between cluster solutions; and standard cluster validation approaches including resampled measures of cluster stability, standard metrics of cluster quality, and label propagation to evaluate generalizability in unseen data. Associated vignettes guide the user through using the package to identify patient subtypes while adhering to best practices for unsupervised learning.

Maintained by Prashanth S Velayudhan. Last updated 10 hours ago.

bioinformatics clustering metaclustering snf

8 stars 8.23 score 30 scripts

dynverse

dynutils:Common Functionality for the 'dynverse' Packages

Provides common functionality for the 'dynverse' packages. 'dynverse' is created to support the development, execution, and benchmarking of trajectory inference methods. For more information, check out <https://dynverse.org>.

Maintained by Robrecht Cannoodt. Last updated 2 years ago.

cpp

3 stars 6.89 score 109 scripts 8 dependents

tom-wolff

ideanet:Integrating Data Exchange and Analysis for Networks ('ideanet')

A suite of convenient tools for social network analysis geared toward students, entry-level users, and non-expert practitioners. ‘ideanet’ features unique functions for the processing and measurement of sociocentric and egocentric network data. These functions automatically generate node- and system-level measures commonly used in the analysis of these types of networks. Outputs from these functions maximize the ability of novice users to employ network measurements in further analyses while making all users less prone to common data analytic errors. Additionally, ‘ideanet’ features an R Shiny graphic user interface that allows novices to explore network data with minimal need for coding.

Maintained by Tom Wolff. Last updated 20 days ago.

6 stars 6.80 score 10 scripts

spsanderson

RandomWalker:Generate Random Walks Compatible with the 'tidyverse'

Generates random walks of various types by providing a set of functions that are compatible with the 'tidyverse'. The functions provided in the package make it simple to create random walks with a variety of properties, such as how many simulations to run, how many steps to take, and the distribution of random walk itself.

Maintained by Steven Sanderson. Last updated 2 months ago.

random-walk random-walks rpackages

5 stars 6.05 score 5 scripts 1 dependents

missiegobeats

OutliersLearn:Educational Outlier Package with Common Outlier Detection Algorithms

Provides implementations of some of the most important outlier detection algorithms. Includes a tutorial mode option that shows a description of each algorithm and provides a step-by-step execution explanation of how it identifies outliers from the given data with the specified input parameters. References include the works of Azzedine Boukerche, Lining Zheng, and Omar Alfandi (2020) <doi:10.1145/3381028>, Abir Smiti (2020) <doi:10.1016/j.cosrev.2020.100306>, and Xiaogang Su, Chih-Ling Tsai (2011) <doi:10.1002/widm.19>.

Maintained by Andres Missiego Manjon. Last updated 10 months ago.

1 stars 4.54 score 2 scripts

wojtacht

hmsr:Multipopulation Evolutionary Strategy HMS

The HMS (Hierarchic Memetic Strategy) is a composite global optimization strategy consisting of a multi-population evolutionary strategy and some auxiliary methods. The HMS makes use of a dynamically-evolving data structure that provides an organization among the component populations. It is a tree with a fixed maximal height and variable internal node degree. Each component population is governed by a particular evolutionary engine. This package provides a simple R implementation with examples of using different genetic algorithms as the population engines. References: J. Sawicki, M. Łoś, M. Smołka, J. Alvarez-Aramberri (2022) <doi:10.1007/s11047-020-09836-w>.

Maintained by Wojciech Achtelik. Last updated 1 years ago.

3 stars 3.18 score 5 scripts

andriyprotsak5

UAHDataScienceO:Educational Outlier Detection Algorithms with Step-by-Step Tutorials

Maintained by Andriy Protsak Protsak. Last updated 2 months ago.

3.00 score

uncertaintyquantification

RobustGaSP:Robust Gaussian Stochastic Process Emulation

Robust parameter estimation and prediction of Gaussian stochastic process emulators. It allows for robust parameter estimation and prediction using Gaussian stochastic process emulator. It also implements the parallel partial Gaussian stochastic process emulator for computer model with massive outputs See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.

Maintained by Mengyang Gu. Last updated 1 years ago.

cpp

2.35 score 75 scripts 1 dependents