R-universe search: needs:registry

talgalili

heatmaply:Interactive Cluster Heat Maps Using 'plotly' and 'ggplot2'

Create interactive cluster 'heatmaps' that can be saved as a stand- alone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and available in the 'RStudio' viewer pane. Hover the mouse pointer over a cell to show details or drag a rectangle to zoom. A 'heatmap' is a popular graphical method for visualizing high-dimensional data, in which a table of numbers are encoded as a grid of colored cells. The rows and columns of the matrix are ordered to highlight patterns and are often accompanied by 'dendrograms'. 'Heatmaps' are used in many fields for visualizing observations, correlations, missing values patterns, and more. Interactive 'heatmaps' allow the inspection of specific value by hovering the mouse over a cell, as well as zooming into a region of the 'heatmap' by dragging a rectangle around the relevant area. This work is based on the 'ggplot2' and 'plotly.js' engine. It produces similar 'heatmaps' to 'heatmap.2' with the advantage of speed ('plotly.js' is able to handle larger size matrix), the ability to zoom from the 'dendrogram' panes, and the placing of factor variables in the sides of the 'heatmap'.

Maintained by Tal Galili. Last updated 9 months ago.

d3-heatmap dendextend dendrogram ggplot2 heatmap plotly

386 stars 14.21 score 2.0k scripts 45 dependents

mhahsler

seriation:Infrastructure for Ordering Objects Using Seriation

Infrastructure for ordering objects with an implementation of several seriation/sequencing/ordination techniques to reorder matrices, dissimilarity matrices, and dendrograms. Also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT). Hahsler et al (2008) <doi:10.18637/jss.v025.i03>.

Maintained by Michael Hahsler. Last updated 3 days ago.

combinatorial-optimization ordination seriation fortran

77 stars 14.11 score 640 scripts 79 dependents

tidymodels

corrr:Correlations in R

A tool for exploring correlations. It makes it possible to easily perform routine tasks when exploring correlation matrices such as ignoring the diagonal, focusing on the correlations of certain variables against others, or rearranging and visualizing the matrix in terms of the strength of the correlations.

Maintained by Max Kuhn. Last updated 1 years ago.

593 stars 13.82 score 2.9k scripts 7 dependents

pecanproject

PEcAn.data.atmosphere:PEcAn Functions Used for Managing Climate Driver Data

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The PECAn.data.atmosphere package converts climate driver data into a standard format for models integrated into PEcAn. As a standalone package, it provides an interface to access diverse climate data sets.

Maintained by David LeBauer. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 11.61 score 64 scripts 14 dependents

braverock

PortfolioAnalytics:Portfolio Analysis, Including Numerical Methods for Optimization of Portfolios

Portfolio optimization and analysis routines and graphics.

Maintained by Brian G. Peterson. Last updated 4 months ago.

81 stars 11.49 score 626 scripts 2 dependents

mhahsler

arulesViz:Visualizing Association Rules and Frequent Itemsets

Extends package 'arules' with various visualization techniques for association rules and itemsets. The package also includes several interactive visualizations for rule exploration. Michael Hahsler (2017) <doi:10.32614/RJ-2017-047>.

Maintained by Michael Hahsler. Last updated 7 months ago.

arules association-rules frequent-itemsets interactive-visualizations visualization

54 stars 11.03 score 1.7k scripts 2 dependents

mhahsler

recommenderlab:Lab for Developing and Testing Recommender Algorithms

Provides a research infrastructure to develop and evaluate collaborative filtering recommender algorithms. This includes a sparse representation for user-item matrices, many popular algorithms, top-N recommendations, and cross-validation. Hahsler (2022) <doi:10.48550/arXiv.2205.12371>.

Maintained by Michael Hahsler. Last updated 4 days ago.

collaborative-filtering recommender-system

214 stars 10.42 score 840 scripts 2 dependents

pecanproject

PEcAn.assim.batch:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by Istem Fer. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 9.96 score 20 scripts 2 dependents

statdivlab

corncob:Count Regression for Correlated Observations with the Beta-Binomial

Statistical modeling for correlated count data using the beta-binomial distribution, described in Martin et al. (2020) <doi:10.1214/19-AOAS1283>. It allows for both mean and overdispersion covariates.

Maintained by Amy D Willis. Last updated 11 days ago.

106 stars 9.82 score 248 scripts 1 dependents

pecanproject

PEcAnRTM:PEcAn Functions Used for Radiative Transfer Modeling

Functions for performing forward runs and inversions of radiative transfer models (RTMs). Inversions can be performed using maximum likelihood, or more complex hierarchical Bayesian methods. Underlying numerical analyses are optimized for speed using Fortran code.

Maintained by Alexey Shiklomanov. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants fortran jags cpp

216 stars 9.70 score 132 scripts

bioc

pcaExplorer:Interactive Visualization of RNA-seq Data Using a Principal Components Approach

This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis.

Maintained by Federico Marini. Last updated 3 months ago.

immunooncology visualization rnaseq dimensionreduction principalcomponent qualitycontrol gui reportwriting shinyapps bioconductor principal-components reproducible-research rna-seq-analysis rna-seq-data shiny transcriptome user-friendly

56 stars 9.63 score 180 scripts

bioc

clusterExperiment:Compare Clusterings for Single-Cell Sequencing

Provides functionality for running and comparing many different clusterings of single-cell sequencing data or other large mRNA Expression data sets.

Maintained by Elizabeth Purdom. Last updated 5 months ago.

clustering rnaseq sequencing software singlecell cpp

38 stars 9.62 score 192 scripts 1 dependents

shixiangwang

sigminer:Extract, Analyze and Visualize Mutational Signatures for Genomic Variations

Genomic alterations including single nucleotide substitution, copy number alteration, etc. are the major force for cancer initialization and development. Due to the specificity of molecular lesions caused by genomic alterations, we can generate characteristic alteration spectra, called 'signature' (Wang, Shixiang, et al. (2021) <DOI:10.1371/journal.pgen.1009557> & Alexandrov, Ludmil B., et al. (2020) <DOI:10.1038/s41586-020-1943-3> & Steele Christopher D., et al. (2022) <DOI:10.1038/s41586-022-04738-6>). This package helps users to extract, analyze and visualize signatures from genomic alteration records, thus providing new insight into cancer study.

Maintained by Shixiang Wang. Last updated 6 months ago.

bayesian-nmf bioinformatics cancer-research cnv copynumber-signatures cosmic-signatures dbs easy-to-use indel mutational-signatures nmf nmf-extraction sbs signature-extraction somatic-mutations somatic-variants visualization cpp

150 stars 9.48 score 123 scripts 2 dependents

bioc

OUTRIDER:OUTRIDER - OUTlier in RNA-Seq fInDER

Identification of aberrant gene expression in RNA-seq data. Read count expectations are modeled by an autoencoder to control for confounders in the data. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. Furthermore, OUTRIDER provides useful plotting functions to analyze and visualize the results.

Maintained by Christian Mertes. Last updated 5 months ago.

immunooncology rnaseq transcriptomics alignment sequencing geneexpression genetics count-data diagnostics expression-analysis mendelian-genetics outlier-detection rna-seq openblas cpp

50 stars 9.07 score 110 scripts 1 dependents

pecanproject

PEcAn.all:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PEcAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by David LeBauer. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 9.00 score 266 scripts

pecanproject

PEcAn.MAAT:PEcAn Package for Integration of the MAAT Model

This module provides functions to wrap the MAAT model into the PEcAn workflows.

Maintained by Shawn Serbin. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 8.96 score 12 scripts

pecanproject

PEcAn.BIOCRO:PEcAn Package for Integration of the BioCro Model

This module provides functions to link BioCro to PEcAn.

Maintained by David LeBauer. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 8.94 score 23 scripts

pecanproject

PEcAn.workflow:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PEcAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. This package provides workhorse functions that can be used to run the major steps of a PEcAn analysis.

Maintained by David LeBauer. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 8.83 score 15 scripts 4 dependents

pecanproject

PEcAn.ED2:PEcAn Package for Integration of ED2 Model

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. This package provides functions to link the Ecosystem Demography Model, version 2, to PEcAn.

Maintained by Mike Dietze. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 8.74 score 145 scripts

bioc

FRASER:Find RAre Splicing Events in RNA-Seq Data

Detection of rare aberrant splicing events in transcriptome profiles. Read count ratio expectations are modeled by an autoencoder to control for confounding factors in the data. Given these expectations, the ratios are assumed to follow a beta-binomial distribution with a junction specific dispersion. Outlier events are then identified as read-count ratios that deviate significantly from this distribution. FRASER is able to detect alternative splicing, but also intron retention. The package aims to support diagnostics in the field of rare diseases where RNA-seq is performed to identify aberrant splicing defects.

Maintained by Christian Mertes. Last updated 5 months ago.

rnaseq alternativesplicing sequencing software genetics coverage aberrant-splicing diagnostics outlier-detection rare-disease rna-seq splicing openblas cpp

41 stars 8.50 score 155 scripts

bioc

projectR:Functions for the projection of weights from PCA, CoGAPS, NMF, correlation, and clustering

Functions for the projection of data into the spaces defined by PCA, CoGAPS, NMF, correlation, and clustering.

Maintained by Genevieve Stein-OBrien. Last updated 11 days ago.

functionalprediction generegulation biologicalquestion software

62 stars 8.42 score 70 scripts

bioc

SPOTlight:`SPOTlight`: Spatial Transcriptomics Deconvolution

`SPOTlight`provides a method to deconvolute spatial transcriptomics spots using a seeded NMF approach along with visualization tools to assess the results. Spatially resolved gene expression profiles are key to understand tissue organization and function. However, novel spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots).

Maintained by Marc Elosua-Bayes. Last updated 5 months ago.

singlecell spatial statisticalmethod

172 stars 8.37 score 170 scripts

pecanproject

PEcAn.SIPNET:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by Mike Dietze. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 8.36 score 61 scripts

chuhousen

amerifluxr:Interface to 'AmeriFlux' Data Services

Programmatic interface to the 'AmeriFlux' database (<https://ameriflux.lbl.gov/>). Provide query, download, and data summary tools.

Maintained by Housen Chu. Last updated 3 months ago.

ameriflux api carbon-flux data time-series

22 stars 8.36 score 29 scripts 15 dependents

pecanproject

PEcAnAssimSequential:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by Mike Dietze. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 8.12 score 35 scripts

bioc

debrowser:Interactive Differential Expresion Analysis Browser

Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps.

Maintained by Alper Kucukural. Last updated 5 months ago.

sequencing chipseq rnaseq differentialexpression geneexpression clustering immunooncology

61 stars 7.80 score 65 scripts

r-forge

ROI:R Optimization Infrastructure

The R Optimization Infrastructure ('ROI') <doi:10.18637/jss.v094.i15> is a sophisticated framework for handling optimization problems in R. Additional information can be found on the 'ROI' homepage <http://roi.r-forge.r-project.org/>.

Maintained by Stefan Theussl. Last updated 2 years ago.

7.68 score 506 scripts 47 dependents

bioc

signeR:Empirical Bayesian approach to mutational signature discovery

The signeR package provides an empirical Bayesian approach to mutational signature discovery. It is designed to analyze single nucleotide variation (SNV) counts in cancer genomes, but can also be applied to other features as well. Functionalities to characterize signatures or genome samples according to exposure patterns are also provided.

Maintained by Renan Valieris. Last updated 5 months ago.

genomicvariation somaticmutation statisticalmethod visualization bioconductor bioinformatics openblas cpp

13 stars 7.67 score 22 scripts

pecanproject

PEcAn.BASGRA:PEcAn Package for Integration of the BASGRA Model

This module provides functions to link the BASGRA model to PEcAn.

Maintained by Istem Fer. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants fortran glibc

216 stars 7.58 score 1 scripts

pecanproject

PEcAn.JULES:PEcAn Package for Integration of the JULES Model

This module provides functions to link the (JULES) to PEcAn.

Maintained by Mike Dietze. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.58 score

pecanproject

PEcAn.MAESPA:PEcAn Functions Used for Ecological Forecasts and Reanalysis using MAESPA

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.This package allows for MAESPA to be run through the PEcAN workflow.

Maintained by Tony Gardella. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.58 score 2 scripts

pecanproject

PEcAn.LDNDC:PEcAn package for integration of the LDNDC model

This module provides functions to link the (LDNDC) to PEcAn.

Maintained by Henri Kajasilta. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 7.58 score

pecanproject

PEcAn.PRELES:PEcAn Package for Integration of the PRELES Model

This module provides functions to run the PREdict Light use efficiency Evapotranspiration and Soil moisture (PRELES) model on the PEcAn project. The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool designed to simplify the management of model parameterization,execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by Tony Gardella. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.58 score 4 scripts

bioc

netSmooth:Network smoothing for scRNAseq

netSmooth is an R package for network smoothing of single cell RNA sequencing data. Using bio networks such as protein-protein interactions as priors for gene co-expression, netsmooth improves cell type identification from noisy, sparse scRNAseq data.

Maintained by Jonathan Ronen. Last updated 5 months ago.

network graphandnetwork singlecell rnaseq geneexpression sequencing transcriptomics normalization preprocessing clustering dimensionreduction bioinformatics genomics single-cell

27 stars 7.41 score 4 scripts

alaninglis

vivid:Variable Importance and Variable Interaction Displays

A suite of plots for displaying variable importance and two-way variable interaction jointly. Can also display partial dependence plots laid out in a pairs plot or 'zenplots' style.

Maintained by Alan Inglis. Last updated 8 months ago.

21 stars 7.39 score 39 scripts

bioc

MutationalPatterns:Comprehensive genome-wide analysis of mutational processes

Mutational processes leave characteristic footprints in genomic DNA. This package provides a comprehensive set of flexible functions that allows researchers to easily evaluate and visualize a multitude of mutational patterns in base substitution catalogues of e.g. healthy samples, tumour samples, or DNA-repair deficient cells. The package covers a wide range of patterns including: mutational signatures, transcriptional and replicative strand bias, lesion segregation, genomic distribution and association with genomic features, which are collectively meaningful for studying the activity of mutational processes. The package works with single nucleotide variants (SNVs), insertions and deletions (Indels), double base substitutions (DBSs) and larger multi base substitutions (MBSs). The package provides functionalities for both extracting mutational signatures de novo and determining the contribution of previously identified mutational signatures on a single sample level. MutationalPatterns integrates with common R genomic analysis workflows and allows easy association with (publicly available) annotation data.

Maintained by Mark van Roosmalen. Last updated 5 months ago.

genetics somaticmutation

7.27 score 251 scripts 1 dependents

bioc

sechm:sechm: Complex Heatmaps from a SummarizedExperiment

sechm provides a simple interface between SummarizedExperiment objects and the ComplexHeatmap package. It enables plotting annotated heatmaps from SE objects, with easy access to rowData and colData columns, and implements a number of features to make the generation of heatmaps easier and more flexible. These functionalities used to be part of the SEtools package.

Maintained by Pierre-Luc Germain. Last updated 1 months ago.

geneexpression visualization

6 stars 7.03 score 60 scripts 2 dependents

bioc

musicatk:Mutational Signature Comprehensive Analysis Toolkit

Mutational signatures are carcinogenic exposures or aberrant cellular processes that can cause alterations to the genome. We created musicatk (MUtational SIgnature Comprehensive Analysis ToolKit) to address shortcomings in versatility and ease of use in other pre-existing computational tools. Although many different types of mutational data have been generated, current software packages do not have a flexible framework to allow users to mix and match different types of mutations in the mutational signature inference process. Musicatk enables users to count and combine multiple mutation types, including SBS, DBS, and indels. Musicatk calculates replication strand, transcription strand and combinations of these features along with discovery from unique and proprietary genomic feature associated with any mutation type. Musicatk also implements several methods for discovery of new signatures as well as methods to infer exposure given an existing set of signatures. Musicatk provides functions for visualization and downstream exploratory analysis including the ability to compare signatures between cohorts and find matching signatures in COSMIC V2 or COSMIC V3.

Maintained by Joshua D. Campbell. Last updated 5 months ago.

software biologicalquestion somaticmutation variantannotation

13 stars 6.97 score 20 scripts

bioc

SomaticSignatures:Somatic Signatures

The SomaticSignatures package identifies mutational signatures of single nucleotide variants (SNVs). It provides a infrastructure related to the methodology described in Nik-Zainal (2012, Cell), with flexibility in the matrix decomposition algorithms.

Maintained by Julian Gehring. Last updated 5 months ago.

sequencing somaticmutation visualization clustering genomicvariation statisticalmethod

22 stars 6.85 score 54 scripts 1 dependents

ludovikcoba

rrecsys:Environment for Evaluating Recommender Systems

Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi: 10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.

Maintained by Ludovik Çoba. Last updated 3 years ago.

cpp

23 stars 6.84 score 25 scripts

bioc

ideal:Interactive Differential Expression AnaLysis

This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. Support for reproducibility of the whole analysis is provided by means of a template report which gets automatically compiled and can be stored/shared.

Maintained by Federico Marini. Last updated 3 months ago.

immunooncology geneexpression differentialexpression rnaseq sequencing visualization qualitycontrol gui genesetenrichment reportwriting shinyapps bioconductor differential-expression reproducible-research rna-seq rna-seq-analysis shiny user-friendly

29 stars 6.78 score 5 scripts

ikosmidis

detectseparation:Detect and Check for Separation and Infinite Maximum Likelihood Estimates

Provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in generalized linear models with categorical responses. The pre-fit methods apply on binomial-response generalized liner models such as logit, probit and cloglog regression, and can be directly supplied as fitting methods to the glm() function. They solve the linear programming problems for the detection of separation developed in Konis (2007, <https://ora.ox.ac.uk/objects/uuid:8f9ee0d0-d78e-4101-9ab4-f9cbceed2a2a>) using 'ROI' <https://cran.r-project.org/package=ROI> or 'lpSolveAPI' <https://cran.r-project.org/package=lpSolveAPI>. The post-fit methods apply to models with categorical responses, including binomial-response generalized linear models and multinomial-response models, such as baseline category logits and adjacent category logits models; for example, the models implemented in the 'brglm2' <https://cran.r-project.org/package=brglm2> package. The post-fit methods successively refit the model with increasing number of iteratively reweighted least squares iterations, and monitor the ratio of the estimated standard error for each parameter to what it has been in the first iteration. According to the results in Lesaffre & Albert (1989, <https://www.jstor.org/stable/2345845>), divergence of those ratios indicates data separation.

Maintained by Ioannis Kosmidis. Last updated 3 years ago.

7 stars 6.74 score 23 scripts 4 dependents

mingzehuang

latentcor:Fast Computation of Latent Correlations for Mixed Data

The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated), comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation. The estimation is based on latent copula Gaussian models. For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017). For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) <arXiv:1809.06255>. For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) <doi:10.1093/biomet/asaa007>. For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) <doi:10.1080/10618600.2021.1882468>. The latter method uses multi-linear interpolation originally implemented in the R package <https://cran.r-project.org/package=chebpol>.

Maintained by Mingze Huang. Last updated 3 years ago.

data-analysis data-mining data-processing data-science data-structures machine-learning mixed-types statistics

16 stars 6.65 score 46 scripts 1 dependents

bioc

ViSEAGO:ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity

The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility.

Maintained by Aurelien Brionne. Last updated 3 months ago.

software annotation go genesetenrichment multiplecomparison clustering visualization

6.64 score 22 scripts

pavlakrotka

NCC:Simulation and Analysis of Platform Trials with Non-Concurrent Controls

Design and analysis of flexible platform trials with non-concurrent controls. Functions for data generation, analysis, visualization and running simulation studies are provided. The implemented analysis methods are described in: Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>, Saville et al. (2022) <doi:10.1177/17407745221112013> and Schmidli et al. (2014) <doi:10.1111/biom.12242>.

Maintained by Pavla Krotka. Last updated 19 days ago.

clinical-trials platform-trials simulation statistical-inference jags cpp

5 stars 6.64 score 29 scripts

loukiaspin

rnmamod:Bayesian Network Meta-Analysis with Missing Participants

A comprehensive suite of functions to perform and visualise pairwise and network meta-analysis with aggregate binary or continuous missing participant outcome data. The package covers core Bayesian one-stage models implemented in a systematic review with multiple interventions, including fixed-effect and random-effects network meta-analysis, meta-regression, evaluation of the consistency assumption via the node-splitting approach and the unrelated mean effects model (original and revised model proposed by Spineli, (2022) <doi:10.1177/0272989X211068005>), and sensitivity analysis (see Spineli et al., (2021) <doi:10.1186/s12916-021-02195-y>). Missing participant outcome data are addressed in all models of the package (see Spineli, (2019) <doi:10.1186/s12874-019-0731-y>, Spineli et al., (2019) <doi:10.1002/sim.8207>, Spineli, (2019) <doi:10.1016/j.jclinepi.2018.09.002>, and Spineli et al., (2021) <doi:10.1002/jrsm.1478>). The robustness to primary analysis results can also be investigated using a novel intuitive index (see Spineli et al., (2021) <doi:10.1177/0962280220983544>). Methods to evaluate the transitivity assumption quantitatively are provided (see Spineli, (2024) <doi:10.1186/s12874-024-02436-7>). A novel index to facilitate interpretation of local inconsistency is also available (see Spineli, (2024) <doi:0.1186/s13643-024-02680-4>) The package also offers a rich, user-friendly visualisation toolkit that aids in appraising and interpreting the results thoroughly and preparing the manuscript for journal submission. The visualisation tools comprise the network plot, forest plots, panel of diagnostic plots, heatmaps on the extent of missing participant outcome data in the network, league heatmaps on estimation and prediction, rankograms, Bland-Altman plot, leverage plot, deviance scatterplot, heatmap of robustness, barplot of Kullback-Leibler divergence, heatmap of comparison dissimilarities and dendrogram of comparison clustering. The package also allows the user to export the results to an Excel file at the working directory.

Maintained by Loukia Spineli. Last updated 23 days ago.

jags cpp

5 stars 6.64 score 12 scripts

bioc

SparseSignatures:SparseSignatures

Point mutations occurring in a genome can be divided into 96 categories based on the base being mutated, the base it is mutated into and its two flanking bases. Therefore, for any patient, it is possible to represent all the point mutations occurring in that patient's tumor as a vector of length 96, where each element represents the count of mutations for a given category in the patient. A mutational signature represents the pattern of mutations produced by a mutagen or mutagenic process inside the cell. Each signature can also be represented by a vector of length 96, where each element represents the probability that this particular mutagenic process generates a mutation of the 96 above mentioned categories. In this R package, we provide a set of functions to extract and visualize the mutational signatures that best explain the mutation counts of a large number of patients.

Maintained by Luca De Sano. Last updated 4 days ago.

biomedicalinformatics somaticmutation

11 stars 6.42 score 4 scripts

bioc

YAPSA:Yet Another Package for Signature Analysis

This package provides functions and routines for supervised analyses of mutational signatures (i.e., the signatures have to be known, cf. L. Alexandrov et al., Nature 2013 and L. Alexandrov et al., Bioaxiv 2018). In particular, the family of functions LCD (LCD = linear combination decomposition) can use optimal signature-specific cutoffs which takes care of different detectability of the different signatures. Moreover, the package provides different sets of mutational signatures, including the COSMIC and PCAWG SNV signatures and the PCAWG Indel signatures; the latter infering that with YAPSA, the concept of supervised analysis of mutational signatures is extended to Indel signatures. YAPSA also provides confidence intervals as computed by profile likelihoods and can perform signature analysis on a stratified mutational catalogue (SMC = stratify mutational catalogue) in order to analyze enrichment and depletion patterns for the signatures in different strata.

Maintained by Zuguang Gu. Last updated 5 months ago.

sequencing dnaseq somaticmutation visualization clustering genomicvariation statisticalmethod biologicalquestion

6.41 score 57 scripts

bioc

SpliceWiz:interactive analysis and visualization of alternative splicing in R

The analysis and visualization of alternative splicing (AS) events from RNA sequencing data remains challenging. SpliceWiz is a user-friendly and performance-optimized R package for AS analysis, by processing alignment BAM files to quantify read counts across splice junctions, IRFinder-based intron retention quantitation, and supports novel splicing event identification. We introduce a novel visualization for AS using normalized coverage, thereby allowing visualization of differential AS across conditions. SpliceWiz features a shiny-based GUI facilitating interactive data exploration of results including gene ontology enrichment. It is performance optimized with multi-threaded processing of BAM files and a new COV file format for fast recall of sequencing coverage. Overall, SpliceWiz streamlines AS analysis, enabling reliable identification of functionally relevant AS events for further characterization.

Maintained by Alex Chit Hei Wong. Last updated 17 days ago.

software transcriptomics rnaseq alternativesplicing coverage differentialsplicing differentialexpression gui sequencing cpp openmp

16 stars 6.41 score 8 scripts

rickhelmus

patRoon:Workflows for Mass-Spectrometry Based Non-Target Analysis

Provides an easy-to-use interface to a mass spectrometry based non-target analysis workflow. Various (open-source) tools are combined which provide algorithms for extraction and grouping of features, extraction of MS and MS/MS data, automatic formula and compound annotation and grouping related features to components. In addition, various tools are provided for e.g. data preparation and cleanup, plotting results and automatic reporting.

Maintained by Rick Helmus. Last updated 8 days ago.

mass-spectrometry non-target cpp openjdk

65 stars 6.24 score 43 scripts

lhdjung

scrutiny:Error Detection in Science

Test published summary statistics for consistency (Brown and Heathers, 2017, <doi:10.1177/1948550616673876>; Allard, 2018, <https://aurelienallard.netlify.app/post/anaytic-grimmer-possibility-standard-deviations/>; Heathers and Brown, 2019, <https://osf.io/5vb3u/>). The package also provides infrastructure for implementing new error detection techniques.

Maintained by Lukas Jung. Last updated 6 months ago.

8 stars 6.22 score 38 scripts

david-barnett

microViz:Microbiome Data Analysis and Visualization

Microbiome data visualization and statistics tools built upon phyloseq.

Maintained by David Barnett. Last updated 3 months ago.

microbiome microbiome-analysis microbiota

114 stars 6.22 score 480 scripts

dirkschumacher

ompr.roi:A Solver for 'ompr' that Uses the R Optimization Infrastructure ('ROI')

A solver for 'ompr' based on the R Optimization Infrastructure ('ROI'). The package makes all solvers in 'ROI' available to solve 'ompr' models. Please see the 'ompr' website <https://dirkschumacher.github.io/ompr/> and package docs for more information and examples on how to use it.

Maintained by Dirk Schumacher. Last updated 2 years ago.

8 stars 6.09 score 209 scripts 4 dependents

bioc

metaseqR2:An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms

Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way.

Maintained by Panagiotis Moulos. Last updated 18 days ago.

software geneexpression differentialexpression workflowstep preprocessing qualitycontrol normalization reportwriting rnaseq transcription sequencing transcriptomics bayesian clustering cellbiology biomedicalinformatics functionalgenomics systemsbiology immunooncology alternativesplicing differentialsplicing multiplecomparison timecourse dataimport atacseq epigenetics regression proprietaryplatforms genesetenrichment batcheffect chipseq

7 stars 6.05 score 3 scripts

malaga-fca-group

fcaR:Formal Concept Analysis

Provides tools to perform fuzzy formal concept analysis, presented in Wille (1982) <doi:10.1007/978-3-642-01815-2_23> and in Ganter and Obiedkov (2016) <doi:10.1007/978-3-662-49291-8>. It provides functions to load and save a formal context, extract its concept lattice and implications. In addition, one can use the implications to compute semantic closures of fuzzy sets and, thus, build recommendation systems.

Maintained by Domingo Lopez Rodriguez. Last updated 2 years ago.

formal-concept-analysis cpp

6 stars 6.02 score 70 scripts

bioc

dar:Differential Abundance Analysis by Consensus

Differential abundance testing in microbiome data challenges both parametric and non-parametric statistical methods, due to its sparsity, high variability and compositional nature. Microbiome-specific statistical methods often assume classical distribution models or take into account compositional specifics. These produce results that range within the specificity vs sensitivity space in such a way that type I and type II error that are difficult to ascertain in real microbiome data when a single method is used. Recently, a consensus approach based on multiple differential abundance (DA) methods was recently suggested in order to increase robustness. With dar, you can use dplyr-like pipeable sequences of DA methods and then apply different consensus strategies. In this way we can obtain more reliable results in a fast, consistent and reproducible way.

Maintained by Francesc Catala-Moll. Last updated 14 days ago.

software sequencing microbiome metagenomics multiplecomparison normalization bioconductor biomarker-discovery differential-abundance-analysis feature-selection microbiology phyloseq

2 stars 5.98 score 8 scripts

yonicd

shinyHeatmaply:Deploy 'heatmaply' using 'shiny'

Access functionality of the 'heatmaply' package through 'Shiny UI'.

Maintained by Jonathan Sidi. Last updated 5 years ago.

47 stars 5.95 score 42 scripts 1 dependents

tillbirkner

metadeconfoundR:Covariate-Sensitive Analysis of Cross-Sectional High-Dimensional Data

Using non-parametric tests, naive associations between omics features and metadata in cross-sectional data-sets are detected. In a second step, confounding effects between metadata associated to the same omics feature are detected and labeled using nested post-hoc model comparison tests, as first described in Forslund, Chakaroun, Zimmermann-Kogadeeva, et al. (2021) <doi:10.1038/s41586-021-04177-9>. The generated output can be graphically summarized using the built-in plotting function.

Maintained by Till Birkner. Last updated 5 months ago.

18 stars 5.91 score 15 scripts

bioc

DiscoRhythm:Interactive Workflow for Discovering Rhythmicity in Biological Data

Set of functions for estimation of cyclical characteristics, such as period, phase, amplitude, and statistical significance in large temporal datasets. Supporting functions are available for quality control, dimensionality reduction, spectral analysis, and analysis of experimental replicates. Contains a R Shiny web interface to execute all workflow steps.

Maintained by Matthew Carlucci. Last updated 5 months ago.

software timecourse qualitycontrol visualization gui principalcomponent bioconductor data-visualization oscillations rhythm-detection webapp

13 stars 5.89 score 9 scripts

cbhurley

condvis2:Interactive Conditional Visualization for Supervised and Unsupervised Models in Shiny

Constructs a shiny app function with interactive displays for conditional visualization of models, data and density functions. An extended version of package 'condvis'. Catherine B. Hurley, Mark O'Connell,Katarina Domijan (2021) <10.1080/10618600.2021.1983439>.

Maintained by Catherine Hurley. Last updated 3 years ago.

6 stars 5.86 score 16 scripts 1 dependents

bayer-group

adepro:A 'shiny' Application for the (Audio-)Visualization of Adverse Event Profiles

Contains a 'shiny' application called AdEPro (Animation of Adverse Event Profiles) which (audio-)visualizes adverse events occurring in clinical trials. As this data is usually considered sensitive, this tool is provided as a stand-alone application that can be launched from any local machine on which the data is stored.

Maintained by Nicole Rethemeier. Last updated 6 days ago.

adverse-events bayer-not-classified bayer-reg-none beat-not-applicable clinical-trials data-insights shiny-apps visualization

7 stars 5.84 score 11 scripts

bioc

benchdamic:Benchmark of differential abundance methods on microbiome data

Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization.

Maintained by Matteo Calgaro. Last updated 4 months ago.

metagenomics microbiome differentialexpression multiplecomparison normalization preprocessing software benchmark differential-abundance-methods

8 stars 5.78 score 8 scripts

trangdata

treeheatr:Heatmap-Integrated Decision Tree Visualizations

Creates interpretable decision tree visualizations with the data represented as a heatmap at the tree's leaf nodes. 'treeheatr' utilizes the customizable 'ggparty' package for drawing decision trees.

Maintained by Trang Le. Last updated 2 years ago.

dataviz decision-trees ggplot heatmap visualization

57 stars 5.71 score 18 scripts

bioc

debCAM:Deconvolution by Convex Analysis of Mixtures

An R package for fully unsupervised deconvolution of complex tissues. It provides basic functions to perform unsupervised deconvolution on mixture expression profiles by Convex Analysis of Mixtures (CAM) and some auxiliary functions to help understand the subpopulation-specific results. It also implements functions to perform supervised deconvolution based on prior knowledge of molecular markers, S matrix or A matrix. Combining molecular markers from CAM and from prior knowledge can achieve semi-supervised deconvolution of mixtures.

Maintained by Lulu Chen. Last updated 5 months ago.

software cellbiology geneexpression openjdk

7 stars 5.69 score 14 scripts

bioc

SEtools:SEtools: tools for working with SummarizedExperiment

This includes a set of convenience functions for working with the SummarizedExperiment class. Note that plotting functions historically in this package have been moved to the sechm package (see vignette for details).

Maintained by Pierre-Luc Germain. Last updated 5 months ago.

geneexpression

2 stars 5.64 score 72 scripts

courtiol

IsoriX:Isoscape Computation and Inference of Spatial Origins using Mixed Models

Building isoscapes using mixed models and inferring the geographic origin of samples based on their isotopic ratios. This package is essentially a simplified interface to several other packages which implements a new statistical framework based on mixed models. It uses 'spaMM' for fitting and predicting isoscapes, and assigning an organism's origin depending on its isotopic ratio. 'IsoriX' also relies heavily on the package 'rasterVis' for plotting the maps produced with 'terra' using 'lattice'.

Maintained by Alexandre Courtiol. Last updated 7 months ago.

14 stars 5.59 score 56 scripts

cbhurley

bullseye:Visualising Multiple Pairwise Variable Correlations and Other Scores

We provide a tidy data structure and visualisations for multiple or grouped variable correlations, general association measures scagnostics and other pairwise scores suitable for numerical, ordinal and nominal variables. Supported measures include distance correlation, maximal information, ace correlation, Kendall's tau, and polychoric correlation.

Maintained by Catherine Hurley. Last updated 23 days ago.

2 stars 5.58 score 14 scripts

bioc

miRSM:Inferring miRNA sponge modules in heterogeneous data

The package aims to identify miRNA sponge or ceRNA modules in heterogeneous data. It provides several functions to study miRNA sponge modules at single-sample and multi-sample levels, including popular methods for inferring gene modules (candidate miRNA sponge or ceRNA modules), and two functions to identify miRNA sponge modules at single-sample and multi-sample levels, as well as several functions to conduct modular analysis of miRNA sponge modules.

Maintained by Junpeng Zhang. Last updated 5 months ago.

geneexpression biomedicalinformatics clustering genesetenrichment microarray software generegulation genetarget cerna mirna mirna-sponge mirna-targets modules openjdk

4 stars 5.51 score 5 scripts

keefe-murphy

MEDseq:Mixtures of Exponential-Distance Models with Covariates

Implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2021) <doi:10.1111/rssa.12712>. A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics and sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided.

Maintained by Keefe Murphy. Last updated 20 days ago.

distance-based-clustering mixture-of-experts model-based-clustering sequence-analysis

5 stars 5.49 score 25 scripts

maxconway

fbar:An Extensible Approach to Flux Balance Analysis

A toolkit for Flux Balance Analysis and related metabolic modeling techniques. Functions are provided for: parsing models in tabular format, converting parsed metabolic models to input formats for common linear programming solvers, and evaluating and applying gene-protein-reaction mappings. In addition, there are wrappers to parse a model, select a solver, find the metabolic fluxes, and return the results applied to the original model. Compared to other packages in this field, this package puts a much heavier focus on providing reusable components that can be used in the design of new implementation of new techniques, in particular those that involve large parameter sweeps. For a background on the theory, see What is Flux Balance Analysis <doi:10.1038/nbt.1614>.

Maintained by Max Conway. Last updated 5 years ago.

9 stars 5.49 score 23 scripts

paulgovan

BayesianNetwork:Bayesian Network Modeling and Analysis

A "Shiny"" web application for creating interactive Bayesian Network models, learning the structure and parameters of Bayesian networks, and utilities for classic network analysis.

Maintained by Paul Govan. Last updated 4 months ago.

bayesian-networks learning-algorithm network-measures

121 stars 5.48 score 1 scripts

bioc

MotifPeeker:Benchmarking Epigenomic Profiling Methods Using Motif Enrichment

MotifPeeker is used to compare and analyse datasets from epigenomic profiling methods with motif enrichment as the key benchmark. The package outputs an HTML report consisting of three sections: (1. General Metrics) Overview of peaks-related general metrics for the datasets (FRiP scores, peak widths and motif-summit distances). (2. Known Motif Enrichment Analysis) Statistics for the frequency of user-provided motifs enriched in the datasets. (3. De-Novo Motif Enrichment Analysis) Statistics for the frequency of de-novo discovered motifs enriched in the datasets and compared with known motifs.

Maintained by Hiranyamaya Dash. Last updated 3 months ago.

epigenetics genetics qualitycontrol chipseq multiplecomparison functionalgenomics motifdiscovery sequencematching software alignment bioconductor bioconductor-package chip-seq epigenomics interactive-report motif-enrichment-analysis

2 stars 5.48 score 6 scripts

bsaul

inferference:Methods for Causal Inference with Interference

Provides methods for estimating causal effects in the presence of interference described in B. Saul and M. Hugdens (2017) <doi:10.18637/jss.v082.i02>. Currently it implements the inverse-probability weighted (IPW) estimators proposed by E.J. Tchetgen Tchetgen and T.J. Vanderweele (2012) <doi:10.1177/0962280210386779>.

Maintained by Bradley Saul. Last updated 4 years ago.

6 stars 5.47 score 49 scripts

r-forge

ROI.plugin.glpk:'ROI' Plug-in 'GLPK'

Enhances the 'R' Optimization Infrastructure ('ROI') package by registering the free 'GLPK' solver. It allows for solving mixed integer linear programming ('MILP') problems as well as all variants/combinations of 'LP', 'IP'.

Maintained by Stefan Theussl. Last updated 2 years ago.

5.35 score 342 scripts 8 dependents

bioc

GEOexplorer:GEOexplorer: a webserver for gene expression analysis and visualisation

GEOexplorer is a webserver and R/Bioconductor package and web application that enables users to perform gene expression analysis. The development of GEOexplorer was made possible because of the excellent code provided by GEO2R (https: //www.ncbi.nlm.nih.gov/geo/geo2r/).

Maintained by Guy Hunt. Last updated 5 months ago.

software geneexpression mrnamicroarray differentialexpression microarray micrornaarray transcriptomics rnaseq

5 stars 5.32 score 14 scripts

signaturescience

skater:Utilities for SNP-Based Kinship Analysis

Utilities for single nucleotide polymorphism (SNP) based kinship analysis testing and evaluation. The 'skater' package contains functions for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing identity by descent (IBD) segment data. Package functions and methods are described in Turner et al. (2021) "skater: An R package for SNP-based Kinship Analysis, Testing, and Evaluation" <doi:10.1101/2021.07.21.453083>.

Maintained by Stephen Turner. Last updated 2 years ago.

9 stars 5.26 score 7 scripts

bioc

DegNorm:DegNorm: degradation normalization for RNA-seq data

This package performs degradation normalization in bulk RNA-seq data to improve differential expression analysis accuracy.

Maintained by Ji-Ping Wang. Last updated 5 months ago.

rnaseq normalization geneexpression alignment coverage differentialexpression batcheffect software sequencing immunooncology qualitycontrol dataimport openblas cpp openmp

1 stars 5.08 score 3 scripts

craig-parylo

plotor:Produces an Odds Ratio Plot from a Logistic Regression Model

Produces an Odds Ratio (OR) Plot to visualise the result of a logistic regression analysis. Provide it with a binomial regression model produced by 'glm()' and it will convert the estimates to odds ratios with a 95% confidence interval and plot the results using 'ggplot2'.

Maintained by Craig Parylo. Last updated 13 days ago.

ggplot2 glm logistic-regression odds-ratio

2 stars 5.04 score 7 scripts

bioc

broadSeq:broadSeq : for streamlined exploration of RNA-seq data

This package helps user to do easily RNA-seq data analysis with multiple methods (usually which needs many different input formats). Here the user will provid the expression data as a SummarizedExperiment object and will get results from different methods. It will help user to quickly evaluate different methods.

Maintained by Rishi Das Roy. Last updated 5 months ago.

geneexpression differentialexpression rnaseq transcriptomics sequencing coverage genesetenrichment go

4 stars 5.00 score 7 scripts

bioc

shinyepico:ShinyÉPICo

ShinyÉPICo is a graphical pipeline to analyze Illumina DNA methylation arrays (450k or EPIC). It allows to calculate differentially methylated positions and differentially methylated regions in a user-friendly interface. Moreover, it includes several options to export the results and obtain files to perform downstream analysis.

Maintained by Octavio Morante-Palacios. Last updated 5 months ago.

differentialmethylation dnamethylation microarray preprocessing qualitycontrol

5 stars 5.00 score 1 scripts

bioc

mspms:Tools for the analysis of MSP-MS data

This package provides functions for the analysis of data generated by the multiplex substrate profiling by mass spectrometry for proteases (MSP-MS) method. Data exported from upstream proteomics software is accepted as input and subsequently processed for analysis. Tools for statistical analysis, visualization, and interpretation of the data are provided.

Maintained by Charlie Bayne. Last updated 4 months ago.

proteomics massspectrometry preprocessing protease proteomics-data-analysis

4.95 score 4 scripts

f-rousset

spaMM:Mixed-Effect Models, with or without Spatial Random Effects

Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.

Maintained by François Rousset. Last updated 10 months ago.

gsl cpp openmp

4.94 score 208 scripts 5 dependents

bioc

methylscaper:Visualization of Methylation Data

methylscaper is an R package for processing and visualizing data jointly profiling methylation and chromatin accessibility (MAPit, NOMe-seq, scNMT-seq, nanoNOMe, etc.). The package supports both single-cell and single-molecule data, and a common interface for jointly visualizing both data types through the generation of ordered representational methylation-state matrices. The Shiny app allows for an interactive seriation process of refinement and re-weighting that optimally orders the cells or DNA molecules to discover methylation patterns and nucleosome positioning.

Maintained by Bacher Rhonda. Last updated 5 months ago.

dnamethylation epigenetics sequencing visualization singlecell nucleosomepositioning

1 stars 4.90 score 3 scripts

dschuhmacher

kanjistat:A Statistical Framework for the Analysis of Japanese Kanji Characters

Various tools and data sets that support the study of kanji, including their morphology, decomposition and concepts of distance and similarity between them.

Maintained by Dominic Schuhmacher. Last updated 10 months ago.

cpp

4 stars 4.90 score 6 scripts

bioc

PolySTest:PolySTest: Detection of differentially regulated features. Combined statistical testing for data with few replicates and missing values

The complexity of high-throughput quantitative omics experiments often leads to low replicates numbers and many missing values. We implemented a new test to simultaneously consider missing values and quantitative changes, which we combined with well-performing statistical tests for high confidence detection of differentially regulated features. The package contains functions to run the test and to visualize the results.

Maintained by Veit Schwämmle. Last updated 4 months ago.

massspectrometry proteomics software differentialexpression

4.86 score 12 scripts

irinagain

mixedCCA:Sparse Canonical Correlation Analysis for High-Dimensional Mixed Data

Semi-parametric approach for sparse canonical correlation analysis which can handle mixed data types: continuous, binary and truncated continuous. Bridge functions are provided to connect Kendall's tau to latent correlation under the Gaussian copula model. The methods are described in Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and Yoon, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.

Maintained by Irina Gaynanova. Last updated 3 years ago.

openblas cpp

21 stars 4.75 score 27 scripts

shixiangwang

gcap:Gene-level Circular Amplicon Prediction

Provides data processing pipeline feeding paired bam files (or allele-specific copy number data) and XGBOOST model for predicting tumor circular amplicons (also known as ecDNA) in gene level.

Maintained by Shixiang Wang. Last updated 3 months ago.

ecdna pipeline tumor-genomics wes xgboost cpp

18 stars 4.72 score 42 scripts

bioc

funOmics:Aggregating Omics Data into Higher-Level Functional Representations

The 'funOmics' package ggregates or summarizes omics data into higher level functional representations such as GO terms gene sets or KEGG metabolic pathways. The aggregated data matrix represents functional activity scores that facilitate the analysis of functional molecular sets while allowing to reduce dimensionality and provide easier and faster biological interpretations. Coordinated functional activity scores can be as informative as single molecules!

Maintained by Elisa Gomez de Lope. Last updated 5 months ago.

software transcriptomics metabolomics proteomics pathways go kegg

5 stars 4.70 score 3 scripts

stla

kantorovich:Kantorovich Distance Between Probability Measures

Computes the Kantorovich distance between two probability measures on a finite set. The Kantorovich distance is also known as the Monge-Kantorovich distance or the first Wasserstein distance.

Maintained by Stéphane Laurent. Last updated 11 months ago.

kantorovich-distance

4.62 score 14 scripts 1 dependents

openpharma

elaborator:A 'shiny' Application for Exploring Laboratory Data

A novel concept for generating knowledge and gaining insights into laboratory data. You will be able to efficiently and easily explore your laboratory data from different perspectives. Janitza, S., Majumder, M., Mendolia, F., Jeske, S., & Kulmann, H. (2021) <doi:10.1007/s43441-021-00318-4>.

Maintained by Bodo Kirsch. Last updated 6 months ago.

7 stars 4.62 score

fcampelo

CALANGO:Comparative Analysis with Annotation-Based Genomic Components

A first-principle, phylogeny-aware comparative genomics tool for investigating associations between terms used to annotate genomic components (e.g., Pfam IDs, Gene Ontology terms,) with quantitative or rank variables such as number of cell types, genome size, or density of specific genomic elements. See the project website for more information, documentation and examples, and <doi:10.1016/j.patter.2023.100728> for the full paper.

Maintained by Felipe Campelo. Last updated 7 months ago.

2 stars 4.60 score 4 scripts

bioc

RESOLVE:RESOLVE: An R package for the efficient analysis of mutational signatures from cancer genomes

Cancer is a genetic disease caused by somatic mutations in genes controlling key biological functions such as cellular growth and division. Such mutations may arise both through cell-intrinsic and exogenous processes, generating characteristic mutational patterns over the genome named mutational signatures. The study of mutational signatures have become a standard component of modern genomics studies, since it can reveal which (environmental and endogenous) mutagenic processes are active in a tumor, and may highlight markers for therapeutic response. Mutational signatures computational analysis presents many pitfalls. First, the task of determining the number of signatures is very complex and depends on heuristics. Second, several signatures have no clear etiology, casting doubt on them being computational artifacts rather than due to mutagenic processes. Last, approaches for signatures assignment are greatly influenced by the set of signatures used for the analysis. To overcome these limitations, we developed RESOLVE (Robust EStimation Of mutationaL signatures Via rEgularization), a framework that allows the efficient extraction and assignment of mutational signatures. RESOLVE implements a novel algorithm that enables (i) the efficient extraction, (ii) exposure estimation, and (iii) confidence assessment during the computational inference of mutational signatures.

Maintained by Luca De Sano. Last updated 4 days ago.

biomedicalinformatics somaticmutation

1 stars 4.60 score 3 scripts

bioc

CARDspa:Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics

CARD is a reference-based deconvolution method that estimates cell type composition in spatial transcriptomics based on cell type specific expression information obtained from a reference scRNA-seq data. A key feature of CARD is its ability to accommodate spatial correlation in the cell type composition across tissue locations, enabling accurate and spatially informed cell type deconvolution as well as refined spatial map construction. CARD relies on an efficient optimization algorithm for constrained maximum likelihood estimation and is scalable to spatial transcriptomics with tens of thousands of spatial locations and tens of thousands of genes.

Maintained by Jing Fu. Last updated 2 days ago.

spatial singlecell transcriptomics visualization openblas cpp openmp

4.60 score 3 scripts

tjfarrar

skedastic:Handling Heteroskedasticity in the Linear Regression Model

Implements numerous methods for testing for, modelling, and correcting for heteroskedasticity in the classical linear regression model. The most novel contribution of the package is found in the functions that implement the as-yet-unpublished auxiliary linear variance models and auxiliary nonlinear variance models that are designed to estimate error variances in a heteroskedastic linear regression model. These models follow principles of statistical learning described in Hastie (2009) <doi:10.1007/978-0-387-21606-5>. The nonlinear version of the model is estimated using quasi-likelihood methods as described in Seber and Wild (2003, ISBN: 0-471-47135-6). Bootstrap methods for approximate confidence intervals for error variances are implemented as described in Efron and Tibshirani (1993, ISBN: 978-1-4899-4541-9), including also the expansion technique described in Hesterberg (2014) <doi:10.1080/00031305.2015.1089789>. The wild bootstrap employed here follows the description in Davidson and Flachaire (2008) <doi:10.1016/j.jeconom.2008.08.003>. Tuning of hyper-parameters makes use of a golden section search function that is modelled after the MATLAB function of Zarnowiec (2022) <https://www.mathworks.com/matlabcentral/fileexchange/25919-golden-section-method-algorithm>. A methodological description of the algorithm can be found in Fox (2021, ISBN: 978-1-003-00957-3). There are 25 different functions that implement hypothesis tests for heteroskedasticity. These include a test based on Anscombe (1961) <https://projecteuclid.org/euclid.bsmsp/1200512155>, Ramsey's (1969) BAMSET Test <doi:10.1111/j.2517-6161.1969.tb00796.x>, the tests of Bickel (1978) <doi:10.1214/aos/1176344124>, Breusch and Pagan (1979) <doi:10.2307/1911963> with and without the modification proposed by Koenker (1981) <doi:10.1016/0304-4076(81)90062-2>, Carapeto and Holt (2003) <doi:10.1080/0266476022000018475>, Cook and Weisberg (1983) <doi:10.1093/biomet/70.1.1> (including their graphical methods), Diblasi and Bowman (1997) <doi:10.1016/S0167-7152(96)00115-0>, Dufour, Khalaf, Bernard, and Genest (2004) <doi:10.1016/j.jeconom.2003.10.024>, Evans and King (1985) <doi:10.1016/0304-4076(85)90085-5> and Evans and King (1988) <doi:10.1016/0304-4076(88)90006-1>, Glejser (1969) <doi:10.1080/01621459.1969.10500976> as formulated by Mittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4), Godfrey and Orme (1999) <doi:10.1080/07474939908800438>, Goldfeld and Quandt (1965) <doi:10.1080/01621459.1965.10480811>, Harrison and McCabe (1979) <doi:10.1080/01621459.1979.10482544>, Harvey (1976) <doi:10.2307/1913974>, Honda (1989) <doi:10.1111/j.2517-6161.1989.tb01749.x>, Horn (1981) <doi:10.1080/03610928108828074>, Li and Yao (2019) <doi:10.1016/j.ecosta.2018.01.001> with and without the modification of Bai, Pan, and Yin (2016) <doi:10.1007/s11749-017-0575-x>, Rackauskas and Zuokas (2007) <doi:10.1007/s10986-007-0018-6>, Simonoff and Tsai (1994) <doi:10.2307/2986026> with and without the modification of Ferrari, Cysneiros, and Cribari-Neto (2004) <doi:10.1016/S0378-3758(03)00210-6>, Szroeter (1978) <doi:10.2307/1913831>, Verbyla (1993) <doi:10.1111/j.2517-6161.1993.tb01918.x>, White (1980) <doi:10.2307/1912934>, Wilcox and Keselman (2006) <doi:10.1080/10629360500107923>, Yuce (2008) <https://dergipark.org.tr/en/pub/iuekois/issue/8989/112070>, and Zhou, Song, and Thompson (2015) <doi:10.1002/cjs.11252>. Besides these heteroskedasticity tests, there are supporting functions that compute the BLUS residuals of Theil (1965) <doi:10.1080/01621459.1965.10480851>, the conditional two-sided p-values of Kulinskaya (2008) <arXiv:0810.2124v1>, and probabilities for the nonparametric trend statistic of Lehmann (1975, ISBN: 0-816-24996-1). For handling heteroskedasticity, in addition to the new auxiliary variance model methods, there is a function to implement various existing Heteroskedasticity-Consistent Covariance Matrix Estimators from the literature, such as those of White (1980) <doi:10.2307/1912934>, MacKinnon and White (1985) <doi:10.1016/0304-4076(85)90158-7>, Cribari-Neto (2004) <doi:10.1016/S0167-9473(02)00366-3>, Cribari-Neto et al. (2007) <doi:10.1080/03610920601126589>, Cribari-Neto and da Silva (2011) <doi:10.1007/s10182-010-0141-2>, Aftab and Chang (2016) <doi:10.18187/pjsor.v12i2.983>, and Li et al. (2017) <doi:10.1080/00949655.2016.1198906>.

Maintained by Thomas Farrar. Last updated 1 years ago.

7 stars 4.60 score 73 scripts

r-forge

stops:Structure Optimized Proximity Scaling

Methods that use flexible variants of multidimensional scaling (MDS) which incorporate parametric nonlinear distance transformations and trade-off the goodness-of-fit fit with structure considerations to find optimal hyperparameters, also known as structure optimized proximity scaling (STOPS) (Rusch, Mair & Hornik, 2023,<doi:10.1007/s11222-022-10197-w>). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different 1-way MDS models with ratio, interval, ordinal optimal scaling in a STOPS framework. These cover essentially the functionality of the package smacofx, including Torgerson (classical) scaling with power transformations of dissimilarities, SMACOF MDS with powers of dissimilarities, Sammon mapping with powers of dissimilarities, elastic scaling with powers of dissimilarities, spherical SMACOF with powers of dissimilarities, (ALSCAL) s-stress MDS with powers of dissimilarities, r-stress MDS, MDS with powers of dissimilarities and configuration distances, elastic scaling powers of dissimilarities and configuration distances, Sammon mapping powers of dissimilarities and configuration distances, power stress MDS (POST-MDS), approximate power stress, Box-Cox MDS, local MDS, Isomap, curvilinear component analysis (CLCA), curvilinear distance analysis (CLDA) and sparsified (power) multidimensional scaling and (power) multidimensional distance analysis (experimental models from smacofx influenced by CLCA). All of these models can also be fit by optimizing over hyperparameters based on goodness-of-fit fit only (i.e., no structure considerations). The package further contains functions for optimization, specifically the adaptive Luus-Jaakola algorithm and a wrapper for Bayesian optimization with treed Gaussian process with jumps to linear models, and functions for various c-structuredness indices.

Maintained by Thomas Rusch. Last updated 3 months ago.

openjdk

1 stars 4.48 score 23 scripts

roigrp

ROI.plugin.lpsolve:'lp_solve' Plugin for the 'R' Optimization Infrastructure

Enhances the 'R' Optimization Infrastructure ('ROI') package with the 'lp_solve' solver.

Maintained by Florian Schwendinger. Last updated 4 years ago.

1 stars 4.32 score 14 scripts 7 dependents

bioc

qmtools:Quantitative Metabolomics Data Processing Tools

The qmtools (quantitative metabolomics tools) package provides basic tools for processing quantitative metabolomics data with the standard SummarizedExperiment class. This includes functions for imputation, normalization, feature filtering, feature clustering, dimension-reduction, and visualization to help users prepare data for statistical analysis. This package also offers a convenient way to compute empirical Bayes statistics for which metabolic features are different between two sets of study samples. Several functions in this package could also be used in other types of omics data.

Maintained by Jaehyun Joo. Last updated 5 months ago.

metabolomics preprocessing normalization dimensionreduction massspectrometry

1 stars 4.30 score 5 scripts

loosolab

wilson:Web-Based Interactive Omics Visualization

Tool-set of modules for creating web-based applications that use plot based strategies to visualize and analyze multi-omics data. This package utilizes the 'shiny' and 'plotly' frameworks to provide a user friendly dashboard for interactive plotting.

Maintained by Hendrik Schultheis. Last updated 4 years ago.

2 stars 4.30 score 7 scripts

bioc

HarmonizR:Handles missing values and makes more data available

An implementation, which takes input data and makes it available for proper batch effect removal by ComBat or Limma. The implementation appropriately handles missing values by dissecting the input matrix into smaller matrices with sufficient data to feed the ComBat or limma algorithm. The adjusted data is returned to the user as a rebuild matrix. The implementation is meant to make as much data available as possible with minimal data loss.

Maintained by Simon Schlumbohm. Last updated 5 months ago.

batcheffect

4.20 score 16 scripts

wenxuanliu1996

MetabolSSMF:Simplex-Structured Matrix Factorisation for Metabolomics Analysis

Provides a framework to perform soft clustering using simplex-structured matrix factorisation (SSMF). The package contains a set of functions for determining the optimal number of prototypes, the optimal algorithmic parameters, the estimation confidence intervals and the diversity of clusters. Abdolali, Maryam & Gillis, Nicolas (2020) <doi:10.1137/20M1354982>.

Maintained by Wenxuan Liu. Last updated 26 days ago.

4.18 score

danielebizzarri

MiMIR:Metabolomics-Based Models for Imputing Risk

Provides an intuitive framework for ad-hoc statistical analysis of 1H-NMR metabolomics by Nightingale Health. It allows to easily explore new metabolomics measurements assayed by Nightingale Health, comparing the distributions with a large Consortium (BBMRI-nl); project previously published metabolic scores [<doi:10.1016/j.ebiom.2021.103764>, <doi:10.1161/CIRCGEN.119.002610>, <doi:10.1038/s41467-019-11311-9>, <doi:10.7554/eLife.63033>, <doi:10.1161/CIRCULATIONAHA.114.013116>, <doi:10.1007/s00125-019-05001-w>]; and calibrate the metabolic surrogate values to a desired dataset.

Maintained by Daniele Bizzarri. Last updated 2 years ago.

binary-risk-factors biomarkers linear-regression metabolites metabolomics nightingale-metabolomics risk-factor-models risk-factors surrogate-models

8 stars 4.11 score 32 scripts

cysouw

qlcVisualize:Visualization for Quantitative Language Comparison

Collection of visualizations as used in quantitative language comparison. Currently implemented are visualisations dealing nominal data with multiple levels ("level map" and "factor map"), and assistance for making weighted geographical Voronoi-maps ("weighted map").

Maintained by Michael Cysouw. Last updated 6 months ago.

4.03 score 24 scripts

bioc

microbiomeExplorer:Microbiome Exploration App

The MicrobiomeExplorer R package is designed to facilitate the analysis and visualization of marker-gene survey feature data. It allows a user to perform and visualize typical microbiome analytical workflows either through the command line or an interactive Shiny application included with the package. In addition to applying common analytical workflows the application enables automated analysis report generation.

Maintained by Janina Reeder. Last updated 5 months ago.

classification clustering geneticvariability differentialexpression microbiome metagenomics normalization visualization multiplecomparison sequencing software immunooncology

4.00 score 8 scripts

bioc

scTensor:Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition

The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor.

Maintained by Koki Tsuyuzaki. Last updated 5 months ago.

dimensionreduction singlecell software geneexpression

4.00 score 2 scripts

william-swl

baizer:Useful Functions for Data Processing

In ancient Chinese mythology, Bai Ze is a divine creature that knows the needs of everything. 'baizer' provides data processing functions frequently used by the author. Hope this package also knows what you want!

Maintained by William Song. Last updated 1 years ago.

dataframe numbers strings tidyverse

6 stars 3.95 score 5 scripts 1 dependents

rhochreiter

modopt.matlab:'MatLab'-Style Modeling of Optimization Problems

'MatLab'-Style Modeling of Optimization Problems with 'R'. This package provides a set of convenience functions to transform a 'MatLab'-style optimization modeling structure to its 'ROI' equivalent.

Maintained by Ronald Hochreiter. Last updated 2 years ago.

6 stars 3.95 score 4 scripts 1 dependents

r-forge

ROI.plugin.symphony:'SYMPHONY' Plug-in for the 'R' Optimization Interface

Enhances the R Optimization Infrastructure ('ROI') package by registering the 'SYMPHONY' open-source solver from the COIN-OR suite. It allows for solving mixed integer linear programming (MILP) problems as well as all variants/combinations of LP, IP.

Maintained by Stefan Theussl. Last updated 2 years ago.

3.82 score 27 scripts 4 dependents

r-forge

ROI.plugin.quadprog:'quadprog' Plug-in for the 'R' Optimization Infrastructure

Enhances the R Optimization Infrastructure ('ROI') package by registering the 'quadprog' solver. It allows for solving quadratic programming (QP) sproblems.

Maintained by Stefan Theussl. Last updated 2 years ago.

3.79 score 85 scripts 2 dependents

cbhurley

DendSer:Dendrogram Seriation: Ordering for Visualisation

Re-arranges a dendrogram to optimize visualisation-based cost functions.

Maintained by Catherine Hurley. Last updated 3 years ago.

3.74 score 27 scripts 5 dependents

alaninglis

corrViz:Visualise Correlations

An investigative tool designed to help users visualize correlations between variables in their datasets. This package aims to provide an easy and effective way to explore and visualize these correlations, making it easier to interpret and communicate results.

Maintained by Alan Inglis. Last updated 1 years ago.

1 stars 3.70 score 4 scripts

olbapjose

FuzzyLP:Fuzzy Linear Programming

Provides methods to solve Fuzzy Linear Programming Problems with fuzzy constraints (following different approaches proposed by Verdegay, Zimmermann, Werners and Tanaka), fuzzy costs, and fuzzy technological matrix.

Maintained by Pablo J. Villacorta. Last updated 2 years ago.

fuzzy-logic linear-programming

3.70 score 8 scripts

martinrd3d

PCRA:Companion to Portfolio Construction and Risk Analysis

A collection of functions and data sets that support teaching a quantitative finance MS level course on Portfolio Construction and Risk Analysis, and the writing of a textbook for such a course. The package is unique in providing several real-world data sets that may be used for problem assignments and student projects. The data sets include cross-sections of stock data from the Center for Research on Security Prices, LLC (CRSP), corresponding factor exposures data from S&P Global, and several SP500 data sets.

Maintained by Doug Martin. Last updated 2 years ago.

3.67 score 94 scripts

william-swl

plutor:Useful Functions for Visualization

In ancient Roman mythology, 'Pluto' was the ruler of the underworld and presides over the afterlife. 'Pluto' was frequently conflated with 'Plutus', the god of wealth, because mineral wealth was found underground. When plotting with R, you try once, twice, practice again and again, and finally you get a pretty figure you want. It's a 'plot tour', a tour about repetition and reward. Hope 'plutor' helps you on the tour!

Maintained by William Song. Last updated 1 years ago.

3 stars 3.62 score 28 scripts

liuy12

SCdeconR:Deconvolution of Bulk RNA-Seq Data using Single-Cell RNA-Seq Data as Reference

Streamlined workflow from deconvolution of bulk RNA-seq data to downstream differential expression and gene-set enrichment analysis. Provide various visualization functions.

Maintained by Yuanhang Liu. Last updated 10 months ago.

bulk-rna-seq-deconvolution deconvolution differential-expression ffpe geneset-enrichment-analysis scdeconr single-cell

4 stars 3.60 score 4 scripts

bioc

NetSAM:Network Seriation And Modularization

The NetSAM (Network Seriation and Modularization) package takes an edge-list representation of a weighted or unweighted network as an input, performs network seriation and modularization analysis, and generates as files that can be used as an input for the one-dimensional network visualization tool NetGestalt (http://www.netgestalt.org) or other network analysis. The NetSAM package can also generate correlation network (e.g. co-expression network) based on the input matrix data, perform seriation and modularization analysis for the correlation network and calculate the associations between the sample features and modules or identify the associated GO terms for the modules.

Maintained by Zhiao Shi. Last updated 5 months ago.

3.60 score 1 scripts

ericdunipace

WpProj:Linear p-Wasserstein Projections

Performs Wasserstein projections from the predictive distributions of any model into the space of predictive distributions of linear models. We utilize L1 penalties to also reduce the complexity of the model space. This package employs the methods as described in Dunipace, Eric and Lorenzo Trippa (2020) <doi:10.48550/arXiv.2012.09999>.

Maintained by Eric Dunipace. Last updated 2 months ago.

cpp

3.48 score

david-hammond

tidymodlr:An R6 Class to Perform Analysis on Long Tidy Data

Transforms long data into a matrix form to allow for ease of input into modelling packages for regression, principal components, imputation or machine learning. It does this by pivoting on user defined columns, generating a key-value table for variable names to ensure one-to-one mappings are preserved. It is particularly useful when the indicator names in the columns are long descriptive strings, for example "Energy imports, net (% of energy use)". High level analysis wrapper functions for correlation and principal components analysis are provided.

Maintained by David Hammond. Last updated 7 months ago.

3.48 score 4 scripts

r-forge

ROI.plugin.ecos:'ECOS' Plugin for the 'R' Optimization Infrastructure

Enhances the 'R' Optimization Infrastructure ('ROI') package with the Embedded Conic Solver ('ECOS') for solving conic optimization problems.

Maintained by Florian Schwendinger. Last updated 2 years ago.

3.35 score 25 scripts 3 dependents

rhochreiter

portfolio.optimization:Contemporary Portfolio Optimization

Simplify your portfolio optimization process by applying a contemporary modeling way to model and solve your portfolio problems. While most approaches and packages are rather complicated this one tries to simplify things and is agnostic regarding risk measures as well as optimization solvers. Some of the methods implemented are described by Konno and Yamazaki (1991) <doi:10.1287/mnsc.37.5.519>, Rockafellar and Uryasev (2001) <doi:10.21314/JOR.2000.038> and Markowitz (1952) <doi:10.1111/j.1540-6261.1952.tb01525.x>.

Maintained by Ronald Hochreiter. Last updated 7 years ago.

2 stars 3.26 score 18 scripts

mondrus96

fabisearch:Change Point Detection in High-Dimensional Time Series Networks

Implementation of the Factorized Binary Search (FaBiSearch) methodology for the estimation of the number and the location of multiple change points in the network (or clustering) structure of multivariate high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI) data. FaBiSearch uses non-negative matrix factorization (NMF), an unsupervised dimension reduction technique, and a new binary search algorithm to identify multiple change points. It requires minimal assumptions. Lastly, we provide interactive, 3-dimensional, brain-specific network visualization capability in a flexible, stand-alone function. This function can be conveniently used with any node coordinate atlas, and nodes can be color coded according to community membership, if applicable. The output is an elegantly displayed network laid over a cortical surface, which can be rotated in the 3-dimensional space. The main routines of the package are detect.cps(), for multiple change point detection, est.net(), for estimating a network between stationary multivariate time series, net.3dplot(), for plotting the estimated functional connectivity networks, and opt.rank(), for finding the optimal rank in NMF for a given data set. The functions have been extensively tested on simulated multivariate high-dimensional time series data and fMRI data. For details on the FaBiSearch methodology, please see Ondrus et al. (2021) <arXiv:2103.06347>. For a more detailed explanation and applied examples of the fabisearch package, please see Ondrus and Cribben (2022), preprint.

Maintained by Martin Ondrus. Last updated 7 months ago.

1 stars 3.00 score 2 scripts

cran

OTrecod:Data Fusion using Optimal Transportation Theory

In the context of data fusion, the package provides a set of functions dedicated to the solving of 'recoding problems' using optimal transportation theory (Gares, Guernec, Savy (2019) <doi:10.1515/ijb-2018-0106> and Gares, Omer (2020) <doi:10.1080/01621459.2020.1775615>). From two databases with no overlapping part except a subset of shared variables, the functions of the package assist users until obtaining a unique synthetic database, where the missing information is fully completed.

Maintained by Gregory Guernec. Last updated 2 years ago.

3.00 score

r-forge

ROI.plugin.neos:'NEOS' Plug-in for the 'R' Optimization Interface

Enhances the 'R' Optimization Infrastructure ('ROI') package with a connection to the 'neos' server. 'ROI' optimization problems can be directly be sent to the 'neos' server and solution obtained in the typical 'ROI' style.

Maintained by Florian Schwendinger. Last updated 2 years ago.

3.00 score 7 scripts

robustport

facmodTS:Time Series Models for Asset Returns

Supports teaching methods of estimating and testing time series models for use in robust portfolio construction and analysis. Unique in providing not only classical least squares, but also modern robust model fitting methods which are not much influenced by outliers. Includes returns and risk decompositions, with user choice of standard deviation, value-at-risk, and expected shortfall risk measures. "Robust Statistics Theory and Methods (with R)", R. A. Maronna, R. D. Martin, V. J. Yohai, M. Salibian-Barrera (2019) <doi:10.1002/9781119214656>.

Maintained by Doug Martin. Last updated 22 days ago.

1 stars 3.00 score

ben-schwen

holiglm:Holistic Generalized Linear Models

Holistic generalized linear models (HGLMs) extend generalized linear models (GLMs) by enabling the possibility to add further constraints to the model. The 'holiglm' package simplifies estimating HGLMs using convex optimization. Additional information about the package can be found in the reference manual, the 'README' and the accompanying paper <doi:10.18637/jss.v108.i07>.

Maintained by Benjamin Schwendinger. Last updated 3 months ago.

3 stars 2.78 score

abeyrann

Path.Analysis:Path Coefficient Analysis

Facilitates the performance of several analyses, including simple and sequential path coefficient analysis, correlation estimate, drawing correlogram, Heatmap, and path diagram. When working with raw data, that includes one or more dependent variables along with one or more independent variables are available, the path coefficient analysis can be conducted. It allows for testing direct effects, which can be a vital indicator in path coefficient analysis. The process of preparing the dataset rule is explained in detail in the vignette file "Path.Analysis_manual.Rmd". You can find this in the folders labelled "data" and "~/inst/extdata". Also see: 1)the 'lavaan', 2)a sample of sequential path analysis in 'metan' suggested by Olivoto and Lúcio (2020) <doi:10.1111/2041-210X.13384>, 3)the simple 'PATHSAS' macro written in 'SAS' by Cramer et al. (1999) <doi:10.1093/jhered/90.1.260>, and 4)the semPlot() function of 'OpenMx' as initial tools for conducting path coefficient analyses and SEM (Structural Equation Modeling). To gain a comprehensive understanding of path coefficient analysis, both in theory and practice, see a 'Minitab' macro developed by Arminian, A. in the paper by Arminian et al. (2008) <doi:10.1080/15427520802043182>.

Maintained by Ali Arminian. Last updated 6 months ago.

2.70 score

stla

bratteli:Deal with Bratteli Graphs

Utilities for Bratteli graphs. A tree is an example of a Bratteli graph. The package provides a function which generates a 'LaTeX' file that renders the given Bratteli graph. It also provides functions to compute the dimensions of the vertices, the intrinsic kernels and the intrinsic distances. Intrinsic kernels and distances were introduced by Vershik (2014) <doi:10.1007/s10958-014-1958-0>.

Maintained by Stéphane Laurent. Last updated 1 years ago.

graphs latex

1 stars 2.70 score

mhahsler

recommenderlabBX:Book-Crossing Dataset (BX) for 'recommenderlab'

Provides the Book-Crossing Dataset for the package recommenderlab.

Maintained by Michael Hahsler. Last updated 3 years ago.

recommender-systems

2.70 score 1 scripts

joe-chelladurai

starry:Explore Data with Plots and Tables

Provides modular functions and applications for quickly generating plots and tables. Each modular function opens a graphical user interface providing the user with options to create and customise plots and tables.

Maintained by Joe Chelladurai. Last updated 2 years ago.

1 stars 2.70 score 1 scripts

r-forge

ROI.models.miplib:'ROI' Access to 'MIPLIB' 2010 Benchmark Instances

The mixed integer programming library 'MIPLIB' (see <http://miplib.zib.de/>) is commonly used to compare the performance of mixed integer optimization solvers. This package provides functions to access 'MIPLIB' from the 'R' Optimization Infrastructure ('ROI'). More information about 'MIPLIB' can be found in the paper by Koch et al. available at <http://mpc.zib.de/index.php/MPC/article/viewFile/56/28>. The 'README.md' file illustrates how to use this package.

Maintained by Florian Schwendinger. Last updated 2 years ago.

2.70 score 5 scripts

philboileau

neatmaps:Heatmaps for Multiple Network Data

Simplify the exploratory data analysis process for multiple network data sets with the help of hierarchical clustering, consensus clustering and heatmaps. Multiple network data consists of multiple disjoint networks that have common variables (e.g. ego networks). This package contains the necessary tools for exploring such data, from the data pre-processing stage to the creation of dynamic visualizations.

Maintained by Philippe Boileau. Last updated 3 years ago.

1 stars 2.70 score 7 scripts

mhahsler

recommenderlabJester:Jester Dataset for 'recommenderlab'

Provides the Jester Dataset for package recommenderlab.

Maintained by Michael Hahsler. Last updated 3 years ago.

recommender-systems

2.70 score 1 scripts

shixiangwang

sigminer.prediction:Train and Predict Cancer Subtype with Keras Model based on Mutational Signatures

Mutational signatures represent mutational processes occured in cancer evolution, thus are stable and genetic resources for subtyping. This tool provides functions for training neutral network models to predict the subtype a sample belongs to based on 'keras' and 'sigminer' packages.

Maintained by Shixiang Wang. Last updated 3 years ago.

keras mutational-signatures prostate-cancer sigminer

8 stars 2.60 score 2 scripts

r-forge

ROI.plugin.optimx:'optimx' Plug-in for the 'R' Optimization Infrastructure

Enhances the R Optimization Infrastructure ('ROI') package with the 'optimx' package.

Maintained by Florian Schwendinger. Last updated 2 years ago.

2.48 score 3 scripts 1 dependents

wjschne

WJSmisc:Miscellaneous functions from W. Joel Schneider

Several functions I find useful.

Maintained by W. Joel Schneider. Last updated 2 years ago.

5 stars 2.40 score 10 scripts

cran

InterSIM:Simulation of Inter-Related Genomic Datasets

Generates three inter-related genomic datasets: methylation, gene expression and protein expression having user specified cluster patterns. The simulation utilizes the realistic inter- and intra- relationships from real DNA methylation, mRNA expression and protein expression data from the TCGA ovarian cancer study, Chalise (2016) <doi:10.1016/j.cmpb.2016.02.011>.

Maintained by Prabhakar Chalise. Last updated 3 months ago.

2.26 score 2 dependents

ablommaert

spectralAnalysis:Pre-Process, Visualize and Analyse Spectral Data

Infrared, near-infrared and Raman spectroscopic data measured during chemical reactions, provide structural fingerprints by which molecules can be identified and quantified. The application of these spectroscopic techniques as inline process analytical tools (PAT), provides the pharmaceutical and chemical industry with novel tools, allowing to monitor their chemical processes, resulting in a better process understanding through insight in reaction rates, mechanistics, stability, etc. Data can be read into R via the generic spc-format, which is generally supported by spectrometer vendor software. Versatile pre-processing functions are available to perform baseline correction by linking to the 'baseline' package; noise reduction via the 'signal' package; as well as time alignment, normalization, differentiation, integration and interpolation. Implementation based on the S4 object system allows storing a pre-processing pipeline as part of a spectral data object, and easily transferring it to other datasets. Interactive plotting tools are provided based on the 'plotly' package. Non-negative matrix factorization (NMF) has been implemented to perform multivariate analyses on individual spectral datasets or on multiple datasets at once. NMF provides a parts-based representation of the spectral data in terms of spectral signatures of the chemical compounds and their relative proportions. See 'hNMF'-package for references on available methods. The functionality to read in spc-files was adapted from the 'hyperSpec' package.

Maintained by Adriaan Blommaert. Last updated 1 years ago.

2.26 score 18 scripts

r-forge

ROI.plugin.alabama:'alabama' Plug-in for the 'R' Optimization Infrastructure

Enhances the R Optimization Infrastructure ('ROI') package with the 'alabama' solver for solving nonlinear optimization problems.

Maintained by Florian Schwendinger. Last updated 2 years ago.

2.15 score 14 scripts

r-forge

ROI.plugin.scs:'SCS' Plug-in for the 'R' Optimization Infrastructure

Enhances the 'R' Optimization Infrastructure ('ROI') package with the 'SCS' solver for solving convex cone problems.

Maintained by Florian Schwendinger. Last updated 2 years ago.

2.15 score 14 scripts

r-forge

ROI.plugin.deoptim:'DEoptim' and 'DEoptimR' Plugin for the 'R' Optimization Interface

Enhances the R Optimization Infrastructure ('ROI') package with the 'DEoptim' and 'DEoptimR' package. 'DEoptim' is used for unconstrained optimization and 'DEoptimR' for constrained optimization.

Maintained by Florian Schwendinger. Last updated 2 years ago.

2.08 score 12 scripts

r-forge

ROI.plugin.osqp:'osqp' Plugin for the 'R' Optimization Infrastructure

Enhances the 'R' Optimization Infrastructure ('ROI') package with the quadratic solver 'OSQP'. More information about 'OSQP' can be found at <https://osqp.org>.

Maintained by Florian Schwendinger. Last updated 2 years ago.

2.00 score 2 scripts

r-forge

ROI.plugin.nloptr:'nloptr' Plug-in for the 'R' Optimization Infrastructure

Enhances the R Optimization Infrastructure ('ROI') package with the 'NLopt' solver for solving nonlinear optimization problems.

Maintained by Florian Schwendinger. Last updated 2 years ago.

2.00 score 8 scripts

r-forge

ROI.plugin.msbinlp:'Multi-Solution' Binary Linear Problem Plug-in for the 'R' Optimization Interface

Enhances the 'R' Optimization Infrastructure ('ROI') package with the possibility to obtain multiple solutions for linear problems with binary variables. The main function is copied (with small modifications) from the relations package.

Maintained by Florian Schwendinger. Last updated 2 years ago.

2.00 score 4 scripts

cran

M3JF:Multi-Modal Matrix Joint Factorization for Integrative Multi-Omics Data Analysis

Multi modality data matrices are factorized conjointly into the multiplication of a shared sub-matrix and multiple modality specific sub-matrices, group sparse constraint is applied to the shared sub-matrix to capture the homogeneous and heterogeneous information, respectively. Then the samples are classified by clustering the shared sub-matrix with kmeanspp(), a new version of kmeans() developed here to obtain concordant results. The package also provides the cluster number estimation by rotation cost. Moreover, cluster specific features could be retrieved using hypergeometric tests.

Maintained by Xiaoyao Yin. Last updated 2 years ago.

2.00 score

cran

bartMan:Create Visualisations for BART Models

Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman, H. A., George, E. I., & McCulloch, R. E. 2010) <doi:10.1214/09-AOAS285> model fits. We construct conventional plots to analyze a model’s performance and stability as well as create new tree-based plots to analyze variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our visualisations are designed to work with the most popular BART R packages available, namely 'BART' Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 <doi:10.18637/jss.v097.i01>, 'dbarts' (Vincent Dorie 2023) <https://CRAN.R-project.org/package=dbarts>, and 'bartMachine' (Adam Kapelner and Justin Bleich 2016) <doi:10.18637/jss.v070.i04>.

Maintained by Alan Inglis. Last updated 8 months ago.

openjdk

2.00 score

mmm-uca

adea:Alternate DEA Package

The meaning of adea is "alternate DEA". This package is devoted to provide the alternative method of DEA described in the paper entitled "Stepwise Selection of Variables in DEA Using Contribution Load", by F. Fernandez-Palacin, M. A. Lopez-Sanchez and M. Munoz-Marquez. Pesquisa Operacional 38 (1), pg. 1-24, 2018. <doi:10.1590/0101-7438.2018.038.01.0031>. A full functional on-line and interactive version is available at <https://knuth.uca.es/shiny/DEA/>.

Maintained by Manuel Munoz-Marquez. Last updated 5 months ago.

2.00 score 1 scripts

cran

ClusteredMutations:Location and Visualization of Clustered Somatic Mutations

Identification and visualization of groups of closely spaced mutations in the DNA sequence of cancer genome. The extremely mutated zones are searched in the symmetric dissimilarity matrix using the anti-Robinson matrix properties. Different data sets are obtained to describe and plot the clustered mutations information.

Maintained by David Lora. Last updated 9 years ago.

2.00 score

r-forge

ROI.plugin.ipop:'ipop' Plug-in for the 'R' Optimization Interface

Enhances the 'R' Optimization Infrastructure ('ROI') package by registering the 'ipop' solver from package 'kernlab'.

Maintained by Florian Schwendinger. Last updated 2 years ago.

2.00 score 2 scripts

j-mitchel

scITD:Single-Cell Interpretable Tensor Decomposition

Single-cell Interpretable Tensor Decomposition (scITD) employs the Tucker tensor decomposition to extract multicell-type gene expression patterns that vary across donors/individuals. This tool is geared for use with single-cell RNA-sequencing datasets consisting of many source donors. The method has a wide range of potential applications, including the study of inter-individual variation at the population-level, patient sub-grouping/stratification, and the analysis of sample-level batch effects. Each "multicellular process" that is extracted consists of (A) a multi cell type gene loadings matrix and (B) a corresponding donor scores vector indicating the level at which the corresponding loadings matrix is expressed in each donor. Additional methods are implemented to aid in selecting an appropriate number of factors and to evaluate stability of the decomposition. Additional tools are provided for downstream analysis, including integration of gene set enrichment analysis and ligand-receptor analysis. Tucker, L.R. (1966) <doi:10.1007/BF02289464>. Unkel, S., Hannachi, A., Trendafilov, N. T., & Jolliffe, I. T. (2011) <doi:10.1007/s13253-011-0055-9>. Zhou, G., & Cichocki, A. (2012) <doi:10.2478/v10175-012-0051-4>.

Maintained by Jonathan Mitchel. Last updated 2 years ago.

cpp

1.98 score 19 scripts

f-rousset

blackbox:Black Box Optimization and Exploration of Parameter Space

Performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. Includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014 <doi:10.1093/molbev/msu212>; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.

Maintained by François Rousset. Last updated 1 years ago.

cpp

1.79 score 8 scripts 1 dependents

nsauwen

hNMF:Hierarchical Non-Negative Matrix Factorization

Hierarchical and single-level non-negative matrix factorization. Several NMF algorithms are available.

Maintained by Nicolas Sauwen. Last updated 4 years ago.

2 stars 1.78 score 7 scripts 1 dependents

schweflo

ROI.plugin.highs:'HiGHS' Plugin for the 'R' Optimization Infrastructure

Enhances the 'R' Optimization Infrastructure ('ROI') package with the quadratic solver 'HiGHS'. More information about 'HiGHS' can be found at <https://highs.dev>.

Maintained by Florian Schwendinger. Last updated 2 years ago.

1.70 score

cran

popstudy:Applied Techniques to Demographic and Time Series Analysis

The use of overparameterization is proposed with combinatorial analysis to test a broader spectrum of possible ARIMA models. In the selection of ARIMA models, the most traditional methods such as correlograms or others, do not usually cover many alternatives to define the number of coefficients to be estimated in the model, which represents an estimation method that is not the best. The popstudy package contains several tools for statistical analysis in demography and time series based in Shryock research (Shryock et. al. (1980) <https://books.google.co.cr/books?id=8Oo6AQAAMAAJ>).

Maintained by Cesar Gamboa-Sanabria. Last updated 1 years ago.

1.70 score

schweflo

ROI.models.globalOptTests:'ROI' Optimization Problems Based on 'globalOptTests'

A collection of non-linear optimization problems with box bounds transformed into 'ROI' optimization problems. This package provides a wrapper around the 'globalOptTests' which provides a collection of global optimization problems. More information can be found in the 'README' file.

Maintained by Florian Schwendinger. Last updated 3 months ago.

1.70 score 3 scripts

blansche

fdm2id:Data Mining and R Programming for Beginners

Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".

Maintained by Alexandre Blansché. Last updated 2 years ago.

1 stars 1.62 score 42 scripts

cran

IntNMF:Integrative Clustering of Multiple Genomic Dataset

Carries out integrative clustering analysis using multiple types of genomic dataset using integrative Non-negative Matrix factorization.

Maintained by Prabhakar Chalise. Last updated 3 months ago.

2 stars 1.30 score

f-rousset

Infusion:Inference Using Simulation

Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated. The package implements more advanced methods than the ones first described in: Rousset, Gouy, Almoyna and Courtiol (2017) <doi:10.1111/1755-0998.12627>.

Maintained by François Rousset. Last updated 6 months ago.

1.18 score 15 scripts

cran

DBNMFrank:Rank Selection for Non-Negative Matrix Factorization

Given the non-negative data and its distribution, the package estimates the rank parameter for Non-negative Matrix Factorization. The method is based on hypothesis testing, using a deconvolved bootstrap distribution to assess the significance level accurately despite the large amount of optimization error. The distribution of the non-negative data can be either Normal distributed or Poisson distributed.

Maintained by Yun Cai. Last updated 3 years ago.

1.00 score

schweflo

ROI.models.netlib:'ROI' Optimization Problems Based on 'NETLIB-LP'

A collection of 'ROI' optimization problems based on the 'NETLIB-LP' collection. 'Netlib' is a software repository, which amongst many other software for scientific computing contains a collection of linear programming problems. The purpose of this package is to make this problems easily accessible from 'R' as 'ROI' optimization problems.

Maintained by Florian Schwendinger. Last updated 2 years ago.

1.00 score 8 scripts

cran

TELP:Social Representation Theory Application: The Free Evocation of Words Technique

Using The Free Evocation of Words Technique method with some functions, this package will make a social representation and other analysis. The Free Evocation of Words Technique consists of collecting a number of words evoked by a subject facing exposure to an inducer term. The purpose of this technique is to understand the relationships created between words evoked by the individual and the inducer term. This technique is included in the theory of social representations, therefore, on the information transmitted by an individual, seeks to create a profile that define a social group.

Maintained by Gabriel Henrique Oliveira Assuncao. Last updated 2 years ago.

1.00 score

cran

adana:Adaptive Nature-Inspired Algorithms for Hybrid Genetic Optimization

The Genetic Algorithm (GA) is a type of optimization method of Evolutionary Algorithms. It uses the biologically inspired operators such as mutation, crossover, selection and replacement.Because of their global search and robustness abilities, GAs have been widely utilized in machine learning, expert systems, data science, engineering, life sciences and many other areas of research and business. However, the regular GAs need the techniques to improve their efficiency in computing time and performance in finding global optimum using some adaptation and hybridization strategies. The adaptive GAs (AGA) increase the convergence speed and success of regular GAs by setting the parameters crossover and mutation probabilities dynamically. The hybrid GAs combine the exploration strength of a stochastic GAs with the exact convergence ability of any type of deterministic local search algorithms such as simulated-annealing, in addition to other nature-inspired algorithms such as ant colony optimization, particle swarm optimization etc. The package 'adana' includes a rich working environment with its many functions that make possible to build and work regular GA, adaptive GA, hybrid GA and hybrid adaptive GA for any kind of optimization problems. Cebeci, Z. (2021, ISBN: 9786254397448).

Maintained by Erkut Tekeli. Last updated 3 years ago.

1.00 score

cran

RMaCzek:Czekanowski's Diagrams

Allows for production of Czekanowski's Diagrams with clusters. See K. Bartoszek, A. Vasterlund (2020) <doi:10.2478/bile-2020-0008> and K. Bartoszek, Y. Luo (2023) <doi:10.14708/ma.v51i2.7259>.

Maintained by Krzysztof Bartoszek. Last updated 9 months ago.

1.00 score

cran

MultiplierDEA:Multiplier Data Envelopment Analysis and Cross Efficiency

Functions are provided for calculating efficiency using multiplier DEA (Data Envelopment Analysis): Measuring the efficiency of decision making units (Charnes et al., 1978 <doi:10.1016/0377-2217(78)90138-8>) and cross efficiency using single and two-phase approach. In addition, it includes some datasets for calculating efficiency and cross efficiency.

Maintained by Aurobindh Kalathil Puthanpura. Last updated 3 years ago.

1 stars 1.00 score

mellorf

shattering:Estimate the Shattering Coefficient for a Particular Dataset

The Statistical Learning Theory (SLT) provides the theoretical background to ensure that a supervised algorithm generalizes the mapping f:X -> Y given f is selected from its search space bias F. This formal result depends on the Shattering coefficient function N(F,2n) to upper bound the empirical risk minimization principle, from which one can estimate the necessary training sample size to ensure the probabilistic learning convergence and, most importantly, the characterization of the capacity of F, including its under and overfitting abilities while addressing specific target problems. In this context, we propose a new approach to estimate the maximal number of hyperplanes required to shatter a given sample, i.e., to separate every pair of points from one another, based on the recent contributions by Har-Peled and Jones in the dataset partitioning scenario, and use such foundation to analytically compute the Shattering coefficient function for both binary and multi-class problems. As main contributions, one can use our approach to study the complexity of the search space bias F, estimate training sample sizes, and parametrize the number of hyperplanes a learning algorithm needs to address some supervised task, what is specially appealing to deep neural networks. Reference: de Mello, R.F. (2019) "On the Shattering Coefficient of Supervised Learning Algorithms" <arXiv:1911.05461>; de Mello, R.F., Ponti, M.A. (2018, ISBN: 978-3319949888) "Machine Learning: A Practical Approach on the Statistical Learning Theory".

Maintained by Rodrigo F. de Mello. Last updated 4 years ago.

1 stars 1.00 score 1 scripts

cran

SpNMF:Supervised NMF

Non-negative Matrix Factorization(NMF) is a powerful tool for identifying the key features of microbial communities and a dimension-reduction method. When we are interested in the differences between the structures of two groups of communities, supervised NMF(Yun Cai, Hong Gu and Tobby Kenney (2017),<doi:10.1186/s40168-017-0323-1>) provides a better way to do this, while retaining all the advantages of NMF -- such as interpretability, and being based on a simple biological intuition.

Maintained by Yun Cai. Last updated 6 years ago.

1.00 score

ben-schwen

ROI.plugin.clarabel:'clarabel' Plug-in for the 'R' Optimization Infrastructure

Enhances the 'R' Optimization Infrastructure ('ROI') package with the 'clarabel' solver for solving convex cone problems. More information about 'clarabel' can be found at <https://oxfordcontrol.github.io/ClarabelDocs/stable/>.

Maintained by Benjamin Schwendinger. Last updated 2 years ago.

1.00 score 5 scripts