Showing 200 of total 311 results (show query)
bioc
mixOmics:Omics Data Integration Project
Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.
Maintained by Eva Hamrud. Last updated 2 days ago.
immunooncologymicroarraysequencingmetabolomicsmetagenomicsproteomicsgenepredictionmultiplecomparisonclassificationregressionbioconductorgenomicsgenomics-datagenomics-visualizationmultivariate-analysismultivariate-statisticsomicsr-pkgr-project
185 stars 13.75 score 1.3k scripts 22 dependentsbioc
variancePartition:Quantify and interpret drivers of variation in multilevel gene expression experiments
Quantify and interpret multiple sources of biological and technical variation in gene expression experiments. Uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. Includes dream differential expression analysis for repeated measures.
Maintained by Gabriel E. Hoffman. Last updated 3 months ago.
rnaseqgeneexpressiongenesetenrichmentdifferentialexpressionbatcheffectqualitycontrolregressionepigeneticsfunctionalgenomicstranscriptomicsnormalizationpreprocessingmicroarrayimmunooncologysoftware
7 stars 11.69 score 1.1k scripts 3 dependents
sachaepskamp
qgraph:Graph Plotting Methods, Psychometric Data Visualization and Graphical Model Estimation
Fork of qgraph - Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>.
Maintained by Sacha Epskamp. Last updated 1 years ago.
69 stars 11.43 score 1.2k scripts 63 dependents
r4ss
r4ss:R Code for Stock Synthesis
A collection of R functions for use with Stock Synthesis, a fisheries stock assessment modeling platform written in ADMB by Dr. Richard D. Methot at the NOAA Northwest Fisheries Science Center. The functions include tools for summarizing and plotting results, manipulating files, visualizing model parameterizations, and various other common stock assessment tasks. This version of '{r4ss}' is compatible with Stock Synthesis versions 3.24 through 3.30 (specifically version 3.30.23.1, from December 2024). Support for 3.24 models is only through the core functions for reading output and plotting.
Maintained by Ian G. Taylor. Last updated 18 days ago.
fisheriesfisheries-stock-assessmentstock-synthesis
43 stars 11.38 score 1.0k scripts 2 dependents
biometry
bipartite:Visualising Bipartite Networks and Calculating Some (Ecological) Indices
Functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) webs. It focuses on webs consisting of only two levels (bipartite), e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the web's topology.
Maintained by Carsten F. Dormann. Last updated 20 days ago.
37 stars 10.93 score 592 scripts 15 dependentsbioc
muscat:Multi-sample multi-group scRNA-seq data analysis tools
`muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data.
Maintained by Helena L. Crowell. Last updated 5 months ago.
immunooncologydifferentialexpressionsequencingsinglecellsoftwarestatisticalmethodvisualization
184 stars 10.74 score 686 scripts 1 dependentssachaepskamp
semPlot:Path Diagrams and Visual Analysis of Various SEM Packages' Output
Path diagrams and visual analysis of various SEM packages' output.
Maintained by Sacha Epskamp. Last updated 3 years ago.
63 stars 10.64 score 2.1k scripts 13 dependents
thej022214
corHMM:Hidden Markov Models of Character Evolution
Fits hidden Markov models of discrete character evolution which allow different transition rate classes on different portions of a phylogeny. Beaulieu et al (2013) <doi:10.1093/sysbio/syt034>.
Maintained by Jeremy Beaulieu. Last updated 1 months ago.
13 stars 9.52 score 422 scripts 2 dependents
jclavel
mvMORPH:Multivariate Comparative Tools for Fitting Evolutionary Models to Morphometric Data
Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. 'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood. See Clavel et al. (2015) <DOI:10.1111/2041-210X.12420>, Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, and Clavel & Morlon (2020) <DOI:10.1093/sysbio/syaa010>.
Maintained by Julien Clavel. Last updated 2 months ago.
17 stars 9.46 score 189 scripts 3 dependentssachaepskamp
bootnet:Bootstrap Methods for Various Network Estimation Routines
Bootstrap methods to assess accuracy and stability of estimated network structures and centrality indices <doi:10.3758/s13428-017-0862-1>. Allows for flexible specification of any undirected network estimation procedure in R, and offers default sets for various estimation routines.
Maintained by Sacha Epskamp. Last updated 5 months ago.
32 stars 8.94 score 155 scripts 3 dependents
merck
gsDesign2:Group Sequential Design with Non-Constant Effect
The goal of 'gsDesign2' is to enable fixed or group sequential design under non-proportional hazards. To enable highly flexible enrollment, time-to-event and time-to-dropout assumptions, 'gsDesign2' offers piecewise constant enrollment, failure rates, and dropout rates for a stratified population. This package includes three methods for designs: average hazard ratio, weighted logrank tests in Yung and Liu (2019) <doi:10.1111/biom.13196>, and MaxCombo tests. Substantial flexibility on top of what is in the 'gsDesign' package is intended for selecting boundaries.
Maintained by Yujie Zhao. Last updated 2 days ago.
22 stars 8.91 score 186 scripts
jarrodhadfield
MCMCglmm:MCMC Generalised Linear Mixed Models
Fits Multivariate Generalised Linear Mixed Models (and related models) using Markov chain Monte Carlo techniques (Hadfield 2010 J. Stat. Soft.).
Maintained by Jarrod Hadfield. Last updated 3 months ago.
2 stars 8.83 score 1.2k scripts 14 dependents
ss3sim
ss3sim:Fisheries Stock Assessment Simulation Testing with Stock Synthesis
A framework for fisheries stock assessment simulation testing with Stock Synthesis (SS3) as described in Anderson et al. (2014) <doi:10.1371/journal.pone.0092725>.
Maintained by Kelli F. Johnson. Last updated 5 months ago.
fisheriessimulationstock-synthesis
39 stars 8.72 score 149 scripts
alexiosg
rmgarch:Multivariate GARCH Models
Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH.
Maintained by Alexios Galanos. Last updated 3 months ago.
14 stars 8.51 score 294 scripts 2 dependents
thej022214
hisse:Hidden State Speciation and Extinction
Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character sets to test for hidden shifts in trait dependent rates of diversification. Beaulieu and O'Meara (2016) <doi:10.1093/sysbio/syw022>.
Maintained by Jeremy Beaulieu. Last updated 2 months ago.
6 stars 8.45 score 152 scriptsthej022214
OUwie:Analysis of Evolutionary Rates in an OU Framework
Estimates rates for continuous character evolution under Brownian motion and a new set of Ornstein-Uhlenbeck based Hansen models that allow both the strength of the pull and stochastic motion to vary across selective regimes. Beaulieu et al (2012).
Maintained by Jeremy Beaulieu. Last updated 12 days ago.
9 stars 8.42 score 161 scriptsbioc
flowStats:Statistical methods for the analysis of flow cytometry data
Methods and functionality to analyse flow data that is beyond the basic infrastructure provided by the flowCore package.
Maintained by Greg Finak. Last updated 5 months ago.
immunooncologyflowcytometrycellbasedassays
14 stars 8.27 score 195 scripts 1 dependents
jmbh
mgm:Estimating Time-Varying k-Order Mixed Graphical Models
Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) <doi:10.18637/jss.v093.i08>.
Maintained by Jonas Haslbeck. Last updated 19 days ago.
29 stars 8.16 score 125 scripts 6 dependentsbioc
POMA:Tools for Omics Data Analysis
The POMA package offers a comprehensive toolkit designed for omics data analysis, streamlining the process from initial visualization to final statistical analysis. Its primary goal is to simplify and unify the various steps involved in omics data processing, making it more accessible and manageable within a single, intuitive R package. Emphasizing on reproducibility and user-friendliness, POMA leverages the standardized SummarizedExperiment class from Bioconductor, ensuring seamless integration and compatibility with a wide array of Bioconductor tools. This approach guarantees maximum flexibility and replicability, making POMA an essential asset for researchers handling omics datasets. See https://github.com/pcastellanoescuder/POMAShiny. Paper: Castellano-Escuder et al. (2021) <doi:10.1371/journal.pcbi.1009148> for more details.
Maintained by Pol Castellano-Escuder. Last updated 4 months ago.
batcheffectclassificationclusteringdecisiontreedimensionreductionmultidimensionalscalingnormalizationpreprocessingprincipalcomponentregressionrnaseqsoftwarestatisticalmethodvisualizationbioconductorbioinformaticsdata-visualizationdimension-reductionexploratory-data-analysismachine-learningomics-data-integrationpipelinepre-processingstatistical-analysisuser-friendlyworkflow
11 stars 8.16 score 20 scripts 1 dependentsbioc
dreamlet:Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs
Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample.
Maintained by Gabriel Hoffman. Last updated 4 days ago.
rnaseqgeneexpressiondifferentialexpressionbatcheffectqualitycontrolregressiongenesetenrichmentgeneregulationepigeneticsfunctionalgenomicstranscriptomicsnormalizationsinglecellpreprocessingsequencingimmunooncologysoftwarecpp
12 stars 8.14 score 128 scripts
gfellerlab
SuperCell:Simplification of scRNA-seq data by merging together similar cells
Aggregates large single-cell data into metacell dataset by merging together gene expression of very similar cells.
Maintained by The package maintainer. Last updated 8 months ago.
softwarecoarse-grainingscrna-seq-analysisscrna-seq-data
72 stars 8.08 score 93 scriptsbioc
TOAST:Tools for the analysis of heterogeneous tissues
This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include 1. detect cell-type specific or cross-cell type differential signals 2. tree-based differential analysis 3. improve variable selection in reference-free deconvolution 4. partial reference-free deconvolution with prior knowledge.
Maintained by Ziyi Li. Last updated 5 months ago.
dnamethylationgeneexpressiondifferentialexpressiondifferentialmethylationmicroarraygenetargetepigeneticsmethylationarray
11 stars 8.01 score 104 scripts 3 dependentsbioc
netZooR:Unified methods for the inference and analysis of gene regulatory networks
netZooR unifies the implementations of several Network Zoo methods (netzoo, netzoo.github.io) into a single package by creating interfaces between network inference and network analysis methods. Currently, the package has 3 methods for network inference including PANDA and its optimized implementation OTTER (network reconstruction using mutliple lines of biological evidence), LIONESS (single-sample network inference), and EGRET (genotype-specific networks). Network analysis methods include CONDOR (community detection), ALPACA (differential community detection), CRANE (significance estimation of differential modules), MONSTER (estimation of network transition states). In addition, YARN allows to process gene expresssion data for tissue-specific analyses and SAMBAR infers missing mutation data based on pathway information.
Maintained by Tara Eicher. Last updated 12 days ago.
networkinferencenetworkgeneregulationgeneexpressiontranscriptionmicroarraygraphandnetworkgene-regulatory-networktranscription-factors
105 stars 7.98 score
fbertran
plsRglm:Partial Least Squares Regression for Generalized Linear Models
Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <arXiv:1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Maintained by Frederic Bertrand. Last updated 2 years ago.
16 stars 7.75 score 103 scripts 5 dependents
gateslab
gimme:Group Iterative Multiple Model Estimation
Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.
Maintained by Kathleen M Gates. Last updated 9 days ago.
26 stars 7.61 score 53 scriptsbioc
AlpsNMR:Automated spectraL Processing System for NMR
Reads Bruker NMR data directories both zipped and unzipped. It provides automated and efficient signal processing for untargeted NMR metabolomics. It is able to interpolate the samples, detect outliers, exclude regions, normalize, detect peaks, align the spectra, integrate peaks, manage metadata and visualize the spectra. After spectra proccessing, it can apply multivariate analysis on extracted data. Efficient plotting with 1-D data is also available. Basic reading of 1D ACD/Labs exported JDX samples is also available.
Maintained by Sergio Oller Moreno. Last updated 5 months ago.
softwarepreprocessingvisualizationclassificationcheminformaticsmetabolomicsdataimport
15 stars 7.59 score 12 scripts 1 dependents
sebastian-engelke
graphicalExtremes:Statistical Methodology for Graphical Extreme Value Models
Statistical methodology for sparse multivariate extreme value models. Methods are provided for exact simulation and statistical inference for multivariate Pareto distributions on graphical structures as described in the paper 'Graphical Models for Extremes' by Engelke and Hitz (2020) <doi:10.1111/rssb.12355>.
Maintained by Sebastian Engelke. Last updated 3 months ago.
16 stars 7.38 score 28 scripts 1 dependents
r-forge
pcalg:Methods for Graphical Models and Causal Inference
Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.
Maintained by Markus Kalisch. Last updated 7 months ago.
7.30 score 700 scripts 19 dependents
sfcheung
semptools:Customizing Structural Equation Modelling Plots
Most function focus on specific ways to customize a graph. They use a 'qgraph' output as the first argument, and return a modified 'qgraph' object. This allows the functions to be chained by a pipe operator.
Maintained by Shu Fai Cheung. Last updated 3 months ago.
diagramgraphlavaanplotsemstructural-equation-modeling
7 stars 7.12 score 87 scripts
alexchristensen
NetworkToolbox:Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis
Implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.
Maintained by Alexander Christensen. Last updated 2 years ago.
23 stars 7.04 score 101 scripts 4 dependentsbioc
lfa:Logistic Factor Analysis for Categorical Data
Logistic Factor Analysis is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter. The main method estimates genetic population structure from genotype data. There are also methods for estimating individual-specific allele frequencies using the population structure. Lastly, a structured Hardy-Weinberg equilibrium (HWE) test is developed, which quantifies the goodness of fit of the genotype data to the estimated population structure, via the estimated individual-specific allele frequencies (all of which generalizes traditional HWE tests).
Maintained by Alejandro Ochoa. Last updated 5 months ago.
snpdimensionreductionprincipalcomponentregressionopenblas
16 stars 7.04 score 57 scripts 1 dependents
psoerensen
qgg:Statistical Tools for Quantitative Genetic Analyses
Provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for: 1) fitting linear mixed models, 2) constructing marker-based genomic relationship matrices, 3) estimating genetic parameters (heritability and correlation), 4) performing genomic prediction and genetic risk profiling, and 5) single or multi-marker association analyses. Rohde et al. (2019) <doi:10.1101/503631>.
Maintained by Peter Soerensen. Last updated 11 days ago.
36 stars 7.01 score 47 scriptssachaepskamp
psychonetrics:Structural Equation Modeling and Confirmatory Network Analysis
Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data <doi:10.31234/osf.io/8ha93>. Allows for confirmatory testing and fit as well as exploratory model search.
Maintained by Sacha Epskamp. Last updated 3 days ago.
51 stars 6.88 score 41 scripts 1 dependents
cvborkulo
IsingFit:Fitting Ising Models Using the ELasso Method
This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.
Maintained by Sacha Epskamp. Last updated 1 years ago.
10 stars 6.85 score 25 scripts 5 dependents
vast-lib
tinyVAST:Multivariate Spatio-Temporal Models using Structural Equations
Fits a wide variety of multivariate spatio-temporal models with simultaneous and lagged interactions among variables (including vector autoregressive spatio-temporal ('VAST') dynamics) for areal, continuous, or network spatial domains. It includes time-variable, space-variable, and space-time-variable interactions using dynamic structural equation models ('DSEM') as expressive interface, and the 'mgcv' package to specify splines via the formula interface. See Thorson et al. (2024) <doi:10.48550/arXiv.2401.10193> for more details.
Maintained by James T. Thorson. Last updated 9 days ago.
vector-autoregressive-spatio-temporal-modelcpp
14 stars 6.83 score
tom-wolff
ideanet:Integrating Data Exchange and Analysis for Networks ('ideanet')
A suite of convenient tools for social network analysis geared toward students, entry-level users, and non-expert practitioners. ‘ideanet’ features unique functions for the processing and measurement of sociocentric and egocentric network data. These functions automatically generate node- and system-level measures commonly used in the analysis of these types of networks. Outputs from these functions maximize the ability of novice users to employ network measurements in further analyses while making all users less prone to common data analytic errors. Additionally, ‘ideanet’ features an R Shiny graphic user interface that allows novices to explore network data with minimal need for coding.
Maintained by Tom Wolff. Last updated 16 days ago.
6 stars 6.80 score 10 scripts
jmcurran
Hotelling:Hotelling's T^2 Test and Variants
A set of R functions which implements Hotelling's T^2 test and some variants of it. Functions are also included for Aitchison's additive log ratio and centred log ratio transformations.
Maintained by James Curran. Last updated 4 years ago.
2 stars 6.78 score 139 scripts 3 dependents
paytonjjones
networktools:Tools for Identifying Important Nodes in Networks
Includes assorted tools for network analysis. Bridge centrality; goldbricker; MDS, PCA, & eigenmodel network plotting.
Maintained by Payton Jones. Last updated 1 months ago.
10 stars 6.75 score 93 scripts 5 dependents
vlyubchich
funtimes:Functions for Time Series Analysis
Nonparametric estimators and tests for time series analysis. The functions use bootstrap techniques and robust nonparametric difference-based estimators to test for the presence of possibly non-monotonic trends and for synchronicity of trends in multiple time series.
Maintained by Vyacheslav Lyubchich. Last updated 2 years ago.
7 stars 6.69 score 93 scripts
bioc
M3C:Monte Carlo Reference-based Consensus Clustering
M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1.
Maintained by Christopher John. Last updated 5 months ago.
clusteringgeneexpressiontranscriptionrnaseqsequencingimmunooncology
6.59 score 174 scripts 1 dependents
sonsoleslp
tna:Transition Network Analysis (TNA)
Provides tools for performing Transition Network Analysis (TNA) to study relational dynamics, including functions for building and plotting TNA models, calculating centrality measures, and identifying dominant events and patterns. TNA statistical techniques (e.g., bootstrapping and permutation tests) ensure the reliability of observed insights and confirm that identified dynamics are meaningful. See (Saqr et al., 2025) <doi:10.1145/3706468.3706513> for more details on TNA.
Maintained by Sonsoles López-Pernas. Last updated 4 days ago.
educational-data-mininglearning-analyticsmarkov-modeltemporal-analysis
4 stars 6.51 score 5 scripts
jazznbass
scan:Single-Case Data Analyses for Single and Multiple Baseline Designs
A collection of procedures for analysing, visualising, and managing single-case data. These include piecewise linear regression models, multilevel models, overlap indices ('PND', 'PEM', 'PAND', 'PET', 'tau-u', 'baseline corrected tau', 'CDC'), and randomization tests. Data preparation functions support outlier detection, handling missing values, scaling, and custom transformations. An export function helps to generate html, word, and latex tables in a publication friendly style. More details can be found in the online book 'Analyzing single-case data with R and scan', Juergen Wilbert (2025) <https://jazznbass.github.io/scan-Book/>.
Maintained by Juergen Wilbert. Last updated 11 days ago.
4 stars 6.47 score 62 scripts 1 dependents
tidymodels
plsmod:Model Wrappers for Projection Methods
Bindings for additional regression models for use with the 'parsnip' package, including ordinary and spare partial least squares models for regression and classification (Rohart et al (2017) <doi:10.1371/journal.pcbi.1005752>).
Maintained by Max Kuhn. Last updated 6 months ago.
14 stars 6.47 score 59 scripts 1 dependentsbioc
zenith:Gene set analysis following differential expression using linear (mixed) modeling with dream
Zenith performs gene set analysis on the result of differential expression using linear (mixed) modeling with dream by considering the correlation between gene expression traits. This package implements the camera method from the limma package proposed by Wu and Smyth (2012). Zenith is a simple extension of camera to be compatible with linear mixed models implemented in variancePartition::dream().
Maintained by Gabriel Hoffman. Last updated 5 days ago.
rnaseqgeneexpressiongenesetenrichmentdifferentialexpressionbatcheffectqualitycontrolregressionepigeneticsfunctionalgenomicstranscriptomicsnormalizationpreprocessingmicroarrayimmunooncologysoftware
6.39 score 91 scripts 1 dependents
biorgeo
bioregion:Comparison of Bioregionalisation Methods
The main purpose of this package is to propose a transparent methodological framework to compare bioregionalisation methods based on hierarchical and non-hierarchical clustering algorithms (Kreft & Jetz (2010) <doi:10.1111/j.1365-2699.2010.02375.x>) and network algorithms (Lenormand et al. (2019) <doi:10.1002/ece3.4718> and Leroy et al. (2019) <doi:10.1111/jbi.13674>).
Maintained by Maxime Lenormand. Last updated 24 days ago.
biogeographybioregionbioregionalizationcpp
7 stars 6.27 score 11 scripts
jsakaluk
dySEM:Dyadic Structural Equation Modeling
Scripting of structural equation models via 'lavaan' for Dyadic Data Analysis, and helper functions for supplemental calculations, tabling, and model visualization. Current models supported include Dyadic Confirmatory Factor Analysis, the Actor–Partner Interdependence Model (observed and latent), the Common Fate Model (observed and latent), Mutual Influence Model (latent), and the Bifactor Dyadic Model (latent).
Maintained by John Sakaluk. Last updated 4 days ago.
6 stars 6.12 score 10 scripts
kosukehamazaki
RAINBOWR:Genome-Wide Association Study with SNP-Set Methods
By using 'RAINBOWR' (Reliable Association INference By Optimizing Weights with R), users can test multiple SNPs (Single Nucleotide Polymorphisms) simultaneously by kernel-based (SNP-set) methods. This package can also be applied to haplotype-based GWAS (Genome-Wide Association Study). Users can test not only additive effects but also dominance and epistatic effects. In detail, please check our paper on PLOS Computational Biology: Kosuke Hamazaki and Hiroyoshi Iwata (2020) <doi:10.1371/journal.pcbi.1007663>.
Maintained by Kosuke Hamazaki. Last updated 4 months ago.
22 stars 5.99 score 22 scriptsbioc
timeOmics:Time-Course Multi-Omics data integration
timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step.
Maintained by Antoine Bodein. Last updated 5 months ago.
clusteringfeatureextractiontimecoursedimensionreductionsoftwaresequencingmicroarraymetabolomicsmetagenomicsproteomicsclassificationregressionimmunooncologygenepredictionmultiplecomparisonclusterintegrationmulti-omicstime-series
24 stars 5.98 score 10 scripts
bmaitner
S4DM:Small Sample Size Species Distribution Modeling
Implements a set of distribution modeling methods that are suited to species with small sample sizes (e.g., poorly sampled species or rare species). While these methods can also be used on well-sampled taxa, they are united by the fact that they can be utilized with relatively few data points. More details on the currently implemented methodologies can be found in Drake and Richards (2018) <doi:10.1002/ecs2.2373>, Drake (2015) <doi:10.1098/rsif.2015.0086>, and Drake (2014) <doi:10.1890/ES13-00202.1>.
Maintained by Brian S. Maitner. Last updated 2 months ago.
open-sciencerange-modellingrare-speciesspecies-distribution-modelingspecies-distribution-modelling
4 stars 5.97 score 33 scripts
leoegidi
pivmet:Pivotal Methods for Bayesian Relabelling and k-Means Clustering
Collection of pivotal algorithms for: relabelling the MCMC chains in order to undo the label switching problem in Bayesian mixture models; fitting sparse finite mixtures; initializing the centers of the classical k-means algorithm in order to obtain a better clustering solution. For further details see Egidi, Pappadà, Pauli and Torelli (2018b)<ISBN:9788891910233>.
Maintained by Leonardo Egidi. Last updated 10 months ago.
5 stars 5.94 score 25 scripts
caetanods
ratematrix:Bayesian Estimation of the Evolutionary Rate Matrix
The Evolutionary Rate Matrix is a variance-covariance matrix which describes both the rates of trait evolution and the evolutionary correlation among multiple traits. This package has functions to estimate these parameters using Bayesian MCMC. It is possible to test if the pattern of evolutionary correlations among traits has changed between predictive regimes painted along the branches of the phylogenetic tree. Regimes can be created a priori or estimated as part of the MCMC under a joint estimation approach. The package has functions to run MCMC chains, plot results, evaluate convergence, and summarize posterior distributions.
Maintained by Daniel Caetano. Last updated 2 years ago.
10 stars 5.91 score 18 scripts 1 dependentsbioc
PathoStat:PathoStat Statistical Microbiome Analysis Package
The purpose of this package is to perform Statistical Microbiome Analysis on metagenomics results from sequencing data samples. In particular, it supports analyses on the PathoScope generated report files. PathoStat provides various functionalities including Relative Abundance charts, Diversity estimates and plots, tests of Differential Abundance, Time Series visualization, and Core OTU analysis.
Maintained by Solaiappan Manimaran. Last updated 5 months ago.
microbiomemetagenomicsgraphandnetworkmicroarraypatternlogicprincipalcomponentsequencingsoftwarevisualizationrnaseqimmunooncology
8 stars 5.90 score 8 scriptsbioc
miRspongeR:Identification and analysis of miRNA sponge regulation
This package provides several functions to explore miRNA sponge (also called ceRNA or miRNA decoy) regulation from putative miRNA-target interactions or/and transcriptomics data (including bulk, single-cell and spatial gene expression data). It provides eight popular methods for identifying miRNA sponge interactions, and an integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of miRNA sponge modules, and conduct survival analysis of miRNA sponge modules. By using a sample control variable strategy, it provides a function to infer sample-specific miRNA sponge interactions. In terms of sample-specific miRNA sponge interactions, it implements three similarity methods to construct sample-sample correlation network.
Maintained by Junpeng Zhang. Last updated 5 months ago.
geneexpressionbiomedicalinformaticsnetworkenrichmentsurvivalmicroarraysoftwaresinglecellspatialrnaseqcernamirnasponge
5 stars 5.88 score 8 scriptsbioc
epiNEM:epiNEM
epiNEM is an extension of the original Nested Effects Models (NEM). EpiNEM is able to take into account double knockouts and infer more complex network signalling pathways. It is tailored towards large scale double knock-out screens.
Maintained by Martin Pirkl. Last updated 5 months ago.
pathwayssystemsbiologynetworkinferencenetwork
1 stars 5.83 score 1 scripts 3 dependentsbioc
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.
metagenomicsmicrobiomedifferentialexpressionmultiplecomparisonnormalizationpreprocessingsoftwarebenchmarkdifferential-abundance-methods
8 stars 5.78 score 8 scripts
ugroempi
relaimpo:Relative Importance of Regressors in Linear Models
Provides several metrics for assessing relative importance in linear models. These can be printed, plotted and bootstrapped. The recommended metric is lmg, which provides a decomposition of the model explained variance into non-negative contributions. There is a version of this package available that additionally provides a new and also recommended metric called pmvd. If you are a non-US user, you can download this extended version from Ulrike Groempings web site.
Maintained by Ulrike Groemping. Last updated 1 years ago.
3 stars 5.75 score 632 scripts 3 dependents
svazzole
sparsevar:Sparse VAR/VECM Models Estimation
A wrapper for sparse VAR/VECM time series models estimation using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped Absolute Deviation) and MCP (Minimax Concave Penalty). Based on the work of Sumanta Basu and George Michailidis <doi:10.1214/15-AOS1315>.
Maintained by Simone Vazzoler. Last updated 4 years ago.
econometricslassomcpscadsparsestatisticstime-seriesvarvecm
11 stars 5.69 score 30 scripts 1 dependentsbioc
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.
softwarecellbiologygeneexpressionopenjdk
7 stars 5.69 score 14 scriptsbioc
methyLImp2:Missing value estimation of DNA methylation data
This package allows to estimate missing values in DNA methylation data. methyLImp method is based on linear regression since methylation levels show a high degree of inter-sample correlation. Implementation is parallelised over chromosomes since probes on different chromosomes are usually independent. Mini-batch approach to reduce the runtime in case of large number of samples is available.
Maintained by Anna Plaksienko. Last updated 2 months ago.
dnamethylationmicroarraysoftwaremethylationarrayregressionimputationmethylationmissing-value-imputation
6 stars 5.62 score 3 scripts
transbiozi
RMTL:Regularized Multi-Task Learning
Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. Based on the accelerated gradient descent method, the algorithms feature a state-of-art computational complexity O(1/k^2). Sparse model structure is induced by the solving the proximal operator. The detail of the package is described in the paper of Han Cao and Emanuel Schwarz (2018) <doi:10.1093/bioinformatics/bty831>.
Maintained by Han Cao. Last updated 6 years ago.
low-rank-representaionmulti-task-learningregularizationsparse-coding
19 stars 5.60 score 21 scripts
benyamindsmith
ig.degree.betweenness:"Smith-Pittman Community Detection Algorithm for 'igraph' Objects (2024)"
Implements the "Smith-Pittman" community detection algorithm for network analysis using 'igraph' objects. This algorithm combines node degree and betweenness centrality measures to identify communities within networks, with a gradient evident in social partitioning. The package provides functions for community detection, visualization, and analysis of the resulting community structure. Methods are based on results from Smith, Pittman and Xu (2024) <doi:10.48550/arXiv.2411.01394>.
Maintained by Benjamin Smith. Last updated 14 days ago.
community-detection-algorithmsigraph
38 stars 5.50 score 11 scripts
molinlab
Holomics:An User-Friendly R 'shiny' Application for Multi-Omics Data Integration and Analysis
A 'shiny' application, which allows you to perform single- and multi-omics analyses using your own omics datasets. After the upload of the omics datasets and a metadata file, single-omics is performed for feature selection and dataset reduction. These datasets are used for pairwise- and multi-omics analyses, where automatic tuning is done to identify correlations between the datasets - the end goal of the recommended 'Holomics' workflow. Methods used in the package were implemented in the package 'mixomics' by Florian Rohart,Benoît Gautier,Amrit Singh,Kim-Anh Lê Cao (2017) <doi:10.1371/journal.pcbi.1005752> and are described there in further detail.
Maintained by Katharina Munk. Last updated 10 months ago.
7 stars 5.45 score 7 scripts
bips-hb
cpi:Conditional Predictive Impact
A general test for conditional independence in supervised learning algorithms as proposed by Watson & Wright (2021) <doi:10.1007/s10994-021-06030-6>. Implements a conditional variable importance measure which can be applied to any supervised learning algorithm and loss function. Provides statistical inference procedures without parametric assumptions and applies equally well to continuous and categorical predictors and outcomes.
Maintained by Marvin N. Wright. Last updated 4 months ago.
11 stars 5.42 score 24 scripts
andybega
spduration:Split-Population Duration (Cure) Regression
An implementation of split-population duration regression models. Unlike regular duration models, split-population duration models are mixture models that accommodate the presence of a sub-population that is not at risk for failure, e.g. cancer patients who have been cured by treatment. This package implements Weibull and Loglogistic forms for the duration component, and focuses on data with time-varying covariates. These models were originally formulated in Boag (1949) and Berkson and Gage (1952), and extended in Schmidt and Witte (1989).
Maintained by Andreas Beger. Last updated 1 years ago.
mixture-modelregressionsplit-populationsurvival-analysiscpp
4 stars 5.38 score 40 scriptsbioc
PLSDAbatch:PLSDA-batch
A novel framework to correct for batch effects prior to any downstream analysis in microbiome data based on Projection to Latent Structures Discriminant Analysis. The main method is named “PLSDA-batch”. It first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data whilst preserving biological variation of interest. PLSDA-batch is highly suitable for microbiome data as it is non-parametric, multivariate and allows for ordination and data visualisation. Combined with centered log-ratio transformation for addressing uneven library sizes and compositional structure, PLSDA-batch addresses all characteristics of microbiome data that existing correction methods have ignored so far. Two other variants are proposed for 1/ unbalanced batch x treatment designs that are commonly encountered in studies with small sample sizes, and for 2/ selection of discriminative variables amongst treatment groups to avoid overfitting in classification problems. These two variants have widened the scope of applicability of PLSDA-batch to different data settings.
Maintained by Yiwen (Eva) Wang. Last updated 5 months ago.
statisticalmethoddimensionreductionprincipalcomponentclassificationmicrobiomebatcheffectnormalizationvisualization
13 stars 5.37 score 18 scripts
msesia
knockoff:The Knockoff Filter for Controlled Variable Selection
The knockoff filter is a general procedure for controlling the false discovery rate (FDR) when performing variable selection. For more information, see the website below and the accompanying paper: Candes et al., "Panning for gold: model-X knockoffs for high-dimensional controlled variable selection", J. R. Statist. Soc. B (2018) 80, 3, pp. 551-577.
Maintained by Matteo Sesia. Last updated 3 years ago.
2 stars 5.35 score 248 scripts 5 dependentsbioc
MOSClip:Multi Omics Survival Clip
Topological pathway analysis tool able to integrate multi-omics data. It finds survival-associated modules or significant modules for two-class analysis. This tool have two main methods: pathway tests and module tests. The latter method allows the user to dig inside the pathways itself.
Maintained by Paolo Martini. Last updated 5 months ago.
softwarestatisticalmethodgraphandnetworksurvivalregressiondimensionreductionpathwaysreactome
5.34 score 5 scriptsbioc
GlobalAncova:Global test for groups of variables via model comparisons
The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany.
Maintained by Manuela Hummel. Last updated 5 months ago.
microarrayonechanneldifferentialexpressionpathwaysregression
5.31 score 9 scripts 1 dependents
langejens
CliquePercolation:Clique Percolation for Networks
Clique percolation community detection for weighted and unweighted networks as well as threshold and plotting functions. For more information see Farkas et al. (2007) <doi:10.1088/1367-2630/9/6/180> and Palla et al. (2005) <doi:10.1038/nature03607>.
Maintained by Jens Lange. Last updated 1 years ago.
4 stars 5.30 score 11 scripts 1 dependents
ncchung
jackstraw:Statistical Inference for Unsupervised Learning
Test for association between the observed data and their estimated latent variables. The jackstraw package provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their latent variables. Depending on the data type and the analysis aim, the latent variables may be estimated by principal component analysis (PCA), factor analysis (FA), K-means clustering, and related unsupervised learning algorithms. The jackstraw methods learn over-fitting characteristics inherent in this circular analysis, where the observed data are used to estimate the latent variables and used again to test against that estimated latent variables. When latent variables are estimated by PCA, the jackstraw enables statistical testing for association between observed variables and latent variables, as estimated by low-dimensional principal components (PCs). This essentially leads to identifying variables that are significantly associated with PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds coherent groups in high-dimensional data. The jackstraw estimates statistical significance of cluster membership, by testing association between data and cluster centers. Clustering membership can be improved by using the resulting jackstraw p-values and posterior inclusion probabilities (PIPs), with an application to unsupervised evaluation of cell identities in single cell RNA-seq (scRNA-seq).
Maintained by Neo Christopher Chung. Last updated 3 months ago.
clusteringk-meansmachine-learningpcastatisticsunsupervised
16 stars 5.29 score 35 scripts
manueleleonelli
bnRep:A Repository of Bayesian Networks from the Academic Literature
A collection of Bayesian networks (discrete, Gaussian, and conditional linear Gaussian) collated from recent academic literature. The 'bnRep_summary' object provides an overview of the Bayesian networks in the repository and the package documentation includes details about the variables in each network. A Shiny app to explore the repository can be launched with 'bnRep_app()' and is available online at <https://manueleleonelli.shinyapps.io/bnRep>. For details see <https://github.com/manueleleonelli/bnRep>.
Maintained by Manuele Leonelli. Last updated 6 months ago.
6 stars 5.18 score 7 scriptsbioc
gcatest:Genotype Conditional Association TEST
GCAT is an association test for genome wide association studies that controls for population structure under a general class of trait models. This test conditions on the trait, which makes it immune to confounding by unmodeled environmental factors. Population structure is modeled via logistic factors, which are estimated using the `lfa` package.
Maintained by Alejandro Ochoa. Last updated 5 months ago.
snpdimensionreductionprincipalcomponentgenomewideassociation
5 stars 5.18 score 4 scripts
cbg-ethz
clustNet:Network-Based Clustering
Network-based clustering using a Bayesian network mixture model with optional covariate adjustment.
Maintained by Fritz Bayer. Last updated 1 years ago.
bayesian-networkbayesian-networksclusteringdaggenomicsmixture-modelnetwork-clustering
7 stars 5.16 score 41 scriptsfbertran
plsRcox:Partial Least Squares Regression for Cox Models and Related Techniques
Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings <doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in <arXiv:1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.
Maintained by Frederic Bertrand. Last updated 2 years ago.
4 stars 5.13 score 56 scripts 2 dependentsbioc
DepecheR:Determination of essential phenotypic elements of clusters in high-dimensional entities
The purpose of this package is to identify traits in a dataset that can separate groups. This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. As certain clusters with separating information will be identified, and these clusters are defined by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters.
Maintained by Jakob Theorell. Last updated 5 months ago.
softwarecellbasedassaystranscriptiondifferentialexpressiondatarepresentationimmunooncologytranscriptomicsclassificationclusteringdimensionreductionfeatureextractionflowcytometryrnaseqsinglecellvisualizationcpp
5.08 score 15 scripts
vandenman
NetworkComparisonTest:Statistical Comparison of Two Networks Based on Several Invariance Measures
This permutation based hypothesis test, suited for several types of data supported by the estimateNetwork function of the bootnet package (Epskamp & Fried, 2018), assesses the difference between two networks based on several invariance measures (network structure invariance, global strength invariance, edge invariance, several centrality measures, etc.). Network structures are estimated with l1-regularization. The Network Comparison Test is suited for comparison of independent (e.g., two different groups) and dependent samples (e.g., one group that is measured twice). See van Borkulo et al. (2021, in press; the final article will be available, upon publication, via its DOI: 10.1037/met0000476).
Maintained by Claudia van Borkulo. Last updated 3 years ago.
5.07 score 70 scripts
bonsook
REN:Regularization Ensemble for Robust Portfolio Optimization
Portfolio optimization is achieved through a combination of regularization techniques and ensemble methods that are designed to generate stable out-of-sample return predictions, particularly in the presence of strong correlations among assets. The package includes functions for data preparation, parallel processing, and portfolio analysis using methods such as Mean-Variance, James-Stein, LASSO, Ridge Regression, and Equal Weighting. It also provides visualization tools and performance metrics, such as the Sharpe ratio, volatility, and maximum drawdown, to assess the results.
Maintained by Bonsoo Koo. Last updated 6 months ago.
1 stars 5.04 score 2 scripts
r-forge
plasma:Partial LeAst Squares for Multiomic Analysis
Contains tools for supervised analyses of incomplete, overlapping multiomics datasets. Applies partial least squares in multiple steps to find models that predict survival outcomes. See Yamaguchi et al. (2023) <doi:10.1101/2023.03.10.532096>.
Maintained by Kevin R. Coombes. Last updated 2 months ago.
4.97 score 13 scripts
rikenbit
iTensor:ICA-Based Matrix/Tensor Decomposition
Some functions for performing ICA, MICA, Group ICA, and Multilinear ICA are implemented. ICA, MICA/Group ICA, and Multilinear ICA extract statistically independent components from single matrix, multiple matrices, and single tensor, respectively. For the details of these methods, see the reference section of GitHub README.md <https://github.com/rikenbit/iTensor>.
Maintained by Koki Tsuyuzaki. Last updated 2 years ago.
1 stars 4.95 score 2 scripts 1 dependentsjmbh
mnet:Modeling Group Differences and Moderation Effects in Statistical Network Models
A toolbox for modeling manifest and latent group differences and moderation effects in various statistical network models.
Maintained by Jonas Haslbeck. Last updated 2 months ago.
4.91 score 18 scriptsbioc
mfa:Bayesian hierarchical mixture of factor analyzers for modelling genomic bifurcations
MFA models genomic bifurcations using a Bayesian hierarchical mixture of factor analysers.
Maintained by Kieran Campbell. Last updated 5 months ago.
immunooncologyrnaseqgeneexpressionbayesiansinglecellcpp
4.85 score 35 scripts
netcoupler
NetCoupler:Inference of Causal Links Between a Network and an External Variable
The 'NetCoupler' algorithm identifies potential direct effects of correlated, high-dimensional variables formed as a network with an external variable. The external variable may act as the dependent/response variable or as an independent/predictor variable to the network.
Maintained by Luke Johnston. Last updated 1 years ago.
6 stars 4.78 score 7 scripts
annennenne
causalDisco:Tools for Causal Discovery on Observational Data
Various tools for inferring causal models from observational data. The package includes an implementation of the temporal Peter-Clark (TPC) algorithm. Petersen, Osler and Ekstrøm (2021) <doi:10.1093/aje/kwab087>. It also includes general tools for evaluating differences in adjacency matrices, which can be used for evaluating performance of causal discovery procedures.
Maintained by Anne Helby Petersen. Last updated 27 days ago.
19 stars 4.76 score 10 scripts
jakobbossek
mcMST:A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem
Algorithms to approximate the Pareto-front of multi-criteria minimum spanning tree problems.
Maintained by Jakob Bossek. Last updated 2 years ago.
evolutionary-algorithmsmcmstminimum-spanning-treesmulti-objective-optimizationspanningtrees
4 stars 4.73 score 27 scriptsbioc
miRLAB:Dry lab for exploring miRNA-mRNA relationships
Provide tools exploring miRNA-mRNA relationships, including popular miRNA target prediction methods, ensemble methods that integrate individual methods, functions to get data from online resources, functions to validate the results, and functions to conduct enrichment analyses.
Maintained by Thuc Duy Le. Last updated 5 months ago.
mirnageneexpressionnetworkinferencenetwork
4.72 score 11 scripts
kylehamilton
lavaan.shiny:Latent Variable Analysis with Shiny
Interactive shiny application for working with different kinds of latent variable analysis, with the 'lavaan' package. Graphical output for models are provided and different estimators are supported.
Maintained by William Kyle Hamilton. Last updated 9 years ago.
10 stars 4.70 score 1 scriptsbioc
MetNet:Inferring metabolic networks from untargeted high-resolution mass spectrometry data
MetNet contains functionality to infer metabolic network topologies from quantitative data and high-resolution mass/charge information. Using statistical models (including correlation, mutual information, regression and Bayes statistics) and quantitative data (intensity values of features) adjacency matrices are inferred that can be combined to a consensus matrix. Mass differences calculated between mass/charge values of features will be matched against a data frame of supplied mass/charge differences referring to transformations of enzymatic activities. In a third step, the two levels of information are combined to form a adjacency matrix inferred from both quantitative and structure information.
Maintained by Thomas Naake. Last updated 5 months ago.
immunooncologymetabolomicsmassspectrometrynetworkregression
4.70 score 1 scripts
audreyqyfu
MRPC:PC Algorithm with the Principle of Mendelian Randomization
A PC Algorithm with the Principle of Mendelian Randomization. This package implements the MRPC (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also contains functions to simulate data under a certain topology, to visualize a graph in different ways, and to compare graphs and quantify the differences. See Badsha and Fu (2019) <doi:10.3389/fgene.2019.00460>,Badsha, Martin and Fu (2021) <doi:10.3389/fgene.2021.651812>.
Maintained by Audrey Fu. Last updated 3 years ago.
8 stars 4.68 score 20 scripts
bkeller2
mlmpower:Power Analysis and Data Simulation for Multilevel Models
A declarative language for specifying multilevel models, solving for population parameters based on specified variance-explained effect size measures, generating data, and conducting power analyses to determine sample size recommendations. The specification allows for any number of within-cluster effects, between-cluster effects, covariate effects at either level, and random coefficients. Moreover, the models do not assume orthogonal effects, and predictors can correlate at either level and accommodate models with multiple interaction effects.
Maintained by Brian T. Keller. Last updated 5 months ago.
3 stars 4.65 score 3 scriptsbioc
nempi:Inferring unobserved perturbations from gene expression data
Takes as input an incomplete perturbation profile and differential gene expression in log odds and infers unobserved perturbations and augments observed ones. The inference is done by iteratively inferring a network from the perturbations and inferring perturbations from the network. The network inference is done by Nested Effects Models.
Maintained by Martin Pirkl. Last updated 5 months ago.
softwaregeneexpressiondifferentialexpressiondifferentialmethylationgenesignalingpathwaysnetworkclassificationneuralnetworknetworkinferenceatacseqdnaseqrnaseqpooledscreenscrisprsinglecellsystemsbiology
2 stars 4.60 score 2 scripts
jongheepark
NetworkChange:Bayesian Package for Network Changepoint Analysis
Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.
Maintained by Jong Hee Park. Last updated 3 years ago.
bayesianchangepointlatent-spacenetwork
5 stars 4.60 score 16 scriptsbioc
bnem:Training of logical models from indirect measurements of perturbation experiments
bnem combines the use of indirect measurements of Nested Effects Models (package mnem) with the Boolean networks of CellNOptR. Perturbation experiments of signalling nodes in cells are analysed for their effect on the global gene expression profile. Those profiles give evidence for the Boolean regulation of down-stream nodes in the network, e.g., whether two parents activate their child independently (OR-gate) or jointly (AND-gate).
Maintained by Martin Pirkl. Last updated 5 months ago.
pathwayssystemsbiologynetworkinferencenetworkgeneexpressiongeneregulationpreprocessing
2 stars 4.60 score 5 scriptsbioc
dce:Pathway Enrichment Based on Differential Causal Effects
Compute differential causal effects (dce) on (biological) networks. Given observational samples from a control experiment and non-control (e.g., cancer) for two genes A and B, we can compute differential causal effects with a (generalized) linear regression. If the causal effect of gene A on gene B in the control samples is different from the causal effect in the non-control samples the dce will differ from zero. We regularize the dce computation by the inclusion of prior network information from pathway databases such as KEGG.
Maintained by Kim Philipp Jablonski. Last updated 3 months ago.
softwarestatisticalmethodgraphandnetworkregressiongeneexpressiondifferentialexpressionnetworkenrichmentnetworkkeggbioconductorcausality
13 stars 4.59 score 4 scripts
karolinehuth
easybgm:Extracting and Visualizing Bayesian Graphical Models
Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. The package supports fitting cross-sectional network models fitted using the packages 'BDgraph', 'bgms' and 'BGGM'. The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the posterior density of the parameters. In addition, for 'BDgraph' and 'bgms' it allows to assess the posterior structure space. Furthermore, the package comes with an extensive suite for visualizing results.
Maintained by Karoline Huth. Last updated 5 months ago.
4.51 score 27 scriptsalexchristensen
SemNeT:Methods and Measures for Semantic Network Analysis
Implements several functions for the analysis of semantic networks including different network estimation algorithms, partial node bootstrapping (Kenett, Anaki, & Faust, 2014 <doi:10.3389/fnhum.2014.00407>), random walk simulation (Kenett & Austerweil, 2016 <http://alab.psych.wisc.edu/papers/files/Kenett16CreativityRW.pdf>), and a function to compute global network measures. Significance tests and plotting features are also implemented.
Maintained by Alexander P. Christensen. Last updated 2 years ago.
23 stars 4.51 score 28 scriptsbioc
clipper:Gene Set Analysis Exploiting Pathway Topology
Implements topological gene set analysis using a two-step empirical approach. It exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype.
Maintained by Paolo Martini. Last updated 5 months ago.
4.48 score 19 scripts
jcdterry
cassandRa:Finds Missing Links and Metric Confidence Intervals in Ecological Bipartite Networks
Provides methods to deal with under sampling in ecological bipartite networks from Terry and Lewis (2020) Ecology <doi:10.1002/ecy.3047> Includes tools to fit a variety of statistical network models and sample coverage estimators to highlight most likely missing links. Also includes simple functions to resample from observed networks to generate confidence intervals for common ecological network metrics.
Maintained by Chris Terry. Last updated 10 months ago.
3 stars 4.48 score 4 scripts
kelliejarcher
hdcuremodels:Penalized Mixture Cure Models for High-Dimensional Data
Provides functions for fitting various penalized parametric and semi-parametric mixture cure models with different penalty functions, testing for a significant cure fraction, and testing for sufficient follow-up as described in Fu et al (2022)<doi:10.1002/sim.9513> and Archer et al (2024)<doi:10.1186/s13045-024-01553-6>. False discovery rate controlled variable selection is provided using model-X knock-offs.
Maintained by Kellie J. Archer. Last updated 8 days ago.
4.48 score 5 scripts
baeyc
varTestnlme:Variance Components Testing for Linear and Nonlinear Mixed Effects Models
An implementation of the Likelihood ratio Test (LRT) for testing that, in a (non)linear mixed effects model, the variances of a subset of the random effects are equal to zero. There is no restriction on the subset of variances that can be tested: for example, it is possible to test that all the variances are equal to zero. Note that the implemented test is asymptotic. This package should be used on model fits from packages 'nlme', 'lmer', and 'saemix'. Charlotte Baey and Estelle Kuhn (2019) <doi:10.18637/jss.v107.i06>.
Maintained by Charlotte Baey. Last updated 2 years ago.
2 stars 4.48 score 4 scripts 1 dependents
pwarncke77
ResIN:Response Item Networks
Contains various tools to perform and visualize Response Item Networks ('ResIN's'). 'ResIN' binarizes ordered-categorical and qualitative response choices from (survey) data, calculates pairwise associations and maps the location of each item response as a node in a force-directed network. Please refer to <https://www.resinmethod.net/> for more details.
Maintained by Philip Warncke. Last updated 6 months ago.
4.48 score 3 scriptsbioc
epistasisGA:An R package to identify multi-snp effects in nuclear family studies using the GADGETS method
This package runs the GADGETS method to identify epistatic effects in nuclear family studies. It also provides functions for permutation-based inference and graphical visualization of the results.
Maintained by Michael Nodzenski. Last updated 5 months ago.
geneticssnpgeneticvariabilityopenblascpp
1 stars 4.48 score 5 scriptsbioc
PRONE:The PROteomics Normalization Evaluator
High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed.
Maintained by Lis Arend. Last updated 9 days ago.
proteomicspreprocessingnormalizationdifferentialexpressionvisualizationdata-analysisevaluation
2 stars 4.41 score 9 scriptsbioc
snm:Supervised Normalization of Microarrays
SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest.
Maintained by John D. Storey. Last updated 5 months ago.
microarrayonechanneltwochannelmultichanneldifferentialexpressionexonarraygeneexpressiontranscriptionmultiplecomparisonpreprocessingqualitycontrol
4.41 score 64 scriptsalexiosg
parma:Portfolio Allocation and Risk Management Applications
Provision of a set of models and methods for use in the allocation and management of capital in financial portfolios.
Maintained by Alexios Galanos. Last updated 2 years ago.
4 stars 4.38 score 12 scripts
fbertran
c060:Extended Inference for Lasso and Elastic-Net Regularized Cox and Generalized Linear Models
The c060 package provides additional functions to perform stability selection, model validation and parameter tuning for glmnet models.
Maintained by Frederic Bertrand. Last updated 2 years ago.
3 stars 4.35 score 37 scriptsfbertran
plsRbeta:Partial Least Squares Regression for Beta Regression Models
Provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013, <http://journal-sfds.fr/article/view/215>) and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Maintained by Frederic Bertrand. Last updated 2 years ago.
2 stars 4.34 score 22 scripts
greenwoodlab
pcev:Principal Component of Explained Variance
Principal component of explained variance (PCEV) is a statistical tool for the analysis of a multivariate response vector. It is a dimension- reduction technique, similar to Principal component analysis (PCA), that seeks to maximize the proportion of variance (in the response vector) being explained by a set of covariates.
Maintained by Maxime Turgeon. Last updated 6 years ago.
4 stars 4.30 score 7 scriptsbioc
nethet:A bioconductor package for high-dimensional exploration of biological network heterogeneity
Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013).
Maintained by Nicolas Staedler. Last updated 5 months ago.
4.30 score 7 scriptsbioc
RegionalST:Investigating regions of interest and performing regional cell type-specific analysis with spatial transcriptomics data
This package analyze spatial transcriptomics data through cross-regional cell type-specific analysis. It selects regions of interest (ROIs) and identifys cross-regional cell type-specific differential signals. The ROIs can be selected using automatic algorithm or through manual selection. It facilitates manual selection of ROIs using a shiny application.
Maintained by Ziyi Li. Last updated 4 months ago.
spatialtranscriptomicsreactomekegg
4.30 score 8 scripts
jafarilab
NIMAA:Nominal Data Mining Analysis
Functions for nominal data mining based on bipartite graphs, which build a pipeline for analysis and missing values imputation. Methods are mainly from the paper: Jafari, Mohieddin, et al. (2021) <doi:10.1101/2021.03.18.436040>, some new ones are also included.
Maintained by Mohieddin Jafari. Last updated 2 years ago.
4 stars 4.30 score 7 scripts
jiangyouxiang
TestAnaAPP:A 'shiny' App for Test Analysis and Visualization
This application provides exploratory and confirmatory factor analysis, classical test theory, unidimensional and multidimensional item response theory, and continuous item response model analysis, through the 'shiny' interactive interface. In addition, it offers rich functionalities for visualizing and downloading results. Users can download figures, tables, and analysis reports via the interactive interface.
Maintained by Youxiang Jiang. Last updated 4 months ago.
4 stars 4.30 score 2 scriptsbioc
mogsa:Multiple omics data integrative clustering and gene set analysis
This package provide a method for doing gene set analysis based on multiple omics data.
Maintained by Chen Meng. Last updated 5 months ago.
geneexpressionprincipalcomponentstatisticalmethodclusteringsoftware
4.29 score 49 scripts
piyalkarum
rCNV:Detect Copy Number Variants from SNPs Data
Functions in this package will import filtered variant call format (VCF) files of SNPs data and generate data sets to detect copy number variants, visualize them and do downstream analyses with copy number variants(e.g. Environmental association analyses).
Maintained by Piyal Karunarathne. Last updated 26 days ago.
cnv-analysiscopy-number-variationgene-duplicationgeneticsgenomicslandscape-geneticssnpscpp
6 stars 4.26 score 4 scripts
andrisignorell
ModTools:Building Regression and Classification Models
Consistent user interface to the most common regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, complemented with a handful of auxiliary functions, such as variable importance and a tuning function for the parameters.
Maintained by Andri Signorell. Last updated 2 months ago.
2 stars 4.20 score 3 scriptsbioc
MICSQTL:MICSQTL (Multi-omic deconvolution, Integration and Cell-type-specific Quantitative Trait Loci)
Our pipeline, MICSQTL, utilizes scRNA-seq reference and bulk transcriptomes to estimate cellular composition in the matched bulk proteomes. The expression of genes and proteins at either bulk level or cell type level can be integrated by Angle-based Joint and Individual Variation Explained (AJIVE) framework. Meanwhile, MICSQTL can perform cell-type-specic quantitative trait loci (QTL) mapping to proteins or transcripts based on the input of bulk expression data and the estimated cellular composition per molecule type, without the need for single cell sequencing. We use matched transcriptome-proteome from human brain frontal cortex tissue samples to demonstrate the input and output of our tool.
Maintained by Qian Li. Last updated 5 months ago.
geneexpressiongeneticsproteomicsrnaseqsequencingsinglecellsoftwarevisualizationcellbasedassayscoverage
4.18 score 3 scripts
barbaratarantino
SEMdeep:Structural Equation Modeling with Deep Neural Network and Machine Learning
Training and validation of a custom (or data-driven) Structural Equation Models using layer-wise Deep Neural Networks or node-wise Machine Learning algorithms, which extend the fitting procedures of the 'SEMgraph' R package <doi:10.32614/CRAN.package.SEMgraph>.
Maintained by Barbara Tarantino. Last updated 2 months ago.
4 stars 4.15 score
desanou
mglasso:Multiscale Graphical Lasso
Inference of Multiscale graphical models with neighborhood selection approach. The method is based on solving a convex optimization problem combining a Lasso and fused-group Lasso penalties. This allows to infer simultaneously a conditional independence graph and a clustering partition. The optimization is based on the Continuation with Nesterov smoothing in a Shrinkage-Thresholding Algorithm solver (Hadj-Selem et al. 2018) <doi:10.1109/TMI.2018.2829802> implemented in python.
Maintained by Edmond Sanou. Last updated 2 years ago.
2 stars 4.11 score 13 scripts
topepo
sparsediscrim:Sparse and Regularized Discriminant Analysis
A collection of sparse and regularized discriminant analysis methods intended for small-sample, high-dimensional data sets. The package features the High-Dimensional Regularized Discriminant Analysis classifier from Ramey et al. (2017) <arXiv:1602.01182>. Other classifiers include those from Dudoit et al. (2002) <doi:10.1198/016214502753479248>, Pang et al. (2009) <doi:10.1111/j.1541-0420.2009.01200.x>, and Tong et al. (2012) <doi:10.1093/bioinformatics/btr690>.
Maintained by Max Kuhn. Last updated 4 years ago.
3 stars 4.11 score 86 scripts
juliengamartin
pedtricks:Visualize, Summarize and Simulate Data from Pedigrees
Sensitivity and power analysis, for calculating statistics describing pedigrees from wild populations, and for visualizing pedigrees. This is a reboot of the methods developped by Morrissey and Wilson (2010) <doi: 10.1111/j.1755-0998.2009.02817.x>
Maintained by Julien Martin. Last updated 7 months ago.
2 stars 4.08 score 1 scripts
pachoning
bigmds:Multidimensional Scaling for Big Data
MDS is a statistic tool for reduction of dimensionality, using as input a distance matrix of dimensions n × n. When n is large, classical algorithms suffer from computational problems and MDS configuration can not be obtained. With this package, we address these problems by means of six algorithms, being two of them original proposals: - Landmark MDS proposed by De Silva V. and JB. Tenenbaum (2004). - Interpolation MDS proposed by Delicado P. and C. Pachón-García (2021) <arXiv:2007.11919> (original proposal). - Reduced MDS proposed by Paradis E (2018). - Pivot MDS proposed by Brandes U. and C. Pich (2007) - Divide-and-conquer MDS proposed by Delicado P. and C. Pachón-García (2021) <arXiv:2007.11919> (original proposal). - Fast MDS, proposed by Yang, T., J. Liu, L. McMillan and W. Wang (2006).
Maintained by Cristian Pachón García. Last updated 1 years ago.
17 stars 4.08 score 14 scripts
psychbruce
PsychWordVec:Word Embedding Research Framework for Psychological Science
An integrative toolbox of word embedding research that provides: (1) a collection of 'pre-trained' static word vectors in the '.RData' compressed format <https://psychbruce.github.io/WordVector_RData.pdf>; (2) a series of functions to process, analyze, and visualize word vectors; (3) a range of tests to examine conceptual associations, including the Word Embedding Association Test <doi:10.1126/science.aal4230> and the Relative Norm Distance <doi:10.1073/pnas.1720347115>, with permutation test of significance; (4) a set of training methods to locally train (static) word vectors from text corpora, including 'Word2Vec' <arXiv:1301.3781>, 'GloVe' <doi:10.3115/v1/D14-1162>, and 'FastText' <arXiv:1607.04606>; (5) a group of functions to download 'pre-trained' language models (e.g., 'GPT', 'BERT') and extract contextualized (dynamic) word vectors (based on the R package 'text').
Maintained by Han-Wu-Shuang Bao. Last updated 1 years ago.
bertcosine-similarityfasttextglovegptlanguage-modelnatural-language-processingnlppretrained-modelspsychologysemantic-analysistext-analysistext-miningtsneword-embeddingsword-vectorsword2vecopenjdk
22 stars 4.04 score 10 scriptsbioc
splineTimeR:Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction
This package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks.
Maintained by Herbert Braselmann. Last updated 5 months ago.
geneexpressiondifferentialexpressiontimecourseregressiongenesetenrichmentnetworkenrichmentnetworkinferencegraphandnetwork
4.01 score 17 scripts
georgiosseitidis
viscomp:Visualize Multi-Component Interventions in Network Meta-Analysis
A set of functions providing several visualization tools for exploring the behavior of the components in a network meta-analysis of multi-component (complex) interventions: - components descriptive analysis - heat plot of the two-by-two component combinations - leaving one component combination out scatter plot - violin plot for specific component combinations' effects - density plot for components' effects - waterfall plot for the interventions' effects that differ by a certain component combination - network graph of components - rank heat plot of components for multiple outcomes. The implemented tools are described by Seitidis et al. (2023) <doi:10.1002/jrsm.1617>.
Maintained by Georgios Seitidis. Last updated 2 years ago.
cnmacomplexmulticomponentnmavisualization
2 stars 4.00 score 6 scripts
georgekoliopanos
modgo:MOck Data GeneratiOn
Generation of mock data from a real dataset using rank normal inverse transformation.
Maintained by George Koliopanos. Last updated 9 months ago.
1 stars 4.00 score 3 scripts
ttacail
isobxr:Stable Isotope Box Modelling in R
A set of functions to run simple and composite box-models to describe the dynamic or static distribution of stable isotopes in open or closed systems. The package also allows the sweeping of many parameters in both static and dynamic conditions. The mathematical models used in this package are derived from Albarede, 1995, Introduction to Geochemical Modelling, Cambridge University Press, Cambridge <doi:10.1017/CBO9780511622960>.
Maintained by Theo Tacail. Last updated 11 months ago.
1 stars 4.00 score 2 scripts
flr
ss3om:Tools for Conditioning Fisheries Operating Models Using Stock Synthesis 3
Tools for loading Stock Synthesis (SS3) models into FLR. Used in conditioning of Operating Models based on SS3 by considering structural uncertainty in input parameters and assumptions. A grid of SS3 runs can be created and results loaded on objects of various FLR classes.
Maintained by Iago Mosqueira. Last updated 2 months ago.
3.94 score 44 scriptsmanueleleonelli
bnmonitor:An Implementation of Sensitivity Analysis in Bayesian Networks
An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) <doi:10.1016/j.knosys.2023.110882>.
Maintained by Manuele Leonelli. Last updated 6 months ago.
3 stars 3.92 score 14 scriptsalexchristensen
latentFactoR:Data Simulation Based on Latent Factors
Generates data based on latent factor models. Data can be continuous, polytomous, dichotomous, or mixed. Skews, cross-loadings, wording effects, population errors, and local dependencies can be added. All parameters can be manipulated. Data categorization is based on Garrido, Abad, and Ponsoda (2011) <doi:10.1177/0013164410389489>.
Maintained by Alexander Christensen. Last updated 8 months ago.
3 stars 3.88 score 2 scriptspaytonjjones
networktree:Recursive Partitioning of Network Models
Network trees recursively partition the data with respect to covariates. Two network tree algorithms are available: model-based trees based on a multivariate normal model and nonparametric trees based on covariance structures. After partitioning, correlation-based networks (psychometric networks) can be fit on the partitioned data. For details see Jones, Mair, Simon, & Zeileis (2020) <doi:10.1007/s11336-020-09731-4>.
Maintained by Payton Jones. Last updated 3 years ago.
network-analysispsychometricstree-models
13 stars 3.85 score 11 scriptsbioc
flowVS:Variance stabilization in flow cytometry (and microarrays)
Per-channel variance stabilization from a collection of flow cytometry samples by Bertlett test for homogeneity of variances. The approach is applicable to microarrays data as well.
Maintained by Ariful Azad. Last updated 5 months ago.
immunooncologyflowcytometrycellbasedassaysmicroarray
3.82 score 11 scripts
sciurus365
quadVAR:Quadratic Vector Autoregression
Estimate quadratic vector autoregression models with the strong hierarchy using the Regularization Algorithm under Marginality Principle (RAMP) by Hao et al. (2018) <doi:10.1080/01621459.2016.1264956>, compare the performance with linear models, and construct networks with partial derivatives.
Maintained by Jingmeng Cui. Last updated 2 months ago.
3.78 score 3 scripts
cran
fastml:Fast Machine Learning Model Training and Evaluation
Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code by providing comprehensive data preprocessing and support for a wide range of algorithms with hyperparameter tuning. It offers performance metrics and visualization tools to facilitate efficient and effective machine learning workflows.
Maintained by Selcuk Korkmaz. Last updated 23 days ago.
3.76 score
mikehellstern
netgsa:Network-Based Gene Set Analysis
Carry out Network-based Gene Set Analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data. Implements methods described in Shojaie A, Michailidis G (2010) <doi:10.1093/biomet/asq038>, Shojaie A, Michailidis G (2009) <doi:10.1089/cmb.2008.0081>, and Ma J, Shojaie A, Michailidis G (2016) <doi:10.1093/bioinformatics/btw410>
Maintained by Michael Hellstern. Last updated 3 years ago.
4 stars 3.75 score 28 scripts
xinkaidupsy
IVPP:Invariance Partial Pruning Test
An implementation of the Invariance Partial Pruning (IVPP) approach described in Du, X., Johnson, S. U., Epskamp, S. (2025) The Invariance Partial Pruning Approach to The Network Comparison in Longitudinal Data. IVPP is a two-step method that first test for global network structural difference with invariance test and then inspect specific edge difference with partial pruning.
Maintained by Xinkai Du. Last updated 4 days ago.
3.74 score 7 scriptsbiometry
tapnet:Trait Matching and Abundance for Predicting Bipartite Networks
Functions to produce, fit and predict from bipartite networks with abundance, trait and phylogenetic information. Its methods are described in detail in Benadi, G., Dormann, C.F., Fruend, J., Stephan, R. & Vazquez, D.P. (2021) Quantitative prediction of interactions in bipartite networks based on traits, abundances, and phylogeny. The American Naturalist, in press.
Maintained by Carsten Dormann. Last updated 6 months ago.
1 stars 3.70 score 2 scriptsfbertran
bootPLS:Bootstrap Hyperparameter Selection for PLS Models and Extensions
Several implementations of non-parametric stable bootstrap-based techniques to determine the numbers of components for Partial Least Squares linear or generalized linear regression models as well as and sparse Partial Least Squares linear or generalized linear regression models. The package collects techniques that were published in a book chapter (Magnanensi et al. 2016, 'The Multiple Facets of Partial Least Squares and Related Methods', <doi:10.1007/978-3-319-40643-5_18>) and two articles (Magnanensi et al. 2017, 'Statistics and Computing', <doi:10.1007/s11222-016-9651-4>) and (Magnanensi et al. 2021, 'Frontiers in Applied Mathematics and Statistics', <doi:10.3389/fams.2021.693126>).
Maintained by Frederic Bertrand. Last updated 6 months ago.
1 stars 3.70 score 4 scriptsbioc
cypress:Cell-Type-Specific Power Assessment
CYPRESS is a cell-type-specific power tool. This package aims to perform power analysis for the cell-type-specific data. It calculates FDR, FDC, and power, under various study design parameters, including but not limited to sample size, and effect size. It takes the input of a SummarizeExperimental(SE) object with observed mixture data (feature by sample matrix), and the cell-type mixture proportions (sample by cell-type matrix). It can solve the cell-type mixture proportions from the reference free panel from TOAST and conduct tests to identify cell-type-specific differential expression (csDE) genes.
Maintained by Shilin Yu. Last updated 5 months ago.
softwaregeneexpressiondataimportrnaseqsequencing
1 stars 3.70 score 2 scripts
haowang47
PCGII:Partial Correlation Graph with Information Incorporation
Large-scale gene expression studies allow gene network construction to uncover associations among genes. This package is developed for estimating and testing partial correlation graphs with prior information incorporated.
Maintained by Hao Wang. Last updated 1 years ago.
1 stars 3.70 score 10 scripts
dswatson
leakyIV:Leaky Instrumental Variables
Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding. However, classical methods rely on strong assumptions such as the exclusion criterion, which states that instrumental effects must be entirely mediated by treatments. In the so-called "leaky" IV setting, candidate instruments are allowed to have some direct influence on outcomes, rendering the average treatment effect (ATE) unidentifiable. But with limits on the amount of information leakage, we may still recover sharp bounds on the ATE, providing partial identification. This package implements methods for ATE bounding in the leaky IV setting with linear structural equations. For details, see Watson et al. (2024) <doi:10.48550/arXiv.2404.04446>.
Maintained by David S. Watson. Last updated 11 months ago.
1 stars 3.70 score 1 scripts
bips-hb
micd:Multiple Imputation in Causal Graph Discovery
Modified functions of the package 'pcalg' and some additional functions to run the PC and the FCI (Fast Causal Inference) algorithm for constraint-based causal discovery in incomplete and multiply imputed datasets. Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V (2020) <doi:10.1111/rssa.12565>; Andrews RM, Foraita R, Didelez V, Witte J (2021) <arXiv:2108.13395>; Witte J, Foraita R, Didelez V (2022) <doi:10.1002/sim.9535>.
Maintained by Ronja Foraita. Last updated 2 years ago.
causal-discoverygraphical-modelsmultiple-imputation
5 stars 3.70 score 20 scripts
argeorgeson
phantSEM:Create Phantom Variables in Structural Equation Models for Sensitivity Analyses
Create phantom variables, which are variables that were not observed, for the purpose of sensitivity analyses for structural equation models. The package makes it easier for a user to test different combinations of covariances between the phantom variable(s) and observed variables. The package may be used to assess a model's or effect's sensitivity to temporal bias (e.g., if cross-sectional data were collected) or confounding bias.
Maintained by Alexis Georgeson. Last updated 5 months ago.
3.70 score 7 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
suren-rathnayake
deepgmm:Deep Gaussian Mixture Models
Deep Gaussian mixture models as proposed by Viroli and McLachlan (2019) <doi:10.1007/s11222-017-9793-z> provide a generalization of classical Gaussian mixtures to multiple layers. Each layer contains a set of latent variables that follow a mixture of Gaussian distributions. To avoid overparameterized solutions, dimension reduction is applied at each layer by way of factor models.
Maintained by Suren Rathnayake. Last updated 2 years ago.
clusteringdeep-learningmixed-models
9 stars 3.65 score 8 scripts
fkgruber
SID:Structural Intervention Distance
The code computes the structural intervention distance (SID) between a true directed acyclic graph (DAG) and an estimated DAG. Definition and details about the implementation can be found in J. Peters and P. Bühlmann: "Structural intervention distance (SID) for evaluating causal graphs", Neural Computation 27, pages 771-799, 2015.
Maintained by Fred Gruber. Last updated 1 years ago.
2 stars 3.62 score 21 scripts
paullabonne
BayesMultiMode:Bayesian Mode Inference
A two-step Bayesian approach for mode inference following Cross, Hoogerheide, Labonne and van Dijk (2024) <doi:10.1016/j.econlet.2024.111579>). First, a mixture distribution is fitted on the data using a sparse finite mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. The number of mixture components does not have to be known; the size of the mixture is estimated endogenously through the SFM approach. Second, the modes of the estimated mixture at each MCMC draw are retrieved using algorithms specifically tailored for mode detection. These estimates are then used to construct posterior probabilities for the number of modes, their locations and uncertainties, providing a powerful tool for mode inference.
Maintained by Paul Labonne. Last updated 5 months ago.
1 stars 3.60 score 8 scriptsbips-hb
tpc:Tiered PC Algorithm
Constraint-based causal discovery using the PC algorithm while accounting for a partial node ordering, for example a partial temporal ordering when the data were collected in different waves of a cohort study. Andrews RM, Foraita R, Didelez V, Witte J (2021) <arXiv:2108.13395> provide a guide how to use tpc to analyse cohort data.
Maintained by Ronja Foraita. Last updated 2 years ago.
causal-discoverycohort-analysisgraphical-models
5 stars 3.60 score 16 scripts
henry-heppe
adproclus:Additive Profile Clustering Algorithms
Obtain overlapping clustering models for object-by-variable data matrices using the Additive Profile Clustering (ADPROCLUS) method. Also contains the low dimensional ADPROCLUS method for simultaneous dimension reduction and overlapping clustering. For reference see Depril, Van Mechelen, Mirkin (2008) <doi:10.1016/j.csda.2008.04.014> and Depril, Van Mechelen, Wilderjans (2012) <doi:10.1007/s00357-012-9112-5>.
Maintained by Henry Heppe. Last updated 7 months ago.
2 stars 3.60 score 2 scripts
mihaiconstantin
powerly:Sample Size Analysis for Psychological Networks and More
An implementation of the sample size computation method for network models proposed by Constantin et al. (2021) <doi:10.31234/osf.io/j5v7u>. The implementation takes the form of a three-step recursive algorithm designed to find an optimal sample size given a model specification and a performance measure of interest. It starts with a Monte Carlo simulation step for computing the performance measure and a statistic at various sample sizes selected from an initial sample size range. It continues with a monotone curve-fitting step for interpolating the statistic across the entire sample size range. The final step employs stratified bootstrapping to quantify the uncertainty around the fitted curve.
Maintained by Mihai Constantin. Last updated 2 years ago.
network-modelspower-analysispsychologysample-size-calculation
8 stars 3.60 score 3 scripts
jglev
veccompare:Perform Set Operations on Vectors, Automatically Generating All n-Wise Comparisons, and Create Markdown Output
Automates set operations (i.e., comparisons of overlap) between multiple vectors. It also contains a function for automating reporting in 'RMarkdown', by generating markdown output for easy analysis, as well as an 'RMarkdown' template for use with 'RStudio'.
Maintained by Jacob Gerard Levernier. Last updated 8 years ago.
8 stars 3.60 score 10 scripts
bbuchsbaum
multivarious:Extensible Data Structures for Multivariate Analysis
Provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks.
Maintained by Bradley Buchsbaum. Last updated 3 months ago.
3.53 score 17 scriptsbioc
trigger:Transcriptional Regulatory Inference from Genetics of Gene ExpRession
This R package provides tools for the statistical analysis of integrative genomic data that involve some combination of: genotypes, high-dimensional intermediate traits (e.g., gene expression, protein abundance), and higher-order traits (phenotypes). The package includes functions to: (1) construct global linkage maps between genetic markers and gene expression; (2) analyze multiple-locus linkage (epistasis) for gene expression; (3) quantify the proportion of genome-wide variation explained by each locus and identify eQTL hotspots; (4) estimate pair-wise causal gene regulatory probabilities and construct gene regulatory networks; and (5) identify causal genes for a quantitative trait of interest.
Maintained by John D. Storey. Last updated 4 days ago.
geneexpressionsnpgeneticvariabilitymicroarraygenetics
3.48 score 3 scripts
donaldrwilliams
GGMnonreg:Non-Regularized Gaussian Graphical Models
Estimate non-regularized Gaussian graphical models, Ising models, and mixed graphical models. The current methods consist of multiple regression, a non-parametric bootstrap <doi:10.1080/00273171.2019.1575716>, and Fisher z transformed partial correlations <doi:10.1111/bmsp.12173>. Parameter uncertainty, predictability, and network replicability <doi:10.31234/osf.io/fb4sa> are also implemented.
Maintained by Donald Williams. Last updated 3 years ago.
6 stars 3.48 score 4 scripts
fhui28
boral:Bayesian Ordination and Regression AnaLysis
Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via Three. JAGS types of models may be fitted: 1) With explanatory variables only, boral fits independent column Generalized Linear Models (GLMs) to each column of the response matrix; 2) With latent variables only, boral fits a purely latent variable model for model-based unconstrained ordination; 3) With explanatory and latent variables, boral fits correlated column GLMs with latent variables to account for any residual correlation between the columns of the response matrix.
Maintained by Francis K.C. Hui. Last updated 1 years ago.
2 stars 3.45 score 79 scriptsfbertran
penalizedSVM:Feature Selection SVM using Penalty Functions
Support Vector Machine (SVM) classification with simultaneous feature selection using penalty functions is implemented. The smoothly clipped absolute deviation (SCAD), 'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic SCAD' (SCAD and 'L2-norm') penalties are available. The tuning parameters can be found using either a fixed grid or a interval search.
Maintained by Frederic Bertrand. Last updated 2 years ago.
1 stars 3.36 score 76 scripts 1 dependents
bioc
cpvSNP:Gene set analysis methods for SNP association p-values that lie in genes in given gene sets
Gene set analysis methods exist to combine SNP-level association p-values into gene sets, calculating a single association p-value for each gene set. This package implements two such methods that require only the calculated SNP p-values, the gene set(s) of interest, and a correlation matrix (if desired). One method (GLOSSI) requires independent SNPs and the other (VEGAS) can take into account correlation (LD) among the SNPs. Built-in plotting functions are available to help users visualize results.
Maintained by Caitlin McHugh. Last updated 5 months ago.
geneticsstatisticalmethodpathwaysgenesetenrichmentgenomicvariation
3.30 score 3 scripts
finyang
flap:Forecast Linear Augmented Projection
The Forecast Linear Augmented Projection (flap) method reduces forecast variance by adjusting the forecasts of multivariate time series to be consistent with the forecasts of linear combinations (components) of the series by projecting all forecasts onto the space where the linear constraints are satisfied. The forecast variance can be reduced monotonically by including more components. For a given number of components, the flap method achieves maximum forecast variance reduction among linear projections.
Maintained by Yangzhuoran Fin Yang. Last updated 9 months ago.
1 stars 3.30 score 2 scripts
cran
sda:Shrinkage Discriminant Analysis and CAT Score Variable Selection
Provides an efficient framework for high-dimensional linear and diagonal discriminant analysis with variable selection. The classifier is trained using James-Stein-type shrinkage estimators and predictor variables are ranked using correlation-adjusted t-scores (CAT scores). Variable selection error is controlled using false non-discovery rates or higher criticism.
Maintained by Korbinian Strimmer. Last updated 3 years ago.
3.21 score 3 dependentscran
GeneNet:Modeling and Inferring Gene Networks
Analyzes gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Schaefer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions).
Maintained by Korbinian Strimmer. Last updated 3 years ago.
3.18 score 5 dependents
gwpcor
gwpcormapper:Geographically Weighted Partial Correlation Mapper
An interactive mapping tool for geographically weighted correlation and partial correlation. Geographically weighted partial correlation coefficients are calculated following (Percival and Tsutsumida, 2017)<doi:10.1553/giscience2017_01_s36> and are described in greater detail in (Tsutsumida et al., 2019)<doi:10.5194/ica-abs-1-372-2019> and (Percival et al., 2021)<arXiv:2101.03491>.
Maintained by Joseph Emile Honour Percival. Last updated 3 years ago.
3 stars 3.18 score 1 scriptsjmbh
fspe:Estimating the Number of Factors in EFA with Out-of-Sample Prediction Errors
Estimating the number of factors in Exploratory Factor Analysis (EFA) with out-of-sample prediction errors using a cross-validation scheme. Haslbeck & van Bork (Preprint) <https://psyarxiv.com/qktsd>.
Maintained by Jonas Haslbeck. Last updated 2 years ago.
1 stars 3.18 score 2 scripts 1 dependents
epertham
xLLiM:High Dimensional Locally-Linear Mapping
Provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) <DOI:10.1007/s11222-014-9461-5>) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) <DOI:10.1016/j.jmva.2017.09.009>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) <arXiv:1701.07899>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).
Maintained by Emeline Perthame. Last updated 1 years ago.
1 stars 3.02 score 21 scripts
marcvidalbadia
pfica:Independent Components Analysis Techniques for Functional Data
This package includes a set of tools to perform smoothed (and non-smoothed) principal/independent components analysis of functional data. Various functional pre-whitening approaches are implemented as discussed in Vidal and Aguilera (2022) “Novel whitening approaches in functional settings", <doi:10.1002/sta4.516>. Further whitening representations of functional data can be derived in terms of a few principal components, providing a powerful avenue to explore hidden structures in low dimensional settings: see Vidal, Rosso and Aguilera (2021) “Bi-smoothed functional independent component analysis for EEG artifact removal”, <doi:10.3390/math9111243>.
Maintained by Marc Vidal. Last updated 2 years ago.
b-splinesfobiicakurtosispenalization
2 stars 3.00 score 3 scriptsbips-hb
SRSim:Spontaneous Reporting Simulator (SRSim)
A package for simulating spontaneous reporting data as used in the field of pharmacovigilance.
Maintained by Louis Dijkstra. Last updated 2 months ago.
binary-datapharmacovigilancesimulatorcpp
5 stars 3.00 score 4 scriptscran
smdi:Perform Structural Missing Data Investigations
An easy to use implementation of routine structural missing data diagnostics with functions to visualize the proportions of missing observations, investigate missing data patterns and conduct various empirical missing data diagnostic tests. Reference: Weberpals J, Raman SR, Shaw PA, Lee H, Hammill BG, Toh S, Connolly JG, Dandreo KJ, Tian F, Liu W, Li J, Hernández-Muñoz JJ, Glynn RJ, Desai RJ. smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies. JAMIA Open. 2024 Jan 31;7(1):ooae008. <doi:10.1093/jamiaopen/ooae008>.
Maintained by Janick Weberpals. Last updated 6 months ago.
3.00 score
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
niklaspfister
StabilizedRegression:Stabilizing Regression and Variable Selection
Contains an implementation of 'StabilizedRegression', a regression framework for heterogeneous data introduced in Pfister et al. (2021) <arXiv:1911.01850>. The procedure uses averaging to estimate a regression of a set of predictors X on a response variable Y by enforcing stability with respect to a given environment variable. The resulting regression leads to a variable selection procedure which allows to distinguish between stable and unstable predictors. The package further implements a visualization technique which illustrates the trade-off between stability and predictiveness of individual predictors.
Maintained by Niklas Pfister. Last updated 3 years ago.
2 stars 3.00 score 1 scripts
samaneh-bioinformatics
BNrich:Pathway Enrichment Analysis Based on Bayesian Network
Maleknia et al. (2020) <doi:10.1101/2020.01.13.905448>. A novel pathway enrichment analysis package based on Bayesian network to investigate the topology features of the pathways. firstly, 187 kyoto encyclopedia of genes and genomes (KEGG) human non-metabolic pathways which their cycles were eliminated by biological approach, enter in analysis as Bayesian network structures. The constructed Bayesian network were optimized by the Least Absolute Shrinkage Selector Operator (lasso) and the parameters were learned based on gene expression data. Finally, the impacted pathways were enriched by Fisher’s Exact Test on significant parameters.
Maintained by Samaneh Maleknia. Last updated 5 years ago.
networkenrichmentgeneexpressionpathwaysbayesiankegg
3.00 score
pilacuan-bonete-luis
LDABiplots:Biplot Graphical Interface for LDA Models
Contains the development of a tool that provides a web-based graphical user interface (GUI) to perform Biplots representations from a scraping of news from digital newspapers under the Bayesian approach of Latent Dirichlet Assignment (LDA) and machine learning algorithms. Contains LDA methods described by Blei , David M., Andrew Y. Ng and Michael I. Jordan (2003) <https://jmlr.org/papers/volume3/blei03a/blei03a.pdf>, and Biplot methods described by Gabriel K.R(1971) <doi:10.1093/biomet/58.3.453> and Galindo-Villardon P(1986) <https://diarium.usal.es/pgalindo/files/2012/07/Questiio.pdf>.
Maintained by Luis Pilacuan-Bonete. Last updated 3 years ago.
3.00 score 4 scriptsdonaldrwilliams
IRCcheck:Irrepresentable Condition Check
Check the irrepresentable condition (IRC) in both L1-regularized regression <doi:10.1109/TIT.2006.883611> and Gaussian graphical models. The IRC requires that the important and unimportant variables are not correlated, at least not all that much, and it is necessary for consistent model selection. Exploring the IRC as a function of the number of variables, assumed sparsity, and effect size can provide valuable insights into the model selection properties of L1-regularization.
Maintained by Donald Williams. Last updated 4 years ago.
2 stars 3.00 score 1 scripts
rchen18
RNGforGPD:Random Number Generation for Generalized Poisson Distribution
Generation of univariate and multivariate data that follow the generalized Poisson distribution. The details of the univariate part are explained in Demirtas (2017) <doi: 10.1080/03610918.2014.968725>, and the multivariate part is an extension of the correlated Poisson data generation routine that was introduced in Yahav and Shmueli (2012) <doi: 10.1002/asmb.901>.
Maintained by Ruizhe Chen. Last updated 4 years ago.
1 stars 3.00 score 11 scripts 3 dependents
adafede
sapid:A Strategy to Analyze Plant Extracts Taste In Depth
This package provides the infrastructure to implement a Strategy to Analyze Plant Extracts Taste In Depth.
Maintained by Adriano Rutz. Last updated 3 days ago.
computational metabolomicsnatural extractstaste
2.90 score
txm676
nos:Compute Node Overlap and Segregation in Ecological Networks
Calculate NOS (node overlap and segregation) and the associated metrics described in Strona and Veech (2015) <DOI:10.1111/2041-210X.12395> and Strona et al. (2017, In Press). The functions provided in the package enable assessment of structural patterns ranging from complete node segregation to perfect nestedness in a variety of network types. In addition, they provide a measure of network modularity.
Maintained by Thomas J. Matthews. Last updated 1 years ago.
2.88 score 15 scripts
mjafin
GeneCycle:Identification of Periodically Expressed Genes
The GeneCycle package implements the approaches of Wichert et al. (2004) <doi:10.1093/bioinformatics/btg364>, Ahdesmaki et al. (2005) <doi:10.1186/1471-2105-6-117> and Ahdesmaki et al. (2007) <DOI:10.1186/1471-2105-8-233> for detecting periodically expressed genes from gene expression time series data.
Maintained by Miika Ahdesmaki. Last updated 4 years ago.
1 stars 2.81 score 64 scripts
helloworld9293
VARcpDetectOnline:Sequential Change Point Detection for High-Dimensional VAR Models
Implements the algorithm introduced in Tian, Y., and Safikhani, A. (2024) <doi:10.5705/ss.202024.0182>, "Sequential Change Point Detection in High-dimensional Vector Auto-regressive Models". This package provides tools for detecting change points in the transition matrices of VAR models, effectively identifying shifts in temporal and cross-correlations within high-dimensional time series data.
Maintained by Yuhan Tian. Last updated 2 months ago.
3 stars 2.78 score
jmanitz
NetOrigin:Origin Estimation for Propagation Processes on Complex Networks
Performs network-based source estimation. Different approaches are available: effective distance median, recursive backtracking, and centrality-based source estimation. Additionally, we provide public transportation network data as well as methods for data preparation, source estimation performance analysis and visualization.
Maintained by Juliane Manitz. Last updated 2 years ago.
2.74 score 11 scripts
cran
ref.ICAR:Objective Bayes Intrinsic Conditional Autoregressive Model for Areal Data
Implements an objective Bayes intrinsic conditional autoregressive prior. This model provides an objective Bayesian approach for modeling spatially correlated areal data using an intrinsic conditional autoregressive prior on a vector of spatial random effects.
Maintained by Erica M. Porter. Last updated 2 months ago.
2.70 score
