close

Showing 28 of total 28 results (show query)

therneau

survival:Survival Analysis

Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.

Maintained by Terry M Therneau. Last updated 3 months ago.

400 stars 20.40 score 29k scripts 3.9k dependents

topepo

caret:Classification and Regression Training

Misc functions for training and plotting classification and regression models.

Maintained by Max Kuhn. Last updated 4 months ago.

1.6k stars 19.24 score 61k scripts 303 dependents

kkholst

mets:Analysis of Multivariate Event Times

Implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray, Ghosh-Lin, Binomial regression) with fast computation of influence functions.

Maintained by Klaus K. Holst. Last updated 16 hours ago.

multivariate-time-to-eventsurvival-analysistime-to-eventfortranopenblascpp

14 stars 13.45 score 236 scripts 42 dependents

bioc

mia:Microbiome analysis

mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization.

Maintained by Tuomas Borman. Last updated 3 days ago.

microbiomesoftwaredataimportanalysisbioconductorcpp

51 stars 11.51 score 316 scripts 5 dependents

bioc

CATALYST:Cytometry dATa anALYSis Tools

CATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively.

Maintained by Helena L. Crowell. Last updated 4 months ago.

clusteringdataimportdifferentialexpressionexperimentaldesignflowcytometryimmunooncologymassspectrometrynormalizationpreprocessingsinglecellsoftwarestatisticalmethodvisualization

67 stars 10.99 score 362 scripts 2 dependents

ctmm-initiative

ctmm:Continuous-Time Movement Modeling

Functions for identifying, fitting, and applying continuous-space, continuous-time stochastic-process movement models to animal tracking data. The package is described in Calabrese et al (2016) <doi:10.1111/2041-210X.12559>, with models and methods based on those introduced and detailed in Fleming & Calabrese et al (2014) <doi:10.1086/675504>, Fleming et al (2014) <doi:10.1111/2041-210X.12176>, Fleming et al (2015) <doi:10.1103/PhysRevE.91.032107>, Fleming et al (2015) <doi:10.1890/14-2010.1>, Fleming et al (2016) <doi:10.1890/15-1607>, Pรฉron & Fleming et al (2016) <doi:10.1186/s40462-016-0084-7>, Fleming & Calabrese (2017) <doi:10.1111/2041-210X.12673>, Pรฉron et al (2017) <doi:10.1002/ecm.1260>, Fleming et al (2017) <doi:10.1016/j.ecoinf.2017.04.008>, Fleming et al (2018) <doi:10.1002/eap.1704>, Winner & Noonan et al (2018) <doi:10.1111/2041-210X.13027>, Fleming et al (2019) <doi:10.1111/2041-210X.13270>, Noonan & Fleming et al (2019) <doi:10.1186/s40462-019-0177-1>, Fleming et al (2020) <doi:10.1101/2020.06.12.130195>, Noonan et al (2021) <doi:10.1111/2041-210X.13597>, Fleming et al (2022) <doi:10.1111/2041-210X.13815>, Silva et al (2022) <doi:10.1111/2041-210X.13786>, Alston & Fleming et al (2023) <doi:10.1111/2041-210X.14025>.

Maintained by Christen H. Fleming. Last updated 9 days ago.

51 stars 10.54 score 534 scripts 4 dependents

daattali

ddpcr:Analysis and Visualization of Droplet Digital PCR in R and on the Web

An interface to explore, analyze, and visualize droplet digital PCR (ddPCR) data in R. This is the first non-proprietary software for analyzing two-channel ddPCR data. An interactive tool was also created and is available online to facilitate this analysis for anyone who is not comfortable with using R.

Maintained by Dean Attali. Last updated 1 years ago.

61 stars 9.54 score 131 scripts 2 dependents

shaunpwilkinson

kmer:Fast K-Mer Counting and Clustering for Biological Sequence Analysis

Contains tools for rapidly computing distance matrices and clustering large sequence datasets using fast alignment-free k-mer counting and recursive k-means partitioning. See Vinga and Almeida (2003) <doi:10.1093/bioinformatics/btg005> for a review of k-mer counting methods and applications for biological sequence analysis.

Maintained by Shaun Wilkinson. Last updated 6 years ago.

cpp

27 stars 8.24 score 71 scripts 6 dependents

alinamateikondylis

sampling:Survey Sampling

Functions to draw random samples using different sampling schemes are available. Functions are also provided to obtain (generalized) calibration weights, different estimators, as well some variance estimators.

Maintained by Alina Matei. Last updated 1 years ago.

2 stars 8.08 score 772 scripts 29 dependents

markheckmann

OpenRepGrid:Tools to Analyze Repertory Grid Data

Analyze repertory grids, a qualitative-quantitative data collection technique devised by George A. Kelly in the 1950s. Today, grids are used across various domains ranging from clinical psychology to marketing. The package contains functions to quantitatively analyze and visualize repertory grid data (e.g. 'Fransella', 'Bell', & 'Bannister', 2004, ISBN: 978-0-470-09080-0). The package is part of the The package is part of the <https://openrepgrid.org/> project.

Maintained by Mark Heckmann. Last updated 28 days ago.

repertory-gridrepgrid

19 stars 6.69 score 156 scripts

cefet-rj-dal

daltoolbox:Leveraging Experiment Lines to Data Analytics

The natural increase in the complexity of current research experiments and data demands better tools to enhance productivity in Data Analytics. The package is a framework designed to address the modern challenges in data analytics workflows. The package is inspired by Experiment Line concepts. It aims to provide seamless support for users in developing their data mining workflows by offering a uniform data model and method API. It enables the integration of various data mining activities, including data preprocessing, classification, regression, clustering, and time series prediction. It also offers options for hyper-parameter tuning and supports integration with existing libraries and languages. Overall, the package provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.

Maintained by Eduardo Ogasawara. Last updated 2 months ago.

1 stars 6.65 score 536 scripts 4 dependents

cran

frailtypack:Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints

The following several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation can be fit using this R package: 1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox proportional hazard model. Clustered and recurrent survival times can be studied. 2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope). 3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects. 4) Joint frailty models in the context of the joint modelling for recurrent events with terminal event for clustered data or not. A joint frailty model for two semi-competing risks and clustered data is also proposed. 5) Joint general frailty models in the context of the joint modelling for recurrent events with terminal event data with two independent frailty terms. 6) Joint Nested frailty models in the context of the joint modelling for recurrent events with terminal event, for hierarchically clustered data (with two levels of clustering) by including two iid gamma random effects. 7) Multivariate joint frailty models for two types of recurrent events and a terminal event. 8) Joint models for longitudinal data and a terminal event. 9) Trivariate joint models for longitudinal data, recurrent events and a terminal event. 10) Joint frailty models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time and/or longitudinal endpoints with the possibility to use a mediation analysis model. 11) Conditional and Marginal two-part joint models for longitudinal semicontinuous data and a terminal event. 12) Joint frailty-copula models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. 13) Generalized shared and joint frailty models for recurrent and terminal events. Proportional hazards (PH), additive hazard (AH), proportional odds (PO) and probit models are available in a fully parametric framework. For PH and AH models, it is possible to consider type-varying coefficients and flexible semiparametric hazard function. Prediction values are available (for a terminal event or for a new recurrent event). Left-truncated (not for Joint model), right-censored data, interval-censored data (only for Cox proportional hazard and shared frailty model) and strata are allowed. In each model, the random effects have the gamma or normal distribution. Now, you can also consider time-varying covariates effects in Cox, shared and joint frailty models (1-5). The package includes concordance measures for Cox proportional hazards models and for shared frailty models. 14) Competing Joint Frailty Model: A single type of recurrent event and two terminal events. 15) functions to compute power and sample size for four Gamma-frailty-based designs: Shared Frailty Models, Nested Frailty Models, Joint Frailty Models, and General Joint Frailty Models. Each design includes two primary functions: a power function, which computes power given a specified sample size; and a sample size function, which computes the required sample size to achieve a specified power. Moreover, the package can be used with its shiny application, in a local mode or by following the link below.

Maintained by Virginie Rondeau. Last updated 23 days ago.

fortranopenmp

7 stars 5.56 score 1 dependents

speakeasy-2

speakeasyR:Fast and Robust Multi-Scale Graph Clustering

A graph community detection algorithm that aims to be performant on large graphs and robust, returning consistent results across runs. SpeakEasy 2 (SE2), the underlying algorithm, is described in Chris Gaiteri, David R. Connell & Faraz A. Sultan et al. (2023) <doi:10.1186/s13059-023-03062-0>. The core algorithm is written in 'C', providing speed and keeping the memory requirements low. This implementation can take advantage of multiple computing cores without increasing memory usage. SE2 can detect community structure across scales, making it a good choice for biological data, which often has hierarchical structure. Graphs can be passed to the algorithm as adjacency matrices using base 'R' matrices, the 'Matrix' library, 'igraph' graphs, or any data that can be coerced into a matrix.

Maintained by David Connell. Last updated 6 months ago.

arpack

3 stars 5.45 score 1 scripts

bioc

runibic:runibic: row-based biclustering algorithm for analysis of gene expression data in R

This package implements UbiBic algorithm in R. This biclustering algorithm for analysis of gene expression data was introduced by Zhenjia Wang et al. in 2016. It is currently considered the most promising biclustering method for identification of meaningful structures in complex and noisy data.

Maintained by Patryk Orzechowski. Last updated 5 months ago.

microarrayclusteringgeneexpressionsequencingcoveragecppopenmp

4 stars 5.20 score 7 scripts

hendersontrent

theftdlc:Analyse and Interpret Time Series Features

Provides a suite of functions for analysing, interpreting, and visualising time-series features calculated from different feature sets from the 'theft' package. Implements statistical learning methodologies described in Henderson, T., Bryant, A., and Fulcher, B. (2023) <arXiv:2303.17809>.

Maintained by Trent Henderson. Last updated 2 months ago.

data-sciencedata-visualizationmachine-learningstatisticstime-series

4 stars 4.94 score 11 scripts

kmkuesters

pooledpeaks:Genetic Analysis of Pooled Samples

Analyzing genetic data obtained from pooled samples. This package can read in Fragment Analysis output files, process the data, and score peaks, as well as facilitate various analyses, including cluster analysis, calculation of genetic distances and diversity indices, as well as bootstrap resampling for statistical inference. Specifically tailored to handle genetic data efficiently, researchers can explore population structure, genetic differentiation, and genetic relatedness among samples. We updated some functions from Covarrubias-Pazaran et al. (2016) <doi:10.1186/s12863-016-0365-6> to allow for the use of new file formats and referenced the following to write our genetic analysis functions: Long et al. (2022) <doi:10.1038/s41598-022-04776-0>, Jost (2008) <doi:10.1111/j.1365-294x.2008.03887.x>, Nei (1973) <doi:10.1073/pnas.70.12.3321>, Foulley et al. (2006) <doi:10.1016/j.livprodsci.2005.10.021>, Chao et al. (2008) <doi:10.1111/j.1541-0420.2008.01010.x>.

Maintained by Kathleen Kuesters. Last updated 15 days ago.

1 stars 4.85 score 3 scripts

schlosslab

clustur:Clustering

A tool that implements the clustering algorithms from 'mothur' (Schloss PD et al. (2009) <doi:10.1128/AEM.01541-09>). 'clustur' make use of the cluster() and make.shared() command from 'mothur'. Our cluster() function has five different algorithms implemented: 'OptiClust', 'furthest', 'nearest', 'average', and 'weighted'. 'OptiClust' is an optimized clustering method for Operational Taxonomic Units, and you can learn more here, (Westcott SL, Schloss PD (2017) <doi:10.1128/mspheredirect.00073-17>). The make.shared() command is always applied at the end of the clustering command. This functionality allows us to generate and create clustering and abundance data efficiently.

Maintained by Patrick Schloss. Last updated 3 months ago.

cpp

1 stars 4.85 score 7 scripts

wenjie2wang

clusrank:Wilcoxon Rank Tests for Clustered Data

Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data documented in Jiang et. al (2020) <doi:10.18637/jss.v096.i06>.

Maintained by Wenjie Wang. Last updated 1 years ago.

cpp

3 stars 4.39 score 17 scripts

bioc

OMICsPCA:An R package for quantitative integration and analysis of multiple omics assays from heterogeneous samples

OMICsPCA is an analysis pipeline designed to integrate multi OMICs experiments done on various subjects (e.g. Cell lines, individuals), treatments (e.g. disease/control) or time points and to analyse such integrated data from various various angles and perspectives. In it's core OMICsPCA uses Principal Component Analysis (PCA) to integrate multiomics experiments from various sources and thus has ability to over data insufficiency issues by using the ingegrated data as representatives. OMICsPCA can be used in various application including analysis of overall distribution of OMICs assays across various samples /individuals /time points; grouping assays by user-defined conditions; identification of source of variation, similarity/dissimilarity between assays, variables or individuals.

Maintained by Subhadeep Das. Last updated 5 months ago.

immunooncologymultiplecomparisonprincipalcomponentdatarepresentationworkflowvisualizationdimensionreductionclusteringbiologicalquestionepigeneticsworkflowtranscriptiongeneticvariabilityguibiomedicalinformaticsepigeneticsfunctionalgenomicssinglecell

4.00 score 1 scripts

mhahsler

rEMM:Extensible Markov Model for Modelling Temporal Relationships Between Clusters

Implements TRACDS (Temporal Relationships between Clusters for Data Streams), a generalization of Extensible Markov Model (EMM). TRACDS adds a temporal or order model to data stream clustering by superimposing a dynamically adapting Markov Chain. Also provides an implementation of EMM (TRACDS on top of tNN data stream clustering). Development of this package was supported in part by NSF IIS-0948893 and R21HG005912 from the National Human Genome Research Institute. Hahsler and Dunham (2010) <doi:10.18637/jss.v035.i05>.

Maintained by Michael Hahsler. Last updated 7 months ago.

clusteringdata-streamsequence-analysis

2 stars 3.79 score 31 scripts

qiangxyz

habCluster:Detecting Spatial Clustering Based on Connection Cost Between Grids

Based on landscape connectivity, spatial boundaries were identified using community detection algorithm at grid level. Methods using raster as input and the value of each cell of the raster is the "smoothness" to indicate how easy the cell connecting with neighbor cells. Details about the 'habCluster' package methods can be found in Zhang et al. <bioRxiv:2022.05.06.490926>.

Maintained by Qiang Dai. Last updated 3 years ago.

cpp

1 stars 3.70 score 5 scripts

larskotthoff

llama:Leveraging Learning to Automatically Manage Algorithms

Provides functionality to train and evaluate algorithm selection models for portfolios.

Maintained by Lars Kotthoff. Last updated 4 years ago.

openjdk

4 stars 2.80 score 53 scripts 1 dependents

sciviews

exploreit:Exploratory Data Analysis for 'SciViews::R'

Multivariate analysis and data exploration for the 'SciViews::R' dialect.

Maintained by Philippe Grosjean. Last updated 11 months ago.

multivariate-analysissciviewsstatistical-methods

2.70 score 4 scripts

matutosi

ecan:Ecological Analysis and Visualization

Support ecological analyses such as ordination and clustering. Contains consistent and easy wrapper functions of 'stat', 'vegan', and 'labdsv' packages, and visualisation functions of ordination and clustering.

Maintained by Toshikazu Matsumura. Last updated 6 months ago.

2.70 score 5 scripts

joemsong

GridOnClusters:Cluster-Preserving Multivariate Joint Grid Discretization

Discretize multivariate continuous data using a grid that captures the joint distribution via preserving clusters in the original data (Wang et al. 2020) <doi:10.1145/3388440.3412415>. Joint grid discretization is applicable as a data transformation step to prepare data for model-free inference of association, function, or causality.

Maintained by Joe Song. Last updated 11 months ago.

cpp

2.70 score 3 scripts

igordot

phenomenalist:Analysis Toolkit for PhenoCycler (CODEX) Data in R

A collection of tools for cleaning, clustering, and plotting PhenoCycler (CODEX) data.

Maintained by Igor Dolgalev. Last updated 1 years ago.

codexphenocycler

3 stars 2.18 score 1 scripts

cran

SetMethods:Functions for Set-Theoretic Multi-Method Research and Advanced QCA

Functions for performing set-theoretic multi-method research, QCA for clustered data, theory evaluation, Enhanced Standard Analysis, indirect calibration, radar visualisations. Additionally it includes data to replicate the examples in the books by Oana, I.E, C. Q. Schneider, and E. Thomann. Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide. Cambridge University Press and C. Q. Schneider and C. Wagemann "Set Theoretic Methods for the Social Sciences", Cambridge University Press.

Maintained by Ioana-Elena Oana. Last updated 9 days ago.

1 stars 1.10 score

cran

drclust:Simultaneous Clustering and (or) Dimensionality Reduction

Methods for simultaneous clustering and dimensionality reduction such as: Double k-means, Reduced k-means, Factorial k-means, Clustering with Disjoint PCA but also methods for exclusively dimensionality reduction: Disjoint PCA, Disjoint FA. The statistical methods implemented refer to the following articles: de Soete G., Carroll J. (1994) "K-means clustering in a low-dimensional Euclidean space" <doi:10.1007/978-3-642-51175-2_24> ; Vichi M. (2001) "Double k-means Clustering for Simultaneous Classification of Objects and Variables" <doi:10.1007/978-3-642-59471-7_6> ; Vichi M., Kiers H.A.L. (2001) "Factorial k-means analysis for two-way data" <doi:10.1016/S0167-9473(00)00064-5> ; Vichi M., Saporta G. (2009) "Clustering and disjoint principal component analysis" <doi:10.1016/j.csda.2008.05.028> ; Vichi M. (2017) "Disjoint factor analysis with cross-loadings" <doi:10.1007/s11634-016-0263-9>.

Maintained by Ionel Prunila. Last updated 11 months ago.

openblascppopenmp

1.00 score

: