Showing 117 of total 117 results (show query)
bioc
pRoloc:A unifying bioinformatics framework for spatial proteomics
The pRoloc package implements machine learning and visualisation methods for the analysis and interogation of quantitiative mass spectrometry data to reliably infer protein sub-cellular localisation.
Maintained by Lisa Breckels. Last updated 7 days ago.
immunooncologyproteomicsmassspectrometryclassificationclusteringqualitycontrolbioconductorproteomics-dataspatial-proteomicsvisualisationopenblascpp
15 stars 10.31 score 101 scripts 2 dependentsbioc
singleCellTK:Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data
The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk.
Maintained by Joshua David Campbell. Last updated 21 hours ago.
singlecellgeneexpressiondifferentialexpressionalignmentclusteringimmunooncologybatcheffectnormalizationqualitycontroldataimportgui
182 stars 10.21 score 252 scriptsbioc
scMerge:scMerge: Merging multiple batches of scRNA-seq data
Like all gene expression data, single-cell data suffers from batch effects and other unwanted variations that makes accurate biological interpretations difficult. The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple single-cell data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of single-cell data.
Maintained by Yingxin Lin. Last updated 5 months ago.
batcheffectgeneexpressionnormalizationrnaseqsequencingsinglecellsoftwaretranscriptomicsbioinformaticssingle-cell
67 stars 9.52 score 137 scripts 1 dependents
r-forge
distr:Object Oriented Implementation of Distributions
S4-classes and methods for distributions.
Maintained by Peter Ruckdeschel. Last updated 3 months ago.
8.77 score 327 scripts 32 dependentsbioc
MLInterfaces:Uniform interfaces to R machine learning procedures for data in Bioconductor containers
This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers.
Maintained by Vincent Carey. Last updated 5 months ago.
7.63 score 79 scripts 6 dependentsr-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 dependentsbioc
isobar:Analysis and quantitation of isobarically tagged MSMS proteomics data
isobar provides methods for preprocessing, normalization, and report generation for the analysis of quantitative mass spectrometry proteomics data labeled with isobaric tags, such as iTRAQ and TMT. Features modules for integrating and validating PTM-centric datasets (isobar-PTM). More information on http://www.ms-isobar.org.
Maintained by Florian P Breitwieser. Last updated 5 months ago.
immunooncologyproteomicsmassspectrometrybioinformaticsmultiplecomparisonsqualitycontrol
10 stars 6.96 score 19 scriptsbioc
pRolocGUI:Interactive visualisation of spatial proteomics data
The package pRolocGUI comprises functions to interactively visualise spatial proteomics data on the basis of pRoloc, pRolocdata and shiny.
Maintained by Lisa Breckels. Last updated 5 months ago.
8 stars 6.90 score 3 scripts
r-forge
distrEx:Extensions of Package 'distr'
Extends package 'distr' by functionals, distances, and conditional distributions.
Maintained by Matthias Kohl. Last updated 3 months ago.
6.64 score 107 scripts 17 dependents
r-forge
distrMod:Object Oriented Implementation of Probability Models
Implements S4 classes for probability models based on packages 'distr' and 'distrEx'.
Maintained by Peter Ruckdeschel. Last updated 3 months ago.
6.60 score 139 scripts 6 dependents
s-mckay-curtis
mcmcplots:Create Plots from MCMC Output
Functions for convenient plotting and viewing of MCMC output.
Maintained by S. McKay Curtis. Last updated 7 years ago.
4 stars 6.53 score 880 scripts 4 dependentsr-forge
RandVar:Implementation of Random Variables
Implements random variables by means of S4 classes and methods.
Maintained by Matthias Kohl. Last updated 2 months ago.
6.03 score 43 scripts 7 dependents
nicholasjclark
MRFcov:Markov Random Fields with Additional Covariates
Approximate node interaction parameters of Markov Random Fields graphical networks. Models can incorporate additional covariates, allowing users to estimate how interactions between nodes in the graph are predicted to change across covariate gradients. The general methods implemented in this package are described in Clark et al. (2018) <doi:10.1002/ecy.2221>.
Maintained by Nicholas J Clark. Last updated 1 years ago.
conditional-random-fieldsgraphical-modelsmachine-learningmarkov-random-fieldmultivariate-analysismultivariate-statisticsnetwork-analysisnetworks
24 stars 6.03 score 30 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 dependents
sestelo
npregfast:Nonparametric Estimation of Regression Models with Factor-by-Curve Interactions
A method for obtaining nonparametric estimates of regression models with or without factor-by-curve interactions using local polynomial kernel smoothers or splines. Additionally, a parametric model (allometric model) can be estimated.
Maintained by Marta Sestelo. Last updated 3 months ago.
allometricbarnaclecritical-pointscurve-interactionsfactor-by-curvefortraninteractionnonparametricregression-modelstesting
5 stars 5.73 score 89 scripts 2 dependents
stephenslab
fastglmpca:Fast Algorithms for Generalized Principal Component Analysis
Implements fast, scalable optimization algorithms for fitting generalized principal components analysis (GLM-PCA) models, as described in "A Generalization of Principal Components Analysis to the Exponential Family" Collins M, Dasgupta S, Schapire RE (2002, ISBN:9780262271738), and subsequently "Feature Selection and Dimension Reduction for Single-Cell RNA-Seq Based on a Multinomial Model" Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) <doi:10.1186/s13059-019-1861-6>.
Maintained by Eric Weine. Last updated 22 days ago.
11 stars 5.72 score 16 scriptsbioc
bandle:An R package for the Bayesian analysis of differential subcellular localisation experiments
The Bandle package enables the analysis and visualisation of differential localisation experiments using mass-spectrometry data. Experimental methods supported include dynamic LOPIT-DC, hyperLOPIT, Dynamic Organellar Maps, Dynamic PCP. It provides Bioconductor infrastructure to analyse these data.
Maintained by Oliver M. Crook. Last updated 22 hours ago.
bayesianclassificationclusteringimmunooncologyqualitycontroldataimportproteomicsmassspectrometryopenblascppopenmp
4 stars 5.68 score 3 scriptsr-forge
DPQ:Density, Probability, Quantile ('DPQ') Computations
Computations for approximations and alternatives for the 'DPQ' (Density (pdf), Probability (cdf) and Quantile) functions for probability distributions in R. Primary focus is on (central and non-central) beta, gamma and related distributions such as the chi-squared, F, and t. -- For several distribution functions, provide functions implementing formulas from Johnson, Kotz, and Kemp (1992) <doi:10.1002/bimj.4710360207> and Johnson, Kotz, and Balakrishnan (1995) for discrete or continuous distributions respectively. This is for the use of researchers in these numerical approximation implementations, notably for my own use in order to improve standard R pbeta(), qgamma(), ..., etc: {'"dpq"'-functions}.
Maintained by Martin Maechler. Last updated 2 months ago.
5.62 score 43 scripts 1 dependents
cfwp
rags2ridges:Ridge Estimation of Precision Matrices from High-Dimensional Data
Proper L2-penalized maximum likelihood estimators for precision matrices and supporting functions to employ these estimators in a graphical modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) <doi:10.18637/jss.v102.i04> and associated publications.
Maintained by Carel F.W. Peeters. Last updated 1 years ago.
c-plus-plusgraphical-modelsmachine-learningnetworksciencestatisticsopenblascpp
8 stars 5.60 score 46 scriptsr-forge
lokern:Kernel Regression Smoothing with Local or Global Plug-in Bandwidth
Kernel regression smoothing with adaptive local or global plug-in bandwidth selection.
Maintained by Martin Maechler. Last updated 4 months ago.
5.53 score 64 scripts 5 dependents
noramvillanueva
clustcurv:Determining Groups in Multiples Curves
A method for determining groups in multiple curves with an automatic selection of their number based on k-means or k-medians algorithms. The selection of the optimal number is provided by bootstrap methods. The methodology can be applied both in regression and survival framework. Implemented methods are: Grouping multiple survival curves described by Villanueva et al. (2018) <doi:10.1002/sim.8016>.
Maintained by Nora M. Villanueva. Last updated 5 months ago.
clusteringdata-analyticsmachinelearningmultiple-curvesnonparametric-statisticsnumber-of-clustersregressionsurvival-analysis
3 stars 5.53 score 38 scriptsbioc
MethReg:Assessing the regulatory potential of DNA methylation regions or sites on gene transcription
Epigenome-wide association studies (EWAS) detects a large number of DNA methylation differences, often hundreds of differentially methylated regions and thousands of CpGs, that are significantly associated with a disease, many are located in non-coding regions. Therefore, there is a critical need to better understand the functional impact of these CpG methylations and to further prioritize the significant changes. MethReg is an R package for integrative modeling of DNA methylation, target gene expression and transcription factor binding sites data, to systematically identify and rank functional CpG methylations. MethReg evaluates, prioritizes and annotates CpG sites with high regulatory potential using matched methylation and gene expression data, along with external TF-target interaction databases based on manually curation, ChIP-seq experiments or gene regulatory network analysis.
Maintained by Tiago Silva. Last updated 5 months ago.
methylationarrayregressiongeneexpressionepigeneticsgenetargettranscription
5 stars 5.45 score 19 scripts
angabrio
missingHE:Missing Outcome Data in Health Economic Evaluation
Contains a suite of functions for health economic evaluations with missing outcome data. The package can fit different types of statistical models under a fully Bayesian approach using the software 'JAGS' (which should be installed locally and which is loaded in 'missingHE' via the 'R' package 'R2jags'). Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models. In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs. The methods implemented are described in Mason (2018) <doi:10.1002/hec.3793>, Molenberghs (2000) <doi:10.1007/978-1-4419-0300-6_18> and Gabrio (2019) <doi:10.1002/sim.8045>.
Maintained by Andrea Gabrio. Last updated 2 years ago.
cost-effectiveness-analysishealth-economic-evaluationindividual-level-datajagsmissing-dataparametric-modellingsensitivity-analysiscpp
5 stars 5.38 score 24 scripts
cmerow
meteR:Fitting and Plotting Tools for the Maximum Entropy Theory of Ecology (METE)
Fit and plot macroecological patterns predicted by the Maximum Entropy Theory of Ecology (METE).
Maintained by Cory Merow. Last updated 6 years ago.
11 stars 5.35 score 41 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 scripts
ugroempi
FrF2:Fractional Factorial Designs with 2-Level Factors
Regular and non-regular Fractional Factorial 2-level designs can be created. Furthermore, analysis tools for Fractional Factorial designs with 2-level factors are offered (main effects and interaction plots for all factors simultaneously, cube plot for looking at the simultaneous effects of three factors, full or half normal plot, alias structure in a more readable format than with the built-in function alias).
Maintained by Ulrike Groemping. Last updated 2 years ago.
2 stars 5.10 score 185 scripts 8 dependentsr-forge
RobAStBase:Robust Asymptotic Statistics
Base S4-classes and functions for robust asymptotic statistics.
Maintained by Matthias Kohl. Last updated 2 months ago.
4.96 score 64 scripts 4 dependents
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
simulatr
simrel:Simulation of Multivariate Linear Model Data
Researchers have been using simulated data from a multivariate linear model to compare and evaluate different methods, ideas and models. Additionally, teachers and educators have been using a simulation tool to demonstrate and teach various statistical and machine learning concepts. This package helps users to simulate linear model data with a wide range of properties by tuning few parameters such as relevant latent components. In addition, a shiny app as an 'RStudio' gadget gives users a simple interface for using the simulation function. See more on: Sæbø, S., Almøy, T., Helland, I.S. (2015) <doi:10.1016/j.chemolab.2015.05.012> and Rimal, R., Almøy, T., Sæbø, S. (2018) <doi:10.1016/j.chemolab.2018.02.009>.
Maintained by Raju Rimal. Last updated 2 years ago.
bivariate-simulationmultivariate-simulationrelevant-predictor-componentssimulated-datasimulationunivariate-simulation
3 stars 4.78 score 40 scriptsbioc
BiSeq:Processing and analyzing bisulfite sequencing data
The BiSeq package provides useful classes and functions to handle and analyze targeted bisulfite sequencing (BS) data such as reduced-representation bisulfite sequencing (RRBS) data. In particular, it implements an algorithm to detect differentially methylated regions (DMRs). The package takes already aligned BS data from one or multiple samples.
Maintained by Katja Hebestreit. Last updated 5 months ago.
geneticssequencingmethylseqdnamethylation
4.78 score 30 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
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 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 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 4 months ago.
softwarestatisticalmethodgraphandnetworkregressiongeneexpressiondifferentialexpressionnetworkenrichmentnetworkkeggbioconductorcausality
13 stars 4.59 score 4 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 1 months ago.
19 stars 4.58 score 10 scriptsbioc
flowClean:flowClean
A quality control tool for flow cytometry data based on compositional data analysis.
Maintained by Kipper Fletez-Brant. Last updated 5 months ago.
flowcytometryqualitycontrolimmunooncology
4.56 score 18 scripts
kwb-r
kwb.qmra:QMRA (quantitative microbial risk assessment)
QMRA for water supply systems.
Maintained by Michael Rustler. Last updated 4 years ago.
project-aquanesproject-demowareproject-smartcontrolqmraqmra-webapp-backend-engine
4 stars 4.53 score 21 scriptsr-forge
RobLox:Optimally Robust Influence Curves and Estimators for Location and Scale
Functions for the determination of optimally robust influence curves and estimators in case of normal location and/or scale (see Chapter 8 in Kohl (2005) <https://epub.uni-bayreuth.de/839/2/DissMKohl.pdf>).
Maintained by Matthias Kohl. Last updated 2 months ago.
4.50 score 70 scripts 1 dependents
egeminiani
penfa:Single- And Multiple-Group Penalized Factor Analysis
Fits single- and multiple-group penalized factor analysis models via a trust-region algorithm with integrated automatic multiple tuning parameter selection (Geminiani et al., 2021 <doi:10.1007/s11336-021-09751-8>). Available penalties include lasso, adaptive lasso, scad, mcp, and ridge.
Maintained by Elena Geminiani. Last updated 4 years ago.
factor-analysislassolatent-variablesmultiple-groupoptimizationpenalizationpsychometrics
3 stars 4.48 score 5 scripts
pokotylo
ddalpha:Depth-Based Classification and Calculation of Data Depth
Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included. (Pokotylo, Mozharovskyi and Dyckerhoff, 2019 <doi:10.18637/jss.v091.i05>).
Maintained by Oleksii Pokotylo. Last updated 6 months ago.
2 stars 4.45 score 211 scripts 7 dependentsbioc
PREDA:Position Related Data Analysis
Package for the position related analysis of quantitative functional genomics data.
Maintained by Francesco Ferrari. Last updated 5 months ago.
softwarecopynumbervariationgeneexpressiongenetics
4.30 score 9 scriptsr-forge
ROptEst:Optimally Robust Estimation
R infrastructure for optimally robust estimation in general smoothly parameterized models using S4 classes and methods as described Kohl, M., Ruckdeschel, P., and Rieder, H. (2010), <doi:10.1007/s10260-010-0133-0>, and in Rieder, H., Kohl, M., and Ruckdeschel, P. (2008), <doi:10.1007/s10260-007-0047-7>.
Maintained by Matthias Kohl. Last updated 2 months ago.
4.26 score 50 scripts 1 dependentsr-forge
distrDoc:Documentation for 'distr' Family of R Packages
Provides documentation in form of a common vignette to packages 'distr', 'distrEx', 'distrMod', 'distrSim', 'distrTEst', 'distrTeach', and 'distrEllipse'.
Maintained by Peter Ruckdeschel. Last updated 3 months ago.
4.20 score 3 scripts
trn000
norMmix:Direct MLE for Multivariate Normal Mixture Distributions
Multivariate Normal (i.e. Gaussian) Mixture Models (S3) Classes. Fitting models to data using 'MLE' (maximum likelihood estimation) for multivariate normal mixtures via smart parametrization using the 'LDL' (Cholesky) decomposition, see McLachlan and Peel (2000, ISBN:9780471006268), Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>.
Maintained by Nicolas Trutmann. Last updated 7 months ago.
gaussian-mixture-modelsmaximum-likelihood-estimationr-language
4.18 score 3 scriptsr-forge
distrSim:Simulation Classes Based on Package 'distr'
S4-classes for setting up a coherent framework for simulation within the distr family of packages.
Maintained by Peter Ruckdeschel. Last updated 3 months ago.
4.16 score 7 scripts 3 dependents
giampmarra
GJRM:Generalised Joint Regression Modelling
Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.
Maintained by Giampiero Marra. Last updated 5 months ago.
4 stars 4.04 score 67 scripts 5 dependentsr-forge
RobLoxBioC:Infinitesimally Robust Estimators for Preprocessing -Omics Data
Functions for the determination of optimally robust influence curves and estimators for preprocessing omics data, in particular gene expression data (Kohl and Deigner (2010), <doi:10.1186/1471-2105-11-583>).
Maintained by Matthias Kohl. Last updated 2 months ago.
4.03 score 30 scripts
cran
YEAB:Analyze Data from Analysis of Behavior Experiments
Analyze data from behavioral experiments conducted using 'MED-PC' software developed by Med Associates Inc. Includes functions to fit exponential and hyperbolic models for delay discounting tasks, exponential mixtures for inter-response times, and Gaussian plus ramp models for peak procedure data, among others. For more details, refer to Alcala et al. (2023) <doi:10.31234/osf.io/8aq2j>.
Maintained by Emmanuel Alcala. Last updated 2 months ago.
4.00 score
darkeyes
EDOIF:Empirical Distribution Ordering Inference Framework (EDOIF)
A non-parametric framework based on estimation statistics principle. Its main purpose is to infer orders of empirical distributions from different categories based on a probability of finding a value in one distribution that is greater than an expectation of another distribution. Given a set of ordered-pair of real-category values the framework is capable of 1) inferring orders of domination of categories and representing orders in the form of a graph; 2) estimating magnitude of difference between a pair of categories in forms of mean-difference confidence intervals; and 3) visualizing domination orders and magnitudes of difference of categories. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2020) <doi:10.1016/j.heliyon.2020.e05435>.
Maintained by Chainarong Amornbunchornvej. Last updated 4 years ago.
bootstrapping-statisticsdata-scienceestimation-statisticsnonparametric-framework
1 stars 3.70 score 4 scripts
malfly
JAGStree:Automatically Write 'JAGS' Code for Hierarchical Bayesian Models on Trees
When relationships between sources of data can be represented by a tree, the generation of appropriate Markov Chain Monte Carlo modeling code to be used with 'JAGS' to run a Bayesian hierarchical model can be automatically generated by this package. Any admissible tree-structured data can be used, under the assumption that node counts are multinomial and branching probabilities are Dirichlet among sibling groups. The methodological basis used to create this package can be found in Flynn (2023) <http://hdl.handle.net/2429/86174>.
Maintained by Mallory J Flynn. Last updated 5 months ago.
3.70 score
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 scriptsr-forge
distrTEst:Estimation and Testing Classes Based on Package 'distr'
Evaluation (S4-)classes based on package distr for evaluating procedures (estimators/tests) at data/simulation in a unified way.
Maintained by Peter Ruckdeschel. Last updated 3 months ago.
3.68 score 3 scripts 1 dependentsr-forge
distrTeach:Extensions of Package 'distr' for Teaching Stochastics/Statistics in Secondary School
Provides flexible examples of LLN and CLT for teaching purposes in secondary school.
Maintained by Peter Ruckdeschel. Last updated 3 months ago.
3.68 score 6 scripts 1 dependentsr-forge
RobExtremes:Optimally Robust Estimation for Extreme Value Distributions
Optimally robust estimation for extreme value distributions using S4 classes and methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and 'ROptEst'); the underlying theoretic results can be found in Ruckdeschel and Horbenko, (2013 and 2012), \doi{10.1080/02331888.2011.628022} and \doi{10.1007/s00184-011-0366-4}.
Maintained by Peter Ruckdeschel. Last updated 2 months ago.
3.67 score 39 scripts
louisaslett
ReliabilityTheory:Structural Reliability Analysis
Perform structural reliability analysis, including computation and simulation with system signatures, Samaniego (2007) <doi:10.1007/978-0-387-71797-5>, and survival signatures, Coolen and Coolen-Maturi (2013) <doi:10.1007/978-3-642-30662-4_8>. Additionally supports parametric and topological inference given system lifetime data, Aslett (2012) <https://www.louisaslett.com/PhD_Thesis.pdf>.
Maintained by Louis Aslett. Last updated 6 months ago.
7 stars 3.62 score 12 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 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 scriptsr-forge
distrEllipse:S4 Classes for Elliptically Contoured Distributions
Distribution (S4-)classes for elliptically contoured distributions (based on package 'distr').
Maintained by Peter Ruckdeschel. Last updated 3 months ago.
3.46 score 18 scripts
pinduzera
genlogis:Generalized Logistic Distribution
Provides basic distribution functions for a generalized logistic distribution proposed by Rathie and Swamee (2006) <https://www.rroij.com/open-access/on-new-generalized-logistic-distributions-and-applicationsbarreto-fhs-mota-jma-and-rathie-pn-.pdf>. It also has an interactive 'RStudio' plot for better guessing dynamically of initial values for ease of included optimization and simulating.
Maintained by Eduardo Hellas. Last updated 1 years ago.
4 stars 3.30 score 8 scripts
r-forge
distrRmetrics:Distribution Classes for Distributions from Rmetrics
S4-distribution classes based on package distr for distributions from packages 'fBasics' and 'fGarch'.
Maintained by Peter Ruckdeschel. Last updated 3 months ago.
3.20 score 7 scripts
bertcarnell
SOAs:Creation of Stratum Orthogonal Arrays
Creates stratum orthogonal arrays (also known as strong orthogonal arrays). These are arrays with more levels per column than the typical orthogonal array, and whose low order projections behave like orthogonal arrays, when collapsing levels to coarser strata. Details are described in Groemping (2022) "A unifying implementation of stratum (aka strong) orthogonal arrays" <http://www1.bht-berlin.de/FB_II/reports/Report-2022-002.pdf>.
Maintained by Ulrike Groemping. Last updated 1 years ago.
3 stars 3.18 score 2 scripts
vaudigier
micemd:Multiple Imputation by Chained Equations with Multilevel Data
Addons for the 'mice' package to perform multiple imputation using chained equations with two-level data. Includes imputation methods dedicated to sporadically and systematically missing values. Imputation of continuous, binary or count variables are available. Following the recommendations of Audigier, V. et al (2018) <doi:10.1214/18-STS646>, the choice of the imputation method for each variable can be facilitated by a default choice tuned according to the structure of the incomplete dataset. Allows parallel calculation and overimputation for 'mice'.
Maintained by Vincent Audigier. Last updated 1 years ago.
1 stars 3.08 score 80 scripts 1 dependents
kgdunn
pid:Process Improvement using Data
A collection of scripts and data files for the statistics text: "Process Improvement using Data" <https://learnche.org/pid> and the online course "Experimentation for Improvement" found on Coursera. The package contains code for designed experiments, data sets and other convenience functions used in the book.
Maintained by Kevin Dunn. Last updated 6 years ago.
2 stars 3.07 score 66 scriptsbioc
ffpe:Quality assessment and control for FFPE microarray expression data
Identify low-quality data using metrics developed for expression data derived from Formalin-Fixed, Paraffin-Embedded (FFPE) data. Also a function for making Concordance at the Top plots (CAT-plots).
Maintained by Levi Waldron. Last updated 5 months ago.
microarraygeneexpressionqualitycontrol
3.03 score 27 scriptsbioc
SigCheck:Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata
While gene signatures are frequently used to predict phenotypes (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its performance on survival and/or classification tasks against a) random gene signatures of the same length; b) known, related and unrelated gene signatures; and c) permuted data and/or metadata.
Maintained by Rory Stark. Last updated 2 months ago.
geneexpressionclassificationgenesetenrichment
3.00 score 1 scripts
ecoroland2
gasfluxes:Greenhouse Gas Flux Calculation from Chamber Measurements
Functions for greenhouse gas flux calculation from chamber measurements.
Maintained by Roland Fuss. Last updated 8 months ago.
1 stars 2.92 score 21 scripts
sere3s
takos:Analysis of Differential Calorimetry Scans
It includes functions for applying methodologies utilized for single-process kinetic analysis of solid-state processes were recently summarized and described in the Recommendation of ICTAC Kinetic Committee. These methods work with the basic kinetic equation. The Methodologies included refers to Avrami, Friedman, Kissinger, Ozawa, OFM, Mo, Starink, isoconversional methodology (Vyazovkin) according to ICATAC Kinetics Committee recommendations as reported in Vyazovkin S, Chrissafis K, Di Lorenzo ML, et al. ICTAC Kinetics Committee recommendations for collecting experimental thermal analysis data for kinetic computations. Thermochim Acta. 2014; 590:1-23. <doi:10.1016/J.TCA.2014.05.036> .
Maintained by Serena Berretta. Last updated 4 years ago.
4 stars 2.92 score 21 scripts
mariushofert
simsalapar:Tools for Simulation Studies in Parallel
Tools for setting up ("design"), conducting, and evaluating large-scale simulation studies with graphics and tables, including parallel computations.
Maintained by Marius Hofert. Last updated 2 years ago.
2 stars 2.91 score 67 scripts 2 dependentsr-forge
DPQmpfr:DPQ (Density, Probability, Quantile) Distribution Computations using MPFR
An extension to the 'DPQ' package with computations for 'DPQ' (Density (pdf), Probability (cdf) and Quantile) functions, where the functions here partly use the 'Rmpfr' package and hence the underlying 'MPFR' and 'GMP' C libraries.
Maintained by Martin Maechler. Last updated 2 months ago.
2.90 score 1 scriptscran
Anthropometry:Statistical Methods for Anthropometric Data
Statistical methodologies especially developed to analyze anthropometric data. These methods are aimed at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the statistical concept of data depth, statistical shape analysis and archetypal analysis. Please see Vinue (2017) <doi:10.18637/jss.v077.i06>.
Maintained by Guillermo Vinue. Last updated 2 years ago.
1 stars 2.78 score 2 dependentscran
mosqcontrol:Mosquito Control Resource Optimization
This project aims to make an accessible model for mosquito control resource optimization. The model uses data provided by users to estimate the mosquito populations in the sampling area for the sampling time period, and the optimal time to apply a treatment or multiple treatments.
Maintained by Travis Byrum. Last updated 5 years ago.
2.70 score
gdurif
funStatTest:Statistical Testing for Functional Data
Implementation of two sample comparison procedures based on median-based statistical tests for functional data, introduced in Smida et al (2022) <doi:10.1080/10485252.2022.2064997>. Other competitive state-of-the-art approaches proposed by Chakraborty and Chaudhuri (2015) <doi:10.1093/biomet/asu072>, Horvath et al (2013) <doi:10.1111/j.1467-9868.2012.01032.x> or Cuevas et al (2004) <doi:10.1016/j.csda.2003.10.021> are also included in the package, as well as procedures to run test result comparisons and power analysis using simulations.
Maintained by Ghislain Durif. Last updated 10 months ago.
2.70 score 4 scripts
rvaradhan
features:Feature Extraction for Discretely-Sampled Functional Data
Discretely-sampled function is first smoothed. Features of the smoothed function are then extracted. Some of the key features include mean value, first and second derivatives, critical points (i.e. local maxima and minima), curvature of cunction at critical points, wiggliness of the function, noise in data, and outliers in data.
Maintained by Ravi Varadhan. Last updated 9 years ago.
2.53 score 112 scriptsugroempi
DoE.wrapper:Wrapper Package for Design of Experiments Functionality
Various kinds of designs for (industrial) experiments can be created. The package uses, and sometimes enhances, design generation routines from other packages. So far, response surface designs from package 'rsm', Latin hypercube samples from packages 'lhs' and 'DiceDesign', and D-optimal designs from package 'AlgDesign' have been implemented.
Maintained by Ulrike Groemping. Last updated 2 years ago.
2.45 score 27 scripts 2 dependents
klausjung-hannover
RepeatedHighDim:Methods for High-Dimensional Repeated Measures Data
A toolkit for the analysis of high-dimensional repeated measurements, providing functions for outlier detection, differential expression analysis, gene-set tests, and binary random data generation.
Maintained by Klaus Jung. Last updated 11 months ago.
2.36 score 9 scriptscran
qtlnet:Causal Inference of QTL Networks
Functions to Simultaneously Infer Causal Graphs and Genetic Architecture. Includes acyclic and cyclic graphs for data from an experimental cross with a modest number (<10) of phenotypes driven by a few genetic loci (QTL). Chaibub Neto E, Keller MP, Attie AD, Yandell BS (2010) Causal Graphical Models in Systems Genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes. Annals of Applied Statistics 4: 320-339. <doi:10.1214/09-AOAS288>.
Maintained by Brian S. Yandell. Last updated 5 years ago.
2.30 score
haydarde
CryptRndTest:Statistical Tests for Cryptographic Randomness
Performs cryptographic randomness tests on a sequence of random integers or bits. Included tests are greatest common divisor, birthday spacings, book stack, adaptive chi-square, topological binary, and three random walk tests (Ryabko and Monarev, 2005) <doi:10.1016/j.jspi.2004.02.010>. Tests except greatest common divisor and birthday spacings are not covered by standard test suites. In addition to the chi-square goodness-of-fit test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are also generated by some of the cryptographic randomness tests.
Maintained by Haydar Demirhan. Last updated 3 years ago.
2.20 score 16 scripts
bayesstats
jarbes:Just a Rather Bayesian Evidence Synthesis
Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).
Maintained by Pablo Emilio Verde. Last updated 6 days ago.
1 stars 2.03 score 27 scriptscran
DDHFm:Variance Stabilization by Data-Driven Haar-Fisz (for Microarrays)
Contains the normalizing and variance stabilizing Data-Driven Haar-Fisz algorithm. Also contains related algorithms for simulating from certain microarray gene intensity models and evaluation of certain transformations. Contains cDNA and shipping credit flow data.
Maintained by Guy Nason. Last updated 6 months ago.
2.00 score
cran
MethodOpt:Advanced Method Optimization for Spectra-Generating Sampling and Analysis Instrumentation
A graphical user interface to apply an advanced method optimization algorithm to various sampling and analysis instruments. This includes generating experimental designs, uploading and viewing data, and performing various analyses to determine the optimal method. Details of the techniques used in this package are published in Gamble, Granger, & Mannion (2024) <doi:10.1021/acs.analchem.3c05763>.
Maintained by Stephanie Gamble. Last updated 6 months ago.
2.00 score
ugroempi
FrF2.catlg128:Catalogues of Resolution IV 128 Run 2-Level Fractional Factorials Up to 33 Factors that Do Have 5-Letter Words
Catalogues of resolution IV regular fractional factorial designs in 128 runs are provided for up to 33 2-level factors. The catalogues are complete, excluding resolution IV designs without 5-letter words, because these do not add value for a search for unblocked clear designs. The previous package version 1.0 with complete catalogues up to 24 runs (24 runs and a namespace added later) can be downloaded from the authors website.
Maintained by Ulrike Groemping. Last updated 1 years ago.
1.95 score 1 scripts 1 dependentscran
ttScreening:Genome-Wide DNA Methylation Sites Screening by Use of Training and Testing Samples
A screening process utilizing training and testing samples to filter out uninformative DNA methylation sites. Surrogate variables (SVs) of DNA methylation are included in the filtering process to explain unknown factor effects.
Maintained by Meredith Ray. Last updated 6 years ago.
1.85 score
kapelner
ICEbox:Individual Conditional Expectation Plot Toolbox
Implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist.
Maintained by Adam Kapelner. Last updated 3 years ago.
1.72 score 52 scriptsugroempi
RcmdrPlugin.DoE:R Commander Plugin for (Industrial) Design of Experiments
Provides a platform-independent GUI for design of experiments. The package is implemented as a plugin to the R-Commander, which is a more general graphical user interface for statistics in R based on tcl/tk. DoE functionality can be accessed through the menu Design that is added to the R-Commander menus.
Maintained by Ulrike Groemping. Last updated 5 months ago.
3 stars 1.70 score 3 scripts
michelmeulders
plfm:Probabilistic Latent Feature Analysis
Functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data.
Maintained by Michel Meulders. Last updated 1 years ago.
1.43 score 27 scriptscran
miCoPTCM:Promotion Time Cure Model with Mis-Measured Covariates
Fits Semiparametric Promotion Time Cure Models, taking into account (using a corrected score approach or the SIMEX algorithm) or not the measurement error in the covariates, using a backfitting approach to maximize the likelihood.
Maintained by Aurelie Bertrand. Last updated 4 years ago.
1.30 score
wjzhong
mDAG:Inferring Causal Network from Mixed Observational Data Using a Directed Acyclic Graph
Learning a mixed directed acyclic graph based on both continuous and categorical data.
Maintained by Wujuan Zhong. Last updated 6 years ago.
1.30 score 5 scripts
pavlomozharovskyi
curveDepth:Tukey Curve Depth and Distance in the Space of Curves
Data recorded as paths or trajectories may be suitably described by curves, which are independent of their parametrization. For the space of such curves, the package provides functionalities for reading curves, sampling points on curves, calculating distance between curves and for computing Tukey curve depth of a curve w.r.t. to a bundle of curves. For details see Lafaye De Micheaux, Mozharovskyi, and Vimond (2019) <arXiv:1901.00180>.
Maintained by Pavlo Mozharovskyi. Last updated 6 years ago.
1.30 score 20 scripts
jackkuipers
Bestie:Bayesian Estimation of Intervention Effects
An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary or continuous data. First, parameters are estimated or sampled for the DAG and then interventions on each node (variable) are propagated through the network (do-calculus). Both exact computation (for continuous data or for binary data up to around 20 variables) and Monte Carlo schemes (for larger binary networks) are implemented.
Maintained by Jack Kuipers. Last updated 3 years ago.
1.00 score 3 scriptscran
RcmdrPlugin.TeachStat:R Commander Plugin for Teaching Statistical Methods
R Commander plugin for teaching statistical methods. It adds a new menu for making easier the teaching of the main concepts about the main statistical methods.
Maintained by Manuel A. Mosquera Rodríguez. Last updated 1 years ago.
1.00 score
sangkyustat
DiSSMod:Fitting Sample Selection Models for Discrete Response Variables
Tools to fit sample selection models in case of discrete response variables, through a parametric formulation which represents a natural extension of the well-known Heckman selection model are provided in the package. The response variable can be of Bernoulli, Poisson or Negative Binomial type. The sample selection mechanism allows to choose among a Normal, Logistic or Gumbel distribution.
Maintained by Sang Kyu Lee. Last updated 6 years ago.
1.00 scorepavlomozharovskyi
TukeyRegion:Tukey Region and Median
Tukey regions are polytopes in the Euclidean space, viz. upper-level sets of the Tukey depth function on given data. The bordering hyperplanes of a Tukey region are computed as well as its vertices, facets, centroid, and volume. In addition, the Tukey median set, which is the non-empty Tukey region having highest depth level, and its barycenter (= Tukey median) are calculated. Tukey regions are visualized in dimension two and three. For details see Liu, Mosler, and Mozharovskyi (2019, <doi:10.1080/10618600.2018.1546595>). See file LICENSE.note for additional license information.
Maintained by Pavlo Mozharovskyi. Last updated 2 years ago.
1.00 scorecran
ssrm.logmer:Sample Size Determination for Longitudinal Designs with Binary Outcome
Provides the necessary sample size for a longitudinal study with binary outcome in order to attain a pre-specified power while strictly maintaining the Type I error rate. Kapur K, Bhaumik R, Tang XC, Hur K, Reda DJ, Bhaumik D (2014) <doi:10.1002/sim.6203>.
Maintained by Kush Kapur. Last updated 7 years ago.
1.00 scorecran
flexmsm:A General Framework for Flexible Multi-State Survival Modelling
A general estimation framework for multi-state Markov processes with flexible specification of the transition intensities. The log-transition intensities can be specified through Generalised Additive Models which allow for virtually any type of covariate effect. Elementary specifications such as time-homogeneous processes and simple parametric forms are also supported. There are no limitations on the type of process one can assume, with both forward and backward transitions allowed and virtually any number of states.
Maintained by Alessia Eletti. Last updated 9 months ago.
1.00 score
pariya
pcgen:Reconstruction of Causal Networks for Data with Random Genetic Effects
Implements the pcgen algorithm, which is a modified version of the standard pc-algorithm, with specific conditional independence tests and modified orientation rules. pcgen extends the approach of Valente et al. (2010) <doi:10.1534/genetics.109.112979> with reconstruction of direct genetic effects.
Maintained by Pariya Behrouzi. Last updated 6 years ago.
1.00 scorecran
adamethods:Archetypoid Algorithms and Anomaly Detection
Collection of several algorithms to obtain archetypoids with small and large databases, and with both classical multivariate data and functional data (univariate and multivariate). Some of these algorithms also allow to detect anomalies (outliers). Please see Vinue and Epifanio (2020) <doi:10.1007/s11634-020-00412-9>.
Maintained by Guillermo Vinue. Last updated 5 years ago.
1.00 score
johnjsl7
IAcsSPCR:Data and Functions for "An Intro. to Accept. Samp. & SPC/R"
Contains data frames and functions used in the book "An Introduction to Acceptance Sampling and SPC with R". This book is available electronically at <https://bookdown.org/>. A physical copy will be published by CRC Press.
Maintained by John Lawson. Last updated 4 years ago.
1.00 score 7 scriptscran
dGAselID:Genetic Algorithm with Incomplete Dominance for Feature Selection
Feature selection from high dimensional data using a diploid genetic algorithm with Incomplete Dominance for genotype to phenotype mapping and Random Assortment of chromosomes approach to recombination.
Maintained by Nicolae Teodor Melita. Last updated 8 years ago.
1 stars 1.00 score
casua1statistician
BRBVS:Variable Selection and Ranking in Copula Survival Models Affected by General Censoring Scheme
Performs variable selection and ranking based on several measures for the class of copula survival model(s) in high dimensional domain. The package is based on the class of copula survival model(s) implemented in the 'GJRM' package.
Maintained by Danilo Petti. Last updated 9 months ago.
1.00 score
florine-greciet
HSPOR:Hidden Smooth Polynomial Regression for Rupture Detection
Several functions that allow by different methods to infer a piecewise polynomial regression model under regularity constraints, namely continuity or differentiability of the link function. The implemented functions are either specific to data with two regimes, or generic for any number of regimes, which can be given by the user or learned by the algorithm. A paper describing all these methods will be submitted soon. The reference will be added to this file as soon as available.
Maintained by Florine Greciet. Last updated 6 years ago.
1.00 score