Showing 90 of total 90 results (show query)
indrajeetpatil
ggstatsplot:'ggplot2' Based Plots with Statistical Details
Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) <doi:10.21105/joss.03236>.
Maintained by Indrajeet Patil. Last updated 1 months ago.
bayes-factorsdatasciencedatavizeffect-sizeggplot-extensionhypothesis-testingnon-parametric-statisticsregression-modelsstatistical-analysis
2.1k stars 14.46 score 3.0k scripts 1 dependents
singmann
afex:Analysis of Factorial Experiments
Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), automatically aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex_plot() provides a high-level interface for interaction or one-way plots using ggplot2, combining raw data and model estimates. afex uses type 3 sums of squares as default (imitating commercial statistical software).
Maintained by Henrik Singmann. Last updated 7 months ago.
124 stars 14.43 score 1.4k scripts 15 dependents
bioc
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 dependentsbioc
Maaslin2:"Multivariable Association Discovery in Population-scale Meta-omics Studies"
MaAsLin2 is comprehensive R package for efficiently determining multivariable association between clinical metadata and microbial meta'omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods. MaAsLin2 is the next generation of MaAsLin.
Maintained by Lauren McIver. Last updated 5 months ago.
metagenomicssoftwaremicrobiomenormalizationbiobakerybioconductordifferential-abundance-analysisfalse-discovery-ratemultiple-covariatespublicrepeated-measurestools
133 stars 11.03 score 532 scripts 3 dependentsindrajeetpatil
statsExpressions:Tidy Dataframes and Expressions with Statistical Details
Utilities for producing dataframes with rich details for the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for 'ggstatsplot'. References: Patil (2021) <doi:10.21105/joss.03236>.
Maintained by Indrajeet Patil. Last updated 1 months ago.
bayesian-inferencebayesian-statisticscontingency-tablecorrelationeffectsizemeta-analysisparametricrobustrobust-statisticsstatistical-detailsstatistical-teststidy
312 stars 10.92 score 146 scripts 2 dependentsbioc
ANCOMBC:Microbiome differential abudance and correlation analyses with bias correction
ANCOMBC is a package containing differential abundance (DA) and correlation analyses for microbiome data. Specifically, the package includes Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2), Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), and Analysis of Composition of Microbiomes (ANCOM) for DA analysis, and Sparse Estimation of Correlations among Microbiomes (SECOM) for correlation analysis. Microbiome data are typically subject to two sources of biases: unequal sampling fractions (sample-specific biases) and differential sequencing efficiencies (taxon-specific biases). Methodologies included in the ANCOMBC package are designed to correct these biases and construct statistically consistent estimators.
Maintained by Huang Lin. Last updated 17 days ago.
differentialexpressionmicrobiomenormalizationsequencingsoftwareancomancombcancombc2correlationdifferential-abundance-analysissecom
120 stars 10.79 score 406 scripts 1 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 dependents
jinseob2kim
jstable:Create Tables from Different Types of Regression
Create regression tables from generalized linear model(GLM), generalized estimating equation(GEE), generalized linear mixed-effects model(GLMM), Cox proportional hazards model, survey-weighted generalized linear model(svyglm) and survey-weighted Cox model results for publication.
Maintained by Jinseob Kim. Last updated 5 days ago.
28 stars 10.08 score 199 scripts 1 dependents
pitakakariki
simr:Power Analysis for Generalised Linear Mixed Models by Simulation
Calculate power for generalised linear mixed models, using simulation. Designed to work with models fit using the 'lme4' package. Described in Green and MacLeod, 2016 <doi:10.1111/2041-210X.12504>.
Maintained by Peter Green. Last updated 2 years ago.
70 stars 9.82 score 756 scripts
jamovi
jmv:The 'jamovi' Analyses
A suite of common statistical methods such as descriptives, t-tests, ANOVAs, regression, correlation matrices, proportion tests, contingency tables, and factor analysis. This package is also useable from the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org> for more information).
Maintained by Jonathon Love. Last updated 1 months ago.
60 stars 9.48 score 440 scripts
arcaldwell49
Superpower:Simulation-Based Power Analysis for Factorial Designs
Functions to perform simulations of ANOVA designs of up to three factors. Calculates the observed power and average observed effect size for all main effects and interactions in the ANOVA, and all simple comparisons between conditions. Includes functions for analytic power calculations and additional helper functions that compute effect sizes for ANOVA designs, observed error rates in the simulations, and functions to plot power curves. Please see Lakens, D., & Caldwell, A. R. (2021). "Simulation-Based Power Analysis for Factorial Analysis of Variance Designs". <doi:10.1177/2515245920951503>.
Maintained by Aaron Caldwell. Last updated 3 months ago.
67 stars 9.03 score 106 scripts 1 dependentsjinseob2kim
jsmodule:'RStudio' Addins and 'Shiny' Modules for Medical Research
'RStudio' addins and 'Shiny' modules for descriptive statistics, regression and survival analysis.
Maintained by Jinseob Kim. Last updated 16 days ago.
medicalrstudio-addinsshinyshiny-modulesstatistics
21 stars 8.69 score 61 scripts
psychbruce
bruceR:Broadly Useful Convenient and Efficient R Functions
Broadly useful convenient and efficient R functions that bring users concise and elegant R data analyses. This package includes easy-to-use functions for (1) basic R programming (e.g., set working directory to the path of currently opened file; import/export data from/to files in any format; print tables to Microsoft Word); (2) multivariate computation (e.g., compute scale sums/means/... with reverse scoring); (3) reliability analyses and factor analyses; (4) descriptive statistics and correlation analyses; (5) t-test, multi-factor analysis of variance (ANOVA), simple-effect analysis, and post-hoc multiple comparison; (6) tidy report of statistical models (to R Console and Microsoft Word); (7) mediation and moderation analyses (PROCESS); and (8) additional toolbox for statistics and graphics.
Maintained by Han-Wu-Shuang Bao. Last updated 4 days ago.
anovadata-analysisdata-sciencelinear-modelslinear-regressionmultilevel-modelsstatisticstoolbox
176 stars 8.16 score 316 scripts 3 dependentsbioc
maaslin3:"Refining and extending generalized multivariate linear models for meta-omic association discovery"
MaAsLin 3 refines and extends generalized multivariate linear models for meta-omicron association discovery. It finds abundance and prevalence associations between microbiome meta-omics features and complex metadata in population-scale epidemiological studies. The software includes multiple analysis methods (including support for multiple covariates, repeated measures, and ordered predictors), filtering, normalization, and transform options to customize analysis for your specific study.
Maintained by William Nickols. Last updated 8 days ago.
metagenomicssoftwaremicrobiomenormalizationmultiplecomparison
33 stars 8.16 score 34 scriptsbioc
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 8 days ago.
rnaseqgeneexpressiondifferentialexpressionbatcheffectqualitycontrolregressiongenesetenrichmentgeneregulationepigeneticsfunctionalgenomicstranscriptomicsnormalizationsinglecellpreprocessingsequencingimmunooncologysoftwarecpp
12 stars 8.14 score 128 scriptsbioc
MSstatsPTM:Statistical Characterization of Post-translational Modifications
MSstatsPTM provides general statistical methods for quantitative characterization of post-translational modifications (PTMs). Supports DDA, DIA, SRM, and tandem mass tag (TMT) labeling. Typically, the analysis involves the quantification of PTM sites (i.e., modified residues) and their corresponding proteins, as well as the integration of the quantification results. MSstatsPTM provides functions for summarization, estimation of PTM site abundance, and detection of changes in PTMs across experimental conditions.
Maintained by Devon Kohler. Last updated 4 months ago.
immunooncologymassspectrometryproteomicssoftwaredifferentialexpressiononechanneltwochannelnormalizationqualitycontrolpost-translational-modificationcpp
10 stars 8.03 score 36 scripts 2 dependentsbioc
spicyR:Spatial analysis of in situ cytometry data
The spicyR package provides a framework for performing inference on changes in spatial relationships between pairs of cell types for cell-resolution spatial omics technologies. spicyR consists of three primary steps: (i) summarizing the degree of spatial localization between pairs of cell types for each image; (ii) modelling the variability in localization summary statistics as a function of cell counts and (iii) testing for changes in spatial localizations associated with a response variable.
Maintained by Ellis Patrick. Last updated 29 days ago.
singlecellcellbasedassaysspatial
9 stars 8.02 score 57 scripts 1 dependents
apariciojohan
agriutilities:Utilities for Data Analysis in Agriculture
Utilities designed to make the analysis of field trials easier and more accessible for everyone working in plant breeding. It provides a simple and intuitive interface for conducting single and multi-environmental trial analysis, with minimal coding required. Whether you're a beginner or an experienced user, 'agriutilities' will help you quickly and easily carry out complex analyses with confidence. With built-in functions for fitting Linear Mixed Models, 'agriutilities' is the ideal choice for anyone who wants to save time and focus on interpreting their results. Some of the functions require the R package 'asreml' for the 'ASReml' software, this can be obtained upon purchase from 'VSN' international <https://vsni.co.uk/software/asreml-r/>.
Maintained by Johan Aparicio. Last updated 3 months ago.
18 stars 7.46 score 88 scripts 1 dependentsbioc
SpatialDecon:Deconvolution of mixed cells from spatial and/or bulk gene expression data
Using spatial or bulk gene expression data, estimates abundance of mixed cell types within each observation. Based on "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data", Danaher (2022). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data.
Maintained by Maddy Griswold. Last updated 5 months ago.
immunooncologyfeatureextractiongeneexpressiontranscriptomicsspatial
37 stars 7.41 score 58 scriptsapariciojohan
flexFitR:Flexible Non-Linear Least Square Model Fitting
Provides tools for flexible non-linear least squares model fitting using general-purpose optimization techniques. The package supports a variety of optimization algorithms, including those provided by the 'optimx' package, making it suitable for handling complex non-linear models. Features include parallel processing support via the 'future' and 'foreach' packages, comprehensive model diagnostics, and visualization capabilities. Implements methods described in Nash and Varadhan (2011, <doi:10.18637/jss.v043.i09>).
Maintained by Johan Aparicio. Last updated 3 hours ago.
3 stars 7.31 score 77 scriptsbioc
GeomxTools:NanoString GeoMx Tools
Tools for NanoString Technologies GeoMx Technology. Package provides functions for reading in DCC and PKC files based on an ExpressionSet derived object. Normalization and QC functions are also included.
Maintained by Maddy Griswold. Last updated 5 months ago.
geneexpressiontranscriptioncellbasedassaysdataimporttranscriptomicsproteomicsmrnamicroarrayproprietaryplatformsrnaseqsequencingexperimentaldesignnormalizationspatial
7.11 score 239 scripts 3 dependents
tylermorganwall
skpr:Design of Experiments Suite: Generate and Evaluate Optimal Designs
Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible. For details, see Morgan-Wall et al. (2021) <doi:10.18637/jss.v099.i01>.
Maintained by Tyler Morgan-Wall. Last updated 26 days ago.
design-of-experimentslinear-modelslinear-regressionmonte-carlooptimal-designspowersplit-plot-designssurvival-analysiscpp
118 stars 6.89 score 35 scriptsbioc
SIAMCAT:Statistical Inference of Associations between Microbial Communities And host phenoTypes
Pipeline for Statistical Inference of Associations between Microbial Communities And host phenoTypes (SIAMCAT). A primary goal of analyzing microbiome data is to determine changes in community composition that are associated with environmental factors. In particular, linking human microbiome composition to host phenotypes such as diseases has become an area of intense research. For this, robust statistical modeling and biomarker extraction toolkits are crucially needed. SIAMCAT provides a full pipeline supporting data preprocessing, statistical association testing, statistical modeling (LASSO logistic regression) including tools for evaluation and interpretation of these models (such as cross validation, parameter selection, ROC analysis and diagnostic model plots).
Maintained by Jakob Wirbel. Last updated 5 months ago.
immunooncologymetagenomicsclassificationmicrobiomesequencingpreprocessingclusteringfeatureextractiongeneticvariabilitymultiplecomparisonregression
6.72 score 147 scripts
pavlakrotka
NCC:Simulation and Analysis of Platform Trials with Non-Concurrent Controls
Design and analysis of flexible platform trials with non-concurrent controls. Functions for data generation, analysis, visualization and running simulation studies are provided. The implemented analysis methods are described in: Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>, Saville et al. (2022) <doi:10.1177/17407745221112013> and Schmidli et al. (2014) <doi:10.1111/biom.12242>.
Maintained by Pavla Krotka. Last updated 23 days ago.
clinical-trialsplatform-trialssimulationstatistical-inferencejagscpp
5 stars 6.64 score 29 scriptsbioc
lisaClust:lisaClust: Clustering of Local Indicators of Spatial Association
lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution.
Maintained by Ellis Patrick. Last updated 4 months ago.
singlecellcellbasedassaysspatial
3 stars 6.64 score 48 scriptsbioc
MSstatsTMT:Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling
The package provides statistical tools for detecting differentially abundant proteins in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling. It provides multiple functionalities, including aata visualization, protein quantification and normalization, and statistical modeling and inference. Furthermore, it is inter-operable with other data processing tools, such as Proteome Discoverer, MaxQuant, OpenMS and SpectroMine.
Maintained by Devon Kohler. Last updated 28 days ago.
immunooncologymassspectrometryproteomicssoftware
6.60 score 35 scripts 3 dependents
bioc
coMethDMR:Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies
coMethDMR identifies genomic regions associated with continuous phenotypes by optimally leverages covariations among CpGs within predefined genomic regions. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first without using any outcome information. Next, coMethDMR tests association between methylation within the sub-region and continuous phenotype using a random coefficient mixed effects model, which models both variations between CpG sites within the region and differential methylation simultaneously.
Maintained by Fernanda Veitzman. Last updated 5 months ago.
dnamethylationepigeneticsmethylationarraydifferentialmethylationgenomewideassociation
7 stars 6.47 score 42 scriptsbioc
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 8 days ago.
rnaseqgeneexpressiongenesetenrichmentdifferentialexpressionbatcheffectqualitycontrolregressionepigeneticsfunctionalgenomicstranscriptomicsnormalizationpreprocessingmicroarrayimmunooncologysoftware
6.39 score 91 scripts 1 dependentsbioc
MSstatsShiny:MSstats GUI for Statistical Anaylsis of Proteomics Experiments
MSstatsShiny is an R-Shiny graphical user interface (GUI) integrated with the R packages MSstats, MSstatsTMT, and MSstatsPTM. It provides a point and click end-to-end analysis pipeline applicable to a wide variety of experimental designs. These include data-dependedent acquisitions (DDA) which are label-free or tandem mass tag (TMT)-based, as well as DIA, SRM, and PRM acquisitions and those targeting post-translational modifications (PTMs). The application automatically saves users selections and builds an R script that recreates their analysis, supporting reproducible data analysis.
Maintained by Devon Kohler. Last updated 5 months ago.
immunooncologymassspectrometryproteomicssoftwareshinyappsdifferentialexpressiononechanneltwochannelnormalizationqualitycontrolgui
15 stars 6.31 score 4 scripts
dongwenluo
predictmeans:Predicted Means for Linear and Semiparametric Models
Providing functions to diagnose and make inferences from various linear models, such as those obtained from 'aov', 'lm', 'glm', 'gls', 'lme', 'lmer', 'glmmTMB' and 'semireg'. Inferences include predicted means and standard errors, contrasts, multiple comparisons, permutation tests, adjusted R-square and graphs.
Maintained by Dongwen Luo. Last updated 12 months ago.
2 stars 6.26 score 152 scripts 2 dependents
myles-lewis
glmmSeq:General Linear Mixed Models for Gene-Level Differential Expression
Using mixed effects models to analyse longitudinal gene expression can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, and are less optimal for analysing longitudinal data. This package provides negative binomial and Gaussian mixed effects models to fit gene expression and other biological data across repeated samples. This is particularly useful for investigating changes in RNA-Sequencing gene expression between groups of individuals over time, as described in: Rivellese, F., Surace, A. E., Goldmann, K., Sciacca, E., Cubuk, C., Giorli, G., ... Lewis, M. J., & Pitzalis, C. (2022) Nature medicine <doi:10.1038/s41591-022-01789-0>.
Maintained by Myles Lewis. Last updated 3 months ago.
bioinformaticsdifferential-gene-expressiongene-expressionglmmmixed-modelstranscriptomics
20 stars 6.13 score 45 scriptsbioc
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
jwiley
multilevelTools:Multilevel and Mixed Effects Model Diagnostics and Effect Sizes
Effect sizes, diagnostics and performance metrics for multilevel and mixed effects models. Includes marginal and conditional 'R2' estimates for linear mixed effects models based on Johnson (2014) <doi:10.1111/2041-210X.12225>.
Maintained by Joshua F. Wiley. Last updated 8 days ago.
4 stars 5.74 score 136 scriptsbioc
MSstatsLiP:LiP Significance Analysis in shotgun mass spectrometry-based proteomic experiments
Tools for LiP peptide and protein significance analysis. Provides functions for summarization, estimation of LiP peptide abundance, and detection of changes across conditions. Utilizes functionality across the MSstats family of packages.
Maintained by Devon Kohler. Last updated 5 months ago.
immunooncologymassspectrometryproteomicssoftwaredifferentialexpressiononechanneltwochannelnormalizationqualitycontrolcpp
7 stars 5.62 score 5 scripts
jasonmoy28
psycModel:Integrated Toolkit for Psychological Analysis and Modeling in R
A beginner-friendly R package for modeling in psychology or related field. It allows fitting models, plotting, checking goodness of fit, and model assumption violations all in one place. It also produces beautiful and easy-to-read output.
Maintained by Jason Moy. Last updated 7 months ago.
4 stars 5.59 score 14 scriptsbioc
GeoDiff:Count model based differential expression and normalization on GeoMx RNA data
A series of statistical models using count generating distributions for background modelling, feature and sample QC, normalization and differential expression analysis on GeoMx RNA data. The application of these methods are demonstrated by example data analysis vignette.
Maintained by Nicole Ortogero. Last updated 5 months ago.
geneexpressiondifferentialexpressionnormalizationopenblascppopenmp
8 stars 5.51 score 9 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
ashenoy-cmbi
grafify:Easy Graphs for Data Visualisation and Linear Models for ANOVA
Easily explore data by plotting graphs with a few lines of code. Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways, for example, by choosing from colour blind-friendly palettes (12 discreet, 3 continuous and 2 divergent palettes). Use the simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs, and fit non-linear outcomes as a generalised additive model (gam) using mgcv(). Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans()). Also includes small datasets for practising code and teaching basics before users move on to more complex designs. See vignettes for details on usage <https://grafify.shenoylab.com/>. Citation: <doi:10.5281/zenodo.5136508>.
Maintained by Avinash R Shenoy. Last updated 18 days ago.
ggplot2linear-modelspost-hoc-comparisonsstatisticsvignettes
48 stars 5.31 score 107 scriptsbioc
smoppix:Analyze Single Molecule Spatial Omics Data Using the Probabilistic Index
Test for univariate and bivariate spatial patterns in spatial omics data with single-molecule resolution. The tests implemented allow for analysis of nested designs and are automatically calibrated to different biological specimens. Tests for aggregation, colocalization, gradients and vicinity to cell edge or centroid are provided.
Maintained by Stijn Hawinkel. Last updated 2 months ago.
transcriptomicsspatialsinglecellcpp
1 stars 5.10 score 4 scripts
lindanab
mecor:Measurement Error Correction in Linear Models with a Continuous Outcome
Covariate measurement error correction is implemented by means of regression calibration by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331), efficient regression calibration by Spiegelman D, Carroll RJ & Kipnis V (2001) <doi:10.1002/1097-0258(20010115)20:1%3C139::AID-SIM644%3E3.0.CO;2-K> and maximum likelihood estimation by Bartlett JW, Stavola DBL & Frost C (2009) <doi:10.1002/sim.3713>. Outcome measurement error correction is implemented by means of the method of moments by Buonaccorsi JP (2010, ISBN:1420066560) and efficient method of moments by Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI & Freedman LS (2014) <doi:10.1002/sim.7011>. Standard error estimation of the corrected estimators is implemented by means of the Delta method by Rosner B, Spiegelman D & Willett WC (1990) <doi:10.1093/oxfordjournals.aje.a115715> and Rosner B, Spiegelman D & Willett WC (1992) <doi:10.1093/oxfordjournals.aje.a116453>, the Fieller method described by Buonaccorsi JP (2010, ISBN:1420066560), and the Bootstrap by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331).
Maintained by Linda Nab. Last updated 3 years ago.
linear-modelsmeasurement-errorstatistics
6 stars 5.07 score 13 scripts
psychbruce
ChineseNames:Chinese Name Database 1930-2008
A database of Chinese surnames and Chinese given names (1930-2008). This database contains nationwide frequency statistics of 1,806 Chinese surnames and 2,614 Chinese characters used in given names, covering about 1.2 billion Han Chinese population (96.8% of the Han Chinese household-registered population born from 1930 to 2008 and still alive in 2008). This package also contains a function for computing multiple features of Chinese surnames and Chinese given names for scientific research (e.g., name uniqueness, name gender, name valence, and name warmth/competence).
Maintained by Han-Wu-Shuang Bao. Last updated 4 days ago.
big-datachinesechinese-namechinese-namesdatabasenamenames
152 stars 4.88 score 6 scripts
angelgar
voxel:Mass-Univariate Voxelwise Analysis of Medical Imaging Data
Functions for the mass-univariate voxelwise analysis of medical imaging data that follows the NIfTI <http://nifti.nimh.nih.gov> format.
Maintained by Angel Garcia de la Garza. Last updated 5 years ago.
9 stars 4.85 score 78 scripts
helmut01
replicateBE:Average Bioequivalence with Expanding Limits (ABEL)
Performs comparative bioavailability calculations for Average Bioequivalence with Expanding Limits (ABEL). Implemented are 'Method A' / 'Method B' and the detection of outliers. If the design allows, assessment of the empiric Type I Error and iteratively adjusting alpha to control the consumer risk. Average Bioequivalence - optionally with a tighter (narrow therapeutic index drugs) or wider acceptance range (South Africa: Cmax) - is implemented as well.
Maintained by Helmut Schütz. Last updated 3 years ago.
9 stars 4.65 score 10 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
MBECS:Evaluation and correction of batch effects in microbiome data-sets
The Microbiome Batch Effect Correction Suite (MBECS) provides a set of functions to evaluate and mitigate unwated noise due to processing in batches. To that end it incorporates a host of batch correcting algorithms (BECA) from various packages. In addition it offers a correction and reporting pipeline that provides a preliminary look at the characteristics of a data-set before and after correcting for batch effects.
Maintained by Michael Olbrich. Last updated 5 months ago.
batcheffectmicrobiomereportwritingvisualizationnormalizationqualitycontrol
4 stars 4.60 score 4 scripts
jacob-long
dpm:Dynamic Panel Models Fit with Maximum Likelihood
Implements the dynamic panel models described by Allison, Williams, and Moral-Benito (2017 <doi:10.1177/2378023117710578>) in R. This class of models uses structural equation modeling to specify dynamic (lagged dependent variable) models with fixed effects for panel data. Additionally, models may have predictors that are only weakly exogenous, i.e., are affected by prior values of the dependent variable. Options also allow for random effects, dropping the lagged dependent variable, and a number of other specification choices.
Maintained by Jacob A. Long. Last updated 1 years ago.
16 stars 4.55 score 44 scripts
vjilmari
multid:Multivariate Difference Between Two Groups
Estimation of multivariate differences between two groups (e.g., multivariate sex differences) with regularized regression methods and predictive approach. See Lönnqvist & Ilmarinen (2021) <doi:10.1007/s11109-021-09681-2> and Ilmarinen et al. (2023) <doi:10.1177/08902070221088155>. Includes tools that help in understanding difference score reliability, predictions of difference score variables, conditional intra-class correlations, and heterogeneity of variance estimates. Package development was supported by the Academy of Finland research grant 338891.
Maintained by Ville-Juhani Ilmarinen. Last updated 7 months ago.
4.48 score 6 scripts
dcourvoisier
doremi:Dynamics of Return to Equilibrium During Multiple Inputs
Provides models to fit the dynamics of a regulated system experiencing exogenous inputs. The underlying models use differential equations and linear mixed-effects regressions to estimate the coefficients of the equation. With them, the functions can provide an estimated signal. The package provides simulation and analysis functions and also print, summary, plot and predict methods, adapted to the function outputs, for easy implementation and presentation of results.
Maintained by Mongin Denis. Last updated 3 years ago.
4.48 score 25 scripts 1 dependentsbioc
MMUPHin:Meta-analysis Methods with Uniform Pipeline for Heterogeneity in Microbiome Studies
MMUPHin is an R package for meta-analysis tasks of microbiome cohorts. It has function interfaces for: a) covariate-controlled batch- and cohort effect adjustment, b) meta-analysis differential abundance testing, c) meta-analysis unsupervised discrete structure (clustering) discovery, and d) meta-analysis unsupervised continuous structure discovery.
Maintained by Siyuan MA. Last updated 5 months ago.
metagenomicsmicrobiomebatcheffect
4.44 score 46 scriptsbioc
Macarron:Prioritization of potentially bioactive metabolic features from epidemiological and environmental metabolomics datasets
Macarron is a workflow for the prioritization of potentially bioactive metabolites from metabolomics experiments. Prioritization integrates strengths of evidences of bioactivity such as covariation with a known metabolite, abundance relative to a known metabolite and association with an environmental or phenotypic indicator of bioactivity. Broadly, the workflow consists of stratified clustering of metabolic spectral features which co-vary in abundance in a condition, transfer of functional annotations, estimation of relative abundance and differential abundance analysis to identify associations between features and phenotype/condition.
Maintained by Sagun Maharjan. Last updated 5 months ago.
sequencingmetabolomicscoveragefunctionalpredictionclustering
4.41 score 13 scripts
annechao
MF.beta4:Measuring Ecosystem Multi-Functionality and Its Decomposition
Provide simple functions to (i) compute a class of multi-functionality measures for a single ecosystem for given function weights, (ii) decompose gamma multi-functionality for pairs of ecosystems and K ecosystems (K can be greater than 2) into a within-ecosystem component (alpha multi-functionality) and an among-ecosystem component (beta multi-functionality). In each case, the correlation between functions can be corrected for. Based on biodiversity and ecosystem function data, this software also facilitates graphics for assessing biodiversity-ecosystem functioning relationships across scales.
Maintained by Anne Chao. Last updated 4 months ago.
4.40 score 3 scriptsbioc
SpatialOmicsOverlay:Spatial Overlay for Omic Data from Nanostring GeoMx Data
Tools for NanoString Technologies GeoMx Technology. Package to easily graph on top of an OME-TIFF image. Plotting annotations can range from tissue segment to gene expression.
Maintained by Maddy Griswold. Last updated 5 months ago.
geneexpressiontranscriptioncellbasedassaysdataimporttranscriptomicsproteomicsproprietaryplatformsrnaseqspatialdatarepresentationvisualizationopenjdk
4.30 score 8 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 scripts
chrisaberson
pwr2ppl:Power Analyses for Common Designs (Power to the People)
Statistical power analysis for designs including t-tests, correlations, multiple regression, ANOVA, mediation, and logistic regression. Functions accompany Aberson (2019) <doi:10.4324/9781315171500>.
Maintained by Chris Aberson. Last updated 3 years ago.
17 stars 4.16 score 17 scriptsbioc
ReducedExperiment:Containers and tools for dimensionally-reduced -omics representations
Provides SummarizedExperiment-like containers for storing and manipulating dimensionally-reduced assay data. The ReducedExperiment classes allow users to simultaneously manipulate their original dataset and their decomposed data, in addition to other method-specific outputs like feature loadings. Implements utilities and specialised classes for the application of stabilised independent component analysis (sICA) and weighted gene correlation network analysis (WGCNA).
Maintained by Jack Gisby. Last updated 3 months ago.
geneexpressioninfrastructuredatarepresentationsoftwaredimensionreductionnetworkbioconductor-packagebioinformaticsdimensionality-reduction
3 stars 4.13 score 8 scripts
myaseen208
StroupGLMM:R Codes and Datasets for Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup
R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models Modern Concepts, Methods and Applications, CRC Press.
Maintained by Muhammad Yaseen. Last updated 6 months ago.
13 stars 4.11 score 2 scriptspsychbruce
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 group 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; and (4) a set of training methods to locally train (static) word vectors from text corpora, including 'Word2Vec' <doi:10.48550/arXiv.1301.3781>, 'GloVe' <doi:10.3115/v1/D14-1162>, and 'FastText' <doi:10.48550/arXiv.1607.04606>.
Maintained by Han-Wu-Shuang Bao. Last updated 4 days ago.
bertcosine-similarityfasttextglovegptlanguage-modelnatural-language-processingnlppretrained-modelspsychologysemantic-analysistext-analysistext-miningtsneword-embeddingsword-vectorsword2vec
22 stars 4.04 score 10 scripts
roberthyde
stabiliser:Stabilising Variable Selection
A stable approach to variable selection through stability selection and the use of a permutation-based objective stability threshold. Lima et al (2021) <doi:10.1038/s41598-020-79317-8>, Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>.
Maintained by Robert Hyde. Last updated 1 years ago.
4.00 score 4 scripts
medianasoft
MedianaDesigner:Power and Sample Size Calculations for Clinical Trials
Efficient simulation-based power and sample size calculations are supported for a broad class of late-stage clinical trials. The following modules are included in the package: Adaptive designs with data-driven sample size or event count re-estimation, Adaptive designs with data-driven treatment selection, Adaptive designs with data-driven population selection, Optimal selection of a futility stopping rule, Event prediction in event-driven trials, Adaptive trials with response-adaptive randomization (experimental module), Traditional trials with multiple objectives (experimental module). Traditional trials with cluster-randomized designs (experimental module).
Maintained by Alex Dmitrienko. Last updated 2 years ago.
20 stars 3.79 score 31 scriptsmyaseen208
eda4treeR:Experimental Design and Analysis for Tree Improvement
Provides data sets and R Codes for E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement, CSIRO Publishing.
Maintained by Muhammad Yaseen. Last updated 7 months ago.
3.62 score 28 scripts 1 dependents
chiliubio
mecoturn:Decipher Microbial Turnover along a Gradient
Two pipelines are provided to study microbial turnover along a gradient, including the beta diversity and microbial abundance change. The 'betaturn' class consists of the steps of community dissimilarity matrix generation, matrix conversion, differential test and visualization. The workflow of 'taxaturn' class includes the taxonomic abundance calculation, abundance transformation, abundance change summary, statistical analysis and visualization. Multiple statistical approaches can contribute to the analysis of microbial turnover.
Maintained by Chi Liu. Last updated 6 months ago.
3 stars 3.48 score 6 scripts
sistm
vici:Vaccine Induced Cellular Immunogenicity with Bivariate Modeling
A shiny app for accurate estimation of vaccine induced immunogenicity with bivariate linear modeling. Method is detailed in: Lhomme, Hejblum, Lacabaratz, Wiedemann, Lelievre, Levy, Thiebaut & Richert (2019). Submitted.
Maintained by Boris Hejblum. Last updated 9 months ago.
1 stars 3.30 score 3 scripts
juliengamartin
pamm:Power Analysis for Random Effects in Mixed Models
Simulation functions to assess or explore the power of a dataset to estimates significant random effects (intercept or slope) in a mixed model. The functions are based on the "lme4" and "lmerTest" packages.
Maintained by Julien Martin. Last updated 2 years ago.
3 stars 3.18 score 7 scriptsmyaseen208
gvcR:Genotypic Variance Components
Functionalities to compute model based genetic components i.e. genotypic variance, phenotypic variance and heritability for given traits of different genotypes from replicated data using methodology explained by Burton, G. W. & Devane, E. H. (1953) (<doi:10.2134/agronj1953.00021962004500100005x>) and Allard, R.W. (2010, ISBN:8126524154).
Maintained by Muhammad Yaseen. Last updated 6 months ago.
3.00 score 4 scriptsmyaseen208
VetResearchLMM:Linear Mixed Models - An Introduction with Applications in Veterinary Research
R Codes and Datasets for Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Maintained by Muhammad Yaseen. Last updated 7 years ago.
2.95 score 18 scripts
cran
fullfact:Full Factorial Breeding Analysis
We facilitate the analysis of full factorial mating designs with mixed-effects models. The package contains six vignettes containing detailed examples.
Maintained by Aimee Lee Houde. Last updated 1 years ago.
2.78 scorecran
JustifyAlpha:Justifying Alpha Levels for Hypothesis Tests
Functions to justify alpha levels for statistical hypothesis tests by avoiding Lindley's paradox, or by minimizing or balancing error rates. For more information about the package please read the following: Maier & Lakens (2021) <doi:10.31234/osf.io/ts4r6>).
Maintained by Maximilian Maier. Last updated 4 years ago.
2.70 score
tswanson222
modnets:Modeling Moderated Networks
Methods for modeling moderator variables in cross-sectional, temporal, and multi-level networks. Includes model selection techniques and a variety of plotting functions. Implements the methods described by Swanson (2020) <https://www.proquest.com/openview/d151ab6b93ad47e3f0d5e59d7b6fd3d3>.
Maintained by Trevor Swanson. Last updated 3 years ago.
2.70 score 6 scripts
cran
rosetta:Parallel Use of Statistical Packages in Teaching
When teaching statistics, it can often be desirable to uncouple the content from specific software packages. To ease such efforts, the Rosetta Stats website (<https://rosettastats.com>) allows comparing analyses in different packages. This package is the companion to the Rosetta Stats website, aiming to provide functions that produce output that is similar to output from other statistical packages, thereby facilitating 'software-agnostic' teaching of statistics.
Maintained by Gjalt-Jorn Peters. Last updated 2 years ago.
2.70 score
david-hervas
repmod:Create Report Table from Different Objects
Tools for generating descriptives and report tables for different models, data.frames and tables and exporting them to different formats.
Maintained by David Hervas Marin. Last updated 2 months ago.
2.60 score 6 scripts
melff
iimm:Improved Infrence for Multilevel Models with Few Clusters
Support for inference about linear mixed effects models estimated with 'lmer' from package 'lme4' using a Student's t-distribution with degrees of freedom determined by the m-l-1 heuristic or the Kenward-Roger method.
Maintained by Martin Elff. Last updated 6 years ago.
5 stars 2.40 scorecran
rADA:Statistical Analysis and Cut-Point Determination of Immunoassays
Systematically transform immunoassay data, evaluate if the data is normally distributed, and pick the right method for cut point determination based on that evaluation. This package can also produce plots that are needed for reports, so data analysis and visualization can be done easily.
Maintained by Emma Gail. Last updated 4 years ago.
1 stars 2.30 score
tylerpittman
BiostatsUHNplus:Nested Data Summary, Adverse Events and REDCap
Tools and code snippets for summarizing nested data, adverse events and REDCap study information.
Maintained by Tyler Pittman. Last updated 2 months ago.
2.18 score 6 scripts
rned
agriTutorial:Tutorial Analysis of Some Agricultural Experiments
Example software for the analysis of data from designed experiments, especially agricultural crop experiments. The basics of the analysis of designed experiments are discussed using real examples from agricultural field trials. A range of statistical methods using a range of R statistical packages are exemplified . The experimental data is made available as separate data sets for each example and the R analysis code is made available as example code. The example code can be readily extended, as required.
Maintained by Rodney Edmondson. Last updated 6 years ago.
1 stars 2.00 score 8 scriptscran
AOboot:Bootstrapping in Different One-Way and Two-Way ANOVA
To address the violation of the assumption of normally distributed variables, researchers frequently employ bootstrapping. Building upon established packages for R (Sigmann et al. (2024) <doi:10.32614/CRAN.package.afex>, Lenth (2024) <doi:10.32614/CRAN.package.emmeans>), we provide bootstrapping functions to approximate a normal distribution of the parameter estimates for between-subject, within-subject, and mixed one-way and two-way ANOVA.
Maintained by Christian Blötner. Last updated 8 days ago.
1.48 scorecran
MicrobiomeStat:Statistical Methods for Microbiome Compositional Data
A suite of methods for powerful and robust microbiome data analysis addressing zero-inflation, phylogenetic structure and compositional effects (Zhou et al. (2022)<doi:10.1186/s13059-022-02655-5>). The methods can be applied to the analysis of other (high-dimensional) compositional data arising from sequencing experiments.
Maintained by Jun Chen. Last updated 1 years ago.
1.48 score 1 dependents
jphughes9
swCRTdesign:Stepped Wedge Cluster Randomized Trial (SW CRT) Design
A set of tools for examining the design and analysis aspects of stepped wedge cluster randomized trials (SW CRT) based on a repeated cross-sectional sampling scheme (Hussey MA and Hughes JP (2007) Contemporary Clinical Trials 28:182-191. <doi:10.1016/j.cct.2006.05.007>).
Maintained by Jim Hughes. Last updated 2 years ago.
1 stars 1.18 score 15 scripts
