In the omics data association studies, it is common to conduct the p-value corrections to control the false significance. Beyond the P-value corrections, E-value is recently studied to facilitate multiple testing correction based on V. Vovk and R. Wang (2021) <doi:10.1214/20-AOS2020>. This package provides E-value calculation for DNA methylation data and RNA-seq data. Currently, five data formats are supported: DNA methylation levels using DMR detection tools (BiSeq, DMRfinder, MethylKit, Metilene and other DNA methylation tools) and RNA-seq data. The relevant references are listed below: Katja Hebestreit and Hans-Ulrich Klein (2022) <doi:10.18129/B9.bioc.BiSeq>; Altuna Akalin et.al (2012) <doi:10.18129/B9.bioc.methylKit>.

Computing functional traits-based distances between pairs of species for species gathered in assemblages allowing to build several functional spaces. The package allows to compute functional diversity indices assessing the distribution of species (and of their dominance) in a given functional space for each assemblage and the overlap between assemblages in a given functional space, see: Chao et al. (2018) <doi:10.1002/ecm.1343>, Maire et al. (2015) <doi:10.1111/geb.12299>, Mouillot et al. (2013) <doi:10.1016/j.tree.2012.10.004>, Mouillot et al. (2014) <doi:10.1073/pnas.1317625111>, Ricotta and Szeidl (2009) <doi:10.1016/j.tpb.2009.10.001>. Graphical outputs are included. Visit the mFD website for more information, documentation and examples.

Model infectious disease dynamics in populations with multiple subgroups having different vaccination rates, transmission characteristics, and contact patterns. Calculate final and intermediate outbreak sizes, form age-structured contact models with automatic fetching of U.S. census data, and explore vaccination scenarios with an interactive shiny dashboard for a model with two subgroups, as described in Nguyen et al. (2024) <doi:10.1016/j.jval.2024.03.039> and Duong et al. (2026) <doi:10.1093/ofid/ofaf695.217>.

Analysis of annual average ocean water level time series from long (minimum length 80 years) individual records, providing improved estimates of trend (mean sea level) and associated real-time velocities and accelerations. Improved trend estimates are based on Singular Spectrum Analysis methods. Various gap-filling options are included to accommodate incomplete time series records. The package also contains a forecasting module to consider the implication of user defined quantum of sea level rise between the end of the available historical record and the year 2100. A wide range of screen and pdf plotting options are available in the package.

Generates Muller plot from parental/genealogy/phylogeny information and population/abundance/frequency dynamics data. Muller plots are plots which combine information about succession of different OTUs (genotypes, phenotypes, species, ...) and information about dynamics of their abundances (populations or frequencies) over time. They are powerful and fascinating tools to visualize evolutionary dynamics. They may be employed also in study of diversity and its dynamics, i.e. how diversity emerges and how changes over time. They are called Muller plots in honor of Hermann Joseph Muller which used them to explain his idea of Muller's ratchet (Muller, 1932, American Naturalist). A big difference between Muller plots and normal box plots of abundances is that a Muller plot depicts not only the relative abundances but also succession of OTUs based on their genealogy/phylogeny/parental relation. In a Muller plot, horizontal axis is time/generations and vertical axis represents relative abundances of OTUs at the corresponding times/generations. Different OTUs are usually shown with polygons with different colors and each OTU originates somewhere in the middle of its parent area in order to illustrate their succession in evolutionary process. To generate a Muller plot one needs the genealogy/phylogeny/parental relation of OTUs and their abundances over time. MullerPlot package has the tools to generate Muller plots which clearly depict the origin of successors of OTUs.

This package contains functions for performing Mokken scale analysis on test and questionnaire data. It includes an automated item selection algorithm, and various checks of model assumptions.

This package provides tools for univariate and multivariate generalized linear models with model averaging and null model technique.

Monte Carlo simulation is a stochastic method computing trajectories of photons in media. Surface backscattering is performing calculations in semi-infinite media and summarizing photon flux leaving the surface. This simulation is modeling the optical measurement of diffuse reflectance using an incident light beam. The semi-infinite media is considered to have flat surface. Media, typically biological tissue, is described by four optical parameters: absorption coefficient, scattering coefficient, anisotropy factor, refractive index. The media is assumed to be homogeneous. Computational parameters of the simulation include: number of photons, radius of incident light beam, lowest photon energy threshold, intensity profile (halo) radius, spatial resolution of intensity profile. You can find more information and validation in the Open Access paper. Laszlo Baranyai (2020) <doi:10.1016/j.mex.2020.100958>.

This package provides a variety of association tests for microbiome data analysis including Quasi-Conditional Association Tests (QCAT) described in Tang Z.-Z. et al.(2017) <doi:10.1093/bioinformatics/btw804> and Zero-Inflated Generalized Dirichlet Multinomial (ZIGDM) tests described in Tang Z.-Z. & Chen G. (2017, submitted).

This package implements the method of successive dichotomizations by Bradley and Massof (2018) <doi:10.1371/journal.pone.0206106>, which estimates item measures, person measures and ordered rating category thresholds given ordinal rating scale data.

This package provides a lightweight framework for model selection and hyperparameter tuning in R. The package offers intuitive tools for grid search, cross-validation, and combined grid search with cross-validation that work seamlessly with virtually any modeling package. Designed for flexibility and ease of use, it standardizes tuning workflows while remaining fully compatible with a wide range of model interfaces and estimation functions.

Analysis of musical scales (& modes, grooves, etc.) in the vein of Sherrill 2025 <doi:10.1215/00222909-11595194>. The initials MCT in the package title refer to the article's title: "Modal Color Theory." Offers support for conventional musical pitch class set theory as developed by Forte (1973, ISBN: 9780300016109) and David Lewin (1987, ISBN: 9780300034936), as well as for the continuous geometries of Callender, Quinn, & Tymoczko (2008) <doi:10.1126/science.1153021>. Identifies structural properties of scales and calculates derived values (sign vector, color number, brightness ratio, etc.). Creates plots such as "brightness graphs" which visualize these properties.

When choosing proper variable selection methods, it is important to consider the uncertainty of a certain method. The model confidence bound for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level. A good variable selection method is the one of which the model confidence bound under a certain confidence level has the shortest width. When visualizing the variability of model selection and comparing different model selection procedures, model uncertainty curve is a good graphical tool. A good variable selection method is the one of whose model uncertainty curve will tend to arch towards the upper left corner. This function aims to obtain the model confidence bound and draw the model uncertainty curve of certain single model selection method under a coverage rate equal or little higher than user-given confidential level. About what model confidence bound is and how it work please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403. <DOI:10.1111/biom.13024>. Besides, flare is needed only you apply the SQRT or LAD method ('mcb totally has 8 methods). Although flare has been archived by CRAN, you can still get it in <https://CRAN.R-project.org/package=flare> and the latest version is useful for mcb'.

Uses memory-mapping to enable the random access of elements of a text file of characters separated by characters as if it were a simple R(cpp) matrix.

This package provides a multi action button for usage in shiny applications.

This package provides tools to analysis of experiments having two or more quantitative explanatory variables and one quantitative dependent variable. Experiments can be without repetitions or with a statistical design (Hair JF, 2016) <ISBN: 13: 978-0138132637>. Pacote para uma analise de experimentos havendo duas ou mais variaveis explicativas quantitativas e uma variavel dependente quantitativa. Os experimentos podem ser sem repeticoes ou com delineamento estatistico (Hair JF, 2016) <ISBN: 13: 978-0138132637>.

This package contains functions for mapping odds ratios, hazard ratios, or other effect estimates using individual-level data such as case-control study data, using generalized additive models (GAMs) or Cox models for smoothing with a two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures) while adjusting linearly for confounding variables, using methods described by Kelsall and Diggle (1998), Webster at al. (2006), and Bai et al. (2020). Includes convenient functions for mapping point estimates and confidence intervals, efficient control sampling, and permutation tests for the null hypothesis that the two-dimensional predictor is not associated with the outcome variable (adjusting for confounders).

Code to support a systems biology research program from inception through publication. The methods focus on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations. The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques. As a result, we encourage suggested additions to the package, both methodological and graphical.

Datasets, constants, conversion factors, and utilities for MArine', Riverine', Estuarine', LAcustrine and Coastal science. The package contains among others: (1) chemical and physical constants and datasets, e.g. atomic weights, gas constants, the earths bathymetry; (2) conversion factors (e.g. gram to mol to liter, barometric units, temperature, salinity); (3) physical functions, e.g. to estimate concentrations of conservative substances, gas transfer and diffusion coefficients, the Coriolis force and gravity; (4) thermophysical properties of the seawater, as from the UNESCO polynomial or from the more recent derivation based on a Gibbs function.

The mlrMBO package can ordinarily not be used for optimization within mlr3', because of incompatibilities of their respective class systems. mlrintermbo offers a compatibility interface that provides mlrMBO as an mlr3tuning Tuner object, for tuning of machine learning algorithms within mlr3', as well as a bbotk Optimizer object for optimization of general objective functions using the bbotk black box optimization framework. The control parameters of mlrMBO are faithfully reproduced as a paradox ParamSet'.

The target of margaret is help to extract data from Minciencias to analyze scientific production in Colombia.

Build multiscalar territorial analysis based on various contexts.

This package provides a collection of functions for processing and analyzing metabolite data. The namesake function mrbin() converts 1D or 2D Nuclear Magnetic Resonance data into a matrix of values suitable for further data analysis and performs basic processing steps in a reproducible way. Negative values, a common issue in such data, can be replaced by positive values (<doi:10.1021/acs.jproteome.0c00684>). All used parameters are stored in a readable text file and can be restored from that file to enable exact reproduction of the data at a later time. The function fia() ranks features according to their impact on classifier models, especially artificial neural network models.

Employing artificial intelligence to convert data analysis questions into executable code, explanations, and algorithms. The self-correction feature ensures the generated code is optimized for performance and accuracy. mergen features a user-friendly chat interface, enabling users to interact with the AI agent and extract valuable insights from their data effortlessly.