This package provides a simple tool allowing users to easily and dynamically explore or document a data set using a tree structure.

Uses the metadata information stored in metacore objects to check and build metadata associated columns.

Check concordance of a vector of mutation impacts with standard dictionaries such as Sequence Ontology (SO) <http://www.sequenceontology.org/>, Mutation Annotation Format (MAF) <https://docs.gdc.cancer.gov/Encyclopedia/pages/Mutation_Annotation_Format_TCGAv2/> or Prediction and Annotation of Variant Effects (PAVE) <https://github.com/hartwigmedical/hmftools/tree/master/pave>. It enables conversion between SO/PAVE and MAF terms and selection of the most severe consequence where multiple ampersand (&) delimited impacts are given.

There are two functions-meta2d and meta3d for detecting rhythmic signals from time-series datasets. For analyzing time-series datasets without individual information, meta2d is suggested, which could incorporates multiple methods from ARSER, JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For analyzing time-series datasets with individual information, meta3d is suggested, which takes use of any one of these three methods to analyze time-series data individual by individual and gives out integrated values based on analysis result of each individual.

Computes the degrees of freedom of the lasso, elastic net, generalized elastic net and adaptive lasso based on the generalized path seeking algorithm. The optimal model can be selected by model selection criteria including Mallows Cp, bias-corrected AIC (AICc), generalized cross validation (GCV) and BIC.

Estimation/multiple imputation programs for mixed categorical and continuous data.

Regression models can be fitted for multiple outcomes simultaneously. This package computes estimates of parameters across fitted models and returns the matrix of asymptotic covariance. Various applications of this package, including CUPED (Controlled Experiments Utilizing Pre-Experiment Data), multiple comparison adjustment, are illustrated.

Transfer learning, as a prevailing technique in computer sciences, aims to improve the performance of a target model by leveraging auxiliary information from heterogeneous source data. We provide novel tools for multi-source transfer learning under statistical models based on model averaging strategies, including linear regression models, partially linear models. Unlike existing transfer learning approaches, this method integrates the auxiliary information through data-driven weight assignments to avoid negative transfer. This is the first package for transfer learning based on the optimal model averaging frameworks, providing efficient implementations for practitioners in multi-source data modeling. The details are described in Hu and Zhang (2023) <https://jmlr.org/papers/v24/23-0030.html>.

This package creates and manages a PostgreSQL database suitable for storing fisheries data and aggregating ready for use within a Gadget <https://gadget-framework.github.io/gadget2/> model. See <https://mareframe.github.io/mfdb/> for more information.

This package provides a flexible framework for power analysis using Monte Carlo simulation for settings in which considerations of the correlations between predictors are important. Users can set up a data generative model that preserves dependence structures among predictors given existing data (continuous, binary, or ordinal). Users can also generate power curves to assess the trade-offs between sample size, effect size, and power of a design. This package includes several statistical models common in environmental mixtures studies. For more details and tutorials, see Nguyen et al. (2022) <arXiv:2209.08036>.

Implementation of custom tidymodels metrics for multi-class prediction models with a single negative class. Currently are implemented macro-average sensitivity and specificity as in Mortaz, Ebrahim (2020) "Imbalance accuracy metric for model selection in multi-class imbalance classification problemsâ <doi:10.1016/j.knosys.2020.106490> and a generalized weighted Youden index as in Li, D.L., Shen F., Yin Y., Peng J.X and Chen P.Y. (2013) â Weighted Youden index and its two-independent-sample comparison based on weighted sensitivity and specificityâ <doi:10.3760/cma.j.issn.0366-6999.20123102>.

This package implements a methodology for the design and analysis of dose-response studies that combines aspects of multiple comparison procedures and modeling approaches (Bretz, Pinheiro and Branson, 2005, Biometrics 61, 738-748, <doi: 10.1111/j.1541-0420.2005.00344.x>). The package provides tools for the analysis of dose finding trials as well as a variety of tools necessary to plan a trial to be conducted with the MCP-Mod methodology. Please note: The MCPMod package will not be further developed, all future development of the MCP-Mod methodology will be done in the DoseFinding R-package.

Collection of functions to compute within-study covariances for different effect sizes, data visualization, and single and multiple imputations for missing data. Effect sizes include correlation (r), mean difference (MD), standardized mean difference (SMD), log odds ratio (logOR), log risk ratio (logRR), and risk difference (RD).

Two functions for simulating the solution of initial value problems of the form g'(x) = G(x, g) with g(x0) = g0. One is an acceptance-rejection method. The other is a method based on the Mean Value Theorem.

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.

Consistent user interface to the most common regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, complemented with a handful of auxiliary functions, such as variable importance and a tuning function for the parameters.

This package provides methods for high-dimensional multi-view learning based on the multi-view stacking (MVS) framework. For technical details on the MVS and stacked penalized logistic regression (StaPLR) methods see Van Loon, Fokkema, Szabo, & De Rooij (2020) <doi:10.1016/j.inffus.2020.03.007> and Van Loon et al. (2022) <doi:10.3389/fnins.2022.830630>.

An S4 update of the mefa package using sparse matrices for enhanced efficiency. Sparse array-like objects are supported via lists of sparse matrices.

Includes functions for conducting univariate and multivariate meta-analysis. This includes the estimation of the asymptotic variance-covariance matrix of effect sizes. For more details see Becker (1992) <doi:10.2307/1165128>, Cooper, Hedges, and Valentine (2019) <doi:10.7758/9781610448864>, and Schmid, Stijnen, and White (2020) <doi:10.1201/9781315119403>.

Estimation equations are from a variety of sources and associated error estimation.

Algorithms for multivariate outlier detection when missing values occur. Algorithms are based on Mahalanobis distance or data depth. Imputation is based on the multivariate normal model or uses nearest neighbour donors. The algorithms take sample designs, in particular weighting, into account. The methods are described in Bill and Hulliger (2016) <doi:10.17713/ajs.v45i1.86>.

An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.

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.

Extends multiverse package (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021) <doi:10.31219/osf.io/yfbwm>, which allows users perform to create explorable multiverse analysis in R. This extension provides an additional level of abstraction to the multiverse package with the aim of creating user friendly syntax to researchers, educators, and students in statistics. The mverse syntax is designed to allow piping and takes hints from the tidyverse grammar. The package allows users to define and inspect multiverse analysis using familiar syntax in R.