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).

This package provides a suite of mixed-integer linear programming (MILP) model builders and solversâ including Gurobi', HiGHS', Symphony', GNU Linear Programming Kit (GLPK)', and lpSolve'â for automated test assembly (ATA) in multistage testing (MST). Offers filtering of decision variables through itemâ module eligibility and the application of explicit bounds to simplify the MILP model and accelerate the optimization process. Supports bottom up, top down, and hybrid assembly strategies; enemy-item and enemy-stimulus exclusions; stimulus all in/all out or partial selection; anchor item/stimulus specification; and item exposure control. Accommodates both single-objective and multi-objective optimization ('weighted sum', maximin', capped maximin', minimax', and goal programming'). Enables simultaneous assembly of multiple panels with item and stimulus content balancing and exposure control. Provides analytical evaluation of assembled MST performance within seconds. Includes tools for diagnosing infeasible optimization models by systematically identifying sources of infeasibility and reformulating models with slack variables to restore feasibility.Methods implemented in this package build on established work in optimal test assembly (van der Linden, 2005 <doi:10.1007/0-387-29054-0>), item-set constrained test assembly (van der Linden, 2000 <doi:10.1177/01466210022031697>), hybrid assembly (Xiong, 2018 <doi:10.1177/0146621618762739>), recursion-based analytic methods (Lim et al., 2021 <doi:10.1111/jedm.12276>), and classification evaluation (Rudner, 2000 <doi:10.7275/an9m-2035>; Rudner, 2005 <doi:10.7275/56a5-6b14>).

This package implements a general interface for model-based estimations for a wide variety of models, used in the computation of marginal means, contrast analysis and predictions. For a list of supported models, see insight::supported_models()'.

Flexible implementation of a structural change point detection algorithm for multivariate time series. It authorizes inclusion of trends, exogenous variables, and break test on the intercept or on the full vector autoregression system. Bai, Lumsdaine, and Stock (1998) <doi:10.1111/1467-937X.00051>.

Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning.

Generate the monotonic binning and perform the woe (weight of evidence) transformation for the logistic regression used in the consumer credit scorecard development. The woe transformation is a piecewise transformation that is linear to the log odds. For a numeric variable, all of its monotonic functional transformations will converge to the same woe transformation.

This package provides functions used for graphing in multivariate contexts. These functions are designed to support produce reasonable graphs with minimal input of graphing parameters. The motivation for these functions was to support students learning multivariate concepts and R - there may be other functions and packages better-suited to practical data analysis. For details about the ellipse methods see Johnson and Wichern (2007, ISBN:9780131877153).

This package implements the method to analyse weighted mobility networks or distribution networks as outlined in: Block, P., Stadtfeld, C., & Robins, G. (2022) <doi:10.1016/j.socnet.2021.08.003>. The purpose of the model is to analyse the structure of mobility, incorporating exogenous predictors pertaining to individuals and locations known from classical mobility analyses, as well as modelling emergent mobility patterns akin to structural patterns known from the statistical analysis of social networks. Version 2 runs the simulations in C++ (using Rcpp') and, besides stochastic approximation (Robbins-Monro), can estimate parameters with Geyer-Thompson MCMC maximum likelihood estimation.

This package provides readers for easy and consistent importing of Mouse Genome Informatics (MGI) report files: <https://www.informatics.jax.org/downloads/reports/index.html>. These data are provided by Baldarelli RM, Smith CL, Ringwald M, Richardson JE, Bult CJ, Mouse Genome Informatics Group (2024) <doi:10.1093/genetics/iyae031>.

Supports visual interpretation of hierarchical composite endpoints (HCEs). HCEs are complex constructs used as primary endpoints in clinical trials, combining outcomes of different types into ordinal endpoints, in which each patient contributes the most clinically important event (one and only one) to the analysis. See Karpefors M et al. (2022) <doi:10.1177/17407745221134949>.

This package provides methods for the analysis of how ecological drivers affect the multifunctionality of an ecosystem based on methods of Byrnes et al. 2016 <doi:10.1111/2041-210X.12143> and Byrnes et al. 2022 <doi:10.1101/2022.03.17.484802>. Most standard methods in the literature are implemented (see vignettes) in a tidy format.

This package provides a new method to implement clustering from multiple modality data of certain samples, the function M2SMjF() jointly factorizes multiple similarity matrices into a shared sub-matrix and several modality private sub-matrices, which is further used for clustering. Along with this method, we also provide function to calculate the similarity matrix and function to evaluate the best cluster number from the original data.

This package provides a set of tools to perform multiple versions of the Mobility Oriented-Parity metric. This multivariate analysis helps to characterize levels of dissimilarity between a set of conditions of reference and another set of conditions of interest. If predictive models are transferred to conditions different from those over which models were calibrated (trained), this metric helps to identify transfer conditions that differ substantially from those of calibration. These tools are implemented following principles proposed in Owens et al. (2013) <doi:10.1016/j.ecolmodel.2013.04.011>, and expanded to obtain more detailed results that aid in interpretation as in Cobos et al. (2024) <doi:10.21425/fob.17.132916>.

This package provides a set of functions, classes and methods for performing ABC and ABC/XYZ analyses, identifying overperforming, underperforming and constantly performing items, and plotting, analyzing as well as predicting the temporal development of items.

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.

This package performs (simultaneous) inferences for ratios of linear combinations of coefficients in the general linear model, linear mixed model, and for quantiles in a one-way layout. Multiple comparisons and simultaneous confidence interval estimations can be performed for ratios of treatment means in the normal one-way layout with homogeneous and heterogeneous treatment variances, according to Dilba et al. (2007) <https://cran.r-project.org/doc/Rnews/Rnews_2007-1.pdf> and Hasler and Hothorn (2008) <doi:10.1002/bimj.200710466>. Confidence interval estimations for ratios of linear combinations of linear model parameters like in (multiple) slope ratio and parallel line assays can be carried out. Moreover, it is possible to calculate the sample sizes required in comparisons with a control based on relative margins. For the simple two-sample problem, functions for a t-test for ratio-formatted hypotheses and the corresponding confidence interval are provided assuming homogeneous or heterogeneous group variances.

Download meteorological data from the Meteo-France public API Données climatologiques <https://portail-api.meteofrance.fr> and SYNOP open data archives <https://meteo.data.gouv.fr/datasets/686f8595b351c06a3a790867>. It provides functions to authenticate, list stations, retrieve station metadata, request data, download files, and import data directly into R'.

Solve scalar-on-function linear models, including generalized linear mixed effect model and quantile linear regression model, and bias correction estimation methods due to measurement error. Details about the measurement error bias correction methods, see Luan et al. (2023) <doi:10.48550/arXiv.2305.12624>, Tekwe et al. (2022) <doi:10.1093/biostatistics/kxac017>, Zhang et al. (2023) <doi:10.5705/ss.202021.0246>, Tekwe et al. (2019) <doi:10.1002/sim.8179>.

This package contains auxiliary routines for influx software. This packages is not intended to be used directly. Influx was published here: Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.

Efficient way to design and conduct psychological experiments for testing the performance of large language models. It simplifies the process of setting up experiments and data collection via language modelsâ API, facilitating a smooth workflow for researchers in the field of machine behaviour.

This package provides functions for multivariate and propensity score matching and for finding optimal balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance has been obtained are also provided. For details, see the paper by Jasjeet Sekhon (2007, <doi:10.18637/jss.v042.i07>).

This package provides a unified framework for fitting, predicting, and interpreting nonlinear relationships in single-level, multilevel, and longitudinal regression models. Flexible functional forms are supported using natural cubic splines ('splines'), B-splines ('splines'), and GAM smooths ('mgcv'). Supports two-way and nested clustering via lme4', automatic knot selection by AIC or BIC, multilevel R-squared decomposition (marginal and conditional R-squared of Nakagawa and Schielzeth (2013) <doi:10.1111/j.2041-210x.2012.00261.x>, extended to random slopes as in Johnson (2014) <doi:10.1111/2041-210X.12225>, with level-specific variance partitioning), a postestimation suite returning first and second derivatives with confidence bands, turning points and inflection regions, and a model comparison workflow contrasting linear, polynomial, and spline fits by AIC, BIC, and likelihood-ratio tests. Cluster heterogeneity in nonlinear effects is supported via random-slope spline terms. Any lme4 random-effects structure can be supplied, and population-averaged (marginal) curves are available for generalized linear mixed models.

Distance between multivariate generalised Gaussian distributions, as presented by N. Bouhlel and A. Dziri (2019) <doi:10.1109/LSP.2019.2915000>. Manipulation of multivariate generalised Gaussian distributions (methods presented by Gomez, Gomez-Villegas and Marin (1998) <doi:10.1080/03610929808832115> and Pascal, Bombrun, Tourneret and Berthoumieu (2013) <doi:10.1109/TSP.2013.2282909>).

This package provides a disciplined, lazy subset of dplyr semantics for MongoDB aggregation pipelines. Queries remain lazy until collect() and compile into MongoDB-native aggregation stages.