Estimate Multidimensional Poverty Index (MPI) measures from household survey microdata using the Alkire-Foster dual-cutoff counting method (Alkire and Foster, 2011). Load indicator specifications from CSV, Excel, JSON, or plain-text files; compute the headcount ratio (H), intensity (A), and MPI = H x A across any subgroup; and export results to formatted Excel reports. Supports complex survey designs â stratification, clustering, and probability weights â and optionally appends design-based standard errors and confidence intervals.

Functionalities for facilitating systematic reviews, data extractions, and meta-analyses. It includes a GUI (graphical user interface) to help screen the abstracts and titles of bibliographic data; tools to assign screening effort across multiple collaborators/reviewers and to assess inter- reviewer reliability; tools to help automate the download and retrieval of journal PDF articles from online databases; figure and image extractions from PDFs; web scraping of citations; automated and manual data extraction from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools to fill gaps in incomplete or missing study parameters; generation of random effects sizes for Hedges d, log response ratio, odds ratio, and correlation coefficients for Monte Carlo experiments; covariance equations for modelling dependencies among multiple effect sizes (e.g., effect sizes with a common control); and finally summaries that replicate analyses and outputs from widely used but no longer updated meta-analysis software (i.e., metawin). Funding for this package was supported by National Science Foundation (NSF) grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016) Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for R. Methods in Ecology and Evolution 7, 323-330 <doi:10.1111/2041-210X.12472>.

This package provides functions for calculating the point and interval estimates of the natural indirect effect (NIE), total effect (TE), and mediation proportion (MP), based on the product approach. We perform the methods considered in Cheng, Spiegelman, and Li (2021) Estimating the natural indirect effect and the mediation proportion via the product method.

User-friendly Shiny apps for designing and evaluating phase I cancer clinical trials, with the aim to estimate the maximum tolerated dose (MTD) of a novel drug, using a Bayesian decision procedure based on logistic regression.

Estimation of the survivor function for interval censored time-to-event data subject to misclassification using nonparametric maximum likelihood estimation, implementing the methods of Titman (2017) <doi:10.1007/s11222-016-9705-7>. Misclassification probabilities can either be specified as fixed or estimated. Models with time dependent misclassification may also be fitted.

Play and record games of minesweeper using a graphics device that supports event handling. Replay recorded games and save GIF animations of them. Based on classic minesweeper as detailed by Crow P. (1997) <https://minesweepergame.com/math/a-mathematical-introduction-to-the-game-of-minesweeper-1997.pdf>.

This package implements methods for processing a sample of (hard) clusterings, e.g. the MCMC output of a Bayesian clustering model. Among them are methods that find a single best clustering to represent the sample, which are based on the posterior similarity matrix or a relabelling algorithm.

This package provides methods for analyzing DNA methylation data via Most Recurrent Methylation Patterns (MRMPs). Supports cell-type annotation, spatial deconvolution, unsupervised clustering, and cancer cell-of-origin inference. Includes C-backed summaries for YAME â .cg/.cmâ files (overlap counts, log2 odds ratios, beta/depth aggregation), an XGBoost classifier, NNLS deconvolution, and plotting utilities. Scales to large spatial and single-cell methylomes and is robust to extreme sparsity.

Estimate diagnostic classification models (also called cognitive diagnostic models) with Stan'. Diagnostic classification models are confirmatory latent class models, as described by Rupp et al. (2010, ISBN: 978-1-60623-527-0). Automatically generate Stan code for the general loglinear cognitive diagnostic diagnostic model proposed by Henson et al. (2009) <doi:10.1007/s11336-008-9089-5> and other subtypes that introduce additional model constraints. Using the generated Stan code, estimate the model evaluate the model's performance using model fit indices, information criteria, and reliability metrics.

Generation of mock data from a real dataset using rank normal inverse transformation.

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.

This function allows to generate two biological conditions synthetic microarray dataset which has similar behavior to those currently observed with common platforms. User provides a subset of parameters. Available default parameters settings can be modified.

This package contains the data sets for the first and second editions of the textbook "Mathematical Modeling and Applied Calculus" by Joel Kilty and Alex M. McAllister. The first edition of the book was published by Oxford University Press in 2018 with ISBN-13: 978-019882472. The second edition is expected to be published in January 2027.

This package performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals (Beath, 2014 <doi:10.1002/jrsm.1114>).

This package contains a dataset of morphological and structural features of Medicinal LEAves (MedLEA)'. The features of each species is recorded by manually viewing the medicinal plant repository available at (<http://www.instituteofayurveda.org/plants/>). You can also download repository of leaf images of 1099 medicinal plants in Sri Lanka.

This package provides a fast, robust and easy-to-use calculation of multi-class classification evaluation metrics based on confusion matrix.

This package implements policy evaluation primitives from HM Treasury Magenta Book guidance (HM Treasury, 2020): theory of change and log-frame construction, evaluation planning and stakeholder mapping, power and minimum-detectable-effect calculations for randomised designs (including cluster and stepped-wedge designs following Hussey and Hughes (2007) <doi:10.1016/j.cct.2006.05.007> and Hemming et al. (2015) <doi:10.1136/bmj.h391>), Maryland Scientific Methods Scale ratings, structured confidence ratings, light-weight difference-in-differences and interrupted-time-series estimators ('Bernal et al. (2017) <doi:10.1093/ije/dyw098>) with cluster-robust standard errors ('Cameron and Miller (2015) <doi:10.3368/jhr.50.2.317>), pre-treatment balance checks ('Stuart (2010) <doi:10.1214/09-STS313>), and cost-effectiveness analysis (cost per outcome, incremental cost-effectiveness ratio, acceptability curves, incremental net benefit, quality-adjusted and disability-adjusted life years). Designed as the evaluation companion to the appraisal package greenbook'. Bundled rubric and reference tables carry vintage metadata for reproducibility.

This package provides a simple and trustworthy methodology for the analysis of misreported continuous time series using either a frequentist (bootstrap-based EM algorithm) or a Bayesian (MCMC via JAGS) approach. The frequentist method is described in Morina et al. (2021) <doi:10.1038/s41598-021-02620-5>. The Bayesian extension fits the same ARMA model with misreporting structure using a full posterior distribution, providing credible intervals and DIC for model comparison, as described in Morina et al. (2024) <doi:10.1101/2024.02.26.24303373>.

This package performs Bayesian meta-analysis, meta-regression and model-based meta-analysis using Stan'. Includes binomial-normal hierarchical models and option to use weakly informative priors for the heterogeneity parameter and the treatment effect parameter which are described in Guenhan, Roever, and Friede (2020) <doi:10.1002/jrsm.1370>.

Traditional methods typically detect breakpoints from individual signals, which means that when applied separately to multiple signals, the breakpoints are not aligned. However, this package implements a common breakpoint detection approach for multiple piecewise constant signals, resulting in increased detection sensitivity and specificity. By employing various techniques, optimal performance is ensured, and computation is accelerated. We hope that this package will be beneficial for researchers in signal processing, bioinformatics, economy, and other related fields. The segmentation(), lambda_estimator() functions are the main functions of this package.

Parametric modeling of M-quantile regression coefficient functions.

Includes support for Mapbox Navigation APIs, including directions, isochrones, and route optimization; the Search API for forward and reverse geocoding; the Maps API for interacting with Mapbox vector tilesets and visualizing Mapbox maps in R; and Mapbox Tiling Service and tippecanoe for generating map tiles. See <https://docs.mapbox.com/api/> for more information about the Mapbox APIs.

Fits fixed-, random-, or mixed-effects multivariate meta-analysis models using dynamic model estimates from each individual building on and extending Lee and Gates (2023) <doi:10.1080/00273171.2023.2229310>.

Converts NIfTI format T1/FL neuroimages into structured, high-dimensional 2D data frames with a focus on region of interest (ROI) based processing. The package incorporates the partition algorithm, which offers a flexible framework for agglomerative partitioning based on the Direct-Measure-Reduce approach. This method ensures that each reduced variable maintains a user-specified minimum level of information while remaining interpretable, as each maps uniquely to one variable in the reduced dataset. The partition framework is described in Millstein et al. (2020) <doi:10.1093/bioinformatics/btz661>. The package allows customization in variable selection, measurement of information loss, and data reduction methods for neuroimaging analysis and machine learning workflows.