Surface topography calculations of Dirichlet's normal energy, relief index, surface slope, and orientation patch count for teeth using scans of enamel caps. Importantly, for the relief index and orientation patch count calculations to work, the scanned tooth files must be oriented with the occlusal plane parallel to the x and y axes, and perpendicular to the z axis. The files should also be simplified, and smoothed in some other software prior to uploading into R.

The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) <doi:10.1111/j.1750-2659.2012.00422.x>, <doi:10.1111/irv.12330>, allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week.

Our approach uses a mixture of multilayer stochastic block models to group co-membership matrices with similar information into components and to partition observations into different clusters. See De Santiago (2023, ISBN: 978-2-87587-088-9).

An ensemble classifier for multiclass classification. This is a novel classifier that natively works as an ensemble. It projects data on a large number of matrices, and uses very simple classifiers on each of these projections. The results are then combined, ideally via Dempster-Shafer Calculus.

Several classes for moment-based models are defined. The classes are defined for moment conditions derived from a single equation or a system of equations. The conditions can also be expressed as functions or formulas. Several methods are also offered to facilitate the development of different estimation techniques. The methods that are currently provided are the Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), for single equations and systems of equation, and the Generalized Empirical Likelihood (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>). Some work is being done to add tools to deal with weak and/or many instruments. This includes K-Class estimators (Limited Information Maximum Likelihood and Fuller), Anderson and Rubin statistic test, etc.

Computes matching algorithms quickly using Rcpp. Implements the Gale-Shapley Algorithm to compute the stable matching for two-sided markets, such as the stable marriage problem and the college-admissions problem. Implements Irving's Algorithm for the stable roommate problem. Implements the top trading cycle algorithm for the indivisible goods trading problem.

This package provides a toolbox to train a single sample classifier that uses in-sample feature relationships. The relationships are represented as feature1 < feature2 (e.g. gene1 < gene2). We provide two options to go with. First is based on switchBox package which uses Top-score pairs algorithm. Second is a novel implementation based on random forest algorithm. For simple problems we recommend to use one-vs-rest using TSP option due to its simplicity and for being easy to interpret. For complex problems RF performs better. Both lines filter the features first then combine the filtered features to make the list of all the possible rules (i.e. rule1: feature1 < feature2, rule2: feature1 < feature3, etc...). Then the list of rules will be filtered and the most important and informative rules will be kept. The informative rules will be assembled in an one-vs-rest model or in an RF model. We provide a detailed description with each function in this package to explain the filtration and training methodology in each line. Reference: Marzouka & Eriksson (2021) <doi:10.1093/bioinformatics/btab088>.

Prediction of behaviour from movement characteristics using observation and random forest for the analyses of movement data in ecology. From movement information (speed, bearing...) the model predicts the observed behaviour (movement, foraging...) using random forest. The model can then extrapolate behavioural information to movement data without direct observation of behaviours. The specificity of this method relies on the derivation of multiple predictor variables from the movement data over a range of temporal windows. This procedure allows to capture as much information as possible on the changes and variations of movement and ensures the use of the random forest algorithm to its best capacity. The method is very generic, applicable to any set of data providing movement data together with observation of behaviour.

Characterization of a mid-summer drought (MSD) with precipitation based statistics. The MSD is a phenomenon of decreased rainfall during a typical rainy season. It is a feature of rainfall in much of Central America and is also found in other locations, typically those with a Mediterranean climate. Details on the metrics are in Maurer et al. (2022) <doi:10.5194/hess-26-1425-2022>.

This grants the functionality of the Maxar Geospatial Platform (MGP) Streaming API. It can search for images using the WFS method. It can Download images using WMS WMTS. It can also Download a full resolution image.

This package provides a comprehensive suite for assessing multivariate normality using six statistical tests (Mardia, Henzeâ Zirkler, Henzeâ Wagner, Royston, Doornikâ Hansen, Energy). Also includes univariate diagnostics, bivariate density visualization, robust outlier detection, power transformations (e.g., Boxâ Cox, Yeoâ Johnson), and imputation strategies ("mean", "median", "mice") for handling missing data. Bootstrap resampling is supported for selected tests to improve p-value accuracy in small samples. Diagnostic plots are available via both ggplot2 and interactive plotly visualizations. See Korkmaz et al. (2014) <https://journal.r-project.org/articles/RJ-2014-031/RJ-2014-031.pdf>.

This package provides tools for cleaning, processing, and preparing microbiome sequencing data (e.g., 16S rRNA) for downstream analysis. Supports CSV, TXT, and Excel file formats. The main function, ezclean(), automates microbiome data transformation, including format validation, transposition, numeric conversion, and metadata integration. It also handles taxonomic levels efficiently, resolves duplicated taxa entries, and outputs a well-structured, analysis-ready dataset. The companion functions ezstat() run statistical tests and summarize results, while ezviz() produces publication-ready visualizations.

Integrating morphological modeling with machine learning to support structured decision-making (e.g., in management and consulting). The package enumerates a morphospace of feasible configurations and uses random forests to estimate class probabilities over that space, bridging deductive model exploration with empirical validation. It includes utilities for factorizing inputs, model training, morphospace construction, and an interactive shiny app for scenario exploration.

This package provides a framework for deconvolution, alignment and postprocessing of 1-dimensional (1d) nuclear magnetic resonance (NMR) spectra, resulting in a data matrix of aligned signal integrals. The deconvolution part uses the algorithm described in Koh et al. (2009) <doi:10.1016/j.jmr.2009.09.003>. The alignment part is based on functions from the speaq package, described in Beirnaert et al. (2018) <doi:10.1371/journal.pcbi.1006018> and Vu et al. (2011) <doi:10.1186/1471-2105-12-405>. A detailed description and evaluation of an early version of the package, MetaboDecon1D v0.2.2', can be found in Haeckl et al. (2021) <doi:10.3390/metabo11070452>.

This package performs Monte Carlo hypothesis tests, allowing a couple of different sequential stopping boundaries. For example, a truncated sequential probability ratio test boundary (Fay, Kim and Hachey, 2007 <DOI:10.1198/106186007X257025>) and a boundary proposed by Besag and Clifford, 1991 <DOI:10.1093/biomet/78.2.301>. Gives valid p-values and confidence intervals on p-values.

Distance between multivariate Cauchy distributions, as presented by N. Bouhlel and D. Rousseau (2022) <doi:10.3390/e24060838>. Manipulation of multivariate Cauchy distributions.

Extends the base classes and methods of caret package for integration of base learners. The user can input the number of different base learners, and specify the final learner, along with the train-validation-test data partition split ratio. The predictions on the unseen new data is the resultant of the ensemble meta-learning <https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/> of the heterogeneous learners aimed to reduce the generalization error in the predictive models. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.

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>.

This package provides tools for the integration, visualisation, and modelling of spatial epidemiological data using the method described in Azeez, A., & Noel, C. (2025). Predictive Modelling and Spatial Distribution of Pancreatic Cancer in Africa Using Machine Learning-Based Spatial Model <doi:10.5281/zenodo.16529986> and <doi:10.5281/zenodo.16529016>. It facilitates the analysis of geographic health data by combining modern spatial mapping tools with advanced machine learning (ML) algorithms. mlspatial enables users to import and pre-process shapefile and associated demographic or disease incidence data, generate richly annotated thematic maps, and apply predictive models, including Random Forest, XGBoost', and Support Vector Regression, to identify spatial patterns and risk factors. It is suited for spatial epidemiologists, public health researchers, and GIS analysts aiming to uncover hidden geographic patterns in health-related outcomes and inform evidence-based interventions.

Interfaces the Python library zuko implementing Masked Autoregressive Flows. See Rozet, Divo and Schnake (2023) <doi:10.5281/zenodo.7625672> and Papamakarios, Pavlakou and Murray (2017) <doi:10.48550/arXiv.1705.07057>.

To assist biological researchers in assembling taxonomically and marker focused molecular sequence data sets. MACER accepts a list of genera as a user input and uses NCBI-GenBank and BOLD as resources to download and assemble molecular sequence datasets. These datasets are then assembled by marker, aligned, trimmed, and cleaned. The use of this package allows the publication of specific parameters to ensure reproducibility. The MACER package has four core functions and an example run through using all of these functions can be found in the associated repository <https://github.com/rgyoung6/MACER_example>.

Simple tools to perform mixture optimization based on the desirability package by Max Kuhn. It also provides a plot routine using ggplot2 and patchwork'.

An R port of the margins command from Stata', which can be used to calculate marginal (or partial) effects from model objects.

This package provides a suite of convenience functions for generating US state and county thematic maps using datasets from the MazamaSpatialUtils package.