Generate code for use with the Optical Mark Recognition free software Auto Multiple Choice (AMC). More specifically, this package provides functions that use as input the question and answer texts, and output the LaTeX code for AMC.

Data processing tools to compute the rectified, integrated and the averaged EMG. Routines for automatic detection of activation phases. A routine to compute and plot the ensemble average of the EMG. An EMG signal simulator for general purposes.

This package contains all the necessary tools to process audio recordings of various formats (e.g., WAV, WAC, MP3, ZC), filter noisy files, display audio signals, detect and extract automatically acoustic features for further analysis such as classification.

Density, distribution function, quantile function and random generation for the Generalized Binomial Distribution. Functions to compute the Clopper-Pearson Confidence Interval and the required sample size. Enhanced model for burn-in studies, where failures are tackled by countermeasures.

This package provides a collection of various R functions for the purpose of Luminescence dating data analysis. This includes, amongst others, data import, export, application of age models, curve deconvolution, sequence analysis and plotting of equivalent dose distributions.

This package provides a Shiny dashboard for exploring Reporters Without Borders (RWB) Press Freedom Index data from 2002 to the present. Combines RWB scores with United Nations M49 geographic classifications to enable comparisons across countries, regions, and time.

Includes functions implementing the conditionally optimal matching algorithm, which can be used to generate matched samples in designs with multiple groups. The algorithm is described in Nattino, Song and Lu (2022) <doi:10.1016/j.csda.2021.107364>.

Collection of stepwise procedures to conduct multiple hypotheses testing. The details of the stepwise algorithm can be found in Romano and Wolf (2007) <DOI:10.1214/009053606000001622> and Hsu, Kuan, and Yen (2014) <DOI:10.1093/jjfinec/nbu014>.

This package provides a plug-in for the text mining framework tm to support text mining in a distributed way. The package provides a convenient interface for handling distributed corpus objects based on distributed list objects.

Builds tables with customizable rows. Users can specify the type of data to use for each row, as well as how to handle missing data and the types of comparison tests to run on the table columns.

Implementation of the transformation of the Mean Opinion Scores (MOS) to be used before applying the rank based statistical techniques. The method and its necessity is described in: Babak Naderi, Sebastian Möller (2020) <arXiv:2004.11490>.

This package provides support for a variety of spatial data sources in tmap', including remote, tiled, and streaming formats. Enables the use of external vector and raster data without requiring full data import, facilitating efficient visualization workflows.

Organizational framework for web development in R including functions to serve static and dynamic content via HTTP methods, includes the html5 package to create HTML pages, and offers other utility functions for common tasks related to web development.

This package provides a library to find out what is still holding a reference to an object that should be dead. It is used to track down reference leaks in MNE-Python, PyVista and pyvistaqt.

This package provides functions for working with magnetic resonance images. It supports reading and writing of popular file formats (DICOM, Analyze, NIfTI-1, NIfTI-2, MGH); interactive and non-interactive visualization; flexible image manipulation; metadata and sparse image handling.

dominatRData is a data package useful for showcasing dominatR examples. dominatR is an R package for quantifying and visualizing feature dominance in datasets. dominatR makes use of entropy-based triangular projections and compositional comparison metrics.

This package provides functions to compute agricultural crop diversification indices for crop data across zones and years. The package implements widely used diversification and concentration measures including Herfindahl Index,Simpson Index, Entropy Index, Ogive Index, and Maximum Proportion Index.

This package provides the estimation algorithm to perform the demand estimation described in Berry, Levinsohn and Pakes (1995) <DOI:10.2307/2171802> . The routine uses analytic gradients and offers a large number of implemented integration methods and optimization routines.

Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

Computation of test statistics of independence between (continuous) innovations of time series. They can be used with stochastic volatility models and Hidden Markov Models (HMM). This improves the results in Duchesne, Ghoudi & Remillard (2012) <doi:10.1002/cjs.11141>.

Implementation of the sampling and aggregation method for the covariate shift maximin effect, which was proposed in <doi:10.48550/arXiv.2011.07568>. It constructs the confidence interval for any linear combination of the high-dimensional maximin effect.

This package provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.

This package provides classes and methods for trajectory data, with support for nesting individual Track objects in track sets (Tracks) and track sets for different entities in collections of Tracks. Methods include selection, generalization, aggregation, intersection, simulation, and plotting.

This package performs fast variable selection in high-dimensional settings while controlling the false discovery rate (FDR) at a user-defined target level. The package is based on the paper Machkour, Muma, and Palomar (2022) <arXiv:2110.06048>.