Praat <https://www.fon.hum.uva.nl/praat/> is a widely used tool for manipulating, annotating and analyzing speech and acoustic data. It stores annotation data in a format called a TextGrid'. This package provides a way to read these files into R.

Interface to the Dryad "Solr" API, their "OAI-PMH" service, and fetch datasets. Dryad (<https://datadryad.org/>) is a curated host of data underlying scientific publications.

Create interactive radar charts using the Chart.js JavaScript library and the htmlwidgets package. Chart.js <http://www.chartjs.org/> is a lightweight library that supports several types of simple chart using the HTML5 canvas element. This package provides an R interface specifically to the radar chart, sometimes called a spider chart, for visualising multivariate data.

This package provides a data mining approach for longitudinal and clustered data, which combines the structure of mixed effects model with tree-based estimation methods. See Sela, R.J. and Simonoff, J.S. (2012) RE-EM trees: a data mining approach for longitudinal and clustered data <doi:10.1007/s10994-011-5258-3>.

The basic algorithm to perform the folding test of unimodality. Given a dataset X (d dimensional, n samples), the test checks whether the distribution of the data are rather unimodal or rather multimodal. This package stems from the following research publication: Siffer Alban, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët. "Are your data gathered?" In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining, pp. 2210-2218. ACM, 2018. <doi:10.1145/3219819.3219994>.

Implementation of the R-Average method for parameter estimation of averaging models of the Anderson's Information Integration Theory by Vidotto, G., Massidda, D., & Noventa, S. (2010) <https://www.uv.es/psicologica/articulos3FM.10/3Vidotto.pdf>.

This package provides various statistical methods for designing and analyzing two-stage randomized controlled trials using the methods developed by Imai, Jiang, and Malani (2021) <doi:10.1080/01621459.2020.1775612> and (2022+) <doi:10.48550/arXiv.2011.07677>. The package enables the estimation of direct and spillover effects, conduct hypotheses tests, and conduct sample size calculation for two-stage randomized controlled trials.

This package provides a collection of datasets that accompany the forthcoming book "R for Health Care Research".

Electrical properties of resistor networks using matrix methods.

Rare variant association tests: burden tests (Bocher et al. 2019 <doi:10.1002/gepi.22210>) and the Sequence Kernel Association Test (Bocher et al. 2021 <doi:10.1038/s41431-020-00792-8>) in the whole genome using the RAVA-FIRST approach (Bocher et al. 2022 <doi:10.1371/journal.pgen.1009923>). Ravages also enables to perform genetic simulations (Bocher et al. 2023 <doi:10.1002/gepi.22529>).

The ability to plot raster graphics in PDF files can be useful when one needs multi-page documents, but the plots contain so many individual elements that (the usual) use of vector graphics results in inconveniently large file sizes. Internally, the package plots each individual page as a PNG, and then combines them in one PDF file.

The header-only C++ template library FastAD for automatic differentiation <https://github.com/JamesYang007/FastAD> is provided by this package, along with a few illustrative examples that can all be called from R.

Pointwise generation and display of attractors (prefractals) of the random iterated function system (RIFS) for various combinations of probabilistic and geometric parameters of some fixed point sets (protofractals), described by Bukhovets A.G. (2012) <doi:10.1134/S0005117912020154>.

This package implements the regularized exponentially tilted empirical likelihood method. Details of the method are given in Kim, MacEachern, and Peruggia (2023) <doi:10.48550/arXiv.2312.17015>. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.

This package implements the Representation-Level Control Surfaces (RLCS) paradigm for ensuring the reliability of autonomous systems and AI models. It provides three deterministic sensors: Residual Likelihood (ResLik) for population-level anomaly detection, Temporal Consistency Sensor (TCS) for drift and shock detection, and Agreement Sensor for multi-modal redundancy checks. These sensors feed into a standardized control surface that issues PROCEED', DEFER', or ABSTAIN signals based on strict safety invariants, allowing systems to detect and react to out-of-distribution states, sensor failures, and environmental shifts before they propagate to decision-making layers.

This package provides fast, persistent (side-effect-free) stack, queue and deque (double-ended-queue) data structures. While deques include a superset of functionality provided by queues, in these implementations queues are more efficient in some specialized situations. See the documentation for rstack, rdeque, and rpqueue for details.

Enhances the R Optimization Infrastructure ('ROI') package with the quadratic solver qpOASES'. More information about qpOASES can be found at <https://github.com/coin-or/qpOASES>.

Create rich and fully interactive 3D visualizations of molecular data. Visualizations can be included in Shiny apps and R markdown documents, or viewed from the R console and RStudio Viewer. r3dmol includes an extensive API to manipulate the visualization after creation, and supports getting data out of the visualization into R. Based on the 3dmol.js and the htmlwidgets R package.

This package provides functions for dissimilarity analysis and memory-based learning (MBL, a.k.a local modeling) in complex spectral data sets. Most of these functions are based on the methods presented in Ramirez-Lopez et al. (2013) <doi:10.1016/j.geoderma.2012.12.014>.

The implemented R6 class SCM aims to simplify working with structural causal models. The missing data mechanism can be defined as a part of the structural model. The class contains methods for 1) defining a structural causal model via functions, text or conditional probability tables, 2) printing basic information on the model, 3) plotting the graph for the model using packages igraph or qgraph', 4) simulating data from the model, 5) applying an intervention, 6) checking the identifiability of a query using the R packages causaleffect and dosearch', 7) defining the missing data mechanism, 8) simulating incomplete data from the model according to the specified missing data mechanism and 9) checking the identifiability in a missing data problem using the R package dosearch'. In addition, there are functions for running experiments and doing counterfactual inference using simulation.

The Echo nest <http://the.echonest.com> is the industry's leading music intelligence company, providing developer with deepest understanding of music content and music fans. This package can be used to access artist's data including songs, blogs, news, reviews etc. Song's data including audio summary, style, danceability, tempo etc can also be accessed.

We implement linear regression when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked, based on D'Haultfoeuille, Gaillac, Maurel (2022) <doi:10.3386/w29953>. The package allows for common regressors observed in both datasets, and for various shape constraints on the effect of covariates on the outcome of interest. It also provides the tools to perform a test of point identification. See the associated vignette <https://github.com/cgaillac/RegCombin/blob/master/RegCombin_vignette.pdf> for theory and code examples.

This package provides methods for estimating online robust reduced-rank regression. The Gaussian maximum likelihood estimation method is described in Johansen, S. (1991) <doi:10.2307/2938278>. The majorisation-minimisation estimation method is partly described in Zhao, Z., & Palomar, D. P. (2017) <doi:10.1109/GlobalSIP.2017.8309093>. The description of the generic stochastic successive upper-bound minimisation method and the sample average approximation can be found in Razaviyayn, M., Sanjabi, M., & Luo, Z. Q. (2016) <doi:10.1007/s10107-016-1021-7>.

Various statistical, graphics, and data-management functions used by the Rcmdr package in the R Commander GUI for R.