Datasets and utilities for teaching linear models in the context of the BetaBit universe (StatPunk). The package provides simulated datasets based on the fictional LifeCalc algorithmic scoring system, illustrating concepts such as simple regression, ANOVA, ANCOVA, hierarchical models, multicollinearity, model selection (AIC, BIC), and regularisation (LASSO, Ridge). Each dataset is accompanied by a narrative context connecting statistical methodology to questions of algorithmic fairness and social consequence.

This package provides a dependency-light OpenTimelineIO (OTIO) <https://github.com/AcademySoftwareFoundation/OpenTimelineIO> document layer in pure R. Provides constructors for the OTIO object model (timelines, tracks, clips, gaps, media references, rational times and time ranges), functional builders that return new objects, and readers and writers for canonical .otio files through jsonlite'. The optional RcppOTIO package validates output against the real OpenTimelineIO C++ library. No compiled code.

Download and handle spatial and temporal data from the CAMELS-CL dataset (Catchment Attributes and Meteorology for Large Sample Studies, Chile) <https://camels.cr2.cl/>, developed by Alvarez-Garreton et al. (2018) <doi:10.5194/hess-22-5817-2018>. The package does not generate new data, it only facilitates direct access to the original dataset for hydrological analyses.

Set of classes and methods to read data and metadata documents exchanged through the Statistical Data and Metadata Exchange (SDMX) framework, currently focusing on the SDMX XML standard format (SDMX-ML).

The parametric Bayes analysis for the restricted mean survival time (RMST) with cluster effect, as described in Hanada and Kojima (2024) <doi:10.48550/arXiv.2406.06071>. Bayes estimation with random-effect and frailty-effect can be applied to several parametric models useful in survival time analysis. The RMST under these parametric models can be computed from the obtained posterior samples.

This package provides a simple user-friendly library based on the python module reservoirpy'. It provides a flexible interface to implement efficient Reservoir Computing (RC) architectures with a particular focus on Echo State Networks (ESN). Some of its features are: offline and online training, parallel implementation, sparse matrix computation, fast spectral initialization, advanced learning rules (e.g. Intrinsic Plasticity) etc. It also makes possible to easily create complex architectures with multiple reservoirs (e.g. deep reservoirs), readouts, and complex feedback loops. Moreover, graphical tools are included to easily explore hyperparameters. Finally, it includes several tutorials exploring time series forecasting, classification and hyperparameter tuning. For more information about reservoirpy', please see Trouvain et al. (2020) <doi:10.1007/978-3-030-61616-8_40>. This package was developed in the framework of the University of Bordeauxâ s IdEx "Investments for the Future" program / RRI PHDS.

This package provides a simple R -> Stata interface allowing the user to execute Stata commands (both inline and from a .do file) from R.

Designed for the import, analysis, and visualization of dosimetric and volumetric data in Radiation Oncology, the tools herein enable import of dose-volume histogram information from multiple treatment planning system platforms and 3D structural representations and dosimetric information from DICOM-RT files. These tools also enable subsequent visualization and statistical analysis of these data.

Allows users to import data files containing heartbeat positions in the most broadly used formats, to remove outliers or points with unacceptable physiological values present in the time series, to plot HRV data, and to perform time domain, frequency domain and nonlinear HRV analysis. See Garcia et al. (2017) <DOI:10.1007/978-3-319-65355-6>.

This package provides a collection of methods for estimating the basic reproduction number (R0) of infectious diseases. Features a web application to interface with the estimators. Uses the models from: Fisman et al. (2013) <DOI:10.1371/journal.pone.0083622>, Bettencourt and Ribeiro (2008) <DOI:10.1371/journal.pone.0002185>, and White and Pagano (2008) <DOI:10.1002/sim.3136>. Includes datasets for Canadian national and provincial COVID-19 case counts provided by Berry et al. (2021) <DOI:10.1038/s41597-021-00955-2>.

This package provides portable access from R to biomedical image processing toolbox ANTs by Avants et al. (2009) <doi:10.54294/uvnhin> via seamless integration with the Python implementation ANTsPy'. Allows biomedical images to be processed in Python and analyzed in R', and vice versa via shared memory. See citation("rpyANTs") for more reference information.

The rearrangement operator (Hardy, Littlewood, and Polya 1952) for univariate, bivariate, and trivariate point estimates of monotonic functions. The package additionally provides a function that creates simultaneous confidence intervals for univariate functions and applies the rearrangement operator to these confidence intervals.

Facilitating the creation of reproducible statistical report templates. Once created, rapport templates can be exported to various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the converter backend.

This package provides the log-likelihoods with gradients from stan (Carpenter et al (2015), <doi:10.48550/arXiv.1509.07164>) needed for generalized log-likelihood estimation in nlmixr2 (Fidler et al (2019) <doi:10.1002/psp4.12445>). This is split of to reduce computational burden of recompiling rxode2 (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) which runs the nlmixr2 models during estimation.

This package provides tools for preparing inputs, running SOCSIM (SOCial SIMulator) demographic kinship microsimulations, and reading simulation outputs from R. The package includes helpers for creating simulation folders, downloading demographic rate schedules, starting simulations, and loading population and marriage result files.

Algorithms to price American and European equity options, convertible bonds and a variety of other financial derivatives. It uses an extension of the usual Black-Scholes model in which jump to default may occur at a probability specified by a power-law link between stock price and hazard rate as found in the paper by Takahashi, Kobayashi, and Nakagawa (2001) <doi:10.3905/jfi.2001.319302>. We use ideas and techniques from Andersen and Buffum (2002) <doi:10.2139/ssrn.355308> and Linetsky (2006) <doi:10.1111/j.1467-9965.2006.00271.x>.

This package provides a single method implementing multiple approaches to generate pseudo-random vectors whose components sum up to one (see, e.g., Maziero (2015) <doi:10.1007/s13538-015-0337-8>). The components of such vectors can for example be used for weighting objectives when reducing multi-objective optimisation problems to a single-objective problem in the socalled weighted sum scalarisation approach.

This package provides a wrapper for the Deutsche Nationalbibliothek (German National Library) API', available at <https://www.dnb.de/EN/Home/home_node.html>. The German National Library is the German central archival library, collecting, archiving, bibliographically classifying all German and German-language publications, foreign publications about Germany, translations of German works, and the works of German-speaking emigrants published abroad between 1933 and 1945.

Enables the calibration and analysis of radiocarbon dates, often but not exclusively for the purposes of archaeological research. It includes functions not only for basic calibration, uncalibration, and plotting of one or more dates, but also a statistical framework for building demographic and related longitudinal inferences from aggregate radiocarbon date lists, including: Monte-Carlo simulation test (Timpson et al 2014 <doi:10.1016/j.jas.2014.08.011>), random mark permutation test (Crema et al 2016 <doi:10.1371/journal.pone.0154809>) and spatial permutation tests (Crema, Bevan, and Shennan 2017 <doi:10.1016/j.jas.2017.09.007>).

This package implements covariate-adjusted estimation and inference for marginal mean differences in two-arm randomized controlled trials with continuous outcomes. Methods include the unadjusted difference in means, common-slope analysis of covariance, interacted standardization with joint influence-function inference, and cross-fitted augmentation. The package also provides analytic power and sample-size calculations, reproducible simulation designs, and article replication workflows, including tools for verifying and analyzing an authorized local trial-data file. The adjustment methods build on Tsiatis et al. (2008) <doi:10.1002/sim.3113> and Lin (2013) <doi:10.1214/12-AOAS583>.

This package implements an optimized approach to learning risk score models, where sparsity and integer constraints are integrated into the model-fitting process.

Three robust marginal integration procedures for additive models based on local polynomial kernel smoothers. As a preliminary estimator of the multivariate function for the marginal integration procedure, a first approach uses local constant M-estimators, a second one uses local polynomials of order 1 over all the components of covariates, and the third one uses M-estimators based on local polynomials but only in the direction of interest. For this last approach, estimators of the derivatives of the additive functions can be obtained. All three procedures can compute predictions for points outside the training set if desired. See Boente and Martinez (2017) <doi:10.1007/s11749-016-0508-0> for details.

Multivariate optimal allocation for different domains in one and two stages stratified sample design. R2BEAT extends the Neyman (1934) â Tschuprow (1923) allocation method to the case of several variables, adopting a generalization of the Bethelâ s proposal (1989). R2BEAT develops this methodology but, moreover, it allows to determine the sample allocation in the multivariate and multi-domains case of estimates for two-stage stratified samples. It also allows to perform both Primary Stage Units and Secondary Stage Units selection. This package requires the availability of ReGenesees', that can be installed from <https://github.com/DiegoZardetto/ReGenesees>.

Robust tail dependence estimation for bivariate models. This package is based on two papers by the authors:'Robust and bias-corrected estimation of the coefficient of tail dependence and Robust and bias-corrected estimation of probabilities of extreme failure sets'. This work was supported by a research grant (VKR023480) from VILLUM FONDEN and an international project for scientific cooperation (PICS-6416).