Plot rpart models. Extends plot.rpart() and text.rpart() in the rpart package.

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.

Defines storage standard for Read, process, and analyze intracranial electroencephalography and deep-brain stimulation in RAVE', a reproducible framework for analysis and visualization of iEEG by Magnotti, Wang, and Beauchamp, (2020, <doi:10.1016/j.neuroimage.2020.117341>). Supports brain imaging data structure (BIDS) <https://bids.neuroimaging.io> and native file structure to ingest signals from Matlab data files, hierarchical data format 5 (HDF5), European data format (EDF), BrainVision core data format (BVCDF), or BlackRock Microsystem (NEV/NSx); process images in Neuroimaging informatics technology initiative (NIfTI) and FreeSurfer formats, providing brain imaging normalization to template brain, facilitating threeBrain package for comprehensive electrode localization via YAEL (your advanced electrode localizer) by Wang, Magnotti, Zhang, and Beauchamp (2023, <doi:10.1523/ENEURO.0328-23.2023>).

This package contains a function to randomize subjects, patients in groups of sequences (treatment sequences). If a blocksize is given, the randomization will be done within blocks. The randomization may be controlled by a Wald-Wolfowitz runs test. Functions to obtain the p-value of that test are included. The package is mainly intended for randomization of bioequivalence studies but may be used also for other clinical crossover studies. Contains two helper functions sequences() and williams() to get the sequences of commonly used designs in BE studies.

This package provides a toolbox created by members of the International Union for Conservation of Nature (IUCN) Red List of Ecosystems Committee for Scientific Standards. Primarily, it is a set of tools suitable for calculating the metrics required for making assessments of species and ecosystems against the IUCN Red List of Threatened Species and the IUCN Red List of Ecosystems categories and criteria. See the IUCN website for detailed guidelines, the criteria, publications and other information.

Estimates of standard errors of popular risk and performance measures for asset or portfolio returns using methods as described in Chen and Martin (2021) <doi:10.21314/JOR.2020.446>.

The goal of rFIA is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, rFIA simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, rFIA improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., dplyr', tidyr', and sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted and model-based estimators to improve population, change, and ratio estimates.

An implementation of a number of Global Trend models for time series forecasting that are Bayesian generalizations and extensions of some Exponential Smoothing models. The main differences/additions include 1) nonlinear global trend, 2) Student-t error distribution, and 3) a function for the error size, so heteroscedasticity. The methods are particularly useful for short time series. When tested on the well-known M3 dataset, they are able to outperform all classical time series algorithms. The models are fitted with MCMC using the rstan package.

This package provides a parallel function for multivariate outlier detection named modified Stahel-Donoho estimators is contained in this package. The function RMSDp() is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. This function is for higher dimensional datasets that cannot be handled by a single core function RMSD() included in RMSD package. See Wada and Tsubaki (2013) <doi:10.1109/CLOUDCOM-ASIA.2013.86> for the detail of the algorithm.

Provide reproducible R chunks in R Markdown document that automatically check computational results for reproducibility. This is achieved by creating json files storing metadata about computational results. A comprehensive tutorial to the package is available as preprint by Brandmaier & Peikert (2024, <doi:10.31234/osf.io/3zjvf>).

Rasch model and extensions for survey data, using Conditional Maximum likelihood (CML). Carlo Cafiero, Sara Viviani, Mark Nord (2018) <doi:10.1016/j.measurement.2017.10.065>.

This package provides helper functions for authenticating and retrieving data from your ODK-X Sync Endpoint'. This is an early release intended for testing and feedback.

Gene-environment (GÃ E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of GÃ E studies have been commonly encountered, leading to the development of a broad spectrum of robust penalization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a robust Bayesian variable selection method for GÃ E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects. An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++.

An extension for roxygen2 to embed Shinylive applications in the package documentation.

This package provides utilities for the design and analysis of replication studies. Features both traditional methods based on statistical significance and more recent methods such as the sceptical p-value; Held L. (2020) <doi:10.1111/rssa.12493>, Held et al. (2022) <doi:10.1214/21-AOAS1502>, Micheloud et al. (2023) <doi:10.1111/stan.12312>. Also provides related methods including the harmonic mean chi-squared test; Held, L. (2020) <doi:10.1111/rssc.12410>, and intrinsic credibility; Held, L. (2019) <doi:10.1098/rsos.181534>. Contains datasets from five large-scale replication projects.

Imputation of missing numerical outcomes for a longitudinal trial with protocol deviations. The package uses distinct treatment arm-based assumptions for the unobserved data, following the general algorithm of Carpenter, Roger, and Kenward (2013) <doi:10.1080/10543406.2013.834911>, and the causal model of White, Royes and Best (2020) <doi:10.1080/10543406.2019.1684308>. Sensitivity analyses to departures from these assumptions can be done by the Delta method of Roger. The program uses the same algorithm as the mimix Stata package written by Suzie Cro, with additional coding for the causal model and delta method. The reference-based methods are jump to reference (J2R), copy increments in reference (CIR), copy reference (CR), and the causal model, all of which must specify the reference treatment arm. Other methods are missing at random (MAR) and the last mean carried forward (LMCF). Individual-specific imputation methods (and their reference groups) can be specified.

Efficient algorithms for generating ensembles of robust, sparse and diverse models via robust multi-model subset selection (RMSS). The robust ensembles are generated by minimizing the sum of the least trimmed square loss of the models in the ensembles under constraints for the size of the models and the sharing of the predictors. Tuning parameters for the robustness, sparsity and diversity of the robust ensemble are selected by cross-validation.

Reporting tables often have structure that goes beyond simple rectangular data. The rtables package provides a framework for declaring complex multi-level tabulations and then applying them to data. This framework models both tabulation and the resulting tables as hierarchical, tree-like objects which support sibling sub-tables, arbitrary splitting or grouping of data in row and column dimensions, cells containing multiple values, and the concept of contextual summary computations. A convenient pipe-able interface is provided for declaring table layouts and the corresponding computations, and then applying them to data.

Implementation of the RESTK algorithm based on Markov's Inequality from Vilardell, Sergi, Serra, Isabel, Mezzetti, Enrico, Abella, Jaume, Cazorla, Francisco J. and Del Castillo, J. (2022). "Using Markov's Inequality with Power-Of-k Function for Probabilistic WCET Estimation". In 34th Euromicro Conference on Real-Time Systems (ECRTS 2022). Leibniz International Proceedings in Informatics (LIPIcs) 231 20:1-20:24. <doi:10.4230/LIPIcs.ECRTS.2022.20>. This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773).

Collection of tools to develop options strategies, value option contracts using the Black-Scholes-Merten option pricing model and calculate the option Greeks. Hull, John C. "Options, Futures, and Other Derivatives" (1997, ISBN:0-13-601589-1). Fischer Black, Myron Scholes (1973) "The Pricing of Options and Corporate Liabilities" <doi:10.1086/260062>.

Perform structural reliability analysis, including computation and simulation with system signatures, Samaniego (2007) <doi:10.1007/978-0-387-71797-5>, and survival signatures, Coolen and Coolen-Maturi (2013) <doi:10.1007/978-3-642-30662-4_8>. Additionally supports parametric and topological inference given system lifetime data, Aslett (2012) <https://www.louisaslett.com/PhD_Thesis.pdf>.

This package provides functions for risk management and portfolio investment of securities with practical tools for data processing and plotting. Moreover, it contains functions which perform the COS Method, an option pricing method based on the Fourier-cosine series (Fang, F. (2008) <doi:10.1137/080718061>).

Outliers virtually exist in any datasets of any application field. To avoid the impact of outliers, we need to use robust estimators. Classical estimators of multivariate mean and covariance matrix are the sample mean and the sample covariance matrix. Outliers will affect the sample mean and the sample covariance matrix, and thus they will affect the classical factor analysis which depends on the classical estimators (Pison, G., Rousseeuw, P.J., Filzmoser, P. and Croux, C. (2003) <doi:10.1016/S0047-259X(02)00007-6>). So it is necessary to use the robust estimators of the sample mean and the sample covariance matrix. There are several robust estimators in the literature: Minimum Covariance Determinant estimator, Orthogonalized Gnanadesikan-Kettenring, Minimum Volume Ellipsoid, M, S, and Stahel-Donoho. The most direct way to make multivariate analysis more robust is to replace the sample mean and the sample covariance matrix of the classical estimators to robust estimators (Maronna, R.A., Martin, D. and Yohai, V. (2006) <doi:10.1002/0470010940>) (Todorov, V. and Filzmoser, P. (2009) <doi:10.18637/jss.v032.i03>), which is our choice of robust factor analysis. We created an object oriented solution for robust factor analysis based on new S4 classes.

This package provides functions to access, search and download spacetime earth observation data via SpatioTemporal Asset Catalog (STAC). This package supports the version 1.0.0 (and older) of the STAC specification (<https://github.com/radiantearth/stac-spec>). For further details see Simoes et al. (2021) <doi:10.1109/IGARSS47720.2021.9553518>.