This package provides tools for Bayesian learning of spatiotemporal dynamical mechanistic models. Includes methods for parameter estimation, simulation, and inference using hierarchical and state-space modeling approaches, following Banerjee, Chen, Frankenburg and Zhou (2025) <https://jmlr.org/papers/v26/22-0896.html>.

Automatically replaces "misspelled" words in a character vector based on their string distance from a list of words sorted by their frequency in a corpus. The default word list provided in the package comes from the Corpus of Contemporary American English. Uses the Jaro-Winkler distance metric for string similarity as implemented in van der Loo (2014) <doi:10.32614/RJ-2014-011>. The word frequency data is derived from Davies (2008-) "The Corpus of Contemporary American English (COCA)" <https://www.english-corpora.org/coca/>.

Meta-analysis via Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) by simulating pseudo-individual data from published group-level summary statistics. Handles binary, continuous, and generic effect-size outcomes within a one-stage mixed-model framework. Supports subgroup analysis.

Alternative to using withCallingHandlers() in the simple case of catch and rethrow. The `%!%` operator evaluates the expression on its left hand side, and if an error occurs, the right hand side is used to construct a new error that embeds the original error.

This package provides functions to create and manage research compendiums for data analysis. Research compendiums are a standard and intuitive folder structure for organizing the digital materials of a research project, which can significantly improve reproducibility. The package offers several compendium structure options that fit different research project as well as the ability of duplicating the folder structure of existing projects or implementing custom structures. It also simplifies the use of version control.

This package implements stagewise regression for variable selection in joint models of recurrent events and terminal events (semi-competing risks). Supports two model frameworks: the joint frailty model (Cox-type) and the joint scale-change model (AFT-type). Provides cooperative lasso, lasso, and group lasso penalties with cross-validation for tuning parameter selection via cross-fitted estimating equations.

Implementations for two different Bayesian models of differential co-expression. scdeco.cop() fits the bivariate Gaussian copula model from Zichen Ma, Shannon W. Davis, Yen-Yi Ho (2023) <doi:10.1111/biom.13701>, while scdeco.pg() fits the bivariate Poisson-Gamma model from Zhen Yang, Yen-Yi Ho (2022) <doi:10.1111/biom.13457>.

This package provides image-related C/C++ header files from the stb single-file libraries for image loading, writing, and resizing.

Calculates vote-specific and traditional Shapley-Owen power indices (vs-SOVs and SOVs) for spatial voting games in one to four dimensions. Evaluates voter influence through an a posteriori analysis of relative preferences. Supports weighted voting and various voting thresholds. Compatible with ideal point estimates from NOMINATE, Optimal Classification, and MCMCpack'. The method builds on Bibina and Dougherty (2025) <doi:10.2139/ssrn.6324519>.

This package performs automatic creation of short forms of scales with an ant colony optimization algorithm and a Tabu search. As implemented in the package, the ant colony algorithm randomly selects items to build a model of a specified length, then updates the probability of item selection according to the fit of the best model within each set of searches. The algorithm continues until the same items are selected by multiple ants a given number of times in a row. On the other hand, the Tabu search changes one parameter at a time to be either free, constrained, or fixed while keeping track of the changes made and putting changes that result in worse fit in a "tabu" list so that the algorithm does not revisit them for some number of searches. See Leite, Huang, & Marcoulides (2008) <doi:10.1080/00273170802285743> for an applied example of the ant colony algorithm, and Marcoulides & Falk (2018) <doi:10.1080/10705511.2017.1409074> for an applied example of the Tabu search.

Run Leslie Matrix models using Monte Carlo simulations for any specified shark species. This package was developed during the publication of Smart, JJ, White, WT, Baje, L, et al. (2020) "Can multi-species shark longline fisheries be managed sustainably using size limits? Theoretically, yes. Realistically, no".J Appl Ecol. 2020; 57; 1847â 1860. <doi:10.1111/1365-2664.13659>.

Making specification curve analysis easy, fast, and pretty. It improves upon existing offerings with additional features and tidyverse integration. Users can easily visualize and evaluate how their models behave under different specifications with a high degree of customization. For a description and applications of specification curve analysis see Simonsohn, Simmons, and Nelson (2020) <doi:10.1038/s41562-020-0912-z>.

This package provides a set of functions and datasets implementation of small area estimation when auxiliary variable is measured with error. These functions provide a empirical best linear unbiased prediction (EBLUP) estimator and mean squared error (MSE) estimator of the EBLUP. These models were developed by Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>.

This package implements a simple, novel clustering algorithm based on optimizing the silhouette width. See <doi:10.1101/2023.11.07.566055> for details.

Compute ploidy of single cells (or nuclei) based on single-cell (or single-nucleus) ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) data <https://github.com/fumi-github/scPloidy>.

Parametric source-filter synthesis of harmonic-noise signals, such as animal vocalizations and human voice, with control over pitch, formants, noise, amplitude modulation, nonlinear phenomena, and morphing. General signal processing tools for audio analysis and manipulation: pitch tracking, formant and vocal tract length estimation, reassigned and auditory spectrograms, modulation spectra and psychoacoustic roughness, self-similarity and surprisal, audio segmentation, pitch and formant shifting, etc. Includes four interactive web apps for audio synthesis, annotation, formant analysis, and manually correcting pitch contours. Reference: Anikin (2019) <doi:10.3758/s13428-018-1095-7>.

This package provides semiparametric smooth-coefficient stochastic frontier analysis following Sun and Kumbhakar (2013) <doi:10.1016/j.econlet.2013.05.001> where the coefficients of the parametric part vary smoothly with a set of nonparametric variables. Inefficiency term is allowed to depend on a set of determinants through heteroskedasticity. Smooth coefficients are estimated using nonparametric regression and the remaining frontier parameters are estimated by maximum likelihood. Technical efficiency and inefficiency are computed using the Battese and Coelli (1988) <doi:10.1016/0304-4076(88)90053-X> and Jondrow et al. (1982) <doi:10.1016/0304-4076(82)90004-5> methods, respectively. Confidence intervals for technical efficiency are computed using the approach of Horrace and Schmidt (1996) <doi:10.1007/BF00157044>.

Run complex native scripts with a single command, similar to system commands.

Analyse light spectra for visual and non-visual (often called melanopic) needs, wrapped up in a Shiny App. Spectran allows for the import of spectra in various CSV forms but also provides a wide range of example spectra and even the creation of own spectral power distributions. The goal of the app is to provide easy access and a visual overview of the spectral calculations underlying common parameters used in the field. It is thus ideal for educational purposes or the creation of presentation ready graphs in lighting research and application. Spectran uses equations and action spectra described in CIE S026 (2018) <doi:10.25039/S026.2018>, DIN/TS 5031-100 (2021) <doi:10.31030/3287213>, and ISO/CIE 23539 (2023) <doi:10.25039/IS0.CIE.23539.2023>.

Simulate parametric and semi-parametric survival times through a consistent, reusable interface for each distribution, using an object-oriented design. Supported distributions include Exponential, Weibull, Gompertz, Log-Logistic, Log-Normal, and Piecewise Exponential. Random variates can be generated under Proportional Hazards, Accelerated Failure Time, and Extended Hazards models, as well as under renewal and non-homogeneous Poisson recurrent event processes, following the methods described by Bender (2003) <doi:10.5282/UBM/EPUB.1716> and Leemis (1987) in Operations Research, 35(6), 892-894.

This package provides a new reduced-rank LDA method which works for high dimensional multi-class data.

This package provides a pipeline for estimating the stellar age, mass, and radius given observational effective temperature, [Fe/H], and astroseismic parameters. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models, as described in Valle et al. (2014) <doi:10.1051/0004-6361/201322210>.

Sometimes it is useful to serve up alternative shiny UIs depending on information passed in the request object, such as the value of a cookie or a query parameter. This packages facilitates such switches.

Adjusts model-based small area estimates so that their weighted aggregate agrees with the weighted aggregate of the direct estimates, using the difference, ratio, and optimum benchmarking methods described in Rao and Molina (2015, ISBN:978-1-118-73578-7) and Wang, Fuller and Qu (2008). The mean squared error (MSE) of the benchmarked empirical best linear unbiased predictor (EBLUP) under the Fay-Herriot model is estimated with the second-order approximation or the parametric bootstrap of Steorts and Ghosh (2013) <doi:10.5705/ss.2012.053>. The posterior MSE of the benchmarked hierarchical Bayes (HB) estimator follows Datta, Ghosh, Steorts and Maples (2011) <doi:10.1007/s11749-010-0218-y>.