Monte Carlo sampling algorithms for semiparametric Bayesian regression analysis. These models feature a nonparametric (unknown) transformation of the data paired with widely-used regression models including linear regression, spline regression, quantile regression, and Gaussian processes. The transformation enables broader applicability of these key models, including for real-valued, positive, and compactly-supported data with challenging distributional features. The samplers prioritize computational scalability and, for most cases, Monte Carlo (not MCMC) sampling for greater efficiency. Details of the methods and algorithms are provided in Kowal and Wu (2024) <doi:10.1080/01621459.2024.2395586>.

Implement the algorithm provided in scan for estimating the transmission route on railway network using passenger volume. It is a generalization of the scan statistic approach for railway network to identify the hot railway route for transmitting infectious diseases.

This package provides statistical process control tools for stochastic textured surfaces. The current version supports the following tools: (1) generic modeling of stochastic textured surfaces. (2) local defect monitoring and diagnostics in stochastic textured surfaces, which was proposed by Bui and Apley (2018a) <doi:10.1080/00401706.2017.1302362>. (3) global change monitoring in the nature of stochastic textured surfaces, which was proposed by Bui and Apley (2018b) <doi:10.1080/00224065.2018.1507559>. (4) computation of dissimilarity matrix of stochastic textured surface images, which was proposed by Bui and Apley (2019b) <doi:10.1016/j.csda.2019.01.019>.

Efficient procedures for fitting and cross-validating the structurally-regularized time-dependent Cox models.

It involves bibliometric indicators calculation from bibliometric data.It also deals pattern analysis using the text part of bibliometric data.The bibliometric data are obtained from mainly Web of Science and Scopus.

This package provides a framework for performing simulations such as those common in methodological statistics papers. The design principles of this package are described in greater depth in Bien, J. (2016) "The simulator: An Engine to Streamline Simulations," which is available at <arXiv:1607.00021>.

This package implements a parameter expanded variational Bayes algorithm for linear regression models with high-dimensional variable selection. The methodology utilizes spike-and-slab priors to perform simultaneous estimation and selection. Details can be found in Olejua et al. (2024) <doi:10.21203/rs.3.rs-7208847/v1>.

Function library for processing collective movement data (e.g. fish schools, ungulate herds, baboon troops) collected from GPS trackers or computer vision tracking software.

This package provides a pipeline-friendly toolkit for assembling stop motion animations from sequences of still images. Provides functions to read image directories, restructure frame sequences (duplicate, splice, arrange), apply per-frame pixel transformations (rotate, wiggle, flip, flop, blur, scale, crop, trim, border, background), and export the result as a GIF. All transformation functions accept a frames argument to target any subset of frames, bridging the gap between magick functions that operate on an entire image stack and fine-grained stop motion editing. Image processing is performed via ImageMagick Studio LLC (2024) <https://imagemagick.org>.

The scrapeR package utilizes functions that fetch and extract text content from specified web pages. It handles HTTP errors and parses HTML efficiently. The package can handle hundreds of websites at a time using the scrapeR_in_batches() command.

Set of tools to find coherent patterns in gene expression (microarray) data using a Bayesian Sparse Latent Factor Model (SLFM) <DOI:10.1007/978-3-319-12454-4_15>. Considerable effort has been put to build a fast and memory efficient package, which makes this proposal an interesting and computationally convenient alternative to study patterns of gene expressions exhibited in matrices. The package contains the implementation of two versions of the model based on different mixture priors for the loadings: one relies on a degenerate component at zero and the other uses a small variance normal distribution for the spike part of the mixture.

This package provides a dynamic programming solution to segmentation based on maximization of arbitrary similarity measures within segments. The general idea, theory and this implementation are described in Machne, Murray & Stadler (2017) <doi:10.1038/s41598-017-12401-8>. In addition to the core algorithm, the package provides time-series processing and clustering functions as described in the publication. These are generally applicable where a `k-means` clustering yields meaningful results, and have been specifically developed for clustering of the Discrete Fourier Transform of periodic gene expression data (`circadian or `yeast metabolic oscillations'). This clustering approach is outlined in the supplemental material of Machne & Murray (2012) <doi:10.1371/journal.pone.0037906>), and here is used as a basis of segment similarity measures. Notably, the time-series processing and clustering functions can also be used as stand-alone tools, independent of segmentation, e.g., for transcriptome data already mapped to genes.

This package provides a statistical disclosure control tool to protect frequency tables in cases where small values are sensitive. The function PLSrounding() performs small count rounding of necessary inner cells so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. The methodology is described in Langsrud and Heldal (2018) <https://www.researchgate.net/publication/327768398_An_Algorithm_for_Small_Count_Rounding_of_Tabular_Data>.

This package provides a web-based shiny interface for the StepReg package enables stepwise regression analysis across linear, generalized linear (including logistic, Poisson, Gamma, and negative binomial), and Cox models. It supports forward, backward, bidirectional, and best-subset selection under a range of criteria. The package also supports stepwise regression to multivariate settings, allowing multiple dependent variables to be modeled simultaneously. Users can explore and combine multiple selection strategies and criteria to optimize model selection. For enhanced robustness, the package offers optional randomized forward selection to reduce overfitting, and a data-splitting workflow for more reliable post-selection inference. Additional features include logging and visualization of the selection process, as well as the ability to export results in common formats.

This package provides a comprehensive suite of functions designed for constructing and managing ShinyItemAnalysis modules, supplemented with detailed guides, ready-to-use templates, linters, and tests. This package allows developers to seamlessly create and integrate one or more modules into their existing packages or to start a new module project from scratch.

This package provides a pipeline for estimating the average treatment effect via semi-supervised learning. Outcome regression is fit with cross-fitting using various machine learning method or user customized function. Doubly robust ATE estimation leverages both labeled and unlabeled data under a semi-supervised missing-data framework. For more details see Hou et al. (2021) <doi:10.48550/arxiv.2110.12336>. A detailed vignette is included.

This package provides a collection of functions for preparing data and fitting Bayesian count spatial regression models, with a specific focus on the Gamma-Count (GC) model. The GC model is well-suited for modeling dispersed count data, including under-dispersed or over-dispersed counts, or counts with equivalent dispersion, using Integrated Nested Laplace Approximations (INLA). The package includes functions for generating data from the GC model, as well as spatially correlated versions of the model. See Nadifar, Baghishani, Fallah (2023) <doi:10.1007/s13253-023-00550-5>.

This package provides tools to decompose (transformed) spatial connectivity matrices and perform supervised or unsupervised semiparametric spatial filtering in a regression framework. The package supports unsupervised spatial filtering in standard linear as well as some generalized linear regression models.

Integrates clipboard copied data in R Studio, loads and installs libraries within a R script and returns all valid arguments of a selected function.

Datasets used in "Statistical Methods for the Social Sciences" (SMSS) by Alan Agresti and Barbara Finlay.

This package implements a segmentation algorithm for multiple change-point detection in high-dimensional GARCH processes. It simultaneously segments GARCH processes by identifying common change-points, each of which can be shared by a subset or all of the component time series as a change-point in their within-series and/or cross-sectional correlation structure.

This package provides a collection of simple parameter estimation and tests for the comparison of multivariate means and variation, to accompany Chapters 4 and 5 of the book Multivariate Statistical Methods. A Primer (5th edition), by Manly BFJ, Navarro Alberto JA & Gerow K (2024) <doi:10.1201/9781003453482>.

This package provides a collection of forecast verification routines developed for the SPECS FP7 project. The emphasis is on comparative verification of ensemble forecasts of weather and climate.

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