This package provides fast and scalable Gibbs sampling algorithms for Bayesian Lasso regression model in high-dimensional settings. The package implements efficient partially collapsed and nested Gibbs samplers for Bayesian Lasso, with a focus on computational efficiency when the number of predictors is large relative to the sample size. Methods are described at Davoudabadi and Ormerod (2026) <https://github.com/MJDavoudabadi/LassoHiDFastGibbs>.
This package provides tools to estimate the impact of vaccination campaigns at population level (number of events averted, number of avertable events, number needed to vaccinate). Inspired by the methodology proposed by Foppa et al. (2015) <doi:10.1016/j.vaccine.2015.02.042> and Machado et al. (2019) <doi:10.2807/1560-7917.ES.2019.24.45.1900268> for influenza vaccination impact.
Using the novel Relative Distance to cluster datasets. Implementation of a clustering approach based on the k-means algorithm that can be used with any distance. In addition, implementation of the Hartigan and Wong method to accommodate alternative distance metrics. Both methods can operate with any distance measure, provided a suitable method is available to compute cluster centers under the chosen metric. Additionally, the k-medoids algorithm is implemented, offering a robust alternative for clustering without the need of computing cluster centers under the chosen metric. All three methods are designed to support Relative distances, Euclidean distances, and any user-defined distance functions. The Hartigan and Wong method is described in Hartigan and Wong (1979) <doi:10.2307/2346830> and an explanation of the k-medoids algorithm can be found in Reynolds et al (2006) <doi:10.1007/s10852-005-9022-1>.
This package provides profile likelihoods for a parameter of interest in commonly used statistical models. The models include linear models, generalized linear models, proportional odds models, linear mixed-effects models, and linear models for longitudinal responses fitted by generalized least squares. The package also provides plots for normalized profile likelihoods as well as the maximum profile likelihood estimates and the kth likelihood support intervals.
This package provides functions for reading, and in some cases writing, foreign files containing spectral data from spectrometers and their associated software, output from daylight simulation models in common use, and some spectral data repositories. As well as functions for exchange of spectral data with other R packages. Part of the r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
This package provides data access to counts matrices and meta-data for single-cell RNA sequencing data of thymic epithlial cells across mouse ageing using SMARTseq2 and 10X Genommics chemistries. Access is provided as a data package via ExperimentHub. It is designed to facilitate the re-use of data from Baran-Gale _et al._ in a consistent format that includes relevant and informative meta-data.
Jade Lizard and Reverse Jade Lizard Option Strategies are presented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Russell A. Stultz (â The option strategy desk reference: an essential reference for option traders (First edition.)â , 2019, ISBN: 9781949443912).
This package provides variable selection for linear models and generalized linear models using Bayesian information criterion (BIC) and model posterior probability (MPP). Given a set of candidate predictors, it evaluates candidate models and returns model-level summaries (BIC and MPP) and predictor-level posterior inclusion probabilities (PIP). For more details see Xu, S., Ferreira, M. A., & Tegge, A. N. (2025) <doi:10.48550/arXiv.2510.02628>.
This package creates visualization plots for 2D data including ellipse plots, Voronoi tesselation plots, and combined ellipse-Voronoi plots. Designed to visualize class separation in 2D data, raw of from projection techniques like principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) or others. For more details see Lotsch and Kringel (2026) and Lotsch, J., and Kringel, D. (2026) <doi:10.1371/journal.pone.0333653>.
This package provides functions to plot and help understand positive and negative predictive values (PPV and NPV), and their relationship with sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007) <doi:10.1111/j.1651-2227.2006.00180.x> for a theoretical overview of the technical concepts and Navarrete et al. (2015) for a practical explanation about the importance of their understanding <doi:10.3389/fpsyg.2015.01327>.
This package provides a collection of functions for statistical and multivariate analysis of surface-related data, with a focus on antimicrobial activity and omniphobicity. Designed to support materials scientists and researchers in exploring structureâ function relationships in surface-engineered materials through reproducible and interpretable workflows. For more details, see Li et al. (2021) <doi:10.1002/advs.202100368>, and Kwon et al. (2020) <doi:10.3390/polym12081826>.
This module implements the Rijndael cipher which has been selected as the Advanced Encryption Standard. The keysize for Rijndael is 32 bytes. The blocksize is 16 bytes (128 bits). The supported encryption modes are:
MODE_CBC---Cipher Block ChainingMODE_CFB---Cipher feedbackMODE_CTR---Counter modeMODE_ECB---Electronic cookbook modeMODE_OFB---Output feedback
Process results generated by Antares', a powerful open source software developed by RTE (Réseau de Transport dâ à lectricité) to simulate and study electric power systems (more information about Antares here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>). This package provides functions to create new columns like net load, load factors, upward and downward margins or to compute aggregated statistics like economic surpluses of consumers, producers and sectors.
Spectral emission data for some frequently used lamps including bulbs and flashlights based on led emitting diodes (LEDs) but excluding LEDs available as electronic components. Original spectral irradiance data for incandescent-, LED- and discharge lamps are included. They are complemented by data on the effect of temperature on the emission by fluorescent tubes. Part of the r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
This package provides functions that compute the spatial covariance matrix for the matern and power classes of spatial models, for data that arise on rectangular units. This code can also be used for the change of support problem and for spatial data that arise on irregularly shaped regions like counties or zipcodes by laying a fine grid of rectangles and aggregating the integrals in a form of Riemann integration.
The clusterGeneration package provides functions for generating random clusters, generating random covariance/correlation matrices, calculating a separation index (data and population version) for pairs of clusters or cluster distributions, and 1-D and 2-D projection plots to visualize clusters. The package also contains a function to generate random clusters based on factorial designs with factors such as degree of separation, number of clusters, number of variables, number of noisy variables.
This is an external ExperimentData package for LRcell. This data package contains the gene enrichment scores calculated from scRNA-seq dataset which indicates the gene enrichment of each cell type in certain brain region. LRcell package is used to identify specific sub-cell types that drives the changes observed in a bulk RNA-seq differential gene expression experiment. For more details, please visit: https://github.com/marvinquiet/LRcell.
Compendium of the most representative algorithms in print---vector-valued dynamic programming, linear programming, policy iteration, the weighting factor approach---for solving multi-objective Markov decision processes, with or without reward discount, over a finite or infinite horizon. Mifrani, A. (2024) <doi:10.1007/s10479-024-06439-x>; Mifrani, A. & Noll, D. <doi:10.48550/arXiv.2502.13697>; Wakuta, K. (1995) <doi:10.1016/0304-4149(94)00064-Z>.
The expander functions rely on the mathematics developed for the Hessian-definiteness invariance theorem for linear projection transformations of variables, described in authors paper, to generate the full, high-dimensional gradient and Hessian from the lower-dimensional derivative objects. This greatly relieves the computational burden of generating the regression-function derivatives, which in turn can be fed into any optimization routine that utilizes such derivatives. The theorem guarantees that Hessian definiteness is preserved, meaning that reasoning about this property can be performed in the low-dimensional space of the base distribution. This is often a much easier task than its equivalent in the full, high-dimensional space. Definiteness of Hessian can be useful in selecting optimization/sampling algorithms such as Newton-Raphson optimization or its sampling equivalent, the Stochastic Newton Sampler. Finally, in addition to being a computational tool, the regression expansion framework is of conceptual value by offering new opportunities to generate novel regression problems.
Mass spectrometry (MS) data backend supporting import and export of MS/MS library spectra from MassBank record files. Different backends are available that allow handling of data in plain MassBank text file format or allow also to interact directly with MassBank SQL databases. Objects from this package are supposed to be used with the Spectra Bioconductor package. This package thus adds MassBank support to the Spectra package.
This package implements specialized K-Nearest Neighbor (KNN) logic to address the unique challenges of spatial modeling in archipelagic environments. Standard contiguity models often leave significant portions of island nations (e.g., 20% of the Philippines) mathematically isolated. This package provides tools to ensure 100% network connectivity, neutralizing spatial bias and enabling robust econometric inference. Methodology follows Anselin (1988, ISBN:9024737354) and LeSage and Pace (2009) <doi:10.1201/9781420064254>.
This package implements Bayesian Surprise methodology for data visualization, based on Correll and Heer (2017) <doi:10.1109/TVCG.2016.2598839> "Surprise! Bayesian Weighting for De-Biasing Thematic Maps". Provides tools to weight event data relative to spatio-temporal models, highlighting unexpected patterns while de-biasing against known factors like population density or sampling variation. Integrates seamlessly with sf for spatial data and ggplot2 for visualization. Supports temporal/streaming data analysis.
This RStudio addin makes the creation of Shiny and ShinyDashboard apps more efficient. Besides the necessary folder structure, entire apps can be created using a drag and drop interface and customized with respect to a specific use case. The addin allows the export of the required user interface and server code at any time. By allowing the creation of modules, the addin can be used throughout the entire app development process.
Non-parametric tests of independence (mutual or serial) between some quantitative random vectors, as described in Bilodeau M. and Lafaye de Micheaux P. (2009) <doi:10.1016/j.jspi.2008.11.006>, in Beran R., Bilodeau M. and Lafaye de Micheaux P. (2007) <doi:10.1016/j.jmva.2007.01.009> and in Fan Y., Lafaye de Micheaux P., Penev S. and Salopek D. (2017) <doi:10.1016/j.jmva.2016.09.014>.