Calculates voter transitions comparing two elections, using the function solve.QP() in package quadprog'.

Random generation, density function and parameter estimation for the Voigt distribution. The main objective of this package is to provide R users with efficient estimation of Voigt parameters using classic iid data in a Bayesian framework. The estimating function allows flexible prior specification, specification of fixed parameters and several options for Markov Chain Monte Carlo posterior simulation. A basic version of the algorithm is described in: Cannas M. and Piras, N. (2025) <doi:10.1007/978-3-031-96303-2_53>.

New wavelet methodology (vector wavelet coherence) (Oygur, T., Unal, G, 2020 <doi:10.1007/s40435-020-00706-y>) to handle dynamic co-movements of multivariate time series via extending multiple and quadruple wavelet coherence methodologies. This package can be used to perform multiple wavelet coherence, quadruple wavelet coherence, and n-dimensional vector wavelet coherence analyses.

Wrapper around the City of Vancouver Open Data API <https://opendata.vancouver.ca/api/v2/console> to simplify and standardize access to City of Vancouver open data. Functionality to list the data catalogue and access data and geographic records.

An implementation of methods related to sparse clustering and variable importance in clustering. The package currently allows to perform sparse k-means clustering with a group penalty, so that it automatically selects groups of numerical features. It also allows to perform sparse clustering and variable selection on mixed data (categorical and numerical features), by preprocessing each categorical feature as a group of numerical features. Several methods for visualizing and exploring the results are also provided. M. Chavent, J. Lacaille, A. Mourer and M. Olteanu (2020)<https://www.esann.org/sites/default/files/proceedings/2020/ES2020-103.pdf>.

Video interactivity within shiny applications using video.js'. Enables the status of the video to be sent from the UI to the server, and allows events such as playing and pausing the video to be triggered from the server.

This package provides access to data collected by the Ecuadorian Truth Commission. Allows users to extract and analyze systematized information for human rights research in Ecuador. The package contains datasets documenting human rights violations from 1984-2008, including victim information, violation types, perpetrators, and geographic distribution.

Recursive partitioning for varying coefficient generalized linear models and ordinal linear mixed models. Special features are coefficient-wise partitioning, non-varying coefficients and partitioning of time-varying variables in longitudinal regression. A description of a part of this package was published by Burgin and Ritschard (2017) <doi:10.18637/jss.v080.i06>.

This package provides methods for calculating the variance scale exponent to identify memory patterns in time series data. Includes tests for white noise, short memory, and long memory. See Fu, H. et al. (2018) <doi:10.1016/j.physa.2018.06.092>.

Implementation of shiny app to visualize adverse events based on the Common Terminology Criteria for Adverse Events (CTCAE) using stacked correspondence analysis as described in Diniz et. al (2021)<doi:10.1186/s12874-021-01368-w>.

This package provides users with a simple and convenient mechanism to manage and query a Virtuoso database using the DBI (Data-Base Interface) compatible ODBC (Open Database Connectivity) interface. Virtuoso is a high-performance "universal server," which can act as both a relational database, supporting standard Structured Query Language ('SQL') queries, while also supporting data following the Resource Description Framework ('RDF') model for Linked Data. RDF data can be queried using SPARQL ('SPARQL Protocol and RDF Query Language) queries, a graph-based query that supports semantic reasoning. This allows users to leverage the performance of local or remote Virtuoso servers using popular R packages such as DBI and dplyr', while also providing a high-performance solution for working with large RDF triplestores from R. The package also provides helper routines to install, launch, and manage a Virtuoso server locally on Mac', Windows and Linux platforms using the standard interactive installers from the R command-line. By automatically handling these setup steps, the package can make using Virtuoso considerably faster and easier for a most users to deploy in a local environment. Managing the bulk import of triples from common serializations with a single intuitive command is another key feature of this package. Bulk import performance can be tens to hundreds of times faster than the comparable imports using existing R tools, including rdflib and redland packages.

An implementation of the Likelihood ratio Test (LRT) for testing that, in a (non)linear mixed effects model, the variances of a subset of the random effects are equal to zero. There is no restriction on the subset of variances that can be tested: for example, it is possible to test that all the variances are equal to zero. Note that the implemented test is asymptotic. This package should be used on model fits from packages nlme', lmer', and saemix'. Charlotte Baey and Estelle Kuhn (2019) <doi:10.18637/jss.v107.i06>.

Implementation of a Monte Carlo simulation engine for valuing synthetic portfolios of variable annuities, which reflect realistic features of common annuity contracts in practice. It aims to facilitate the development and dissemination of research related to the efficient valuation of a portfolio of large variable annuities. The main valuation methodology was proposed by Gan (2017) <doi:10.1515/demo-2017-0021>.

This package provides a data.frame processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. vtreat prepares variables so that data has fewer exceptional cases, making it easier to safely use models in production. Common problems vtreat defends against: Inf', NA', too many categorical levels, rare categorical levels, and new categorical levels (levels seen during application, but not during training). Reference: "'vtreat': a data.frame Processor for Predictive Modeling", Zumel, Mount, 2016, <DOI:10.5281/zenodo.1173313>.

This package provides methods to calculate the expected value of information from a decision-analytic model. This includes the expected value of perfect information (EVPI), partial perfect information (EVPPI) and sample information (EVSI), and the expected net benefit of sampling (ENBS). A range of alternative computational methods are provided under the same user interface. See Heath et al. (2024) <doi:10.1201/9781003156109>, Jackson et al. (2022) <doi:10.1146/annurev-statistics-040120-010730>.

Using frequency matrices, very low frequency variants (VLFs) are assessed for amino acid and nucleotide sequences. The VLFs are then compared to see if they occur in only one member of a species, singleton VLFs, or if they occur in multiple members of a species, shared VLFs. The amino acid and nucleotide VLFs are then compared to see if they are concordant with one another. Amino acid VLFs are also assessed to determine if they lead to a change in amino acid residue type, and potential changes to protein structures. Based on Stoeckle and Kerr (2012) <doi:10.1371/journal.pone.0043992> and Phillips et al. (2023) <doi:10.3897/BDJ.11.e96480>.

This package implements wild bootstrap tests for autocorrelation in Vector Autoregressive (VAR) models based on Ahlgren and Catani (2016) <doi:10.1007/s00362-016-0744-0>, a combined Lagrange Multiplier (LM) test for Autoregressive Conditional Heteroskedasticity (ARCH) in VAR models from Catani and Ahlgren (2016) <doi:10.1016/j.ecosta.2016.10.006>, and bootstrap-based methods for determining the cointegration rank from Cavaliere, Rahbek, and Taylor (2012) <doi:10.3982/ECTA9099> and Cavaliere, Rahbek, and Taylor (2014) <doi:10.1080/07474938.2013.825175>.

Enables computationally efficient parameters-estimation by variational Bayesian methods for various diagnostic classification models (DCMs). DCMs are a class of discrete latent variable models for classifying respondents into latent classes that typically represent distinct combinations of skills they possess. Recently, to meet the growing need of large-scale diagnostic measurement in the field of educational, psychological, and psychiatric measurements, variational Bayesian inference has been developed as a computationally efficient alternative to the Markov chain Monte Carlo methods, e.g., Yamaguchi and Okada (2020a) <doi:10.1007/s11336-020-09739-w>, Yamaguchi and Okada (2020b) <doi:10.3102/1076998620911934>, Yamaguchi (2020) <doi:10.1007/s41237-020-00104-w>, Oka and Okada (2023) <doi:10.1007/s11336-022-09884-4>, and Yamaguchi and Martinez (2023) <doi:10.1111/bmsp.12308>. To facilitate their applications, variationalDCM is developed to provide a collection of recently-proposed variational Bayesian estimation methods for various DCMs.

Tool for easy and efficient discretization of continuous and categorical data. The package calculates the most optimal binning of a given explanatory variable with respect to a user-specified target variable. The purpose is to assign a unique Weight-of-Evidence value to each of the calculated binpoints in order to recode the original variable. The package allows users to impose certain restrictions on the functional form on the resulting binning while maximizing the overall information value in the original data. The package is well suited for logistic scoring models where input variables may be subject to restrictions such as linearity by e.g. regulatory authorities. An excellent source describing in detail the development of scorecards, and the role of Weight-of-Evidence coding in credit scoring is (Siddiqi 2006, ISBN: 978â 0-471â 75451â 0). The package utilizes the discrete nature of decision trees and Isotonic Regression to accommodate the trade-off between flexible functional forms and maximum information value.

This package provides a variational Bayesian finite mixture model for the clustering of categorical data, and can implement variable selection and semi-supervised outcome guiding if desired. Incorporates an option to perform model averaging over multiple initialisations to reduce the effects of local optima and improve the automatic estimation of the true number of clusters. For further details, see the paper by Rao and Kirk (2024) <doi:10.48550/arXiv.2406.16227>.

Abstract descriptions of (yet) unobserved variables.

Estimate vaccine efficacy (VE) using immunogenicity data. The inclusion of immunogenicity data in regression models can increase precision in VE. The methods are described in the publications "Elucidating vaccine efficacy using a correlate of protection, demographics, and logistic regression" and "Improving precision of vaccine efficacy evaluation using immune correlate data in time-to-event models" by Julie Dudasova, Zdenek Valenta, and Jeffrey R. Sachs (2024).

Position adjustments for ggplot2 to implement "visualize as you randomize" principles, which can be especially useful when plotting experimental data.

This package provides a suite of plots for displaying variable importance and two-way variable interaction jointly. Can also display partial dependence plots laid out in a pairs plot or zenplots style.