This package provides tools for viewscape analysis from one or multiple viewpoints using a digital surface or elevation model. Core functionality includes computing viewsheds, quantifying visual magnitude, calculating a suite of viewscape configuration metrics (extent, depth, relief, Sky View Factor, skyline variation, and patch-based landscape structure), Shannon Diversity Index and land cover feature proportions within the visible area, pairwise intervisibility networks, panoramic view generation, and visualizing results as rasters or polygons. Viewscape configuration metrics follow the methods of Tabrizian et al. (2020) <doi:10.1016/j.landurbplan.2019.103704>. The viewshed algorithm is based on Franklin & Ray (1994) <https://api.semanticscholar.org/CorpusID:10680920> and Wang et al. (2000) <https://api.semanticscholar.org/CorpusID:131687018>. Visual magnitude is derived from Chamberlain & Meitner (2013) <doi:10.1016/j.landurbplan.2013.01.003>. Sky View Factor is computed following Oke (1981) <doi:10.1002/joc.3370010304> as implemented in the shadow package (Dorman et al. 2019) <doi:10.32614/RJ-2019-024>.

Models categorical time series through a Markov Chain when a) covariates are predictors for transitioning into the next state/symbol and b) when the dependence in the past states has variable length. The probability of transitioning to the next state in the Markov Chain is defined by a multinomial regression whose parameters depend on the past states of the chain and, moreover, the number of states in the past needed to predict the next state also depends on the observed states themselves. See Zambom, Kim, and Garcia (2022) <doi:10.1111/jtsa.12615>.

Time series decomposition for univariate time series using the "Verallgemeinerte Berliner Verfahren" (Generalized Berlin Method) as described in Kontinuierliche Messgröà en und Stichprobenstrategien in Raum und Zeit mit Anwendungen in den Natur-, Umwelt-, Wirtschafts- und Finanzwissenschaften', by Hebbel and Steuer, Springer Berlin Heidelberg, 2022 <doi:10.1007/978-3-662-65638-9>, or Decomposition of Time Series using the Generalised Berlin Method (VBV) by Hebbel and Steuer, in Jan Beran, Yuanhua Feng, Hartmut Hebbel (Eds.): Empirical Economic and Financial Research - Theory, Methods and Practice, Festschrift in Honour of Prof. Siegfried Heiler. Series: Advanced Studies in Theoretical and Applied Econometrics. Springer 2014, p. 9-40.

This package provides a R interface to the vouch project (<https://github.com/mitchellh/vouch>), which bills itself as "a community trust management system based on explicit vouches to participate". vouch is a Nushell module, so voucher provides a R application programming interface (API) to modify the VOUCHED.td database without Nushell'. voucher does not depend on vouch or Nushell'.

This package provides a dedicated viral-explainer model tool designed to empower researchers in the field of HIV research, particularly in viral load and CD4 (Cluster of Differentiation 4) lymphocytes regression modeling. Drawing inspiration from the tidymodels framework for rigorous model building of Max Kuhn and Hadley Wickham (2020) <https://www.tidymodels.org>, and the DALEXtra tool for explainability by Przemyslaw Biecek (2020) <doi:10.48550/arXiv.2009.13248>. It aims to facilitate interpretable and reproducible research in biostatistics and computational biology for the benefit of understanding HIV dynamics.

Collects tweets and metadata for threaded conversations and generates networks.

An interactive document on the topic of variance analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://predanalyticssessions1.shinyapps.io/chisquareVarianceTest/>.

An implementation of Vasicek and Song goodness-of-fit tests. Several functions are provided to estimate differential Shannon entropy, i.e., estimate Shannon entropy of real random variables with density, and test the goodness-of-fit of some family of distributions, including uniform, Gaussian, log-normal, exponential, gamma, Weibull, Pareto, Fisher, Laplace and beta distributions; see Lequesne and Regnault (2020) <doi:10.18637/jss.v096.c01>.

This package provides a set of functions to: (1) perform fuzzy clustering of vegetation data (De Caceres et al, 2010) <doi:10.1111/j.1654-1103.2010.01211.x>; (2) to assess ecological community similarity on the basis of structure and composition (De Caceres et al, 2013) <doi:10.1111/2041-210X.12116>.

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>.

Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.

This package implements variable screening techniques for ultra-high dimensional regression settings. Techniques for independent (iid) data, varying-coefficient models, and longitudinal data are implemented. The package currently contains three screen functions: screenIID(), screenLD() and screenVCM(), and six methods for simulating dataset: simulateDCSIS(), simulateLD, simulateMVSIS(), simulateMVSISNY(), simulateSIRS() and simulateVCM(). The package is based on the work of Li-Ping ZHU, Lexin LI, Runze LI, and Li-Xing ZHU (2011) <DOI:10.1198/jasa.2011.tm10563>, Runze LI, Wei ZHONG, & Liping ZHU (2012) <DOI:10.1080/01621459.2012.695654>, Jingyuan LIU, Runze LI, & Rongling WU (2014) <DOI:10.1080/01621459.2013.850086> Hengjian CUI, Runze LI, & Wei ZHONG (2015) <DOI:10.1080/01621459.2014.920256>, and Wanghuan CHU, Runze LI and Matthew REIMHERR (2016) <DOI:10.1214/16-AOAS912>.

This package provides a graphical user interface to integrate, visualize and explore results from linkage and quantitative trait loci analysis, together with genomic information for autopolyploid species. The app is meant for interactive use and allows users to optionally upload different sources of information, including gene annotation and alignment files, enabling the exploitation and search for candidate genes in a genome browser. In its current version, VIEWpoly supports inputs from MAPpoly', polymapR', diaQTL', QTLpoly', polyqtlR', GWASpoly', and HIDECAN packages.

Extending the functionalities of the VGAM package with additional functions and datasets. At present, VGAMextra comprises new family functions (ffs) to estimate several time series models by maximum likelihood using Fisher scoring, unlike popular packages in CRAN relying on optim(), including ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non- seasonal), the Order-(p) VAR model, error correction models for cointegrated time series, and ARMA-structures with Student-t errors. For independent data, new ffs to estimate the inverse- Weibull, the inverse-gamma, the generalized beta of the second kind and the general multivariate normal distributions are available. In addition, VGAMextra incorporates new VGLM-links for the mean-function, and the quantile-function (as an alternative to ordinary quantile modelling) of several 1-parameter distributions, that are compatible with the class of VGLM/VGAM family functions. Currently, only fixed-effects models are implemented. All functions are subject to change; see the NEWS for further details on the latest changes.

This package performs clustering of quantitative variables, assuming that clusters lie in low-dimensional subspaces. Segmentation of variables, number of clusters and their dimensions are selected based on BIC. Candidate models are identified based on many runs of K-means algorithm with different random initializations of cluster centers.

This package provides a Shiny application for the interactive visualisation and analysis of networks that also provides a web interface for collecting social media data using vosonSML'.

Extendable R6 file comparison classes, including a shiny app for combining the comparison functionality into a file comparison application. The package idea originates from pharma companies drug development processes, where statisticians and statistical programmers need to review and compare different versions of the same outputs and datasets. The package implementation itself is not tied to any specific industry and can be used in any context for easy file comparisons between different file version sets.

This package provides an R interface for volesti C++ package. volesti computes estimations of volume of polytopes given by (i) a set of points, (ii) linear inequalities or (iii) Minkowski sum of segments (a.k.a. zonotopes). There are three algorithms for volume estimation as well as algorithms for sampling, rounding and rotating polytopes. Moreover, volesti provides algorithms for estimating copulas useful in computational finance. Methods implemented in volesti are described in A. Chalkis and V. Fisikopoulos (2022) <doi:10.32614/RJ-2021-077> and references therein.

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.

This package implements methods for inference on potential waning of vaccine efficacy and for estimation of vaccine efficacy at a user-specified time after vaccination based on data from a randomized, double-blind, placebo-controlled vaccine trial in which participants may be unblinded and placebo subjects may be crossed over to the study vaccine. The methods also allow adjustment for possible confounding via inverse probability weighting through specification of models for the trial entry process, unblinding mechanisms, and the probability an unblinded placebo participant accepts study vaccine: Tsiatis, A. A. and Davidian, M. (2022) <doi:10.1111/biom.13509>.

Under a different representation of the multivariate normal (MVN) probability, we can use the Vecchia approximation to sample the integrand at a linear complexity with respect to n. Additionally, both the SOV algorithm from Genz (92) and the exponential-tilting method from Botev (2017) can be adapted to linear complexity. The reference for the method implemented in this package is Jian Cao and Matthias Katzfuss (2024) "Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities" <doi:10.48550/arXiv.2311.09426>. Two major references for the development of our method are Alan Genz (1992) "Numerical Computation of Multivariate Normal Probabilities" <doi:10.1080/10618600.1992.10477010> and Z. I. Botev (2017) "The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting" <doi:10.48550/arXiv.1603.04166>.

This package provides functions for downloading, reshaping, culling, cleaning, and analyzing fossil data from the Paleobiology Database <https://paleobiodb.org>.

Procedures for the manipulation, normalization, and plotting of phonetic and sociophonetic vowel formant data. vowels is the backend for the NORM website.

Wrapper around the City of Vancouver Open Data API <https://opendata.vancouver.ca/api-console/explore/v2.1/> to simplify and standardize access to City of Vancouver open data. Functionality to list the data catalogue and access data and geographic records.