This package provides statistical methods for the design and analysis of a calibration study, which aims for calibrating measurements using two different methods. The package includes sample size calculation, sample selection, regression analysis with error-in measurements and change-point regression. The method is described in Tian, Durazo-Arvizu, Myers, et al. (2014) <DOI:10.1002/sim.6235>.

This package provides an interface to a HashiCorp vault server over its http API (typically these are self-hosted; see <https://www.vaultproject.io>). This allows for secure storage and retrieval of secrets over a network, such as tokens, passwords and certificates. Authentication with vault is supported through several backends including user name/password and authentication via GitHub'.

Export dataframes and automatically start importing into Vorteks'. Vorteks Visualization Environment (VVE) and Vorteks Data Manager (VDM) will start an import. Vorteks Processing Environment (VPE) will start a new project and add a file reader with the dataframe file already set. Warning: WINDOWS ONLY. Requires installation of Vorteks software.

Visualizing of distributions of covariance matrices. The package implements the methodology described in Tokuda, T., Goodrich, B., Van Mechelen, I., Gelman, A., & Tuerlinckx, F. (2012) <https://sites.stat.columbia.edu/gelman/research/unpublished/Visualization.pdf>.

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.

This package provides direct access to linked names for the same entity across the world's major name authority files, including national and regional variations in language, character set, and spelling. For more information go to <https://viaf.org/>.

Vector autoregressive (VAR) model is a fundamental and effective approach for multivariate time series analysis. Shrinkage estimation methods can be applied to high-dimensional VAR models with dimensionality greater than the number of observations, contrary to the standard ordinary least squares method. This package is an integrative package delivering nonparametric, parametric, and semiparametric methods in a unified and consistent manner, such as the multivariate ridge regression in Golub, Heath, and Wahba (1979) <doi:10.2307/1268518>, a James-Stein type nonparametric shrinkage method in Opgen-Rhein and Strimmer (2007) <doi:10.1186/1471-2105-8-S2-S3>, and Bayesian estimation methods using noninformative and informative priors in Lee, Choi, and S.-H. Kim (2016) <doi:10.1016/j.csda.2016.03.007> and Ni and Sun (2005) <doi:10.1198/073500104000000622>.

Generation of domain variables, linearization of several non-linear population statistics (the ratio of two totals, weighted income percentile, relative median income ratio, at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, the aggregate replacement ratio, the relative median income ratio, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow, Sample Survey Methods And Theory, vol. I: Methods and Applications; vol. II: Theory. 1953, New York: John Wiley and Sons), variance estimation for longitudinal, cross-sectional measures and measures of change for single and multistage stage cluster sampling designs (Berger, Y. G., 2015, <doi:10.1111/rssa.12116>). Several other precision measures are derived - standard error, the coefficient of variation, the margin of error, confidence interval, design effect.

This package provides R functions to draw lines and curves with the width of the curve allowed to vary along the length of the curve.

An implementation of the Verhoeff algorithm for calculating check digits (Verhoeff, J. (1969) <doi:10.1002/zamm.19710510323>). Functions are provided to calculate a check digit given an input number, calculate and append a check digit to an input number, and validate that a check digit is correct given an input number.

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>.

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.

Implementation of Azure DevOps <https://azure.microsoft.com/> API calls. It enables the extraction of information about repositories, build and release definitions and individual releases. It also helps create repositories and work items within a project without logging into Azure DevOps'. There is the ability to use any API service with a shell for any non-predefined call.

This package contains functions for visualization univariate data: ccdplot and qddplot.

Designed to help the user to determine the sensitivity of an proposed causal effect to unconsidered common causes. Users can create visualizations of sensitivity, effect sizes, and determine which pattern of effects would support a causal claim for between group differences. Number needed to treat formula from Kraemer H.C. & Kupfer D.J. (2006) <doi:10.1016/j.biopsych.2005.09.014>.

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.

Predicate helper functions for testing atomic vectors in R. All functions take a single argument x and check whether it's of the target type of base-R atomic vector (i.e. no class extensions nor attributes other than names'), returning TRUE or FALSE. Some additionally check for value (e.g. absence of missing values, infinities, blank characters, or names attribute; or having length 1).

This package provides a graphical R package designed to visualize behavioral observations over time. Based on raw time data extracted from video recorded sessions of experimental observations, ViSiElse grants a global overview of a process by combining the visualization of multiple actions timestamps for all participants in a single graph. Individuals and/or group behavior can easily be assessed. Supplementary features allow users to further inspect their data by adding summary statistics (mean, standard deviation, quantile or statistical test) and/or time constraints to assess the accuracy of the realized actions.

This package provides functions to run statistical analyses on surface-based neuroimaging data, computing measures including cortical thickness and surface area of the whole-brain and of the hippocampi. It can make use of FreeSurfer', fMRIprep', XCP-D', HCP and CAT12 preprocessed datasets and HippUnfold hippocampal segmentation outputs for a given sample by restructuring the data values into a single file. The single file can then be used by the package for analyses independently from its base dataset and without need for its access.

This package provides a versatile range of functions, including exploratory data analysis, time-series analysis, organizational network analysis, and data validation, whilst at the same time implements a set of best practices in analyzing and visualizing data specific to Microsoft Viva Insights'.

Estimates joint marker (longitudinal) and survival (time-to-event) outcomes using variational approximations. The package supports multivariate markers allowing for correlated error terms and multiple types of survival outcomes which may be left-truncated, right-censored, and recurrent. Time-varying fixed and random covariate effects are supported along with non-proportional hazards.

To computed the variability independent of mean (VIM) or variation independent of mean (VIM). The methodology can be found at Peter M Rothwell et al. (2010) <doi:10.1016/S1474-4422(10)70067-3>.

This package provides a collection of tools for analyzing the field of vision. It provides a framework for development and use of innovative methods for visualization, statistical analysis, and clinical interpretation of visual-field loss and its change over time. It is intended to be a tool for collaborative research. The package is described in Marin-Franch and Swanson (2013) <doi:10.1167/13.4.10> and is part of the Open Perimetry Initiative (OPI) [Turpin, Artes, and McKendrick (2012) <doi:10.1167/12.11.22>].

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.