This package provides a Tool for Semi-Automating the Statistical Disclosure Control of Research Outputs.

Simple animated versions of basic R plots, using the animation package. Includes animated versions of plot, barplot, persp, contour, filled.contour, hist, curve, points, lines, text, symbols, segments, and arrows.

This package provides a customisable set of tools for assessing and grading R or R-markdown scripts from students. It allows for checking correctness of code output, runtime statistics and static code analysis. The latter feature is made possible by representing R expressions using a tree structure.

Sets the alpha level for coefficients in a regression model as a decreasing function of the sample size through the use of Jeffreys Approximate Bayes factor. You tell alphaN() your sample size, and it tells you to which value you must lower alpha to avoid Lindley's Paradox. For details, see Wulff and Taylor (2024) <doi:10.1177/14761270231214429>.

This package provides functions for calculating the acute chronic workload ratio using three different methods: exponentially weighted moving average (EWMA), rolling average coupled (RAC) and rolling averaged uncoupled (RAU). Examples of this methods can be found in Williams et al. (2017) <doi:10.1136/bjsports-2016-096589> for EWMA and Windt & Gabbet (2018) for RAC and RAU <doi: 10.1136/bjsports-2017-098925>.

This package provides a cross-platform R framework that facilitates processing of any number of Affymetrix microarray samples regardless of computer system. The only parameter that limits the number of chips that can be processed is the amount of available disk space. The Aroma Framework has successfully been used in studies to process tens of thousands of arrays. This package has actively been used since 2006.

Parse Autonomous Recording Unit (ARU) data and for sub-sampling recordings. Extract Metadata from your recordings, select a subset of recordings for interpretation, and prepare files for processing on the WildTrax <https://wildtrax.ca/> platform. Read and process metadata from recordings collected using the SongMeter and BAR-LT types of ARUs.

This package provides tools to perform model selection alongside estimation under Linear, Logistic, Negative binomial, Quantile, and Skew-Normal regression. Under the spike-and-slab method, a probability for each possible model is estimated with the posterior mean, credibility interval, and standard deviation of coefficients and parameters under the most probable model.

This package implements the Analytic Hierarchy Process (AHP) method using Gaussian normalization (AHPGaussian) to derive the relative weights of the criteria and alternatives. It also includes functions for visualizing the results and generating graphical outputs. Method as described in: dos Santos, Marcos (2021) <doi:10.13033/ijahp.v13i1.833>.

This package provides Azure Active Directory (AAD) authentication functionality for R users of Microsoft's Azure cloud <https://azure.microsoft.com/en-us>. Use this package to obtain OAuth 2.0 tokens for services including Azure Resource Manager, Azure Storage and others. It supports both AAD v1.0 and v2.0, as well as multiple authentication methods, including device code and resource owner grant. Tokens are cached in a user-specific directory obtained using the rappdirs package. The interface is based on the OAuth framework in the httr package, but customised and streamlined for Azure. Part of the AzureR family of packages.

Deals with many computations related to the thermodynamics of atmospheric processes. It includes many functions designed to consider the density of air with varying degrees of water vapour in it, saturation pressures and mixing ratios, conversion of moisture indices, computation of atmospheric states of parcels subject to dry or pseudoadiabatic vertical evolutions and atmospheric instability indices that are routinely used for operational weather forecasts or meteorological diagnostics.

This package provides tools supporting multi-criteria and group decision making, including variable number of criteria, by means of aggregation operators, spread measures, fuzzy logic connectives, fusion functions, and preordered sets. Possible applications include, but are not limited to, quality management, scientometrics, software engineering, etc.

This package provides a toolbox for programming Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam>). The package is an extension package of the admiral package focusing on the metabolism therapeutic area.

This package provides a few functions aim to provide a statistic tool for three purposes. First, simulate kin pairs data based on the assumption that every trait is affected by genetic effects (A), common environmental effects (C) and unique environmental effects (E).Second, use kin pairs data to fit an ACE model and get model fit output.Third, calculate power of A estimate given a specific condition. For the mechanisms of power calculation, we suggest to check Visscher(2004)<doi:10.1375/twin.7.5.505>.

Presents a series of molecular and genetic routines in the R environment with the aim of assisting in analytical pipelines before and after the use of asreml or another library to perform analyses such as Genomic Selection or Genome-Wide Association Analyses. Methods and examples are described in Gezan, Oliveira, Galli, and Murray (2022) <https://asreml.kb.vsni.co.uk/wp-content/uploads/sites/3/ASRgenomics_Manual.pdf>.

Data processing and generating stratigraphic sections for volcanic deposits and tephrastratigraphy. Package was developed for studies on Alaska volcanoes ("av") where stratigraphic ("strat") figures are needed for interpreting eruptive histories, but the methods are applicable to any sediment stratigraphy project. Plotting styles inspired by "SedLog" (Zervas et al. 2009) <doi:10.1016/j.cageo.2009.02.009> but with more customizable outputs and flexible data input based on best practice recommendations for the tephra community (Wallace et al. 2022) <doi:10.1038/s41597-022-01515-y>.

Clinical trial design for subgroup selection in three-stage group sequential trial as described in Lai, Lavori and Liao (2014, <doi:10.1016/j.cct.2014.09.001>). Includes facilities for design, exploration and analysis of such trials. An implementation of the initial DEFUSE-3 trial is also provided as a vignette.

Provides: (1) Tools to infer dominance hierarchies based on calculating Elo scores, but with custom functions to improve estimates in animals with relatively stable dominance ranks. (2) Tools to plot the shape of the dominance hierarchy and estimate the uncertainty of a given data set.

Create data that displays generative art when mapped into a ggplot2 plot. Functionality includes specialized data frame creation for geometric shapes, tools that define artistic color palettes, tools for geometrically transforming data, and other miscellaneous tools that are helpful when using ggplot2 for generative art.

This package contains some tools for testing, analyzing time series data and fitting popular time series models such as ARIMA, Moving Average and Holt Winters, etc. Most functions also provide nice and clear outputs like SAS does, such as identify, estimate and forecast, which are the same statements in PROC ARIMA in SAS.

Computes low-dimensional point representations of high-dimensional numerical data according to the data visualization method Adaptable Radial Axes described in: Manuel Rubio-Sánchez, Alberto Sanchez, and Dirk J. Lehmann (2017) "Adaptable radial axes plots for improved multivariate data visualization" <doi:10.1111/cgf.13196>.

This package provides methods for fitting identity-link GLMs and GAMs to discrete data, using EM-type algorithms with more stable convergence properties than standard methods.

This package provides a recent method proposed by Yi and Chen (2023) <doi:10.1177/09622802221146308> is used to estimate the average treatment effects using noisy data containing both measurement error and spurious variables. The package AteMeVs contains a set of functions that provide a step-by-step estimation procedure, including the correction of the measurement error effects, variable selection for building the model used to estimate the propensity scores, and estimation of the average treatment effects. The functions contain multiple options for users to implement, including different ways to correct for the measurement error effects, distinct choices of penalty functions to do variable selection, and various regression models to characterize propensity scores.

The adapted pair correlation function transfers the concept of the pair correlation function from point patterns to patterns of objects of finite size and irregular shape (e.g. lakes within a country). The pair correlation function describes the spatial distribution of objects, e.g. random, aggregated or regularly spaced. This is a reimplementation of the method suggested by Nuske et al. (2009) <doi:10.1016/j.foreco.2009.09.050> using the library GEOS <doi:10.5281/zenodo.11396894>.