An efficient Rcpp implementation of the Adaptive Rejection Metropolis Sampling (ARMS) algorithm proposed by Gilks, W. R., Best, N. G. and Tan, K. K. C. (1995) <doi:10.2307/2986138>. This allows for sampling from a univariate target probability distribution specified by its (potentially unnormalised) log density.

Formatter functions in the apa package take the return value of a statistical test function, e.g. a call to chisq.test() and return a string formatted according to the guidelines of the APA (American Psychological Association).

This package provides functions are included for recalling AQL (Acceptable Quality Level or Acceptance Quality Level) Based single, double, and multiple attribute sampling plans from the Military Standard (MIL-STD-105E) - American National Standards Institute/American Society for Quality (ANSI/ASQ Z1.4) tables and for retrieving variable sampling plans from Military Standard (MIL-STD-414) - American National Standards Institute/American Society for Quality (ANSI/ASQ Z1.9) tables. The sources for these tables are listed in the URL: field. Also included are functions for computing the OC (Operating Characteristic) and ASN (Average Sample Number) coordinates for the attribute plans it recalls, and functions for computing the estimated proportion nonconforming and the maximum allowable proportion nonconforming for variable sampling plans. The MIL-STD AQL Sampling schemes were the most used and copied set of standards in the world. They are intended to be used for sampling a stream of lots, and were used in contract agreements between supplier and customer companies. When the US military dropped support of MIL-STD 105E and 414, The American National Standards Institute (ANSI) and the International Standards Organization (ISO) adopted the standard with few changes or no changes to the central tables. This package is useful because its computer implementation of these tables duplicates that available in other commercial software and subscription online calculators.

Auto-GO is a framework that enables automated, high quality Gene Ontology enrichment analysis visualizations. It also features a handy wrapper for Differential Expression analysis around the DESeq2 package described in Love et al. (2014) <doi:10.1186/s13059-014-0550-8>. The whole framework is structured in different, independent functions, in order to let the user decide which steps of the analysis to perform and which plot to produce.

Simulates battles in the board game Axis and Allies Spring 1942, and calculates your probability of winning a battle. This speeds the game up significantly.

This package provides functions to estimate and interpret the alpha-NOMINATE ideal point model developed in Carroll et al. (2013, <doi:10.1111/ajps.12029>). alpha-NOMINATE extends traditional spatial voting frameworks by allowing for a mixture of Gaussian and quadratic utility functions, providing flexibility in modeling political actors preferences. The package uses Markov Chain Monte Carlo (MCMC) methods for parameter estimation, supporting robust inference about individuals ideological positions and the shape of their utility functions. It also contains functions to simulate data from the model and to calculate the probability of a vote passing given the ideal points of the legislators/voters and the estimated location of the choice alternatives.

Providing ways to estimate the value of European stock options given historical stock price data. It includes functions for calculating option values based on autoregressiveâ moving-average (ARMA) models and generates information about these models. This package is made to be easy to understand and for financial analysis capabilities.

This package provides a quick method for visualizing non-aggregated line-list or aggregated census data stratified by age and one or two categorical variables (e.g. gender and health status) with any number of values. It returns a ggplot object, allowing the user to further customize the output. This package is part of the R4Epis project <https://r4epis.netlify.app/>.

Quantile regression with fixed effects solves longitudinal data, considering the individual intercepts as fixed effects. The parametric set of this type of problem used to be huge. Thus penalized methods such as Lasso are currently applied. Adaptive Lasso presents oracle proprieties, which include Gaussianity and correct model selection. Bayesian information criteria (BIC) estimates the optimal tuning parameter lambda. Plot tools are also available.

Facilitates plotting audiometric data (mostly) by preparing the coordinate system according to standards, given e. g. in American Speech-Language-Hearing Association (2005), <doi:10.1044/policy.GL2005-00014>.

Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors.

This package provides functions for I/O, visualisation and analysis of functional Magnetic Resonance Imaging (fMRI) datasets stored in the ANALYZE or NIFTI format. Note that the latest version of XQuartz seems to be necessary under MacOS.

Analysis of moderation (ANOMO) method conceptualizes the difference and equivalence tests as a moderation problem to test the difference and equivalence of two estimates (e.g., two means or two effects).

R wrapper around the argon HTML library. More at <https://demos.creative-tim.com/argon-design-system/>.

When many possible multiplier method estimates of a target population are available, a weighted sum of estimates from each back-calculated path can be achieved with this package. Variance-minimizing weights are used and with any admissible tree-structured data. The methodological basis used to create this package can be found in Flynn (2023) <http://hdl.handle.net/2429/86174>.

This software solves an Advection Bi-Flux Diffusive Problem using the Finite Difference Method FDM. Vasconcellos, J.F.V., Marinho, G.M., Zanni, J.H., 2016, Numerical analysis of an anomalous diffusion with a bimodal flux distribution. <doi:10.1016/j.rimni.2016.05.001>. Silva, L.G., Knupp, D.C., Bevilacqua, L., Galeao, A.C.N.R., Silva Neto, A.J., 2014, Formulation and solution of an Inverse Anomalous Diffusion Problem with Stochastic Techniques. <doi:10.5902/2179460X13184>. In this version, it is possible to include a source as a function depending on space and time, that is, s(x,t).

This package provides a shiny application to assess statistical assumptions and guide users toward appropriate tests. The app is designed for researchers with minimal statistical training and provides diagnostics, plots, and test recommendations for a wide range of analyses. Many statistical assumptions are implemented using the package rstatix (Kassambara, 2019) <doi:10.32614/CRAN.package.rstatix> and performance (Lüdecke et al., 2021) <doi:10.21105/joss.03139>.

Flexible multi-environment trials analysis via MCMC method for Additive Main Effects and Multiplicative Model (AMMI) for continuous data. Biplot with the averages and regions of confidence can be generated. The chains run in parallel on Linux systems and run serially on Windows.

Allows access to selected services that are part of the Google Adwords API <https://developers.google.com/adwords/api/docs/guides/start>. Google Adwords is an online advertising service by Google', that delivers Ads to users. This package offers a authentication process using OAUTH2'. Currently, there are two methods of data of accessing the API, depending on the type of request. One method uses SOAP requests which require building an XML structure and then sent to the API. These are used for the ManagedCustomerService and the TargetingIdeaService'. The second method is by building AWQL queries for the reporting side of the Google Adwords API.

An interface to container functionality in Microsoft's Azure cloud: <https://azure.microsoft.com/en-us/products/category/containers/>. Manage Azure Container Instance (ACI), Azure Container Registry (ACR) and Azure Kubernetes Service (AKS) resources, push and pull images, and deploy services. On the client side, lightweight shells to the docker', docker-compose', kubectl and helm commandline tools are provided. Part of the AzureR family of packages.

Parse R code in a given directory for R packages and attempt to install them from CRAN or GitHub. Optionally use a dependencies file for tighter control over which package versions to install.

It calculates the Air Pollution Tolerance Index (APTI) of plant species using biochemical parameters such as chlorophyll content, leaf extract pH, relative water content, and ascorbic acid content. It helps in identifying tolerant species for greenbelt development and pollution mitigation studies. It includes a shiny app for interactive APTI calculation and visualisation. For method details see, Sahu et al. (2020).<DOI:10.1007/s42452-020-3120-6>.

Simulate the effect of management or demography on allele retention and inbreeding accumulation in bottlenecked populations of animals with overlapping generations.

This package implements anomaly detection as binary classification for cross-sectional data. Uses maximum likelihood estimates and normal probability functions to classify observations as anomalous. The method is presented in the following lecture from the Machine Learning course by Andrew Ng: <https://www.coursera.org/learn/machine-learning/lecture/C8IJp/algorithm/>, and is also described in: Aleksandar Lazarevic, Levent Ertoz, Vipin Kumar, Aysel Ozgur, Jaideep Srivastava (2003) <doi:10.1137/1.9781611972733.3>.