This package provides a decision support tool to strategically prioritise evidence gathering in complex, hierarchical AND-OR decision trees. It is designed for situations with incomplete or uncertain information where the goal is to reach a confident conclusion as efficiently as possible (responding to the minimum number of questions, and only spending resources on generating improved evidence when it is of significant value to the final decision). The framework excels in complex analyses with multiple potential successful pathways to a conclusion ('OR nodes). Key features include a dynamic influence index to guide users to the most impactful question, a system for propagating answers and semi-quantitative confidence scores (0-5) up the tree, and post-conclusion guidance to identify the best actions to increase the final confidence. These components are brought together in an interactive command-line workflow that guides the analysis from start to finish.

Data on Asylum and Resettlement for the UK, provided by the Home Office <https://www.gov.uk/government/statistical-data-sets/immigration-system-statistics-data-tables>.

Plots simulation results of clinical trials. Its main feature is allowing users to simultaneously investigate the impact of several simulation input dimensions through dynamic filtering of the simulation results. A more detailed description of the app can be found in Meyer et al. <DOI:10.1016/j.softx.2023.101347> or the vignettes on GitHub'.

Interactive graphical user interface (GUI) for the package AdhereR', allowing the user to access different data sources, to explore the patterns of medication use therein, and the computation of various measures of adherence. It is implemented using Shiny and HTML/CSS/JavaScript.

In mathematics, rejection sampling is a basic technique used to generate observations from a distribution. It is also commonly called the Acceptance-Rejection method or Accept-Reject algorithm and is a type of Monte Carlo method. Acceptance-Rejection method is based on the observation that to sample a random variable one can perform a uniformly random sampling of the 2D cartesian graph, and keep the samples in the region under the graph of its density function. Package AR is able to generate/simulate random data from a probability density function by Acceptance-Rejection method. Moreover, this package is a useful teaching resource for graphical presentation of Acceptance-Rejection method. From the practical point of view, the user needs to calculate a constant in Acceptance-Rejection method, which package AR is able to compute this constant by optimization tools. Several numerical examples are provided to illustrate the graphical presentation for the Acceptance-Rejection Method.

R codes for the (adaptive) Sum of Powered Score ('SPU and aSPU') tests, inverse variance weighted Sum of Powered score ('SPUw and aSPUw') tests and gene-based and some pathway based association tests (Pathway based Sum of Powered Score tests ('SPUpath'), adaptive SPUpath ('aSPUpath') test, GEEaSPU test for multiple traits - single SNP (single nucleotide polymorphism) association in generalized estimation equations, MTaSPUs test for multiple traits - single SNP association with Genome Wide Association Studies ('GWAS') summary statistics, Gene-based Association Test that uses an extended Simes procedure ('GATES'), Hybrid Set-based Test ('HYST') and extended version of GATES test for pathway-based association testing ('GATES-Simes'). ). The tests can be used with genetic and other data sets with covariates. The response variable is binary or quantitative. Summary; (1) Single trait-'SNP set association with individual-level data ('aSPU', aSPUw', aSPUr'), (2) Single trait-'SNP set association with summary statistics ('aSPUs'), (3) Single trait-pathway association with individual-level data ('aSPUpath'), (4) Single trait-pathway association with summary statistics ('aSPUsPath'), (5) Multiple traits-single SNP association with individual-level data ('GEEaSPU'), (6) Multiple traits- single SNP association with summary statistics ('MTaSPUs'), (7) Multiple traits-'SNP set association with summary statistics('MTaSPUsSet'), (8) Multiple traits-pathway association with summary statistics('MTaSPUsSetPath').

This package implements adaptive gPCA, as described in: Fukuyama, J. (2017) <arXiv:1702.00501>. The package also includes functionality for applying the method to phyloseq objects so that the method can be easily applied to microbiome data and a shiny app for interactive visualization.

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

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 contains tools to fit the additive hazards model to data from a cohort, random sampling, two-phase Bernoulli sampling and two-phase finite population sampling, as well as calibration tool to incorporate phase I auxiliary information into the two-phase data model fitting. This package provides regression parameter estimates and their model-based and robust standard errors. It also offers tools to make prediction of individual specific hazards.

This package provides a Shiny application to access the functionalities and datasets of the archeofrag package for spatial analysis in archaeology from refitting data. Quick and seamless exploration of archaeological refitting datasets, focusing on physical refits only. Features include: built-in documentation and convenient workflow, plot generation and exports, anomaly detection in the spatial distribution of refitting connection, exploration of spatial units merging solutions, simulation of archaeological site formation processes, support for parallel computing, R code generation to re-execute simulations and ensure reproducibility, code generation for the openMOLE model exploration software. A demonstration of the app is available at <https://analytics.huma-num.fr/Sebastien.Plutniak/archeofrag/>.

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.

Offers a graphical user interface for the calculation of the mean measure of divergence, with facilities for trait selection and graphical representations <doi:10.1002/ajpa.23336>.

An interface to the API for arXiv', a repository of electronic preprints for computer science, mathematics, physics, quantitative biology, quantitative finance, and statistics.

This package performs linear regression with respect to a data-driven convex loss function that is chosen to minimize the asymptotic covariance of the resulting M-estimator. The convex loss function is estimated in 5 steps: (1) form an initial OLS (ordinary least squares) or LAD (least absolute deviation) estimate of the regression coefficients; (2) use the resulting residuals to obtain a kernel estimator of the error density; (3) estimate the score function of the errors by differentiating the logarithm of the kernel density estimate; (4) compute the L2 projection of the estimated score function onto the set of decreasing functions; (5) take a negative antiderivative of the projected score function estimate. Newton's method (with Hessian modification) is then used to minimize the convex empirical risk function. Further details of the method are given in Feng et al. (2024) <doi:10.48550/arXiv.2403.16688>.

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

This package provides the alpha-adjustment correction from "Benjamini, Y., & Hochberg, Y. (1995) <doi:10.1111/j.2517-6161.1995.tb02031.x> Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57(1), 289-300". For researchers interested in using the exact mathematical formulas and procedures as used in the original paper.

It performs Canonical Correlation Analysis and provides inferential guaranties on the correlation components. The p-values are computed following the resampling method developed in Winkler, A. M., Renaud, O., Smith, S. M., & Nichols, T. E. (2020). Permutation inference for canonical correlation analysis. NeuroImage, <doi:10.1016/j.neuroimage.2020.117065>. Furthermore, it provides plotting tools to visualize the results.

This toolkit implements a numerical solution algorithm to invert a quality of life measure from observed data. Unlike the traditional Rosen-Roback measure, this measure accounts for mobility frictionsâ generated by idiosyncratic tastes and local ties â and trade frictions â generated by trade costs and non-tradable services, thereby reducing non-classical measurement error. The QoL measure is based on Ahlfeldt, Bald, Roth, Seidel (2024) <https://econpapers.repec.org/RePEc:boc:bocode:s459382> "Measuring Quality of Life under Spatial Frictions". When using this programme or the toolkit in your work, please cite the paper.

Set of tools for fitting the additive partial linear models with symmetric autoregressive errors of order p, or APLMS-AR(p). This setup enables the modeling of a time series response variable using linear and nonlinear structures of a set of explanatory variables, with nonparametric components approximated by natural cubic splines or P-splines. It also accounts for autoregressive error terms with distributions that have lighter or heavier tails than the normal distribution. The package includes various error distributions, such as normal, generalized normal, Student's t, generalized Student's t, power-exponential, and Cauchy distributions. Chou-Chen, S.W., Oliveira, R.A., Raicher, I., Gilberto A. Paula (2024) <doi:10.1007/s00362-024-01590-w>.

Statistical procedures to perform stability analysis in plant breeding and to identify stable genotypes under diverse environments. It is possible to calculate coefficient of homeostaticity by Khangildin et al. (1979), variance of specific adaptive ability by Kilchevsky&Khotyleva (1989), weighted homeostaticity index by Martynov (1990), steadiness of stability index by Udachin (1990), superiority measure by Lin&Binn (1988) <doi:10.4141/cjps88-018>, regression on environmental index by Erberhart&Rassel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>, Tai's (1971) stability parameters <doi:10.2135/cropsci1971.0011183X001100020006x>, stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>, ecovalence by Wricke (1962), nonparametric stability parameters by Nassar&Huehn (1987) <doi:10.2307/2531947>, Francis&Kannenberg's parameters of stability (1978) <doi:10.4141/cjps78-157>.

This package provides functions to calculate the assortment of vertices in social networks. This can be measured on both weighted and binary networks, with discrete or continuous vertex values.

This package performs approximate unconditional and permutation testing for 2x2 contingency tables. Motivated by testing for disease association with rare genetic variants in case-control studies. When variants are extremely rare, these tests give better control of Type I error than standard tests.

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.