The rfacts package is an R interface to the Fixed and Adaptive Clinical Trial Simulator ('FACTS') on Unix-like systems. It programmatically invokes FACTS to run clinical trial simulations, and it aggregates simulation output data into tidy data frames. These capabilities provide end-to-end automation for large-scale simulation pipelines, and they enhance computational reproducibility. For more information on FACTS itself, please visit <https://www.berryconsultants.com/software/>.

Gene-environment (GÃ E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of GÃ E studies have been commonly encountered, leading to the development of a broad spectrum of robust penalization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a robust Bayesian variable selection method for GÃ E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects. An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++.

These functions are especially helpful when writing reports of data analysis using "Sweave".

This package provides tools for manipulating, exploring, and visualising multiple-response data, including scored or ranked responses. Conversions to and from factors, lists, strings, matrices; reordering, lumping, flattening; set operations; tables; frequency and co-occurrence plots.

Facilitates querying data from the â Facebook Marketing API', particularly for social science research <https://developers.facebook.com/docs/marketing-apis/>. Data from the Facebook Marketing API has been used for a variety of social science applications, such as for poverty estimation (Marty and Duhaut (2024) <doi:10.1038/s41598-023-49564-6>), disease surveillance (Araujo et al. (2017) <doi:10.48550/arXiv.1705.04045>), and measuring migration (Alexander, Polimis, and Zagheni (2020) <doi:10.1007/s11113-020-09599-3>). The package facilitates querying the number of Facebook daily/monthly active users for multiple location types (e.g., from around a specific coordinate to an administrative region) and for a number of attribute types (e.g., interests, behaviors, education level, etc). The package supports making complex queries within one API call and making multiple API calls across different locations and/or parameters.

Loads Blackrock <https://blackrockneurotech.com> neural signal data files into the memory, provides utility tools to extract the data into common formats such as plain-text tsv and HDF5'.

This package provides a collection of functions to simulate dice rolls and the like. In particular, experiments and exercises can be performed looking at combinations and permutations of values in dice rolls and coin flips, together with the corresponding frequencies of occurrences. When applying each function, the user has to input the number of times (rolls, flips) to toss the dice. Needless to say, the more the tosses, the more the frequencies approximate the actual probabilities. Moreover, the package provides functions to generate non-transitive sets of dice (like Efron's) and to check whether a given set of dice is non-transitive with given probability.

Assists for presentation and visualization of data from the Norwegian Health Quality Registries following the standardization based on the requirement specified by the National Service for Health Quality Registries. This requirement can be accessed from (<https://www.kvalitetsregistre.no/resultater-til-publisering-pa-nett>). Unfortunately the website is only available in Norwegian.

This package provides an interface to the Python package Geomstats authored by Miolane et al. (2020) <arXiv:2004.04667>.

R packages for genetics research.

This package provides a simplified version of the Portal Project Database designed for teaching. It provides a real world example of life-history, population, and ecological data, with sufficient complexity to teach many aspects of data analysis and management, but with many complexities removed to allow students to focus on the core ideas and skills being taught. The full database (which should be used for research) is available at <https://github.com/weecology/PortalData>.

Assemble the panels of computerized adaptive multistage testing by the bottom-up and the top-down approach, and simulate the administration of the assembled panels. The full documentation and tutorials are at <https://github.com/xluo11/Rmst>. Reference: Luo and Kim (2018) <doi:10.1111/jedm.12174>.

This package implements the Representation-Level Control Surfaces (RLCS) paradigm for ensuring the reliability of autonomous systems and AI models. It provides three deterministic sensors: Residual Likelihood (ResLik) for population-level anomaly detection, Temporal Consistency Sensor (TCS) for drift and shock detection, and Agreement Sensor for multi-modal redundancy checks. These sensors feed into a standardized control surface that issues PROCEED', DEFER', or ABSTAIN signals based on strict safety invariants, allowing systems to detect and react to out-of-distribution states, sensor failures, and environmental shifts before they propagate to decision-making layers.

Processing logical operations such as AND/OR/NOT operations dynamically. It also handles nesting in the operations.

Storing huge data in RData format causes problems because of the necessity to load the whole file to the memory in order to access and manipulate objects inside such file; rtape is a simple solution to this problem. The package contains several wrappers of R built-in serialize/unserialize mechanism allowing user to quickly append objects to a tape-like file and later iterate over them requiring only one copy of each stored object to reside in memory a time.

This package provides functions for reading, analysing and plotting river networks. For this package, river networks consist of sections and nodes with associated attributes, e.g. to characterise their morphological, chemical and biological state. The package provides functions to read this data from text files, to analyse the network structure and network paths and regions consisting of sections and nodes that fulfill prescribed criteria, and to plot the river network and associated properties.

Replication Rate (RR) is the probability of replicating a statistically significant association in genome-wide association studies. This R-package provide the estimation method for replication rate which makes use of the summary statistics from the primary study. We can use the estimated RR to determine the sample size of the replication study, and to check the consistency between the results of the primary study and those of the replication study.

Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval Method is designed to perform multi-criteria decision-making (MCDM), developed by Mališa Žižovic in 2020 (<doi:10.3390/math8061015>). It calculates the final sorted rankings based on a decision matrix where rows represent alternatives and columns represent criteria. The method uses: - A numeric vector of weights for each criterion (the sum of weights must be 1). - A numeric vector of ideal values for each criterion. - A numeric vector of anti-ideal values for each criterion. - Numeric values representing the extent to which the ideal value is preferred over the anti-ideal value, and the extent to which the anti-ideal value is considered worse. The function standardizes the decision matrix, normalizes the data, applies weights, and returns the final sorted rankings.

The output gap indicates the percentage difference between the actual output of an economy and its potential. Since potential output is a latent process, the estimation of the output gap poses a challenge and numerous filtering techniques have been proposed. RGAP facilitates the estimation of a Cobb-Douglas production function type output gap, as suggested by the European Commission (Havik et al. 2014) <https://ideas.repec.org/p/euf/ecopap/0535.html>. To that end, the non-accelerating wage rate of unemployment (NAWRU) and the trend of total factor productivity (TFP) can be estimated in two bivariate unobserved component models by means of Kalman filtering and smoothing. RGAP features a flexible modeling framework for the appropriate state-space models and offers frequentist as well as Bayesian estimation techniques. Additional functionalities include direct access to the AMECO <https://economy-finance.ec.europa.eu/economic-research-and-databases/economic-databases/ameco-database_en> database and automated model selection procedures. See the paper by Streicher (2022) <http://hdl.handle.net/20.500.11850/552089> for details.

We implement full-ranked, rank-penalized, and adaptive nuclear norm penalized estimation methods using multivariate mixture models proposed by Kang, Chen, and Yao (2022+).

This package provides a robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.

Three-step regression and inference for cellwise and casewise contamination.

The function RepaymentPlan() calculates repayment schedule for repayment/mortgage plans.

Linguistic Descriptions of Complex Phenomena (LDCP) is an architecture and methodology that allows us to model complex phenomena, interpreting input data, and generating automatic text reports customized to the user needs (see <doi:10.1016/j.ins.2016.11.002> and <doi:10.1007/s00500-016-2430-5>). The proposed package contains a set of methods that facilitates the development of LDCP systems. It main goal is increasing the visibility and practical use of this research line.