This package provides tools for analysing the geometry of configurations in high-dimensional spaces using the Average Membership Degree (AMD) framework and synthetic configuration generation. The package supports a domain-agnostic approach to studying the shape, dispersion, and internal structure of point clouds, with applications across biological and ecological datasets, including those derived from deep-time records. The AMD framework builds on the idea that strongly coupled systems may occupy a limited set of recurrent regimes in state space, producing high-occupancy regions separated by sparsely populated transitional configurations. The package focuses on detecting these concentration patterns and quantifying their geometric definition without assuming any underlying dynamical model. It provides AMD curve computation, cluster assignment, and sigma-equivalent estimation, together with S3 methods for plotting, printing, and summarising AMD and sigma-equivalent objects. Mendoza (2025) <https://mmendoza1967.github.io/AMDconfigurations/>.

This package provides a set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) <doi:10.1016/j.acags.2020.100026>), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) <doi:10.1007/BF00891269>) and singularity of the covariation matrix with aIc.singular(). Currently tests five data transformations: prop, clr, TMM, TMMwsp, and RLE from the R packages ALDEx2', edgeR and DESeq2 (Fernandes et al (2014) <doi:10.1186/2049-2618-2-15>, Anders et al. (2013)<doi:10.1038/nprot.2013.099>).

Create, upload and run Acumos R models. Acumos (<https://www.acumos.org>) is a platform and open source framework intended to make it easy to build, share, and deploy AI apps. Acumos is part of the LF AI Foundation', an umbrella organization within The Linux Foundation'. With this package, user can create a component, and push it to an Acumos platform.

This package provides a project template to support the data science workflow.

This package creates complex autoregressive distributed lag (ARDL) models and constructs the underlying unrestricted and restricted error correction model (ECM) automatically, just by providing the order. It also performs the bounds-test for cointegration as described in Pesaran et al. (2001) <doi:10.1002/jae.616> and provides the multipliers and the cointegrating equation. The validity and the accuracy of this package have been verified by successfully replicating the results of Pesaran et al. (2001) in Natsiopoulos and Tzeremes (2022) <doi:10.1002/jae.2919>.

Plot party trees in left-right orientation instead of the classical top-down layout.

Wraps the Abseil C++ library for use by R packages. Original files are from <https://github.com/abseil/abseil-cpp>. Patches are located at <https://github.com/doccstat/abseil-r/tree/main/local/patches>.

This package provides a simulations-first sample size determination package that aims at making sample size formulae obsolete for most easily computable statistical experiments ; the main envisioned use case is clinical trials. The proposed clinical trial must be written by the user in the form of a function that takes as argument a sample size and returns a boolean (for whether or not the trial is a success). The adsasi functions will then use it to find the correct sample size empirically. The unavoidable mis-specification is obviated by trying sample size values close to the right value, the latter being understood as the value that gives the probability of success the user wants (usually 80 or 90% in biostatistics, corresponding to 20 or 10% type II error).

This package implements the methodology introduced in Capezza, Lepore, and Paynabar (2025) <doi:10.1080/00401706.2025.2561744> for process monitoring with limited labeling resources. The package provides functions to (i) simulate data streams with true latent states and multivariate Gaussian observations as done in the paper, (ii) fit partially hidden Markov models (pHMMs) using a constrained Baum-Welch algorithm with partial labels, and (iii) perform stream-based active learning that balances exploration and exploitation to decide whether to request labels in real time. The methodology is particularly suited for statistical process monitoring in industrial applications where labeling is costly.

Data sets used in Cayuela and De la Cruz (2022, ISBN:978-84-8476-833-3).

Inference of protein complex states from quantitative proteomics data. The package takes information on known stable protein interactions (i.e. protein components of the same complex) and assesses how protein quantitative ratios change between different conditions. It reports protein pairs for which relative protein quantities to each other have been significantly altered in the tested condition.

This package provides a client for AWS Translate <https://aws.amazon.com/documentation/translate>, a machine translation service that will convert a text input in one language into a text output in another language.

Testing, Implementation, and Forecasting of the ARIMA-ANN hybrid model. The ARIMA-ANN hybrid model combines the distinct strengths of the Auto-Regressive Integrated Moving Average (ARIMA) model and the Artificial Neural Network (ANN) model for time series forecasting.For method details see Zhang, GP (2003) <doi:10.1016/S0925-2312(01)00702-0>.

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.

Functionality for working with virtual machines (VMs) in Microsoft's Azure cloud: <https://azure.microsoft.com/en-us/services/virtual-machines/>. Includes facilities to deploy, startup, shutdown, and cleanly delete VMs and VM clusters. Deployment configurations can be highly customised, and can make use of existing resources as well as creating new ones. A selection of predefined configurations is provided to allow easy deployment of commonly used Linux and Windows images, including Data Science Virtual Machines. With a running VM, execute scripts and install optional extensions. Part of the AzureR family of packages.

In panel data settings, specifies set of candidate models, fits them to data from pre-treatment validation periods, and selects model as average over candidate models, weighting each by posterior probability of being most robust given its differential average prediction errors in pre-treatment validation periods. Subsequent estimation and inference of causal effect's bounds accounts for both model and sampling uncertainty, and calculates the robustness changepoint value at which bounds go from excluding to including 0. The package also includes a range of diagnostic plots, such as those illustrating models differential average prediction errors and the posterior distribution of which model is most robust.

The Langmuir and Freundlich adsorption isotherms are pivotal in characterizing adsorption processes, essential across various scientific disciplines. Proper interpretation of adsorption isotherms involves robust fitting of data to the models, accurate estimation of parameters, and efficiency evaluation of the models, both in linear and non-linear forms. For researchers and practitioners in the fields of chemistry, environmental science, soil science, and engineering, a comprehensive package that satisfies all these requirements would be ideal for accurate and efficient analysis of adsorption data, precise model selection and validation for rigorous scientific inquiry and real-world applications. Details can be found in Langmuir (1918) <doi:10.1021/ja02242a004> and Giles (1973) <doi:10.1111/j.1478-4408.1973.tb03158.x>.

Interface to the Azure Machine Learning Software Development Kit ('SDK'). Data scientists can use the SDK to train, deploy, automate, and manage machine learning models on the Azure Machine Learning service. To learn more about Azure Machine Learning visit the website: <https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml>.

Solves the problem of identifying the densest submatrix in a given or sampled binary matrix, Bombina et al. (2019) <arXiv:1904.03272>.

Exclusion-based parentage assignment is essential for studies in biodiversity conservation and breeding programs - Kang Huang, Rui Mi, Derek W Dunn, Tongcheng Wang, Baoguo Li, (2018), <doi:10.1534/genetics.118.301592>. The tool compares multilocus genotype data of potential parents and offspring, identifying likely parentage relationships while accounting for genotyping errors, missing data, and duplicate genotypes. acoRn includes two algorithms: one generates synthetic genotype data based on user-defined parameters, while the other analyzes existing genotype data to identify parentage patterns. The package is versatile, applicable to diverse organisms, and offers clear visual outputs, making it a valuable resource for researchers.

This package implements a multiple testing approach to the choice of a threshold gamma on the p-values using the Average Power Function (APF) and Bayes False Discovery Rate (FDR) robust estimation. Function apf_fdr() estimates both quantities from either raw data or p-values. Function apf_plot() produces smooth graphs and tables of the relevant results. Details of the methods can be found in Quatto P, Margaritella N, et al. (2019) <doi:10.1177/0962280219844288>.

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.

Estimate the Å estákâ Berggren kinetic model (degradation model) from experimental data. A closed-form (analytic) solution to the degradation model is implemented as a non-linear fit, allowing for the extrapolation of the degradation of a drug product - both in time and temperature. Parametric bootstrap, with kinetic parameters drawn from the multivariate t-distribution, and analytical formulae (the delta method) are available options to calculate the confidence and prediction intervals. The results (modelling, extrapolations and statistical intervals) can be visualised with multiple plots. The examples illustrate the accelerated stability modelling in drugs and vaccines development.

Epidemiological population dynamics models traditionally define a pathogen's virulence as the increase in the per capita rate of mortality of infected hosts due to infection. This package provides functions allowing virulence to be estimated by maximum likelihood techniques. The approach is based on the analysis of relative survival comparing survival in matching cohorts of infected vs. uninfected hosts (Agnew 2019) <doi:10.1101/530709>.