This package provides a toolkit for archaeological time series and time intervals. This package provides a system of classes and methods to represent and work with archaeological time series and time intervals. Dates are represented as "rata die" and can be converted to (virtually) any calendar defined by Reingold and Dershowitz (2018) <doi:10.1017/9781107415058>. This packages offers a simple API that can be used by other specialized packages.

Browse through a continuously updated list of existing RStudio addins and install/uninstall their corresponding packages.

La libreria ACEP contiene funciones especificas para desarrollar analisis computacional de eventos de protesta. Asimismo, contiene base de datos con colecciones de notas sobre protestas y diccionarios de palabras conflictivas. Coleccion de diccionarios que reune diccionarios de diferentes origenes. The ACEP library contains specific functions to perform computational analysis of protest events. It also contains a database with collections of notes on protests and dictionaries of conflicting words. Collection of dictionaries that brings together dictionaries from different sources.

This package provides sleep duration estimates using a Pruned Dynamic Programming (PDP) algorithm that efficiently identifies change-points. PDP applied to physical activity data can identify transitions from wakefulness to sleep and vice versa. Baek, Jonggyu, Banker, Margaret, Jansen, Erica C., She, Xichen, Peterson, Karen E., Pitchford, E. Andrew, Song, Peter X. K. (2021) An Efficient Segmentation Algorithm to Estimate Sleep Duration from Actigraphy Data <doi:10.1007/s12561-021-09309-3>.

An interface for performing all stages of ADMIXTOOLS analyses (<https://github.com/dreichlab/admixtools>) entirely from R. Wrapper functions (D, f4, f3, etc.) completely automate the generation of intermediate configuration files, run ADMIXTOOLS programs on the command-line, and parse output files to extract values of interest. This allows users to focus on the analysis itself instead of worrying about low-level technical details. A set of complementary functions for processing and filtering of data in the EIGENSTRAT format is also provided.

Data sets are referred to in the text "Applied Survival Analysis Using R" by Dirk F. Moore, Springer, 2016, ISBN: 978-3-319-31243-9, <DOI:10.1007/978-3-319-31245-3>.

Set of functions to analyse and estimate Artificial Counterfactual models from Carvalho, Masini and Medeiros (2016) <DOI:10.2139/ssrn.2823687>.

Estimating and analyzing auto regressive integrated moving average (ARIMA) models. The primary function in this package is arima(), which fits an ARIMA model to univariate time series data using a random restart algorithm. This approach frequently leads to models that have model likelihood greater than or equal to that of the likelihood obtained by fitting the same model using the arima() function from the stats package. This package enables proper optimization of model likelihoods, which is a necessary condition for performing likelihood ratio tests. This package relies heavily on the source code of the arima() function of the stats package. For more information, please see Jesse Wheeler and Edward L. Ionides (2025) <doi:10.1371/journal.pone.0333993>.

This package provides adaptive direct sparse regression for high-dimensional multimodal data with heterogeneous missing patterns and measurement errors. AdapDISCOM extends the DISCOM framework with modality-specific adaptive weighting to handle varying data structures and error magnitudes across blocks. The method supports flexible block configurations (any K blocks) and includes robust variants for heavy-tailed distributions ('AdapDISCOM'-Huber) and fast implementations for large-scale applications (Fast-'AdapDISCOM'). Designed for realistic multimodal scenarios where different data sources exhibit distinct missing data patterns and contamination levels. Diakité et al. (2025) <doi:10.48550/arXiv.2508.00120>.

Survival analysis is employed to model the time it takes for events to occur. Survival model examines the relationship between survival and one or more predictors, usually termed covariates in the survival-analysis literature. To this end, Cox-proportional (Cox-PH) hazard rate model introduced in a seminal paper by Cox (1972) <doi:10.1111/j.2517-6161.1972.tb00899.x>, is a broadly applicable and the most widely used method of survival analysis. This package can be used to estimate the effect of fixed and time-dependent covariates and also to compute the survival probabilities of the lactation of dairy animal. This package has been developed using algorithm of Klein and Moeschberger (2003) <doi:10.1007/b97377>.

Use the Amazon Alexa Web Information Services API to find information about domains, including the kind of content that they carry, how popular are they---rank and traffic history, sites linking to them, among other things. See <https://aws.amazon.com/awis/> for more information.

Edit an Antares simulation before running it : create new areas, links, thermal clusters or binding constraints or edit existing ones. Update Antares general & optimization settings. Antares is an open source power system generator, more information available here : <https://antares-simulator.org/>.

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>.

Automatic normalisation of a data frame to third normal form, with the intention of easing the process of data cleaning. (Usage to design your actual database for you is not advised.) Originally inspired by the AutoNormalize library for Python by Alteryx (<https://github.com/alteryx/autonormalize>), with various changes and improvements. Automatic discovery of functional or approximate dependencies, normalisation based on those, and plotting of the resulting "database" via Graphviz', with options to exclude some attributes at discovery time, or remove discovered dependencies at normalisation time.

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.

Calculate users prevalence of a product based on the prevalence of triers in the population. The measurement of triers is relatively easy. It is just a question of whether a person tried a product even once in his life or not. On the other hand, The measurement of people who also adopt it as part of their life is more complicated since adopting an innovative product is a subjective view of the individual. Mickey Kislev and Shira Kislev developed a formula to calculate the prevalence of a product's users to overcome this difficulty. The current package assists in calculating the users prevalence of a product based on the prevalence of triers in the population. See for: Kislev, M. M., and S. Kislev (2020) <doi:10.5539/ijms.v12n4p63>.

Compute the R-squared measure under the accelerated failure time (AFT) models proposed in Chan et. al. (2018) <doi:10.1080/03610918.2016.1177072>.

Adaptive smoothing functions for estimating the blood oxygenation level dependent (BOLD) effect by using functional Magnetic Resonance Imaging (fMRI) data, based on adaptive Gauss Markov random fields, for real as well as simulated data. The implemented models make use of efficient Markov Chain Monte Carlo methods. Implemented methods are based on the research developed by A. Brezger, L. Fahrmeir, A. Hennerfeind (2007) <https://www.jstor.org/stable/4626770>.

This package provides a set of functions for interacting with the DigitalOcean API <https://www.digitalocean.com/>, including creating images, destroying them, rebooting, getting details on regions, and available images.

The agghoo procedure is an alternative to usual cross-validation. Instead of choosing the best model trained on V subsamples, it determines a winner model for each subsample, and then aggregates the V outputs. For the details, see "Aggregated hold-out" by Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle (2021) <arXiv:1909.04890> published in Journal of Machine Learning Research 22(20):1--55.

Automatic model selection for structural time series decomposition into trend, cycle, and seasonal components, plus optionality for structural interpolation, using the Kalman filter. Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models" <doi:10.1093/oxfordhb/9780195398649.013.0006>. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.

Utilities designed to make the analysis of field trials easier and more accessible for everyone working in plant breeding. It provides a simple and intuitive interface for conducting single and multi-environmental trial analysis, with minimal coding required. Whether you're a beginner or an experienced user, agriutilities will help you quickly and easily carry out complex analyses with confidence. With built-in functions for fitting Linear Mixed Models, agriutilities is the ideal choice for anyone who wants to save time and focus on interpreting their results. Some of the functions require the R package asreml for the ASReml software, this can be obtained upon purchase from VSN international <https://vsni.co.uk/software/asreml-r/>.

Collect your data on digital marketing campaigns from Amazon Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

This package provides a wrapper for the Microsoft Azure Maps REST APIs <https://learn.microsoft.com/en-us/rest/api/maps/route?view=rest-maps-2025-01-01>, enabling users to access mapping and geospatial services directly from R. This package simplifies authenticating, building, and sending requests for services like route directions. It handles conversions between R objects (such as sf objects) and the GeoJSON+JSON format required by the API, making it easier to integrate Azure Maps into R-based data analysis workflows.