This package contains data from an observational study concerning possible effects of light daily alcohol consumption on survival and on HDL cholesterol. It also replicates various simple analyses in Rosenbaum (2025) <doi:10.1080/09332480.2025.2473291>. Finally, it includes new R code in wgtRankCef() that implements and replicates a new method for constructing evidence factors in observational block designs in Rosenbaum (2026) <doi:10.1080/01621459.2026.2624858>).

This package performs simple and canonical CA (covariates on rows/columns) on a two-way frequency table (with missings) by means of SVD. Different scaling methods (standard, centroid, Benzecri, Goodman) as well as various plots including confidence ellipsoids are provided.

Exploration of Weather Research & Forecasting ('WRF') Model data of Servicio Meteorologico Nacional (SMN) from Amazon Web Services (<https://registry.opendata.aws/smn-ar-wrf-dataset/>) cloud. The package provides the possibility of data downloading, processing and correction methods. It also has map management and series exploration of available meteorological variables of WRF forecast.

This package contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models.

This package provides methods to analyse spatial units in archaeology from the refitting relationships between fragments of objects scattered in these units (e.g. stratigraphic layers). Graphs are used to model archaeological observations. The package is mainly based on the igraph package for graph analysis. Functions can: 1) create, manipulate, visualise, and simulate fragmentation graphs, 2) measure the cohesion and admixture of archaeological spatial units, and 3) characterise the topology of a specific set of refitting relationships. A series of published empirical datasets is included. Documentation about archeofrag is provided by a vignette and by the accompanying scientific papers: Plutniak (2021, Journal of Archaeological Science, <doi:10.1016/j.jas.2021.105501>) and Plutniak (2022, Journal of Open Source Software, <doi:10.21105/joss.04335>). This package is complemented by the archeofrag.gui R package, a companion GUI application available at <https://analytics.huma-num.fr/Sebastien.Plutniak/archeofrag/>.

Simulation and pricing routines for rare-event options using Adaptive Multilevel Splitting and standard Monte Carlo under Black-Scholes and Heston models. Core routines are implemented in C++ via Rcpp and RcppArmadillo with lightweight R wrappers.

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>.

This package contains functions for retrieving, managing, and analyzing air quality and weather data from the Regione Lombardia open database (<https://www.dati.lombardia.it/>). Data are collected by ARPA Lombardia (Lombardia Environmental Protection Agency), Italy, through its ground monitoring network (<https://www.dati.lombardia.it/stories/s/auv9-c2sj>). See the website <https://www.arpalombardia.it/> for further information on ARPA Lombardia's activities and history. Data quality (e.g., missing values, extreme values, and graphical mapping) has been checked in collaboration with members of ARPA Lombardia's air quality control office. The package provides observations since 1989 (for weather) and 1968 (for air quality), and these data are updated daily by the regional agency. A full description of the package is available in the companion paper Maranzano \& Algieri (2024), "ARPALData: an R package for retrieving and analyzing air quality and weather data from ARPA Lombardia (Italy)", Environmental and Ecological Statistics, <doi:10.1007/s10651-024-00599-6>.

Reads and writes clinical-trial datasets losslessly across SAS XPORT (XPT), Clinical Data Interchange Standards Consortium (CDISC) Dataset-JSON, and Apache Parquet', applying a specification to produce submission-ready Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM) datasets. A single canonical metadata model carries labels, CDISC data types, lengths, SAS display formats, controlled-terminology references, and sort keys identically across every format, so conversion between any two formats is lossless by construction. Pure R and lightweight, with no external SAS or Java runtime. Implements the published format specifications for CDISC Dataset-JSON (<https://cdisc-org.github.io/DataExchange-DatasetJson/doc/dataset-json1-1.html>) and SAS XPORT (<https://www.loc.gov/preservation/digital/formats/fdd/fdd000466.shtml>).

This package implements the Age Band Decomposition (ABD) method for standardizing tree ring width data while preserving both low and high frequency variability. Unlike traditional detrending approaches that can distort long term growth trends, ABD decomposes ring width series into multiple age classes, detrends each class separately, and then recombines them to create standardized chronologies. This approach improves the detection of growth signals linked to past climatic and environmental factors, making it particularly valuable for dendroecological and dendroclimatological studies. The package provides functions to perform ABD-based standardization, compare results with other common methods (e.g., BAI, C method, RCS), and facilitate the interpretation of growth patterns under current and future climate variability.

Computes low-dimensional point representations of high-dimensional numerical data according to the data visualization method Adaptable Radial Axes described in: Manuel Rubio-Sánchez, Alberto Sanchez, and Dirk J. Lehmann (2017) "Adaptable radial axes plots for improved multivariate data visualization" <doi:10.1111/cgf.13196>.

This package provides a modeling package compiling applicability domain methods in R. It combines different methods to measure the amount of extrapolation new samples can have from the training set. See <doi:10.4018/IJQSPR.2016010102> for an overview of applicability domains.

Analyzes autocorrelation and partial autocorrelation using surrogate methods and bootstrapping, and computes the acceleration constants for the vectorized moving block bootstrap provided by this package. It generates percentile, bias-corrected, and accelerated intervals and estimates partial autocorrelations using Durbin-Levinson. This package calculates the autocorrelation power spectrum, computes cross-correlations between two time series, computes bandwidth for any time series, and performs autocorrelation frequency analysis. It also calculates the periodicity of a time series.

Convert populations into integer number of seats for legislative bodies. Implements apportionment methods used historically and currently in the United States for reapportionment after the Census, as described in <https://www.census.gov/history/www/reference/apportionment/methods_of_apportionment.html>.

An R API providing access to a relational database with macroeconomic data for Africa. The database contains >700 macroeconomic time series from mostly international sources, grouped into 50 macroeconomic and development-related topics. Series are carefully selected on the basis of data coverage for Africa, frequency, and relevance to the macro-development context. The project is part of the Kiel Institute Africa Initiative <https://www.ifw-kiel.de/institute/initiatives/kiel-institute-africa-initiative/>, which, amongst other things, aims to develop a parsimonious database with highly relevant indicators to monitor macroeconomic developments in Africa, accessible through a fast API and a web-based platform at <https://africamonitor.ifw-kiel.de/>. The database is maintained at the Kiel Institute for the World Economy <https://www.ifw-kiel.de/>.

This package provides a simple client for the Amazon Web Services ('AWS') Identity and Access Management ('IAM') API <https://aws.amazon.com/iam/>.

Fits a linear-binomial model using a modified Newton-type algorithm for solving the maximum likelihood estimation problem under linear box constraints. Similar methods are described in Wagenpfeil, Schöpe and Bekhit (2025, ISBN:9783111341972) "Estimation of adjusted relative risks in log-binomial regression using the BSW algorithm". In: Mau, Mukhin, Wang and Xu (Eds.), Biokybernetika. De Gruyter, Berlin, pp. 665â 676.

This package provides functions to calculate Gray Level Co-occurrence Matrix(GLCM), RGB-based Vegetative Index(RGB VI) and Normalized Difference Vegetation Index(NDVI) family image features. GLCM calculations are based on Haralick (1973) <doi:10.1109/TSMC.1973.4309314>.

This package provides a systematic framework for neural networkâ based model selection and forecasting using single hidden layer feed-forward networks. It evaluates all possible combinations of predictor variables and hidden layer configurations, selecting the optimal model based on predictive accuracy criteria such as root mean squared error (RMSE) and mean absolute percentage error (MAPE). Predictors are automatically standardized, and model performance is assessed using out-of-sample validation. The package is designed for empirical modelling and forecasting in economics, agriculture, trade, climate, and related applied research domains where nonlinear relationships and robust predictive performance are of primary interest.

Using this package you can interact with the Aplos NCA API'<https://docs.aplosanalytics.com/> using standard R functions. This will allow you to authenticate with your Aplos NCA account, upload input datasets, initiate analyses, and download results.

The real-life time series data are hardly pure linear or nonlinear. Merging a linear time series model like the autoregressive moving average (ARMA) model with a nonlinear neural network model such as the Long Short-Term Memory (LSTM) model can be used as a hybrid model for more accurate modeling purposes. Both the autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models can be implemented. Details can be found in Box et al. (2015, ISBN: 978-1-118-67502-1) and Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>.

Imports Azure Application Insights for web pages into Shiny apps via Microsoft's JavaScript snippet. Allows app developers to submit page tracking and submit events.

This package performs the two-sample Ansariâ Bradley test (Ansari & Bradley, 1960 <https://www.jstor.org/stable/2237814>) for univariate, distinct data in the presence of missing values, as described in Zeng et al. (2025) <doi:10.48550/arXiv.2509.20332>. This method does not make any assumptions about the missingness mechanisms and controls the Type I error regardless of the missing values by taking all possible missing values into account.

This package provides a high-performance, flexible and extensible framework to develop continuous-time agent based models. Its high performance allows it to simulate millions of agents efficiently. Agents are defined by their states (arbitrary R lists). The events are handled in chronological order. This avoids the multi-event interaction problem in a time step of discrete-time simulations, and gives precise outcomes. The states are modified by provided or user-defined events. The framework provides a flexible and customizable implementation of state transitions (either spontaneous or caused by agent interactions), making the framework suitable to apply to epidemiology and ecology, e.g., to model life history stages, competition and cooperation, and disease and information spread. The agent interactions are flexible and extensible. The framework provides random mixing and network interactions, and supports multi-level mixing patterns. It can be easily extended to other interactions such as inter- and intra-households (or workplaces and schools) by subclassing an R6 class. It can be used to study the effect of age-specific, group-specific, and contact- specific intervention strategies, and complex interactions between individual behavior and population dynamics. This modeling concept can also be used in business, economical and political models. As a generic event based framework, it can be applied to many other fields. More information about the implementation and examples can be found at <https://github.com/junlingm/ABM>.