Modern software often poorly support older file formats. This package intends to handle many file formats that were native to the antiquated Commodore Amiga machine. This package focuses on file types from the older Amiga operating systems (<= 3.0). It will read and write specific file formats and coerces them into more contemporary data.

Fits Modern Analogue Technique and Weighted Averaging transfer function models for prediction of environmental data from species data, and related methods used in palaeoecology.

This package provides tools to summarize, analyze, and visualize results from Mendelian randomization studies using summarized genetic association data. The package includes functions for generating forest plots and scatter plots at the single-nucleotide polymorphism and Mendelian randomization method levels, and for fitting multiple estimators in a unified workflow, including inverse-variance weighted estimation, Mendelian randomization Egger regression, the weighted median estimator, the robust adjusted profile score, Mendelian randomization pleiotropy residual sum and outlier, Mendelian randomization with the genotype recoding invariance property, and a Bayesian horseshoe method. Related methods are described by Burgess (2013) <doi:10.1002/gepi.21758>, Bowden (2015) <doi:10.1093/ije/dyv080>, Bowden (2016) <doi:10.1002/gepi.21965>, Zhao (2020) <doi:10.1214/19-AOS1866>, Verbanck (2018) <doi:10.1038/s41588-018-0099-7>, Dudbridge (2025) <doi:10.1371/journal.pgen.1011967>, and Grant and Burgess (2024) <doi:10.1016/j.ajhg.2023.12.002>. Related open-source software includes TwoSampleMR <https://github.com/MRCIEU/TwoSampleMR>, mr.raps <https://github.com/qingyuanzhao/mr.raps>, MR-PRESSO <https://github.com/rondolab/MR-PRESSO>, and MR-Horse <https://github.com/aj-grant/mrhorse>.

Nonparametric data-driven approach to discovering heterogeneous subgroups in a selection-on-observables framework. aggTrees allows researchers to assess whether there exists relevant heterogeneity in treatment effects by generating a sequence of optimal groupings, one for each level of granularity. For each grouping, we obtain point estimation and inference about the group average treatment effects. Please reference the use as Di Francesco (2022) <doi:10.2139/ssrn.4304256>.

This package provides a set of fast and convenient functions to help conducting accessibility analyses. Given a pre-computed travel cost matrix and a land use dataset (containing for example the location of jobs, healthcare and population), the package allows one to calculate accessibility levels, and accessibility poverty and inequality. The package covers the majority of the most commonly used accessibility measures (such as cumulative opportunities, gravity-based and floating catchment areas methods), some cutting edge measures proposed in the literature (e.g. balancing cost and constrained accessibility) as well as the most frequently used inequality and poverty metrics (such as the Palma ratio, the concentration and Theil indices and the FGT family of measures).

Calculate ActiGraph counts from the X, Y, and Z axes of a triaxial accelerometer. This work was inspired by Neishabouri et al. who published the article "Quantification of Acceleration as Activity Counts in ActiGraph Wearables" on February 24, 2022. The link to the article (<https://pubmed.ncbi.nlm.nih.gov/35831446>) and python implementation of this code (<https://github.com/actigraph/agcounts>).

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.

Computation of adherence to medications from Electronic Health care Data and visualization of individual medication histories and adherence patterns. The package implements a set of S3 classes and functions consistent with current adherence guidelines and definitions. It allows the computation of different measures of adherence (as defined in the literature, but also several original ones), their publication-quality plotting, the estimation of event duration and time to initiation, the interactive exploration of patient medication history and the real-time estimation of adherence given various parameter settings. It scales from very small datasets stored in flat CSV files to very large databases and from single-thread processing on mid-range consumer laptops to parallel processing on large heterogeneous computing clusters. It exposes a standardized interface allowing it to be used from other programming languages and platforms, such as Python.

This package provides methods for high-throughput adaptive immune receptor repertoire sequencing (AIRR-Seq; Rep-Seq) analysis. In particular, immunoglobulin (Ig) sequence lineage reconstruction, lineage topology analysis, diversity profiling, amino acid property analysis and gene usage. Citations: Gupta and Vander Heiden, et al (2017) <doi:10.1093/bioinformatics/btv359>, Stern, Yaari and Vander Heiden, et al (2014) <doi:10.1126/scitranslmed.3008879>.

This package provides a comprehensive pipeline for data quality checks and statistical assumption diagnostics in agricultural experimental data. Functions cover outlier detection using Interquartile Range (IQR) fence, Z-score, modified Z-score (Hampel identifier), Grubbs test and Dixon Q-test with consensus flagging; missing data pattern analysis and mechanism classification (Missing Completely At Random/Missing At Random/Missing Not At Random (MCAR/MAR/MNAR)) via Little's test; normality testing using Shapiro-Wilk, Anderson-Darling, Kolmogorov-Smirnov, Lilliefors, Pearson chi-square and Jarque-Bera tests; homogeneity of variance via Bartlett, Levene and Fligner-Killeen tests; independence of errors via Durbin-Watson, Breusch-Godfrey and Wald-Wolfowitz runs tests; experimental design validation for Completely Randomised Design (CRD), Randomised Complete Block Design (RCBD), Latin Square Design (LSD) and factorial designs; qualitative variable consistency checks; and automated HyperText Markup Language (HTML) report generation. Designed to align with Findable, Accessible, Interoperable and Reusable (FAIR) data principles. Methods follow Gomez and Gomez (1984, ISBN:978-0471870920) and Montgomery (2017, ISBN:978-1119492443).

Routines for re-scaling isotope maps using known-origin tissue isotope data, assigning origin of unknown samples, and summarizing and assessing assignment results. Methods are adapted from Wunder (2010, in ISBN:9789048133536) and Vander Zanden, H. B. et al. (2014) <doi:10.1111/2041-210X.12229> as described in Ma, C. et al. (2020) <doi:10.1111/2041-210X.13426>.

An evaluation framework for algorithm portfolios using Item Response Theory (IRT). We use continuous and polytomous IRT models to evaluate algorithms and introduce algorithm characteristics such as stability, effectiveness and anomalousness (Kandanaarachchi, Smith-Miles 2020) <doi:10.13140/RG.2.2.11363.09760>.

Implementation of the autocorrelated conditioned Latin Hypercube Sampling (acLHS) algorithm for 1D (time-series) and 2D (spatial) data. The acLHS algorithm is an extension of the conditioned Latin Hypercube Sampling (cLHS) algorithm that allows sampled data to have similar correlative and statistical features of the original data. Only a properly formatted dataframe needs to be provided to yield subsample indices from the primary function. For more details about the cLHS algorithm, see Minasny and McBratney (2006), <doi:10.1016/j.cageo.2005.12.009>. For acLHS, see Le and Vargas (2024) <doi:10.1016/j.cageo.2024.105539>.

This package provides tools for the multiscale spatial analysis of multivariate data. Several methods are based on the use of a spatial weighting matrix and its eigenvector decomposition (Moran's Eigenvectors Maps, MEM). Several approaches are described in the review Dray et al (2012) <doi:10.1890/11-1183.1>.

This package provides tools for estimating length-based indicators from length frequency data to assess fish stock status and manage fisheries sustainably. Implements methods from Cope and Punt (2009) <doi:10.1577/C08-025.1> for data-limited stock assessment and Froese (2004) <doi:10.1111/j.1467-2979.2004.00144.x> for detecting overfishing using simple indicators. Key functions include: FrequencyTable(): Calculate the frequency table from the collected and also the extract the length frequency data from the frequency table with the upper length_range. A numeric value specifying the bin width for class intervals. If not provided, the bin width is automatically calculated using Wang (2020) <doi:10.1016/j.fishres.2019.105474> formula. FreqTM(): Creates a frequency distribution table for fish length data across multiple months using a consistent length class structure. The bin width is determined by either a custom value or Wang's formula, applied uniformly across all months. The function dynamically detects and renames columns to Month and Length from the input dataframe. The maximum observed length is included as part of the last class, with the upper bound set to the smallest multiple of the bin width greater than or equal to the maximum length. Months can be converted to dates using a configurable day and year, with dates assigned sequentially in day.month.year format (e.g., 15.01.26). FishPar(): Calculates length-based indicators (LBIs) proposed by Froese (2004) <doi:10.1111/j.1467-2979.2004.00144.x> such as the percentage of mature fish (Pmat), percentage of optimal length fish (Popt), percentage of mega spawners (Pmega), and the sum of these as Pobj. This function also estimates confidence intervals for different lengths, visualizes length frequency distributions, and provides data frames containing calculated values. FishSS(): Makes decisions based on input from Cope and Punt (2009) <doi:10.1577/C08-025.1> and parameters calculated by FishPar() (e.g., Pobj, Pmat, Popt, LM_ratio) to determine stock status as target spawning biomass (TSB40) and limit spawning biomass (LSB25), and selectivity. LWR(): Fits and visualizes length-weight relationships using linear regression, with options for log-transformation and customizable plotting.

Extends package arules with various visualization techniques for association rules and itemsets. The package also includes several interactive visualizations for rule exploration. Michael Hahsler (2017) <doi:10.32614/RJ-2017-047>.

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.

Response surface designs (RSDs) are widely used for Response Surface Methodology (RSM) based optimization studies, which aid in exploring the relationship between a group of explanatory variables and one or more response variable(s) (G.E.P. Box and K.B. Wilson (1951), "On the experimental attainment of optimum conditions" ; M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) <DOI: 10.1080/03610926.2021.1944213>."Theoretical developments in response surface designs: an informative review and further thoughts".). Second order rotatable designs are the most prominent and popular class of designs used for process and product optimization trials but it is suitable for situations when all the number of levels for each factor is the same. In many practical situations, RSDs with asymmetric levels (J.S. Mehta and M.N. Das (1968). "Asymmetric rotatable designs and orthogonal transformations" ; M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2020) <DOI: 10.1080/02664763.2020.1864817>. "Sequential asymmetric third order rotatable designs (SATORDs)" .) are more suitable as these designs explore more regions in the design space.This package contains functions named Asords() ,CCD_coded(), CCD_original(), SORD_coded() and SORD_original() for generating asymmetric/symmetric RSDs along with the randomized layout. It also contains another function named Pred.var() for generating the variance of predicted response as well as the moment matrix based on a second order model.

Fast generators and iterators for permutations, combinations, integer partitions and compositions. The arrangements are in lexicographical order and generated iteratively in a memory efficient manner. It has been demonstrated that arrangements outperforms most existing packages of similar kind. Benchmarks could be found at <https://randy3k.github.io/arrangements/articles/benchmark.html>.

This package provides tools for defining recurrence rules and recurrence sets. Recurrence rules are a programmatic way to define a recurring event, like the first Monday of December. Multiple recurrence rules can be combined into larger recurrence sets. A full holiday and calendar interface is also provided that can generate holidays within a particular year, can detect if a date is a holiday, can respect holiday observance rules, and allows for custom holidays.

Computation of the alpha-shape and alpha-convex hull of a given sample of points in the plane. The concepts of alpha-shape and alpha-convex hull generalize the definition of the convex hull of a finite set of points. The programming is based on the duality between the Voronoi diagram and Delaunay triangulation. The package also includes a function that returns the Delaunay mesh of a given sample of points and its dual Voronoi diagram in one single object.

This package provides a number of functions to access the National Energy Research Laboratory Alternate Fuel Locator API <https://developer.nrel.gov/docs/transportation/alt-fuel-stations-v1/>. The Alternate Fuel Locator shows the location of alternate fuel stations in the United States and Canada. This package also includes the data from the US Department of Energy Alternate Fuel database as a data set.

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 Azure Active Directory (AAD) authentication functionality for R users of Microsoft's Azure cloud <https://azure.microsoft.com/en-us>. Use this package to obtain OAuth 2.0 tokens for services including Azure Resource Manager, Azure Storage and others. It supports both AAD v1.0 and v2.0, as well as multiple authentication methods, including device code and resource owner grant. Tokens are cached in a user-specific directory obtained using the rappdirs package. The interface is based on the OAuth framework in the httr package, but customised and streamlined for Azure. Part of the AzureR family of packages.