This package provides three stability-validated pipelines for computing an Aggregated Latent Space Index (ALSI): a binary MCA pipeline (alsi_workflow()), an ordinal pipeline using homals alternating least squares optimal scaling (alsi_workflow_ordinal()), and a continuous ipsatized SVD pipeline (calsi_workflow()). All three pipelines share a common bootstrap dual-criterion stability framework (principal angles and Tucker congruence phi) for determining the number of dimensions to retain before index construction. The package is designed to complement Segmented Profile Analysis (SEPA) and is intended for psychometric scale construction and dimensional reduction in survey and clinical research.

An interface to Azure Computer Vision <https://docs.microsoft.com/azure/cognitive-services/Computer-vision/Home> and Azure Custom Vision <https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/home>, building on the low-level functionality provided by the AzureCognitive package. These services allow users to leverage the cloud to carry out visual recognition tasks using advanced image processing models, without needing powerful hardware of their own. Part of the AzureR family of packages.

An integrated set of functions for building, analyzing, and visualizing Analytic Hierarchy Process (AHP) models, designed to support structured decision-making in consultancy, policy analysis, and research (Bose 2022 <doi:10.1002/mcda.1784>; Bose 2023 <doi:10.1002/mcda.1821>). In addition to tools for assessing and improving the consistency of pairwise comparison matrices (PCMs), the package supports full-hierarchy weight computation, intuitive tree-based visualization, sensitivity analysis, along with convenient PCM generation from user preferences.

This package provides functions are provided to read and convert AIFF audio files to WAVE (WAV) format. This supports, for example, use of the tuneR package, which does not currently handle AIFF files. The AIFF file format is defined in <https://web.archive.org/web/20080125221040/http://www.borg.com/~jglatt/tech/aiff.htm> and <https://www.mmsp.ece.mcgill.ca/Documents/AudioFormats/AIFF/Docs/AIFF-1.3.pdf> .

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.

Penalized variable selection tools for the Cox proportional hazards model with interval censored and possibly left truncated data. It performs variable selection via penalized nonparametric maximum likelihood estimation with an adaptive lasso penalty. The optimal thresholding parameter can be searched by the package based on the profile Bayesian information criterion (BIC). The asymptotic validity of the methodology is established in Li et al. (2019 <doi:10.1177/0962280219856238>). The unpenalized nonparametric maximum likelihood estimation for interval censored and possibly left truncated data is also available.

This package provides simple assertions with sensible defaults and customisable error messages. It offers convenient assertion call wrappers and a general assert function that can handle any condition. Default error messages are user friendly and easily customized with inline code evaluation and styling powered by the cli package.

Predicts amyloid proteins using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI.

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>.

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 implements the Adaptive Multiple Importance Sampling (AMIS) algorithm, as described by Retkute et al. (2021, <doi:10.1214/21-AOAS1486>), to estimate key epidemiological parameters by combining outputs from a geostatistical model of infectious diseases (such as prevalence, incidence, or relative risk) with a disease transmission model. Utilising the resulting posterior distributions, the package enables forward projections at the local level.

This package provides tools for the quantitative analysis of axon integrity in microscopy images. It implements image pre-processing, adaptive thresholding, feature extraction, and support vector machine-based classification to compute indices such as the Axon Integrity Index (AII) and Degeneration Index (DI). The package is designed for reproducible and automated analysis in neuroscience research.

Comprehensive toolkit for reading and analyzing Anki flashcard collection databases. Provides functions to access notes, cards, decks, note types, and review logs with a tidy interface. Features extensive analytics including retention rates, learning curves, forgetting curve fitting, and review patterns. Supports FSRS (Free Spaced Repetition Scheduler) analysis with stability, difficulty, retrievability metrics, parameter comparison, and workload predictions. Includes visualization functions, comparative analysis, time-based analytics, card quality assessment, sibling card analysis, interference detection, predictive features, session simulation, and an interactive Shiny dashboard. Academic/exam preparation tools for medical students and board exam preparation. Export capabilities include CSV, Org-mode, Markdown, SuperMemo, Mochi, Obsidian SR, and JSON formats with progress reports.

Analyze association studies with multiple realizations of a noisy or uncertain exposure. These can be obtained from e.g. a two-dimensional Monte Carlo dosimetry system (Simon et al 2015 <doi:10.1667/RR13729.1>) to characterize exposure uncertainty. The implemented methods are regression calibration (Carroll et al. 2006 <doi:10.1201/9781420010138>), extended regression calibration (Little et al. 2023 <doi:10.1038/s41598-023-42283-y>), Monte Carlo maximum likelihood (Stayner et al. 2007 <doi:10.1667/RR0677.1>), frequentist model averaging (Kwon et al. 2023 <doi:10.1371/journal.pone.0290498>), and Bayesian model averaging (Kwon et al. 2016 <doi:10.1002/sim.6635>). Supported model families are Gaussian, binomial, multinomial, Poisson, proportional hazards, and conditional logistic.

Interact with the Attentional Control Data Collection (ACDC) or the Truth Effect Database (TED). Download the databases using download_acdc() or download_ted(), connect to the database via connect_to_db(), set filter arguments via add_argument() and query the database via query_db().

Machine learning based package to predict anti-angiogenic peptides using heterogeneous sequence descriptors. AntAngioCOOL exploits five descriptor types of a peptide of interest to do prediction including: pseudo amino acid composition, k-mer composition, k-mer composition (reduced alphabet), physico-chemical profile and atomic profile. According to the obtained results, AntAngioCOOL reached to a satisfactory performance in anti-angiogenic peptide prediction on a benchmark non-redundant independent test dataset.

Lite interface for finding locations of addresses or businesses around the world using the ArcGIS REST API service <https://developers.arcgis.com/rest/geocode/api-reference/overview-world-geocoding-service.htm>. Address text can be converted to location candidates and a location can be converted into an address. No API key required.

Create American Psychological Association Style, Seventh Edition documents. Format numbers and text consistent with APA style. Create tables that comply with APA style by extending flextable functions.

It fits a univariate left, right, or interval censored linear regression model with autoregressive errors, considering the normal or the Student-t distribution for the innovations. It provides estimates and standard errors of the parameters, predicts future observations, and supports missing values on the dependent variable. References used for this package: Schumacher, F. L., Lachos, V. H., & Dey, D. K. (2017). Censored regression models with autoregressive errors: A likelihood-based perspective. Canadian Journal of Statistics, 45(4), 375-392 <doi:10.1002/cjs.11338>. Schumacher, F. L., Lachos, V. H., Vilca-Labra, F. E., & Castro, L. M. (2018). Influence diagnostics for censored regression models with autoregressive errors. Australian & New Zealand Journal of Statistics, 60(2), 209-229 <doi:10.1111/anzs.12229>. Valeriano, K. A., Schumacher, F. L., Galarza, C. E., & Matos, L. A. (2024). Censored autoregressive regression models with Studentâ t innovations. Canadian Journal of Statistics, 52(3), 804-828 <doi:10.1002/cjs.11804>.

This package provides tools for raster georeferencing, grid affine transforms, and general raster logic. These functions provide converters between raster specifications, world vector, geotransform, RasterIO window, and RasterIO window in sf package list format. There are functions to offset a matrix by padding any of four corners (useful for vectorizing neighbourhood operations), and helper functions to harvesting user clicks on a graphics device to use for simple georeferencing of images. Methods used are available from <https://en.wikipedia.org/wiki/World_file> and <https://gdal.org/user/raster_data_model.html>.

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.

This package provides a collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737.

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

Estimate the AUC using a variety of methods as follows: (1) frequentist nonparametric methods based on the Mann-Whitney statistic or kernel methods. (2) frequentist parametric methods using the likelihood ratio test based on higher-order asymptotic results, the signed log-likelihood ratio test, the Wald test, or the approximate t solution to the Behrens-Fisher problem. (3) Bayesian parametric MCMC methods.