This package provides methods to evaluate the performance characteristics of various point and interval estimators for optimal adaptive two-stage designs as described in Meis et al. (2024) <doi:10.1002/sim.10020>. Specifically, this package is written to work with trial designs created by the adoptr package (Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09>; Pilz et al. (2021) <doi:10.1002/sim.8953>)). Apart from the a priori evaluation of performance characteristics, this package also allows for the evaluation of the implemented estimators on real datasets, and it implements methods to calculate p-values.

Build and train a variational autoencoder (VAE) for mixed-type tabular data (continuous, binary, categorical). Models are implemented using TensorFlow and Keras via the reticulate interface, enabling reproducible VAE training for heterogeneous tabular datasets.

Implementation of the Artificial Hydrocarbon Networks for data modeling.

This package implements a credential chain for Azure OAuth 2.0 authentication based on the package httr2''s OAuth framework. Sequentially attempts authentication methods until one succeeds. During development allows interactive browser-based flows ('Device Code and Auth Code flows) and non-interactive flow ('Client Secret') in batch mode.

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.

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.

This package provides functions to compute various clinical scores used in healthcare. These include the Charlson Comorbidity Index (CCI), predicting 10-year survival in patients with multiple comorbidities; the EPICES score, an individual indicator of precariousness considering its multidimensional nature; the MELD score for chronic liver disease severity; the Alternative Fistula Risk Score (a-FRS) for postoperative pancreatic fistula risk; and the Distal Pancreatectomy Fistula Risk Score (D-FRS) for risk following distal pancreatectomy. For detailed methodology, refer to Charlson et al. (1987) <doi:10.1016/0021-9681(87)90171-8> , Sass et al. (2006) <doi:10.1007/s10332-006-0131-5>, Kamath et al. (2001) <doi:10.1053/jhep.2001.22172>, Kim et al. (2008) <doi:10.1056/NEJMoa0801209> Kim et al. (2021) <doi:10.1053/j.gastro.2021.08.050>, Mungroop et al. (2019) <doi:10.1097/SLA.0000000000002620>, and de Pastena et al. (2023) <doi:10.1097/SLA.0000000000005497>..

This package provides a dynamic time warping (DTW) algorithm for stratigraphic alignment, translated into R from the original published MATLAB code by Hay et al. (2019) <doi:10.1130/G46019.1>. The DTW algorithm incorporates two geologically relevant parameters (g and edge) for augmenting the typical DTW cost matrix, allowing for a range of sedimentologic and chronologic conditions to be explored, as well as the generation of an alignment library (as opposed to a single alignment solution). The g parameter relates to the relative sediment accumulation rate between the two time series records, while the edge parameter relates to the amount of total shared time between the records. Note that this algorithm is used for all DTW alignments in the Align Shiny application, detailed in Hagen et al. (in review).

This package provides tools for constructing a matched design with multiple comparison groups. Further specifications of refined covariate balance restriction and exact match on covariate can be imposed. Matches are approximately optimal in the sense that the cost of the solution is at most twice the optimal cost, Crama and Spieksma (1992) <doi:10.1016/0377-2217(92)90078-N>, Karmakar, Small and Rosenbaum (2019) <doi:10.1080/10618600.2019.1584900>.

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.

This package provides functions to perform statistical inference in the balanced one-way ANOVA model with a random factor: confidence intervals, prediction interval, and Weerahandi generalized pivotal quantities. References: Burdick & Graybill (1992, ISBN-13: 978-0824786441); Weerahandi (1995) <doi:10.1007/978-1-4612-0825-9>; Lin & Liao (2008) <doi:10.1016/j.jspi.2008.01.001>.

This package provides a simple driver that reads binary data created by the ASD Inc. portable spectrometer instruments, such as the FieldSpec (for more information, see <http://www.asdi.com/products/fieldspec-spectroradiometers>). Spectral data can be extracted from the ASD files as raw (DN), white reference, radiance, or reflectance. Additionally, the metadata information contained in the ASD file header can also be accessed.

Functionalities to simulate space-time data and to estimate dynamic-spatial panel data models. Estimators implemented are the BCML (Elhorst (2010), <doi:10.1016/j.regsciurbeco.2010.03.003>), the MML (Elhorst (2010) <doi:10.1016/j.regsciurbeco.2010.03.003>) and the INLA Bayesian estimator (Lindgren and Rue, (2015) <doi:10.18637/jss.v063.i19>; Bivand, Gomez-Rubio and Rue, (2015) <doi:10.18637/jss.v063.i20>) adapted to panel data. The package contains functions to replicate the analyses of the scientific article entitled "Agricultural Productivity in Space" (Baldoni and Esposti (2021), <doi:10.1111/ajae.12155>)).

Simulates, fits, and predicts long-memory and anti-persistent time series, possibly mixed with ARMA, regression, transfer-function components. Exact methods (MLE, forecasting, simulation) are used. Bug reports should be done via GitHub (at <https://github.com/JQVeenstra/arfima>), where the development version of this package lives; it can be installed using devtools.

Simple functions to convert given Arabic numerals to Kansuji numerical figures that represent numbers written in Chinese characters.

Self-Attention algorithm helper functions and demonstration vignettes of increasing depth on how to construct the Self-Attention algorithm, this is based on Vaswani et al. (2017) <doi:10.48550/arXiv.1706.03762>, Dan Jurafsky and James H. Martin (2022, ISBN:978-0131873216) <https://web.stanford.edu/~jurafsky/slp3/> "Speech and Language Processing (3rd ed.)" and Alex Graves (2020) <https://www.youtube.com/watch?v=AIiwuClvH6k> "Attention and Memory in Deep Learning".

This package provides a cross-platform R framework that facilitates processing of any number of Affymetrix microarray samples regardless of computer system. The only parameter that limits the number of chips that can be processed is the amount of available disk space. The Aroma Framework has successfully been used in studies to process tens of thousands of arrays. This package has actively been used since 2006.

Retrieve air quality data via the AirNow <https://www.airnow.gov/> API.

This package implements several tools that are used in animal social network analysis, as described in Whitehead (2007) Analyzing Animal Societies <University of Chicago Press> and Farine & Whitehead (2015) <doi: 10.1111/1365-2656.12418>. In particular, this package provides the tools to infer groups and generate networks from observation data, perform permutation tests on the data, calculate lagged association rates, and performed multiple regression analysis on social network data.

To address the violation of the assumption of normally distributed variables, researchers frequently employ bootstrapping. Building upon established packages for R (Sigmann et al. (2024) <doi:10.32614/CRAN.package.afex>, Lenth (2024) <doi:10.32614/CRAN.package.emmeans>), we provide bootstrapping functions to approximate a normal distribution of the parameter estimates for between-subject, within-subject, and mixed one-way and two-way ANOVA.

An implementation of the additive polynomial (AP) design matrix. It constructs and appends an AP design matrix to a data frame for use with longitudinal data subject to seasonality.

Age-Period-Cohort (APC) analyses are used to differentiate relevant drivers for long-term developments. The APCtools package offers visualization techniques and general routines to simplify the workflow of an APC analysis. Sophisticated functions are available both for descriptive and regression model-based analyses. For the former, we use density (or ridgeline) matrices and (hexagonally binned) heatmaps as innovative visualization techniques building on the concept of Lexis diagrams. Model-based analyses build on the separation of the temporal dimensions based on generalized additive models, where a tensor product interaction surface (usually between age and period) is utilized to represent the third dimension (usually cohort) on its diagonal. Such tensor product surfaces can also be estimated while accounting for further covariates in the regression model. See Weigert et al. (2021) <doi:10.1177/1354816620987198> for methodological details.

This package provides easy access to the AviList Global Avian Checklist, the first unified global bird taxonomy that harmonizes previous differences between International Ornithological Committee ('IOC'), Clements', and BirdLife checklists. This package contains the complete AviList dataset as R data objects ready for ornithological research and analysis. For more details see AviList Core Team (2025) <doi:10.2173/avilist.v2025>.

This package provides a web framework inspired by express.js to build any web service from multi-page websites to RESTful application programming interfaces.