Analyze repertory grids, a qualitative-quantitative data collection technique devised by George A. Kelly in the 1950s. Today, grids are used across various domains ranging from clinical psychology to marketing. The package contains functions to quantitatively analyze and visualize repertory grid data (e.g. Fransella', Bell', & Bannister', 2004, ISBN: 978-0-470-09080-0). The package is part of the The package is part of the <https://openrepgrid.org/> project.

Medication adherence, defined as medication-taking behavior that aligns with the agreed-upon treatment protocol, is critical for realizing the benefits of prescription medications. Medication adherence can be assessed using electronic adherence monitoring devices (EAMDs), pill bottles or boxes that contain a computer chip that records the date and time of each opening (or â actuationâ ). Before researchers can use EAMD data, they must apply a series of decision rules to transform actuation data into adherence data. The purpose of this R package ('oncmap') is to transform EAMD actuations in the form of a raw .csv file, information about the patient, regimen, and non-monitored periods into two daily adherence values -- Dose Taken and Correct Dose Taken.

Inspired by S-PLUS function objects.summary(), provides a function with the same name that returns data class, storage mode, mode, type, dimension, and size information for R objects in the specified environment. Various filtering and sorting options are also proposed.

Empirical reservoir water quality modelling using Walker's BATHTUB Model 1 (second-order available-phosphorus sedimentation) from Walker (1985) <https://hdl.handle.net/11681/13884> and Walker (1996) <https://hdl.handle.net/11681/4353> as the default retention model. The Vollenweider (1976) hydraulic- residence form and the equivalent formulation of Larsen and Mercier (1976) are available as alternatives. Predicts in-lake total phosphorus, total nitrogen, chlorophyll-a, and Secchi depth from tributary nutrient and hydraulic loading inputs, and computes Carlson (1977) <doi:10.4319/lo.1977.22.2.0361> Trophic State Indices. Optional Oklahoma-specific chlorophyll and Secchi regression coefficients are provided, calibrated from publicly available state lake monitoring data. Supports single-segment and multi-segment reservoir configurations and load-reduction scenario analysis. Designed to complement watershed loading models such as the Soil and Water Assessment Tool ('SWAT'; <https://swat.tamu.edu>) and the U.S. EPA Hydrologic and Water Quality System ('HAWQS'; <https://hawqs.tamu.edu>) in a two-model nutrient management workflow.

An implementation of several functions for feature extraction in ordinal time series datasets. Specifically, some of the features proposed by Weiss (2019) <doi:10.1080/01621459.2019.1604370> can be computed. These features can be used to perform inferential tasks or to feed machine learning algorithms for ordinal time series, among others. The package also includes some interesting datasets containing financial time series. Practitioners from a broad variety of fields could benefit from the general framework provided by otsfeatures'.

This package provides tools to process raster data and apply Otsu-based thresholding for burned area mapping and other image segmentation tasks. Implements the method described by Otsu (1979) <doi:10.1109/TSMC.1979.4310076>, a data-driven technique that determines an optimal threshold by maximizing the inter-class variance of pixel intensities. It includes validation functions to assess segmentation accuracy against reference data using standard accuracy metrics such as precision, recall, and F1-score.

An extension to the Regression Modeling Strategies package that facilitates plotting ordinal regression model predictions together with confidence intervals for each dependent variable level. It also adds a functionality to plot the model summary as a modifiable object.

DNA methylation is an important epigenetic process that regulates gene activity through chemical modifications of DNA without changing its sequence. OpEnCAST is a plant-specific ensemble-based prediction package that identifies 4mC, 5mC and 6mA methylation sites directly from DNA sequences. It combines multiple machine learning algorithms trained on monocot (Oryza sp.) and dicot (Arabidopsis sp.) reference models to deliver accurate predictions. This methodology is being inspired by the ensemble algorithm for methylation prediction developed by Wang et al. (2022) <doi:10.1186/s12859-022-04756-1>.

This package performs the O2PLS data integration method for two datasets, yielding joint and data-specific parts for each dataset. The algorithm automatically switches to a memory-efficient approach to fit O2PLS to high dimensional data. It provides a rigorous and a faster alternative cross-validation method to select the number of components, as well as functions to report proportions of explained variation and to construct plots of the results. See the software article by el Bouhaddani et al (2018) <doi:10.1186/s12859-018-2371-3>, and Trygg and Wold (2003) <doi:10.1002/cem.775>. It also performs Sparse Group (Penalized) O2PLS, see Gu et al (2020) <doi:10.1186/s12859-021-03958-3> and cross-validation for the degree of sparsity.

Plotting toolbox for 2D oceanographic data (satellite data, sea surface temperature, chlorophyll, ocean fronts & bathymetry). Recognized classes and formats include netcdf, Raster, .nc and .gz files.

The online principal component regression method can process the online data set. OPCreg implements the online principal component regression method, which is specifically designed to process online datasets efficiently. This method is particularly useful for handling large-scale, streaming data where traditional batch processing methods may be computationally infeasible.The philosophy of the package is described in Guo (2025) <doi:10.1016/j.physa.2024.130308>.

This package provides a penalized regression framework that can simultaneously estimate the optimal treatment strategy and identify important variables. Appropriate for either censored or uncensored continuous response.

Calculates D-, Ds-, A-, I- and L-optimal designs, weighted combinations of these via a Compound criterion, and KL-optimal designs for model discrimination, for non-linear single- and multi-factor models, via an implementation of the cocktail algorithm (Yu, 2011, <doi:10.1007/s11222-010-9183-2>). Multi-factor models use design variables x1, x2, â ¦ with a named-list design space; single-factor models remain backward compatible. Compares designs via their efficiency, augments any design with a controlled efficiency loss, and provides efficient rounding functions to convert approximate designs to exact ones.

The classical and extended occupancy distributions occur in cases where balls are randomly allocated to bins. The PDF, CDF, quantile functions, generation of random variates, and calculating the first four central moments of the distributions are implemented as described in Oâ Neill (2019) <doi:10.1080/00031305.2019.1699445>.

Obtain and evaluate various optimal designs for the 3, 4, and 5-parameter logistic models. The optimal designs are obtained based on the numerical algorithm in Hyun, Wong, Yang (2018) <doi:10.18637/jss.v083.i05>.

This package contains data from the May 2020 Occupational Employment and Wage Statistics data release from the U.S. Bureau of Labor Statistics. The dataset covers employment and wages across occupations, industries, states, and at the national level. Metropolitan data is not included.

Outlier detection method that flags suspicious values within observations, constrasting them against the normal values in a user-readable format, potentially describing conditions within the data that make a given outlier more rare. Full procedure is described in Cortes (2020) <doi:10.48550/arXiv.2001.00636>. Loosely based on the GritBot <https://www.rulequest.com/gritbot-info.html> software.

Extracts covariates from Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) domains using an R-only pipeline. Supports configurable temporal windows, domain-specific covariates for drug exposure, drug era (including Anatomical Therapeutic Chemical (ATC) groupings), condition occurrence, condition era, concept sets and cohorts. Methods are based on the Observational Health Data Sciences and Informatics (OHDSI) framework described in Hripcsak et al. (2015) <doi:10.1038/sdata.2015.35> and "The Book of OHDSI" OHDSI (2019, ISBN:978-1-7923-0589-8).

Empirical or simulated disease outbreak data, provided either as RData or as text files.

This package provides clustering of genes with similar dose response (or time course) profiles. It implements the method described by Lin et al. (2012).

Functionality to handle and project lat-long coordinates, easily download background maps and add a correct scale bar to OpenStreetMap plots in any map projection.

This package provides a set of commands to manage an abstract optimization method. The goal is to provide a building block for a large class of specialized optimization methods. This package manages: the number of variables, the minimum and maximum bounds, the number of non linear inequality constraints, the cost function, the logging system, various termination criteria, etc...

Generate 2, 3 or 4-dimensional gradient noise. The noise function is comparable to classic Perlin noise, but with less directional artefacts and lower computational overhead. It can have applications in procedural generation or (flow fields) simulations.

This package provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.