This package implements a constrained version of hierarchical agglomerative clustering, in which each observation is associated to a position, and only adjacent clusters can be merged. Typical application fields in bioinformatics include Genome-Wide Association Studies or Hi-C data analysis, where the similarity between items is a decreasing function of their genomic distance. Taking advantage of this feature, the implemented algorithm is time and memory efficient. This algorithm is described in Ambroise et al (2019) <doi:10.1186/s13015-019-0157-4>.

This package provides a stacking solution for modeling imbalanced and severely skewed data. It automates the process of building homogeneous or heterogeneous stacked ensemble models by selecting "best" models according to different criteria. In doing so, it strategically searches for and selects diverse, high-performing base-learners to construct ensemble models optimized for skewed data. This package is particularly useful for addressing class imbalance in datasets, ensuring robust and effective model outcomes through advanced ensemble strategies which aim to stabilize the model, reduce its overfitting, and further improve its generalizability.

Make the compiled Java modules of the Amazon Web Services ('AWS') SDK available to be used in downstream R packages interacting with AWS'. See <https://aws.amazon.com/sdk-for-java> for more information on the AWS SDK for Java.

This package provides functions in this package fit a stratified Cox proportional hazards and a proportional subdistribution hazards model by extending Zhang et al., (2007) <doi: 10.1016/j.cmpb.2007.07.010> and Zhang et al., (2011) <doi: 10.1016/j.cmpb.2010.07.005> respectively to clustered right-censored data. The functions also provide the estimates of the cumulative baseline hazard along with their standard errors. Furthermore, the adjusted survival and cumulative incidence probabilities are also provided along with their standard errors. Finally, the estimate of cumulative incidence and survival probabilities given a vector of covariates along with their standard errors are also provided.

The meaning of adea is "alternate DEA". This package is devoted to provide the alternative method of DEA described in the paper entitled "Stepwise Selection of Variables in DEA Using Contribution Load", by F. Fernandez-Palacin, M. A. Lopez-Sanchez and M. Munoz-Marquez. Pesquisa Operacional 38 (1), pg. 1-24, 2018. <doi:10.1590/0101-7438.2018.038.01.0031>. A full functional on-line and interactive version is available at <https://knuth.uca.es/shiny/DEA/>.

This package provides functions to retrieve information from Web Feature Service (WFS) and Web Map Service (WMS) layers from various Argentine organizations and import them into R for further analysis. WFS and WMS are standardized protocols for serving georeferenced map data over the internet. For more information on these services, see <https://www.ogc.org/publications/standard/wfs/> and <https://www.ogc.org/publications/standard/wms/>.

Designed for the development and application of hidden Markov models and profile HMMs for biological sequence analysis. Contains functions for multiple and pairwise sequence alignment, model construction and parameter optimization, file import/export, implementation of the forward, backward and Viterbi algorithms for conditional sequence probabilities, tree-based sequence weighting, and sequence simulation. Features a wide variety of potential applications including database searching, gene-finding and annotation, phylogenetic analysis and sequence classification. Based on the models and algorithms described in Durbin et al (1998, ISBN: 9780521629713).

An R console utility that lets you ask R related questions to the OpenAI large language model. It can answer how-to questions by providing code, and what-is questions by explaining what given code does. You must provision your own key for the OpenAI API <https://platform.openai.com/docs/api-reference>.

The centralized empirical cumulative average deviation function is utilized to develop both Ada-plot and Uda-plot as alternatives to Ad-plot and Ud-plot introduced by the author. Analogous to Ad-plot, Ada-plot can identify symmetry, skewness, and outliers of the data distribution. The Uda-plot is as exceptional as Ud-plot in assessing normality. The d-value that quantifies the degree of proximity between the Uda-plot and the graph of the estimated normal density function helps guide to make decisions on confirmation of normality. Extreme values in the data can be eliminated using the 1.5IQR rule to create its robust version if user demands. Full description of the methodology can be found in the article by Wijesuriya (2025a) <doi:10.1080/03610926.2025.2558108>. Further, the development of Ad-plot and Ud-plot is contained in both article and the adplots R package by Wijesuriya (2025b & 2025c) <doi:10.1080/03610926.2024.2440583> and <doi:10.32614/CRAN.package.adplots>.

An isotope natural abundance correction algorithm that is needed especially for high resolution mass spectrometers. Supports correction for 13C, 2H and 15N. Su X, Lu W and Rabinowitz J (2017) <doi:10.1021/acs.analchem.7b00396>.

This package provides tools for designing and analyzing Acceptance Sampling plans. Supports both Attributes Sampling (Binomial and Poisson distributions) and Variables Sampling (Normal and Beta distributions), enabling quality control for fractional and compositional data. Uses nonlinear programming for sampling plan optimization, minimizing sample size while controlling producer's and consumer's risks. Operating Characteristic curves are available for plan visualization.

Helps enable adaptive management by codifying knowledge in the form of models generated from numerous analyses and data sets. Facilitates this process by storing all models and data sets in a single object that can be updated and saved, thus tracking changes in knowledge through time. A shiny application called AM Model Manager (modelMgr()) enables the use of these functions via a GUI.

Computes and integrates daily potential evapotranspiration (PET) and a soil water balance model. It allows users to estimate and predict the wet season calendar, including onset, cessation, and duration, based on an agroclimatic approach for a specified period. This functionality helps in managing agricultural water resources more effectively. For detailed methodologies, users can refer to Allen et al. (1998, ISBN:92-5-104219-5); Allen (2005, ISBN:9780784408056); Doorenbos and Pruitt (1975, ISBN:9251002797); Guo et al. (2016) <doi:10.1016/j.envsoft.2015.12.019>; Hargreaves and Samani (1985) <doi:10.13031/2013.26773>; Priestley and Taylor (1972) <https://journals.ametsoc.org/view/journals/apme/18/7/1520-0450_1979_018_0898_tptema_2_0_co_2.xml>.

This package provides an automatic aggregation tool to manage point data privacy, intended to be helpful for the production of official spatial data and for researchers. The package pursues the data accuracy at the smallest possible areas preventing individual information disclosure. The methodology, based on hierarchical geographic data structures performs aggregation and local suppression of point data to ensure privacy as described in Lagonigro, R., Oller, R., Martori J.C. (2017) <doi:10.2436/20.8080.02.55>. The data structures are created following the guidelines for grid datasets from the European Forum for Geography and Statistics.

This package provides a simple client package for the Amazon Web Services ('AWS') Lambda API <https://aws.amazon.com/lambda/>.

Use Monte-Carlo and K-fold cross-validation coupled with machine- learning classification algorithms to perform population assignment, with functionalities of evaluating discriminatory power of independent training samples, identifying informative loci, reducing data dimensionality for genomic data, integrating genetic and non-genetic data, and visualizing results.

This package provides sleep duration estimates using a Pruned Dynamic Programming (PDP) algorithm that efficiently identifies change-points. PDP applied to physical activity data can identify transitions from wakefulness to sleep and vice versa. Baek, Jonggyu, Banker, Margaret, Jansen, Erica C., She, Xichen, Peterson, Karen E., Pitchford, E. Andrew, Song, Peter X. K. (2021) An Efficient Segmentation Algorithm to Estimate Sleep Duration from Actigraphy Data <doi:10.1007/s12561-021-09309-3>.

Manage and analyze animal movement data. The functionality of amt includes methods to calculate home ranges, track statistics (e.g. step lengths, speed, or turning angles), prepare data for fitting habitat selection analyses, and simulation of space-use from fitted step-selection functions.

Fit, interpret, and compute predictions with oblique random forests. Includes support for partial dependence, variable importance, passing customized functions for variable importance and identification of linear combinations of features. Methods for the oblique random survival forest are described in Jaeger et al., (2023) <DOI:10.1080/10618600.2023.2231048>.

This package provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) <doi:10.48550/arXiv.1707.01815> and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2020) <doi:10.48550/arXiv.2004.12655>.

Analysis of complex plant root system architectures (RSA) using the output files created by Data Analysis of Root Tracings (DART), an open-access software dedicated to the study of plant root architecture and development across time series (Le Bot et al (2010) "DART: a software to analyse root system architecture and development from captured images", Plant and Soil, <DOI:10.1007/s11104-009-0005-2>), and RSA data encoded with the Root System Markup Language (RSML) (Lobet et al (2015) "Root System Markup Language: toward a unified root architecture description language", Plant Physiology, <DOI:10.1104/pp.114.253625>). More information can be found in Delory et al (2016) "archiDART: an R package for the automated computation of plant root architectural traits", Plant and Soil, <DOI:10.1007/s11104-015-2673-4>.

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.

Implementation of the augmented Simulation-Extrapolation (SIMEX) algorithm proposed by Yi et al. (2015) <doi:10.1080/01621459.2014.922777> for analyzing the data with mixed measurement error and misclassification. The main function provides a similar summary output as that of glm() function. Both parametric and empirical SIMEX are considered in the package.

Collect your data on digital marketing campaigns from Awin using the Windsor.ai API <https://windsor.ai/api-fields/>.