This package implements the generalized order-restricted information criterion approximation (GORICA), an AIC-like information criterion that can be utilized to evaluate informative hypotheses specifying directional relationships between model parameters in terms of (in)equality constraints (see Altinisik, Van Lissa, Hoijtink, Oldehinkel, & Kuiper, 2021), <doi:10.31234/osf.io/t3c8g>. The GORICA is applicable not only to normal linear models, but also to generalized linear models (GLMs), generalized linear mixed models (GLMMs), structural equation models (SEMs), and contingency tables. For contingency tables, restrictions on cell probabilities can be non-linear.

This package provides specialized visualization tools for Single-Case Experimental Design (SCED) research using ggplot2'. SCED studies are a crucial methodology in behavioral and educational research where individual participants serve as their own controls through carefully designed experimental phases. This package extends ggplot2 to create publication-ready graphics with professional phase change lines, support for multiple baseline designs, and styling functions that follow SCED visualization conventions. Key functions include adding phase change demarcation lines to existing plots and formatting axes with broken axis appearance commonly used in single-case research.

Create network-style visualizations of pairwise relationships using custom edge glyphs built on top of ggplot2'. The package supports both statistical and non-statistical data and allows users to represent directed relationships. This enables clear, publication-ready graphics for exploring and communicating relational structures in a wide range of domains. The method was first used in Abu-Akel et al. (2021) <doi:10.1371/journal.pone.0245100>. Code is released under the MIT License; included datasets are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0).

Fast, model-agnostic implementation of different H-statistics introduced by Jerome H. Friedman and Bogdan E. Popescu (2008) <doi:10.1214/07-AOAS148>. These statistics quantify interaction strength per feature, feature pair, and feature triple. The package supports multi-output predictions and can account for case weights. In addition, several variants of the original statistics are provided. The shape of the interactions can be explored through partial dependence plots or individual conditional expectation plots. DALEX explainers, meta learners ('mlr3', tidymodels', caret') and most other models work out-of-the-box.

Rank-based tests for enrichment of KOG (euKaryotic Orthologous Groups) classes with up- or down-regulated genes based on a continuous measure. The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed.

This toolbox allows you to do simple mediation and moderation analysis. Models are estimated with the lavaan package by Rosseel (2012) <doi:10.18637/jss.v048.i02>; standard errors for the mediation estimates are computed with the delta method following Sobel (1982) <doi:10.2307/270723> or by bootstrapping. It is also available as a module for jamovi (see <https://www.jamovi.org> for more information). You can find an in depth tutorial on the lavaan model syntax used for this package on <https://lavaan.ugent.be/tutorial/index.html>.

This package provides a comprehensive toolkit for calculating and visualizing Nitrogen Use Efficiency (NUE) indicators in agricultural research. The package implements 23 parameters categorized into fertilizer-based, plant-based, soil-based, isotope-based, ecology-based, and system-based indicators based on Congreves et al. (2021) <doi:10.3389/fpls.2021.637108>. Key features include vectorized calculations for paired-plot experimental designs, batch processing capabilities for handling large datasets, and built-in visualization tools using ggplot2'. Designed to streamline the workflow from raw agronomic data to publication-ready metrics and plots.

This package contains sixteen moisture sorption isotherm models, which evaluate the fitness of adsorption and desorption curves for further understanding of the relationship between moisture content and water activity. Fitness evaluation is conducted through parameter estimation and error analysis. Moreover, graphical representation, hysteresis area estimation, and isotherm classification through the equation of Blahovec & Yanniotis (2009) <doi:10.1016/j.jfoodeng.2008.08.007> which is based on the classification system introduced by Brunauer et. al. (1940) <doi:10.1021/ja01864a025> are also included for the visualization of models and hysteresis.

This package provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after finding good starting values via a K-means clustering of the genetic data. Details can be found in: Foster, S. D.; Feutry, P.; Grewe, P. M.; Berry, O.; Hui, F. K. C. & Davies (2020) <doi:10.1111/1755-0998.12920>.

This package provides a domain-agnostic visualization utility designed to bypass structural rendering limitations within multi-trace three-dimensional animations in Plotly by implementing a decoupled rendering pipeline. Computational processing of time-dependent physical states (markers and nodes) is managed within the R environment, while a custom hardware-accelerated WebGL injection handles the synchronous rendering of complex topological frameworks (lines and edges) directly within the UI browser interface via htmlwidgets'. This dual-layer architecture ensures smooth execution of synchronized multi-component 3D animations without framework degradation or controller loss.

This package provides a computational framework for analyzing mutations in immunoglobulin (Ig) sequences. Includes methods for Bayesian estimation of antigen-driven selection pressure, mutational load quantification, building of somatic hypermutation (SHM) models, and model-dependent distance calculations. Also includes empirically derived models of SHM for both mice and humans. Citations: Gupta and Vander Heiden, et al (2015) <doi:10.1093/bioinformatics/btv359>, Yaari, et al (2012) <doi:10.1093/nar/gks457>, Yaari, et al (2013) <doi:10.3389/fimmu.2013.00358>, Cui, et al (2016) <doi:10.4049/jimmunol.1502263>.

The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Min-Max transformation has been used for data preparation. Here, we have used one LSTM layer as a simple LSTM model and a Dense layer is used as the output layer. Then, compile the model using the loss function, optimizer and metrics. This package is based on Keras and TensorFlow modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.

This package provides a dedicated viral-explainer model tool designed to empower researchers in the field of HIV research, particularly in viral load and CD4 (Cluster of Differentiation 4) lymphocytes regression modeling. Drawing inspiration from the tidymodels framework for rigorous model building of Max Kuhn and Hadley Wickham (2020) <https://www.tidymodels.org>, and the DALEXtra tool for explainability by Przemyslaw Biecek (2020) <doi:10.48550/arXiv.2009.13248>. It aims to facilitate interpretable and reproducible research in biostatistics and computational biology for the benefit of understanding HIV dynamics.

New tools for the imputation of missing values in high-dimensional data are introduced using the non-parametric nearest neighbor methods. It includes weighted nearest neighbor imputation methods that use specific distances for selected variables. It includes an automatic procedure of cross validation and does not require prespecified values of the tuning parameters. It can be used to impute missing values in high-dimensional data when the sample size is smaller than the number of predictors. For more information see Faisal and Tutz (2017) <doi:10.1515/sagmb-2015-0098>.

Estimation of time of concentration and lag times for watersheds based on their morphometric characteristics. It includes various methods for calculation and offers plotting functionalities for comparative analysis. For more details see Bransby-Williams (1922, ISSN 2214-5818), Kirpich (1940) <https://hess.copernicus.org/articles/24/2655/2020/>, Kerby (1959, ISBN-13, 979-8355357214), Johnstone & Cross (1949, ISBN:9780823211234), California Division of Highways (1942, ISSN:0012-7353), Clark (1945) <doi:10.1061/TACEAT.0005800>, Giandotti (1934) <doi:10.1080/02626667.2017.1384549>, Passini (1972, ISBN:84-7433-040-8), Témez (1978, ISBN:84-7433-040-8), Pérez (1962, ISSN:0012-7353), Pilgrim (1977) <doi:10.1029/WR013i003p00587>, Bureau of Reclamation (1973, ISBN:9780913232123), Valencia-Zuluaga (1983) <https://repositorio.unal.edu.co/>, Ventura & Heras (1964) <doi:10.1061/9780784413548.005>, Soil Conservation Service (1972, ISBN:OL15009517M), Soil Conservation Service (1986) <https://www.ars.usda.gov/research/software/download/?softwareid=527>, US Navy - Technical Publication Navdocks (1972) <ISBN:978-1289256234>, Federal Aviation Administration (1970, ISBN:9780913236543), Natural Environment Research Council (1975, ISBN:9780114501234), Mimikou (1984) <doi:10.1080/02626668409490922>, Watt & Chow (1985) <doi:10.1139/l85-031>, Haktanir & Sezen (1990) <doi:10.1080/02626669009492423>.

Alternating Manifold Proximal Gradient Method for Sparse PCA uses the Alternating Manifold Proximal Gradient (AManPG) method to find sparse principal components from a data or covariance matrix. Provides a novel algorithm for solving the sparse principal component analysis problem which provides advantages over existing methods in terms of efficiency and convergence guarantees. Chen, S., Ma, S., Xue, L., & Zou, H. (2020) <doi:10.1287/ijoo.2019.0032>. Zou, H., Hastie, T., & Tibshirani, R. (2006) <doi:10.1198/106186006X113430>. Zou, H., & Xue, L. (2018) <doi:10.1109/JPROC.2018.2846588>.

Count transformation models featuring parameters interpretable as discrete hazard ratios, odds ratios, reverse-time discrete hazard ratios, or transformed expectations. An appropriate data transformation for a count outcome and regression coefficients are simultaneously estimated by maximising the exact discrete log-likelihood using the computational framework provided in package mlt', technical details are given in Siegfried & Hothorn (2020) <DOI:10.1111/2041-210X.13383>. The package also features joint count transformation models with covariate-dependent correlations applied to a species community of three aquatic birds <DOI:10.48550/arXiv.2201.13095>.

Collection of ancillary functions and utilities for Partial Linear Single Index Models for Environmental mixture analyses, which currently provides functions for scalar, binary and count outcomes. The outputs of these functions include the single index function, single index coefficients, partial linear coefficients, mixture overall effect, exposure main and interaction effects, and differences of quartile effects. In the future, we will add functions for ordinal, survival, and longitudinal outcomes, as well as models for time-dependent exposures. See Wang et al (2020) <doi:10.1186/s12940-020-00644-4> for an overview.

This package produces diversity estimates and species lists with associated global distribution for any vascular plant family and genus from Plants of the World Online database, by interacting with the source code of each plant taxon page. It also creates global maps of species richness, graphics of species discoveries and name changes over time. For more details: Zuanny, D.C., B.Vilela, P.W.Moonlight, T.E.Särkinen, and D.Cardoso. 2024. expowo: An R package for mining global plant diversity and distribution data. Applications in Plant Sciences 12: e11609'.

Build a map of path-based geometry, this is a simple description of the number of parts in an object and their basic structure. Translation and restructuring operations for planar shapes and other hierarchical types require a data model with a record of the underlying relationships between elements. The gibble() function creates a geometry map, a simple record of the underlying structure in path-based hierarchical types. There are methods for the planar shape types in the sf and sp packages and for types in the trip and silicate packages.

This package implements the GAMbag, GAMrsm and GAMens ensemble classifiers for binary classification (De Bock et al., 2010) <doi:10.1016/j.csda.2009.12.013>. The ensembles implement Bagging (Breiman, 1996) <doi:10.1023/A:1010933404324>, the Random Subspace Method (Ho, 1998) <doi:10.1109/34.709601> , or both, and use Hastie and Tibshirani's (1990, ISBN:978-0412343902) generalized additive models (GAMs) as base classifiers. Once an ensemble classifier has been trained, it can be used for predictions on new data. A function for cross validation is also included.

Mapper-based survival analysis with transcriptomics data is designed to carry out. Mapper-based survival analysis is a modification of Progression Analysis of Disease (PAD) where survival data is taken into account in the filtering function. More details in: J. Fores-Martos, B. Suay-Garcia, R. Bosch-Romeu, M.C. Sanfeliu-Alonso, A. Falco, J. Climent, "Progression Analysis of Disease with Survival (PAD-S) by SurvMap identifies different prognostic subgroups of breast cancer in a large combined set of transcriptomics and methylation studies" <doi:10.1101/2022.09.08.507080>.

The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as GoMiner (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. To capture the benefit of all three ontologies, I developed HTGM3D', a three-dimensional version of GoMiner'.

This package provides methods for linear regression in the presence of missing data, including missingness in covariates and responses. The package implements two estimators: oss_estimator(), a low-dimensional semi-supervised method, and dantzig_missing(), a high-dimensional approach. The tuning parameter can be selected automatically via cv_dantzig_missing(). See Risebrow and Berrett (2026) <doi:10.48550/arXiv.2602.13729>. Optional support for the gurobi optimizer via the gurobi R package (available from Gurobi, see <https://docs.gurobi.com/projects/optimizer/en/current/reference/r.html>).