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. Microarray studies are usually analyzed with BP, whereas proteomics researchers often prefer CC. To capture the benefit of both of those ontologies, I now present an enhancement of the existing two-dimensional version of High-Throughput GoMiner ('HTGM2D'), which is called HTGM4D'. The original HTGM2D is augmented by adding two instances of the original GoMiner genes versus categories heatmaps, aligned with the categories axes of the HTGM2D heatmap.

This package provides a molecular genetics tool that processes binary data from fragment analysis. It consolidates replicate sample pairs, outputs summary statistics, and produces hierarchical clustering trees and nMDS plots. This package was developed from the publication available here: <doi:10.1016/j.biocontrol.2020.104426>. The GUI version of this package is available on the R Shiny online server at: <https://clarkevansteenderen.shinyapps.io/BINMAT/> or it is accessible via GitHub by typing: shiny::runGitHub("BinMat", "clarkevansteenderen") into the console in R. Two real-world datasets accompany the package: an AFLP dataset of Bunias orientalis samples from Tewes et. al. (2017) <doi:10.1111/1365-2745.12869>, and an ISSR dataset of Nymphaea specimens from Reid et. al. (2021) <doi:10.1016/j.aquabot.2021.103372>. The authors of these publications are thanked for allowing the use of their data.

Hidden Markov models (HMMs) are a formal foundation for making probabilistic models of linear sequence. They provide a conceptual toolkit for building complex models just by drawing an intuitive picture. They are at the heart of a diverse range of programs, including genefinding, profile searches, multiple sequence alignment and regulatory site identification. HMMs are the Legos of computational sequence analysis. In graph theory, a tree is an undirected graph in which any two vertices are connected by exactly one path, or equivalently a connected acyclic undirected graph. Tree represents the nodes connected by edges. It is a non-linear data structure. A poly-tree is simply a directed acyclic graph whose underlying undirected graph is a tree. The model proposed in this package is the same as an HMM but where the states are linked via a polytree structure rather than a simple path.

Objects and methods to handle and solve the min-sum location problem, also known as Fermat-Weber problem. The min-sum location problem search for a point such that the weighted sum of the distances to the demand points are minimized. See "The Fermat-Weber location problem revisited" by Brimberg, Mathematical Programming, 1, pg. 71-76, 1995. <DOI:10.1007/BF01592245>. General global optimization algorithms are used to solve the problem, along with the adhoc Weiszfeld method, see "Sur le point pour lequel la Somme des distances de n points donnes est minimum", by Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pg. 355-386, 1937 or "On the point for which the sum of the distances to n given points is minimum", by E. Weiszfeld and F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. <DOI:10.1007/s10479-008-0352-z>.

An implementation of a hybrid method of person-oriented method and perturbation on the model. Pompom is the initials of the two methods. The hybrid method will provide a multivariate intraindividual variability metric (iRAM). The person-oriented method used in this package refers to uSEM (unified structural equation modeling, see Kim et al., 2007, Gates et al., 2010 and Gates et al., 2012 for details). Perturbation on the model was conducted according to impulse response analysis introduced in Lutkepohl (2007). Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) <doi:10.1002/hbm.20259>. Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) <doi:10.1016/j.neuroimage.2009.12.117>. Gates, K. M., & Molenaar, P. C. M. (2012) <doi:10.1016/j.neuroimage.2012.06.026>. Lutkepohl, H. (2007, ISBN:3540262393).

This package provides the functions to perform a Welch's one-way Anova with fixed effects based on summary statistics (sample size, means, standard deviation) and the Games-Howell post hoc test for multiple comparisons and provides the effect size estimator adjusted omega squared. In addition sample size estimation can be computed based on Levy's method, and a Monte Carlo simulation is included to bootstrap residual normality and homoscedasticity Welch, B. L. (1951) <doi:10.1093/biomet/38.3-4.330> Kirk, R. E. (1996) <doi:10.1177/0013164496056005002> Carroll, R. M., & Nordholm, L. A. (1975) <doi:10.1177/001316447503500304> Albers, C., & Lakens, D. (2018) <doi:10.1016/j.jesp.2017.09.004> Games, P. A., & Howell, J. F. (1976) <doi:10.2307/1164979> Levy, K. J. (1978a) <doi:10.1080/00949657808810246> Show-Li, J., & Gwowen, S. (2014) <doi:10.1111/bmsp.12006>.

Bayesian optimal interval based on both efficacy and toxicity outcomes (BOIN-ET) design is a model-assisted oncology phase I/II trial design, aiming to establish an optimal biological dose accounting for efficacy and toxicity in the framework of dose-finding. Some extensions of BOIN-ET design are also available to allow for time-to-event efficacy and toxicity outcomes based on cumulative and pending data (time-to-event BOIN-ET: TITE-BOIN-ET), ordinal graded efficacy and toxicity outcomes (generalized BOIN-ET: gBOIN-ET), and their combination (TITE-gBOIN-ET). boinet is a package to implement the BOIN-ET design family and supports the conduct of simulation studies to assess operating characteristics of BOIN-ET, TITE-BOIN-ET, gBOIN-ET, and TITE-gBOIN-ET, where users can choose design parameters in flexible and straightforward ways depending on their own application.

Calculate the bark beetle phenology based on raster data or point-related data. There are multiple models implemented for two bark beetle species. The models can be customized and their submodels (onset of infestation, beetle development, diapause initiation, mortality) can be combined. The following models are available in the package: PHENIPS-Clim (first-time release in this package), PHENIPS (Baier et al. 2007) <doi:10.1016/j.foreco.2007.05.020>, RITY (Ogris et al. 2019) <doi:10.1016/j.ecolmodel.2019.108775>, CHAPY (Ogris et al. 2020) <doi:10.1016/j.ecolmodel.2020.109137>, BSO (Jakoby et al. 2019) <doi:10.1111/gcb.14766>, Lange et al. (2008) <doi:10.1007/978-3-540-85081-6_32>, Jönsson et al. (2011) <doi:10.1007/s10584-011-0038-4>. The package may be expanded by models for other bark beetle species in the future.

Includes wrapper functions around existing functions for the analysis of categorical data and introduces functions for calculating risk differences and matched odds ratios. R currently supports a wide variety of tools for the analysis of categorical data. However, many functions are spread across a variety of packages with differing syntax and poor compatibility with each another. prop_test() combines the functions binom.test(), prop.test() and BinomCI() into one output. prop_power() allows for power and sample size calculations for both balanced and unbalanced designs. riskdiff() is used for calculating risk differences and matched_or() is used for calculating matched odds ratios. For further information on methods used that are not documented in other packages see Nathan Mantel and William Haenszel (1959) <doi:10.1093/jnci/22.4.719> and Alan Agresti (2002) <ISBN:0-471-36093-7>.

High-dimensional visualization methods for data envelopment analysis (DEA), gathering in one place techniques that have appeared in the literature but remained scattered and largely unimplemented: cross-efficiency matrix unfolding, the Porembski network with lambda edges, principal component analysis biplots, multidimensional-scaling colour-plots, self-organizing maps, the Costa bi-dimensional efficient frontier, parallel coordinates, radar charts, panel-data trajectory biplots, peer and reference networks, and a set of descriptive plots. The package is built around a single validated dea_data() object and uses the Benchmarking package as its DEA engine. The implemented methods draw on a body of literature; representative references include Doyle and Green (1994) <doi:10.1057/jors.1994.84>, Porembski, Breitenstein and Alpar (2005) <doi:10.1007/s11123-005-1328-5> and Bana e Costa, Soares de Mello and Angulo Meza (2016) <doi:10.1016/j.ejor.2016.05.012>.

This package implements the Fourier Bootstrap Autoregressive Distributed Lag (FBARDL) bounds testing approach for cointegration analysis. Combines the Pesaran, Shin and Smith (2001) <doi:10.1002/jae.616> ARDL bounds testing framework with Fourier terms to capture smooth structural breaks, as in Yilanci, Bozoklu and Gorus (2020) <doi:10.1016/j.scs.2020.102035>, and recursive bootstrap critical values following McNown, Sam and Goh (2018) <doi:10.1080/00036846.2017.1366643> and Bertelli, Vacca and Zoia (2022) <doi:10.1016/j.econmod.2022.105987>, with the Fourier frequency and the lags selected again in every bootstrap replication (a package choice); finite-sample bounds test critical values from Kripfganz and Schneider (2020) <doi:10.1111/obes.12377> for models without Fourier terms. Features include lag selection via AIC/BIC, Fourier frequency selection by minimum SSR, long-run and short-run coefficient estimation and diagnostic tests.

This package provides a user-friendly shiny application for Bayesian machine learning analysis of marine species distributions. GLOSSA (Global Ocean Species Spatio-temporal Analysis) uses Bayesian Additive Regression Trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) to model species distributions with intuitive workflows for data upload, processing, model fitting, and result visualization. It supports presence-absence and presence-only data (with pseudo-absence generation), spatial thinning, cross-validation, and scenario-based projections. GLOSSA is designed to facilitate ecological research by providing easy-to-use tools for analyzing and visualizing marine species distributions across different spatial and temporal scales. Optionally, pseudo-absences can be generated within the environmental space using the external package flexsdm (not on CRAN), which can be downloaded from <https://github.com/sjevelazco/flexsdm>; this functionality is used conditionally when available and all core features work without it.

Interpreting the differences between mean scale scores across various forms of an assessment can be challenging. This difficulty arises from different mappings between raw scores and scale scores, complex mathematical relationships, adjustments based on judgmental procedures, and diverse equating functions applied to different assessment forms. An alternative method involves running simulations to explore the effect of incrementing raw scores on mean scale scores. The idmact package provides an implementation of this approach based on the algorithm detailed in Schiel (1998) <https://www.act.org/content/dam/act/unsecured/documents/ACT_RR98-01.pdf> which was developed to help interpret differences between mean scale scores on the American College Testing (ACT) assessment. The function idmact_subj() within the package offers a framework for running simulations on subject-level scores. In contrast, the idmact_comp() function provides a framework for conducting simulations on composite scores.

This package provides a Graphical user interface to calculate the rainfall-runoff relation using the Natural Resources Conservation Service - Curve Number method (NRCS-CN method) but include modifications by Hawkins et al., (2002) about the Initial Abstraction. This GUI follows the programming logic of a previously published software (Hernandez-Guzman et al., 2011)<doi:10.1016/j.envsoft.2011.07.006>. It is a raster-based GIS tool that outputs runoff estimates from Land use/land cover and hydrologic soil group maps. This package has already been published in Journal of Hydroinformatics (Hernandez-Guzman et al., 2021)<doi:10.2166/hydro.2020.087> but it is under constant development at the Institute about Natural Resources Research (INIRENA) from the Universidad Michoacana de San Nicolas de Hidalgo and represents a collaborative effort between the Hydro-Geomatic Lab (INIRENA) with the Environmental Management Lab (CIAD, A.C.).

An integrated wavelet-based spatial time series modelling framework designed to enhance predictive accuracy under noisy and nonstationary conditions by jointly exploiting multi-resolution (wavelet) information and spatial dependence. The package implements WaveSARIMA() (Wavelet Based Spatial AutoRegressive Integrated Moving Average model using regression features with forecast::auto.arima()) and WaveSNN() (Wavelet Based Spatial Neural Network model using neuralnet with hyperparameter search). Both functions support spatial transformation via a user-supplied spatial matrix, lag feature construction, MODWT-based wavelet sub-series feature generation, time-ordered train/test splitting, and performance evaluation (Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), and Mean Absolute Percentage Error (MAPE)), returning fitted models and actual vs predicted values for train and test sets. The package has been developed using the algorithm of Paul et al. (2023) <doi:10.1007/s43538-025-00581-1>.

This package provides a decision support tool to strategically prioritise evidence gathering in complex, hierarchical AND-OR decision trees. It is designed for situations with incomplete or uncertain information where the goal is to reach a confident conclusion as efficiently as possible (responding to the minimum number of questions, and only spending resources on generating improved evidence when it is of significant value to the final decision). The framework excels in complex analyses with multiple potential successful pathways to a conclusion ('OR nodes). Key features include a dynamic influence index to guide users to the most impactful question, a system for propagating answers and semi-quantitative confidence scores (0-5) up the tree, and post-conclusion guidance to identify the best actions to increase the final confidence. These components are brought together in an interactive command-line workflow that guides the analysis from start to finish.

This package provides a post-estimation method for categorical response models (CRM). Inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(), vglm() and glm() are currently supported. Available tests include the Hosmer-Lemeshow tests for the binary, multinomial and ordinal logistic regression; the Lipsitz and the Pulkstenis-Robinson tests for the ordinal models. The proportional odds, adjacent-category, and constrained continuation-ratio models are particularly supported at ordinal level. Tests for the proportional odds assumptions in ordinal models are also possible with the Brant and the Likelihood-Ratio tests. Moreover, several summary measures of predictive strength (Pseudo R-squared), and some useful error metrics, including, the brier score, misclassification rate and logloss are also available for the binary, multinomial and ordinal models. Ugba, E. R. and Gertheiss, J. (2018) <http://www.statmod.org/workshops_archive_proceedings_2018.html>.

The saemix package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. It (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for saemix': <https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.

This natural language processing toolkit provides language-agnostic tokenization', parts of speech tagging', lemmatization and dependency parsing of raw text. Next to text parsing, the package also allows you to train annotation models based on data of treebanks in CoNLL-U format as provided at <https://universaldependencies.org/format.html>. The techniques are explained in detail in the paper: Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe', available at <doi:10.18653/v1/K17-3009>. The toolkit also contains functionalities for commonly used data manipulations on texts which are enriched with the output of the parser. Namely functionalities and algorithms for collocations, token co-occurrence, document term matrix handling, term frequency inverse document frequency calculations, information retrieval metrics (Okapi BM25), handling of multi-word expressions, keyword detection (Rapid Automatic Keyword Extraction, noun phrase extraction, syntactical patterns) sentiment scoring and semantic similarity analysis.

This package provides raster grid logic, operations that describe a discretized rectangular domain and do not require access to materialized data. Grids are arrays with dimension and extent, and many operations are functions of dimension only: number of columns, number of rows, or they are a combination of the dimension and the extent the range in x and the range in y in that order. Here we provide direct access to this logic without need for connection to any materialized data or formats. Grid logic includes functions that relate the cell index to row and column, or row and column to cell index, row, column or cell index to position. These methods are described in Loudon, TV, Wheeler, JF, Andrew, KP (1980) <doi:10.1016/0098-3004(80)90015-1>, and implementations were in part derived from Hijmans R (2024) <doi:10.32614/CRAN.package.terra>.

Functional enrichment analysis methods such as gene set enrichment analysis (GSEA) have been widely used for analyzing gene expression data. GSEA is a powerful method to infer results of gene expression data at a level of gene sets by calculating enrichment scores for predefined sets of genes. GSEA depends on the availability and accuracy of gene sets. There are overlaps between terms of gene sets or categories because multiple terms may exist for a single biological process, and it can thus lead to redundancy within enriched terms. In other words, the sets of related terms are overlapping. Using deep learning, this pakage is aimed to predict enrichment scores for unique tokens or words from text in names of gene sets to resolve this overlapping set issue. Furthermore, we can coin a new term by combining tokens and find its enrichment score by predicting such a combined tokens.

Learning, manipulation and evaluation of mixtures of truncated basis functions (MoTBFs), which include mixtures of polynomials (MOPs) and mixtures of truncated exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian networks (I. Pérez-Bernabé, A. Salmerón, H. Langseth (2015) <doi:10.1007/978-3-319-20807-7_36>; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) <doi:10.1016/j.ijar.2013.09.012>; I. Pérez-Bernabé, A. Fernández, R. Rumà , A. Salmerón (2016) <doi:10.1007/s10618-015-0429-7>). The package provides functionality for learning univariate, multivariate and conditional densities, with the possibility of incorporating prior knowledge. Structural learning of hybrid Bayesian networks is also provided. A set of useful tools is provided, including plotting, printing and likelihood evaluation. This package makes use of S3 objects, with two new classes called motbf and jointmotbf'.

This natural language processing toolkit provides language-agnostic tokenization, parts of speech tagging, lemmatization and dependency parsing of raw text. Next to text parsing, the package also allows you to train annotation models based on data of treebanks in CoNLL-U format as provided at https://universaldependencies.org/format.html. The techniques are explained in detail in the paper: 'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe', available at doi:10.18653/v1/K17-3009. The toolkit also contains functionalities for commonly used data manipulations on texts which are enriched with the output of the parser. Namely functionalities and algorithms for collocations, token co-occurrence, document term matrix handling, term frequency inverse document frequency calculations, information retrieval metrics (Okapi BM25), handling of multi-word expressions, keyword detection (Rapid Automatic Keyword Extraction, noun phrase extraction, syntactical patterns) sentiment scoring and semantic similarity analysis.

This package implements PRIDIT (Principal Component Analysis applied to RIDITs'), an unsupervised, nonparametric method for aggregating ordinal, categorical, and continuous indicators into a single interpretable composite score. Originally proposed by Brockett et al. (2002) <doi:10.1111/1539-6975.00027> for insurance fraud detection and extended to hospital quality measurement by Lieberthal (2008) <doi:10.1111/j.1475-6773.2007.00821.x> and Lieberthal and Comer (2013) <doi:10.1111/rmir.12009>. The package provides: (1) low-level functions ridit(), PRIDITweight(), and PRIDITscore(); (2) a unified pridit() entry point returning a classed object with print, summary, autoplot', and coef methods; (3) pridit_boot() for bootstrap confidence intervals on scores and weights; (4) a step_pridit() recipe step for out-of-sample scoring within the tidymodels framework; and (5) pridit_longitudinal() for panel data, computing cross-period stability of scores and weights.