This package contains datasets and several smaller functions suitable for analysis of interval-censored data. The package complements the book Bogaerts, Komárek and Lesaffre (2017, ISBN: 978-1-4200-7747-6) "Survival Analysis with Interval-Censored Data: A Practical Approach" <https://www.routledge.com/Survival-Analysis-with-Interval-Censored-Data-A-Practical-Approach-with/Bogaerts-Komarek-Lesaffre/p/book/9781420077476>. Full R code related to the examples presented in the book can be found at <https://ibiostat.be/online-resources/icbook/supplemental>. Packages mentioned in the "Suggests" section are used in those examples.

Comparative evaluation of families and candidate variants in rare-variant association studies. The package can be used for two methodologically overlapping but distinct purposes. First, the prior to any genetic or genomic evaluation, evaluation of relative detection power of pedigrees, can direct recruitment efforts by showing which individuals not yet sampled would be the most meaningful additions to a study. Second, after sequencing and analysis, variants based on association with disease status and familial relationships of individuals, aids in variant prioritization. Methodology is described in Nugent (2025) <doi:10.1101/2025.10.06.25337426>.

It computes arbitrary products moments (mean vector and variance-covariance matrix), for some double truncated (and folded) multivariate distributions. These distributions belong to the family of selection elliptical distributions, which includes well known skewed distributions as the unified skew-t distribution (SUT) and its particular cases as the extended skew-t (EST), skew-t (ST) and the symmetric student-t (T) distribution. Analogous normal cases unified skew-normal (SUN), extended skew-normal (ESN), skew-normal (SN), and symmetric normal (N) are also included. Density, probabilities and random deviates are also offered for these members.

The unique function of this package allows representing in a single graph the relative occurrence and co-occurrence of events measured in a sample. As examples, the package was applied to describe the occurrence and co-occurrence of different species of bacterial or viral symbionts infecting arthropods at the individual level. The graphics allows determining the prevalence of each symbiont and the patterns of multiple infections (i.e. how different symbionts share or not the same individual hosts). We named the package after the famous painter as the graphical output recalls Mondrianâ s paintings.

Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data from species related by a common evolutionary history. See K. Bartoszek, J, Pienaar, P. Mostad, S. Andersson, T. F. Hansen (2012) <doi:10.1016/j.jtbi.2012.08.005> and K. Bartoszek, and J. Tredgett Clarke, J. Fuentes-Gonzalez, V. Mitov, J. Pienaar, M. Piwczynski, R. Puchalka, K. Spalik, K. L. Voje (2024) <doi:10.1111/2041-210X.14376>. The suggested PCMBaseCpp package (which significantly speeds up the likelihood calculations) can be obtained from <https://github.com/venelin/PCMBaseCpp/>.

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>.

Fit linear mixed models dedicated to the genetic study of plant mixtures, such as those based on general and specific mixing abilities (GMA-SMA) as well as direct and social breeding values (DBV-SBV), also known as direct and indirect genetic effects (DGE-IGE). More details in Forst et al (2019, <doi:10.1016/j.fcr.2019.107571>) for GMA-SMA models, and Salomon et al (2026, <doi:10.64898/2026.03.27.714849>) for DBV-SBV models. The package also provides functions to optimize experimental designs, simulate data sets and compute interaction indices.

Fitting models for, and simulation of, trend locally stationary wavelet (TLSW) time series models, which take account of time-varying trend and dependence structure in a univariate time series. The TLSW model, and its estimation, is described in McGonigle, Killick and Nunes (2022a) <doi:10.1111/jtsa.12643>, (2022b) <doi:10.1214/22-EJS2044>. Further information regarding the use of the package, along with detailed examples, can be found in McGonigle, Killick and Nunes (2025) <doi:10.18637/jss.v115.i10>. New users will likely want to start with the TLSW function.

This package provides a collection of utilities that grew out of day-to-day non-life actuarial work at Com-PASS Advisory. Provides helpers for building chain-ladder triangles (cumulative, decumulative, run-off, development factors with optional weighting), constructing exposure columns from policy start/end dates, parsing Czech birth numbers ('rodné Ä Ã slo') into dates, generating smooth RGB color palettes for charts, and loading multi-sheet xlsx'/'xlsb files into a list of data frames. The chain-ladder helpers follow the standard methodology of Mack (1993) <doi:10.2143/AST.23.2.2005092>.

SBGNview is a tool set for pathway based data visalization, integration and analysis. SBGNview is similar and complementary to the widely used Pathview, with the following key features: 1. Pathway definition by the widely adopted Systems Biology Graphical Notation (SBGN); 2. Supports multiple major pathway databases beyond KEGG (Reactome, MetaCyc, SMPDB, PANTHER, METACROP) and user defined pathways; 3. Covers 5,200 reference pathways and over 3,000 species by default; 4. Extensive graphics controls, including glyph and edge attributes, graph layout and sub-pathway highlight; 5. SBGN pathway data manipulation, processing, extraction and analysis.

Using site polymorphism is one of the ways to cluster DNA/protein sequences but it is possible for the sequences with the same polymorphism on a single site to be genetically distant. This package is aimed at clustering sequences using site polymorphism and their corresponding phylogenetic trees. By considering their location on the tree, only the structurally adjacent sequences will be clustered. However, the adjacent sequences may not necessarily have the same polymorphism. So a branch-and-bound like algorithm is used to minimize the entropy representing the purity of site polymorphism of each cluster.

This package provides tools designed to perform and evaluate cluster analysis (including Tocher's algorithm), discriminant analysis and path analysis (standard and under collinearity), as well as some useful miscellaneous tools for dealing with sample size and optimum plot size calculations. A test for seed sample heterogeneity is now available. Mantel's permutation test can be found in this package. A new approach for calculating its power is implemented. biotools also contains tests for genetic covariance components. Heuristic approaches for performing non-parametric spatial predictions of generic response variables and spatial gene diversity are implemented.

General optimisation and specific tools for the parameter estimation (i.e. calibration) of complex models, including stochastic ones. It implements generic functions that can be used for fitting any type of models, especially those with non-differentiable objective functions, with the same syntax as base::optim. It supports multiple phases estimation (sequential parameter masking), constrained optimization (bounding box restrictions) and automatic parallel computation of numerical gradients. Some common maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs is provided. See <https://roliveros-ramos.github.io/calibrar/> for more details.

This package provides a reproducible framework for constructing and comparing gene regulatory networks by integrating chromatin accessibility footprint scores with matched RNA expression data. It implements context-specific enhancer-gene linking, transcription factor focused network analysis, differential network analysis, and regulatory topic modeling workflows for systematic exploration of gene regulation across conditions. Methodological background is described in Bentsen and others (2020) <doi:10.1038/s41467-020-18035-1>, Blei, Ng and Jordan (2003) <https://www.jmlr.org/papers/v3/blei03a.html>, and Chen, Li, Zhu and Chen (2015) <doi:10.48550/arXiv.1510.08628>.

Statistical methods for retrospectively detecting changes in location and/or dispersion of univariate and multivariate variables. Data values are assumed to be independent, can be individual (one observation at each instant of time) or subgrouped (more than one observation at each instant of time). Control limits are computed, often using a permutation approach, so that a prescribed false alarm probability is guaranteed without making any parametric assumptions on the stable (in-control) distribution. See G. Capizzi and G. Masarotto (2018) <doi:10.1007/978-3-319-75295-2_1> for an introduction to the package.

This package provides a shiny-based front end (the ExPanD app) and a set of functions for exploratory data analysis. Run as a web-based app, ExPanD enables users to assess the robustness of empirical evidence without providing them access to the underlying data. You can export a notebook containing the analysis of ExPanD and/or use the functions of the package to support your exploratory data analysis workflow. Refer to the vignettes of the package for more information on how to use ExPanD and/or the functions of this package.

This package provides tools for simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic individual-contact models, and stochastic network models. Network models use the robust statistical methods of exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an API for extending these templates to address novel scientific research aims. Full methods for EpiModel are detailed in Jenness et al. (2018, <doi:10.18637/jss.v084.i08>).

Identifying labeled compounds in a 13C-tracer experiment in non-targeted fashion is a cumbersome process. This package facilitates such type of analyses by providing high level quality control plots, deconvoluting and evaluating spectra and performing a multitude of tests in an automatic fashion. The main idea is to use changing intensity ratios of ion pairs from peak list generated with xcms as candidates and evaluate those against base peak chromatograms and spectra information within the raw measurement data automatically. The functionality is described in Hoffmann et al. (2018) <doi:10.1021/acs.analchem.8b00356>.

An RStudio addin that integrates large language model (LLM) assistance directly into the code-editing workflow. Features include: (1) generate R code from inline comments; (2) obtain LLM-assisted fixes for console errors; (3) insert plain-English explanations of selected code blocks; (4) a multi-turn Chat Panel with session-context awareness (loaded packages, global objects, source editor contents, console history). Supports OpenAI', Anthropic (Claude), DeepSeek', Groq', Together AI', OpenRouter', Ollama (fully local, no API key required), and any OpenAI'-compatible custom endpoint (e.g. LM Studio', vLLM', llama.cpp').

Normative data are often used to estimate the relative position of a raw test score in the population. This package allows for deriving regression-based normative data. It includes functions that enable the fitting of regression models for the mean and residual (or variance) structures, test the model assumptions, derive the normative data in the form of normative tables or automatic scoring sheets, and estimate confidence intervals for the norms. This package accompanies the book Van der Elst, W. (2024). Regression-based normative data for psychological assessment. A hands-on approach using R. Springer Nature.

Dealing with neutrosophic data of the form N=D+I(where N is a Neutrosophic number ,D is the determinant part of the number and I is the indeterminacy part) using the neutrosophic two way anova test keeps the type I error low. This algorithm calculates the fisher statistics when we have a neutrosophic data, also tests two hypothesizes, first is to test differences between treatments, and second is to test differences between sectors. For more information see Miari, Mahmoud; Anan, Mohamad Taher; Zeina, Mohamed Bisher(2022) <https://www.americaspg.com/articleinfo/21/show/1058>.

The comprehensive knowledge of epigenetic modifications in plants, encompassing histone modifications in regulating gene expression, is not completely ingrained. It is noteworthy that histone deacetylation and histone H3 lysine 27 trimethylation (H3K27me3) play a role in repressing transcription in eukaryotes. In contrast, histone acetylation (H3K9ac) and H3K4me3 have been inevitably linked to the stimulation of gene expression, which significantly influences plant development and plays a role in plant responses to biotic and abiotic stresses. To our knowledge this the first multiclass classifier for predicting histone modification in plants. <doi:10.1186/s12864-019-5489-4>.

Datetimes and timestamps are invariably an imprecise notation, with any partial representation implying some amount of uncertainty. To handle this, parttime provides classes for embedding partial missingness as a central part of its datetime classes. This central feature allows for more ergonomic use of datetimes for challenging datetime computation, including calculations of overlapping date ranges, imputations, and more thoughtful handling of ambiguity that arises from uncertain time zones. This package was developed first and foremost with pharmaceutical applications in mind, but aims to be agnostic to application to accommodate general use cases just as conveniently.

The goal of PlotFTIR is to easily and quickly kick-start the production of journal-quality Fourier Transform Infra-Red (FTIR) spectral plots in R using ggplot2'. The produced plots can be published directly or further modified by ggplot2 functions. L'objectif de PlotFTIR est de démarrer facilement et rapidement la production des tracés spectraux de spectroscopie infrarouge à transformée de Fourier (IRTF) de qualité journal dans R à l'aide de ggplot2'. Les tracés produits peuvent être publiés directement ou modifiés davantage par les fonctions ggplot2'.