This package provides R wrappers of several on-target and off-target scoring methods for CRISPR guide RNAs (gRNAs). The following nucleases are supported: SpCas9, AsCas12a, enAsCas12a, and RfxCas13d (CasRx). The available on-target cutting efficiency scoring methods are RuleSet1, RuleSet3, DeepHF, enPAM+GB, and CRISPRscan. Both the CFD and MIT scoring methods are available for off-target specificity prediction. The package also provides a Lindel-derived score to predict the probability of a gRNA to produce indels inducing a frameshift for the Cas9 nuclease. Note that DeepHF and enPAM+GB are not available on Windows machines.

This package provides a Bayesian framework for Dynamic Energy Budget (DEB) modelling via Stan'. Implements the standard DEB model of Kooijman (2010, <doi:10.1017/CBO9780511805400>) as a state-space model with Hamiltonian Monte Carlo inference (Carpenter et al., 2017, <doi:10.18637/jss.v076.i01>). Includes individual-level growth models, growth-reproduction models, hierarchical multi-individual models with partial pooling, and toxicokinetic-toxicodynamic (TKTD) models for ecotoxicology following the DEBtox framework (Jager et al., 2006, <doi:10.1007/s10646-006-0060-x>). Supports prior specification from biological knowledge, convergence diagnostics (Vehtari et al., 2021, <doi:10.1214/20-BA1221>), posterior predictive checks, derived quantity estimation, and visualisation via ggplot2'.

Fit and simulate bivariate correlated frailty models with proportional hazard structure. Frailty distributions, such as gamma and lognormal models are supported semiparametric procedures. Frailty variances of the two subjects can be varied or equal. Details on the models are available in book of Wienke (2011,ISBN:978-1-4200-7388-1). Bivariate gamma fit is obtained using the approach given in Kifle et al (2023) <DOI: 10.4310/22-SII738> with modifications. Lognormal fit is based on the approach by Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>. Frailty distributions, such as gamma, inverse gaussian and power variance frailty models are supported for parametric approach.

Allows plotting data on bathymetric maps using ggplot2'. Plotting oceanographic spatial data is made as simple as feasible, but also flexible for custom modifications. Data that contain geographic information from anywhere around the globe can be plotted on maps generated by the basemap() or qmap() functions using ggplot2 layers separated by the + operator. The package uses spatial shape- ('sf') and raster ('stars') files, geospatial packages for R to manipulate, and the ggplot2 package to plot these files. The package ships with low-resolution spatial data files and higher resolution files for detailed maps are stored in the ggOceanMapsLargeData repository on GitHub and downloaded automatically when needed.

Most of the time floating point arithmetic does approximately the right thing. When adding sums or having products of numbers that greatly differ in magnitude, the floating point arithmetic may be incorrect. This package implements the Kahan (1965) sum <doi:10.1145/363707.363723>, Neumaier (1974) sum <doi:10.1002/zamm.19740540106>, pairwise-sum (adapted from NumPy', See Castaldo (2008) <doi:10.1137/070679946> for a discussion of accuracy), and arbitrary precision sum (adapted from the fsum in Python ; Shewchuk (1997) <https://people.eecs.berkeley.edu/~jrs/papers/robustr.pdf>). In addition, products are changed to long double precision for accuracy, or changed into a log-sum for accuracy.

Data in multidimensional systems is obtained from operational systems and is transformed to adapt it to the new structure. Frequently, the operations to be performed aim to transform a flat table into a star schema. Transformations can be carried out using professional extract, transform and load tools or tools intended for data transformation for end users. With the tools mentioned, this transformation can be carried out, but it requires a lot of work. The main objective of this package is to define transformations that allow obtaining stars from flat tables easily. In addition, it includes basic data cleaning, dimension enrichment, incremental data refresh and query operations, adapted to this context.

This package provides a conditional independence test that can be applied both to univariate and multivariate random variables. The test is based on a weighted form of the sample covariance of the residuals after a nonlinear regression on the conditioning variables. Details are described in Scheidegger, Hoerrmann and Buehlmann (2022) "The Weighted Generalised Covariance Measure" <http://jmlr.org/papers/v23/21-1328.html>. The test is a generalisation of the Generalised Covariance Measure (GCM) implemented in the R package GeneralisedCovarianceMeasure by Jonas Peters and Rajen D. Shah based on Shah and Peters (2020) "The Hardness of Conditional Independence Testing and the Generalised Covariance Measure" <doi:10.1214/19-AOS1857>.

Conducts a goodness-of-fit test for the Weibull distribution (referred to as the weibullness test) and furnishes parameter estimations for both the two-parameter and three-parameter Weibull distributions. Notably, the threshold parameter is derived through correlation from the Weibull plot. Additionally, this package conducts goodness-of-fit assessments for the exponential, Gumbel, and inverse Weibull distributions, accompanied by parameter estimations. For more details, see Park (2017) <doi:10.23055/ijietap.2017.24.4.2848>, Park (2018) <doi:10.1155/2018/6056975>, and Park (2023) <doi:10.3390/math11143156>. This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (No. 2022R1A2C1091319).

This package allows biologists to judge in the first place whether the sequence surrounding the polymorphism is a good match, and in the second place how much information is gained or lost in one allele of the polymorphism relative to another. This package gives a choice of algorithms for interrogation of genomes with motifs from public sources:

1. a weighted-sum probability matrix;

2. log-probabilities;

3. weighted by relative entropy.

This package can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within Bioconductor.

Get z-scores, percentiles, absolute values, and percent of predicted of a reference cohort. Functionality requires installing the data packages adiposerefdata and musclerefdata from p-mq.github.io/drat. For more information on the underlying research, please visit our website which also includes a graphical interface. The models and underlying data are described in Marquardt J. Peter et al (2025), "Subcutaneous and Visceral adipose tissue Reference Values from Framingham Heart Study Thoracic and Abdominal CT", *Investigative Radiology* <doi:10.1097/RLI.0000000000001104> and Tonnesen PE et al. (2023), "Muscle Reference Values from Thoracic and Abdominal CT for Sarcopenia Assessment [column] The Framingham Heart Study", *Investigative Radiology*, <doi:10.1097/RLI.0000000000001012>.

Generates interactive Jellyfish plots to visualize spatiotemporal tumor evolution by integrating sample and phylogenetic trees into a unified plot. This approach provides an intuitive way to analyze tumor heterogeneity and evolution over time and across anatomical locations. The Jellyfish plot visualization design was first introduced by Lahtinen, Lavikka, et al. (2023, <doi:10.1016/j.ccell.2023.04.017>). This package also supports visualizing ClonEvol results, a tool developed by Dang, et al. (2017, <doi:10.1093/annonc/mdx517>), for analyzing clonal evolution from multi-sample sequencing data. The clonevol package is not available on CRAN but can be installed from its GitHub repository (<https://github.com/hdng/clonevol>).

This package provides a set of vectorised functions to calculate medical equations used in transplantation, focused mainly on transplantation of abdominal organs. These functions include donor and recipient risk indices as used by NHS Blood & Transplant, OPTN/UNOS and Eurotransplant, tools for quantifying HLA mismatches, functions for calculating estimated glomerular filtration rate (eGFR), a function to calculate the APRI (AST to platelet ratio) score used in initial screening of suitability to receive a transplant from a hepatitis C seropositive donor and some biochemical unit converter functions. All functions are designed to work with either US or international units. References for the equations are provided in the vignettes and function documentation.

This package provides a screening process utilizing training and testing samples to filter out uninformative DNA methylation sites. Surrogate variables (SVs) of DNA methylation are included in the filtering process to explain unknown factor effects. This package also provides two screening functions for screening high-dimensional predictors when the events are rare. The firth method is called Rare-Screening which employs a repeated random sampling with replacement and using linear modeling with Bayes adjustment. The Second method is called Firth-ttScreening which uses ttScreening method with additional Firth correction term in the maximum likelihood for the logistic regression model. These methods handle the high-dimensionality and low event rates.

This is a collection of tools for assessment of feature importance and feature effects. Key functions are:

• feature_importance() for assessment of global level feature importance,

• ceteris_paribus() for calculation of the what-if plots,

• partial_dependence() for partial dependence plots,

• conditional_dependence() for conditional dependence plots,

• accumulated_dependence() for accumulated local effects plots,

• aggregate_profiles() and cluster_profiles() for aggregation of ceteris paribus profiles,

• generic print() and plot() for better usability of selected explainers,

• generic plotD3() for interactive, D3 based explanations, and

• generic describe() for explanations in natural language.

Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the jagstargets R package is leverages targets and R2jags to ease this burden. jagstargets makes it super easy to set up scalable JAGS pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than targets alone. For the underlying methodology, please refer to the documentation of targets <doi:10.21105/joss.02959> and JAGS (Plummer 2003) <https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf>.

This package provides a framework for deconvolution, alignment and postprocessing of 1-dimensional (1d) nuclear magnetic resonance (NMR) spectra, resulting in a data matrix of aligned signal integrals. The deconvolution part uses the algorithm described in Koh et al. (2009) <doi:10.1016/j.jmr.2009.09.003>. The alignment part is based on functions from the speaq package, described in Beirnaert et al. (2018) <doi:10.1371/journal.pcbi.1006018> and Vu et al. (2011) <doi:10.1186/1471-2105-12-405>. A detailed description and evaluation of an early version of the package, MetaboDecon1D v0.2.2', can be found in Haeckl et al. (2021) <doi:10.3390/metabo11070452>.

This package provides a streamlined workflow for cleaning, transforming, filtering, aggregating, and exporting epidemiological line list data. The package is designed for public health surveillance and clinical datasets where each row represents an individual case. It supports common data-wrangling tasks and multi-format data import/export (e.g., csv', rds', xlsx', json', dbf'). The functions are designed to be combined into a clear and reproducible pipeline while remaining flexible enough for use in standalone data-processing steps. data4health is part of the 4health toolkit, which integrates health, climate, land-use, and socioeconomic data workflows. More information on the 4health tools can be found on the HARMONIZE website <https://harmonize-tools.org/toolkits>.

Este paquete pretende apoyar el proceso enseñanza-aprendizaje de estadà stica descriptiva e inferencial. Las funciones contenidas en el paquete estadistica cubren los conceptos básicos estudiados en un curso introductorio. Muchos conceptos son ilustrados con gráficos dinámicos o web apps para facilitar su comprensión. This package aims to help the teaching-learning process of descriptive and inferential statistics. The functions contained in the package estadistica cover the basic concepts studied in a statistics introductory course. Many concepts are illustrated with dynamic graphs or web apps to make the understanding easier. See: Esteban et al. (2005, ISBN: 9788497323741), Newbold et al.(2019, ISBN:9781292315034 ), Murgui et al. (2002, ISBN:9788484424673) .

This package provides functions for the identification, classification, and ranking of influential nodes and candidate features from network and omics data. The package implements the Integrated Value of Influence (IVI) for integrative network centrality analysis, the SIR-based Influence Ranking (SIRIR) model for unsupervised influence ranking, and the Experimental data-based Integrative Ranking (ExIR) model for prioritizing candidate driver, biomarker, and mediator features from experimental omics data. Functions are provided for network reconstruction from adjacency matrices and data frames, topological analysis, centrality calculation, assessment of associations between centrality measures, and conditional probability analysis. ExIR supports bulk and single-cell omics data, including matrices, sparse matrices, data frames, tibbles, and Seurat objects.

Classical Ising Model is a land mark system in statistical physics.The model explains the physics of spin glasses and magnetic materials, and cooperative phenomenon in general, for example phase transitions and neural networks.This package provides utilities to simulate one dimensional Ising Model with Metropolis and Glauber Monte Carlo with single flip dynamics in periodic boundary conditions. Utility functions for exact solutions are provided. Such as transfer matrix for 1D. Utility functions for exact solutions are provided. Example use cases are as follows: Measuring effective ergodicity and power-laws in so called functional-diffusion. Example usage contains parallel runs, fitting power-laws, finite size scaling, computing autocorrelation, uncertainty analysis and plotting utilities.

There are three different modules: (1) model fitting and selection using a set of the most commonly used equations describing developmental responses to temperature helped by already existing R packages ('rTPC') and nonlinear regression model functions from nls.multstart (Padfield et al. 2021, <doi:10.1111/2041-210X.13585>), with visualization of model predictions to guide ecological criteria for model selection; (2) calculation of suitability thermal limits, which consist on a temperature interval delimiting the optimal performance zone or suitability; and (3) climatic data extraction and visualization inspired on previous research (Taylor et al. 2019, <doi:10.1111/1365-2664.13455>), with either exportable rasters, static map images or html, interactive maps.

This package performs pathway enrichment analysis using a voting-based framework that integrates CpGâ gene regulatory information from expression quantitative trait methylation (eQTM) data. For a grid of top-ranked CpGs and filtering thresholds, gene sets are generated and refined using an entropy-based pruning strategy that balances information richness, stability, and probe bias correction. In particular, gene lists dominated by genes with disproportionately high numbers of CpG mappings are penalized to mitigate active probe biasâ a common artifact in methylation data analysis. Enrichment results across parameter combinations are then aggregated using a voting scheme, prioritizing pathways that are consistently recovered under diverse settings and robust to parameter perturbations.

The package provides functionality that can be useful for the analysis of the high-density tiling microarray data (such as from Affymetrix genechips) or for measuring the transcript abundance and the architecture. The main functionalities of the package are:

1. the class segmentation for representing partitionings of a linear series of data;

2. the function segment for fitting piecewise constant models using a dynamic programming algorithm that is both fast and exact;

3. the function confint for calculating confidence intervals using the strucchange package;

4. the function plotAlongChrom for generating pretty plots;

5. the function normalizeByReference for probe-sequence dependent response adjustment from a (set of) reference hybridizations.

This package provides functions to access data from public RESTful APIs including the ArgentinaDatos API', REST Countries API', and World Bank API related to Argentina's exchange rates, inflation, political figures, holidays, economic indicators, and general country-level statistics. Additionally, the package includes curated datasets related to Argentina, covering topics such as economic indicators, biodiversity, agriculture, human rights, genetic data, and consumer prices. The package supports research and analysis focused on Argentina by integrating open APIs with high-quality datasets from various domains. For more details on the APIs, see: ArgentinaDatos API <https://argentinadatos.com/>, REST Countries API <https://restcountries.com/>, and World Bank API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>.