MHCnuggets (<https://github.com/KarchinLab/mhcnuggets>) is a Python tool to predict MHC class I and MHC class II epitopes. This package allows one to call MHCnuggets from R.

Calculates Model-Averaged Tail Area Wald (MATA-Wald) confidence intervals, and MATA-Wald confidence densities and distributions, which are constructed using single-model frequentist estimators and model weights. See Turek and Fletcher (2012) <doi:10.1016/j.csda.2012.03.002> and Fletcher et al (2019) <doi:10.1007/s10651-019-00432-5> for details.

Efficient finite difference method for valuing European and American multi-asset options.

An R interface to version 0.3 of the ROPTLIB optimization library (see <https://www.math.fsu.edu/~whuang2/> for more information). Optimize real-valued functions over manifolds such as Stiefel, Grassmann, and symmetric positive definite matrices. For details see Martin et al. (2020) <doi:10.18637/jss.v093.i01>. Note that the optional ldr package used in some of this package's examples can be obtained from either the article <doi:10.18637/jss.v061.i03> or from the ldr package <https://cran.r-project.org/package=ldr>.

This package provides methods for performing genetic risk prediction from genotype data. You can use it to perform risk prediction for individuals, or for families with missing data.

Facilitates creation and manipulation of metric graphs, such as street or river networks. Further facilitates operations and visualizations of data on metric graphs, and the creation of a large class of random fields and stochastic partial differential equations on such spaces. These random fields can be used for simulation, prediction and inference. In particular, linear mixed effects models including random field components can be fitted to data based on computationally efficient sparse matrix representations. Interfaces to the R packages INLA and inlabru are also provided, which facilitate working with Bayesian statistical models on metric graphs. The main references for the methods are Bolin, Simas and Wallin (2024) <doi:10.3150/23-BEJ1647>, Bolin, Kovacs, Kumar and Simas (2023) <doi:10.1090/mcom/3929> and Bolin, Simas and Wallin (2023) <doi:10.48550/arXiv.2304.03190> and <doi:10.48550/arXiv.2304.10372>.

Converts NIfTI format T1/FL neuroimages into structured, high-dimensional 2D data frames with a focus on region of interest (ROI) based processing. The package incorporates the partition algorithm, which offers a flexible framework for agglomerative partitioning based on the Direct-Measure-Reduce approach. This method ensures that each reduced variable maintains a user-specified minimum level of information while remaining interpretable, as each maps uniquely to one variable in the reduced dataset. The partition framework is described in Millstein et al. (2020) <doi:10.1093/bioinformatics/btz661>. The package allows customization in variable selection, measurement of information loss, and data reduction methods for neuroimaging analysis and machine learning workflows.

This package provides the function mixor for fitting a mixed-effects ordinal and binary response models and associated methods for printing, summarizing, extracting estimated coefficients and variance-covariance matrix, and estimating contrasts for the fitted models.

This package provides a novel framework to estimate mixed models via gradient boosting. The implemented functions are based on the mboost and lme4 packages, and the family range is therefore determined by lme4'. A correction mechanism for cluster-constant covariates is implemented, as well as estimation of the covariance of random effects. These methods are described in the accompanying publication; see <doi:10.1007/s11222-025-10612-y> for details.

Extends traditional random-effects meta-analysis by embedding it within a generalized linear mixed-effects model framework. The package supports covariate adjustment and non-normal responses using aggregate data, and provides likelihood-based inference with computationally efficient likelihood evaluation. The underlying methodology is described in Hanada and Sugimoto (2026) <doi:10.1093/biomtc/ujag148>.

Estimation of relative risks, risk differences, and partial effects from mixed model. Marginalisation over random effect terms is accomplished using Markov Chain Monte Carlo.

Encodes several methods for performing Mendelian randomization analyses with summarized data. Similar to the MendelianRandomization package, but with fewer bells and whistles, and less frequent updates. As described in Yavorska (2017) <doi:10.1093/ije/dyx034> and Broadbent (2020) <doi:10.12688/wellcomeopenres.16374.2>.

Run multiple Large Language Model predictions against a table. The predictions run row-wise over a specified column. It works using a one-shot prompt, along with the current row's content. The prompt that is used will depend of the type of analysis needed.

Acoustic template detection and monitoring database interface. Create, modify, save, and use templates for detection of animal vocalizations. View, verify, and extract results. Upload a MySQL schema to a existing instance, manage survey metadata, write and read templates and detections locally or to the database.

This package implements the multicorrelation coefficient for p-variables as described in Cankaya (2023). The package provides a numerically stable implementation using logarithmic transformations and a log-sum-exp approach to reduce numerical overflow and underflow when calculations involve a large number of variables.

This package provides a general framework for clinical trial simulations based on the Clinical Scenario Evaluation (CSE) approach. The package supports a broad class of data models (including clinical trials with continuous, binary, survival-type and count-type endpoints as well as multivariate outcomes that are based on combinations of different endpoints), analysis strategies and commonly used evaluation criteria.

Michel Rodange was a Luxembourguish writer and poet who lived in the 19th century. His most notable work is Rodange (1872, ISBN:1166177424), ("Renert oder de Fuuà am Frack an a Ma'nsgrëà t"), but he also wrote many more works, including Rodange, Tockert (1928) <https://www.autorenlexikon.lu/page/document/361/3614/1/FRE/index.html> ("D'Léierchen - Dem Léiweckerche säi Lidd") and Rodange, Welter (1929) <https://www.autorenlexikon.lu/page/document/361/3615/1/FRE/index.html> ("Dem Grow Sigfrid seng Goldkuommer"). This package contains three datasets, each made from the plain text versions of his works available on <https://data.public.lu/fr/datasets/the-works-in-luxembourguish-of-michel-rodange/>.

Apply the marginal classification method to achieve the purpose of providing the point and interval estimates for the minimal clinically important difference based on the classical anchor-based method. For more details of the methodology, please see Zehua Zhou, Leslie J. Bisson and Jiwei Zhao (2021) <arXiv:2108.11589>.

An implementation of the iterative proportional fitting (IPFP), maximum likelihood, minimum chi-square and weighted least squares procedures for updating a N-dimensional array with respect to given target marginal distributions (which, in turn can be multidimensional). The package also provides an application of the IPFP to simulate multivariate Bernoulli distributions.

This package implements the Mittag-Leffler function, distribution, random variate generation, and estimation. Based on the Laplace-Inversion algorithm by Garrappa, R. (2015) <doi:10.1137/140971191>.

Generate central composite designs (CCD)with full as well as fractional factorial points (half replicate) and Box Behnken designs (BBD) with minimally changed run sequence.

Computes regression deletion diagnostics for multivariate linear models and provides some associated diagnostic plots. The diagnostic measures include hat-values (leverages), generalized Cook's distance, and generalized squared studentized residuals. Several types of plots to detect influential observations are provided.

This package provides tools for likelihood-based inference in one-dimensional stochastic differential equations with mixed effects using expectationâ maximization (EM) algorithms. The package supports Wiener and Ornsteinâ Uhlenbeck diffusion processes with user-specified drift functions, allowing flexible parametric forms including polynomial, exponential, and trigonometric structures. Estimation is performed via Markov chain Monte Carlo EM.

Toolbox and shiny application to help researchers design movement ecology studies, focusing on two key objectives: estimating home range areas, and estimating fine-scale movement behavior, specifically speed and distance traveled. It provides interactive simulations and methodological guidance to support study planning and decision-making. The application is described in Silva et al. (2023) <doi:10.1111/2041-210X.14153>.