Transfer learning, as a prevailing technique in computer sciences, aims to improve the performance of a target model by leveraging auxiliary information from heterogeneous source data. We provide novel tools for multi-source transfer learning under statistical models based on model averaging strategies, including linear regression models, partially linear models. Unlike existing transfer learning approaches, this method integrates the auxiliary information through data-driven weight assignments to avoid negative transfer. This is the first package for transfer learning based on the optimal model averaging frameworks, providing efficient implementations for practitioners in multi-source data modeling. The details are described in Hu and Zhang (2023) <https://jmlr.org/papers/v24/23-0030.html>.

Gene Expression datasets for the MM2S package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594'). Deena Gendoo et al. (2015) <doi:10.1016/j.ygeno.2015.05.002>.

Electronic health records (EHR) linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. Towards that end, we developed an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP). Specifically, our proposed method, called MAP (Map Automated Phenotyping algorithm), fits an ensemble of latent mixture models on aggregated ICD and NLP counts along with healthcare utilization. The MAP algorithm yields a predicted probability of phenotype for each patient and a threshold for classifying subjects with phenotype yes/no (See Katherine P. Liao, et al. (2019) <doi:10.1093/jamia/ocz066>.).

This is the very popular mine sweeper game! The game requires you to find out tiles that contain mines through clues from unmasking neighboring tiles. Each tile that does not contain a mine shows the number of mines in its adjacent tiles. If you unmask all tiles that do not contain mines, you win the game; if you unmask any tile that contains a mine, you lose the game. For further game instructions, please run `help(run_game)` and check details. This game runs in X11-compatible devices with `grDevices::x11()`.

Equivalence tests and related confidence intervals for the comparison of two treatments, simultaneously for one or many normally distributed, primary response variables (endpoints). The step-up procedure of Quan et al. (2001) is both applied for differences and extended to ratios of means. A related single-step procedure is also available.

This package provides a modeltime extension that implements time series ensemble forecasting methods including model averaging, weighted averaging, and stacking. These techniques are popular methods to improve forecast accuracy and stability.

Helping psychologists and other behavioural scientists to analyze mouse movement (and other 2-D trajectory) data. Bundles together several functions that compute spatial measures (e.g., maximum absolute deviation, area under the curve, sample entropy) or provide a shorthand for procedures that are frequently used (e.g., time normalization, linear interpolation, extracting initiation and movement times). For more information on these dependent measures, see Wirth et al. (2020) <doi:10.3758/s13428-020-01409-0>.

Automated cell type annotation for single-cell RNA sequencing data using consensus predictions from multiple large language models. Integrates with Seurat objects and provides uncertainty quantification for annotations. Supports various LLM providers including OpenAI, Anthropic, and Google. For details see Yang et al. (2025) <doi:10.1101/2025.04.10.647852>.

This package provides functions for measuring population divergence from genotypic data.

Set of tools for descriptive analysis of metaproteomics data generated from high-throughput mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance, expressed as spectral counts, and to manipulate them in groups of metaproteins. This information can be represented using multiple visualization functions to portray the global metaproteome landscape and to differentiate samples or conditions, in terms of abundance of metaproteins, taxonomic levels and/or functional annotation. The provided tools allow to implement flexible analytical pipelines that can be easily applied to studies interested in metaproteomics analysis.

MTrackJ is an ImageJ plugin for motion tracking and analysis (see <https://imagescience.org/meijering/software/mtrackj/>). This package reads and writes MTrackJ Data Files ('.mdf', see <https://imagescience.org/meijering/software/mtrackj/format/>). It supports 2D data and read/writes cluster, point, and channel information. If desired, generates track identifiers that are unique over the clusters. See the project page for more information and examples.

This package provides helper functions, metadata utilities, and workflows for administering and managing databases on the Motherduck cloud platform. Some features require a Motherduck account (<https://motherduck.com/>).

This package provides functionality to generate compound optimal designs for targeting the multiple experimental objectives directly, ensuring that the full set of research questions is answered as economically as possible. Designs can be found using point or coordinate exchange algorithms combining estimation, inference and lack-of-fit criteria that account for model inadequacy. Details and examples are given by Koutra et al. (2024) <doi:10.48550/arXiv.2412.17158>.

Multivariate generalized Gaussian distribution, Multivariate Cauchy distribution, Multivariate t distribution. Distance between two distributions (see N. Bouhlel and A. Dziri (2019): <doi:10.1109/LSP.2019.2915000>, N. Bouhlel and D. Rousseau (2022): <doi:10.3390/e24060838>, N. Bouhlel and D. Rousseau (2023): <doi:10.1109/LSP.2023.3324594>). Manipulation of these multivariate probability distributions. This package replaces mggd', mcauchyd and mstudentd'.

Build multiscalar territorial analysis based on various contexts.

Test for monotonicity in financial variables sorted by portfolios. It is conventional practice in empirical research to form portfolios of assets ranked by a certain sort variable. A t-test is then used to consider the mean return spread between the portfolios with the highest and lowest values of the sort variable. Yet comparing only the average returns on the top and bottom portfolios does not provide a sufficient way to test for a monotonic relation between expected returns and the sort variable. This package provides nonparametric tests for the full set of monotonic patterns by Patton, A. and Timmermann, A. (2010) <doi:10.1016/j.jfineco.2010.06.006> and compares the proposed results with extant alternatives such as t-tests, Bonferroni bounds, and multivariate inequality tests through empirical applications and simulations.

Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.

Estimation and bootstrap utilities for single layer and multilayer Mixed Graphical Models, including functions for centrality, bridge metrics, membership stability, and plotting (De Martino et al. (2026) <doi:10.48550/arXiv.2602.05716>).

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

Computes robust and bias-corrected sandwich variance estimators for multi-state Cox models with clustered time-to-event data. The methodology extends the marginal Cox model bias-correction framework of Wang et al. (2023) <doi:10.1002/bimj.202200113> to the multi-state setting.

This package creates an object that stores a matrix ensemble, matrices that share the same common properties, where rows and columns can be annotated. Matrices must have the same dimension and dimnames. Operators to manipulate these objects are provided as well as mechanisms to apply functions to these objects.

Semi-parametric approach for sparse canonical correlation analysis which can handle mixed data types: continuous, binary and truncated continuous. Bridge functions are provided to connect Kendall's tau to latent correlation under the Gaussian copula model. The methods are described in Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and Yoon, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.

Fit Maximum Entropy Optimality Theory models to data sets, generate the predictions made by such models for novel data, and compare the fit of different models using a variety of metrics. The package is described in Mayer, C., Tan, A., Zuraw, K. (in press) <https://sites.socsci.uci.edu/~cjmayer/papers/cmayer_et_al_maxent_ot_accepted.pdf>.

This package performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors. These model-based non-parametric clustering techniques can extract clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default.