Some enhancements, extensions and additions to the facilities of the recommended MASS package that are useful mainly for teaching purposes, with more convenient default settings and user interfaces. Key functions from MASS are imported and re-exported to avoid masking conflicts. In addition we provide some additional functions mainly used to illustrate coding paradigms and techniques, such as Gramm-Schmidt orthogonalisation and generalised eigenvalue problems.

With high-dimensional omics features, repeated measure ANOVA leads to longitudinal gene-environment interaction studies that have intra-cluster correlations, outlying observations and structured sparsity arising from the ANOVA design. In this package, we have developed robust sparse Bayesian mixed effect models tailored for the above studies (Fan et al. (2025) <doi:10.1093/jrsssc/qlaf027>). An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++'. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.

Rudimentary functions for sampling and calculating density from the matrix-variate variance-gamma distribution.

This package provides exact and approximate algorithms for the horseshoe prior in linear regression models, which were proposed by Johndrow et al. (2020) <https://www.jmlr.org/papers/v21/19-536.html>.

Takes QC signal for each day and normalize metabolomic data that has been acquired in a certain period of time. At least three QC per day are required.

Discrete event simulation using both R and C++ (Karlsson et al 2016; <doi:10.1109/eScience.2016.7870915>). The C++ code is adapted from the SSIM library <https://www.inf.usi.ch/carzaniga/ssim/>, allowing for event-oriented simulation. The code includes a SummaryReport class for reporting events and costs by age and other covariates. The C++ code is available as a static library for linking to other packages. A priority queue implementation is given in C++ together with an S3 closure and a reference class implementation. Finally, some tools are provided for cost-effectiveness analysis.

Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes and all the nonzero effects were further identified by likelihood ratio test for true QTL. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018) <doi:10.1093/bib/bbw145>.

To determine the number of quantitative assays needed for a sample of data using pooled testing methods, which include mini-pooling (MP), MP with algorithm (MPA), and marker-assisted MPA (mMPA). To estimate the number of assays needed, the package also provides a tool to conduct Monte Carlo (MC) to simulate different orders in which the sample would be collected to form pools. Using MC avoids the dependence of the estimated number of assays on any specific ordering of the samples to form pools.

Compute the average of a sequence of random vectors in a moving expanding window using a fixed amount of memory.

This package is deprecated. Please use redatamx instead. Provides an API to work with Redatam (see <https://redatam.org>) databases in both formats: RXDB (new format) and DICX (old format) and running Redatam programs written in SPC language. It's a wrapper around Redatam core and provides functions to open/close a database (redatam_open()/redatam_close()), list entities and variables from the database (redatam_entities(), redatam_variables()) and execute a SPC program and gets the results as data frames (redatam_query(), redatam_run()).

Many useful functions and extensions for dealing with meteorological data in the tidy data framework. Extends ggplot2 for better plotting of scalar and vector fields and provides commonly used analysis methods in the atmospheric sciences.

Includes support for Mapbox Navigation APIs, including directions, isochrones, and route optimization; the Search API for forward and reverse geocoding; the Maps API for interacting with Mapbox vector tilesets and visualizing Mapbox maps in R; and Mapbox Tiling Service and tippecanoe for generating map tiles. See <https://docs.mapbox.com/api/> for more information about the Mapbox APIs.

Allows the user to generate a list of features (gene, pseudo, RNA, CDS, and/or UTR) directly from NCBI database for any species with a current build available. Option to save downloaded and formatted files is available, and the user can prioritize the feature list based on type and assembly builds present in the current build used. The user can then use the list of features generated or provide a list to map a set of markers (designed for SNP markers with a single base pair position available) to the closest feature based on the map build. This function does require map positions of the markers to be provided and the positions should be based on the build being queried through NCBI.

Local adaptation and evaluation of maps of continuous attributes in raster format by use of point location data.

Estimation functions and diagnostic tools for mean length-based total mortality estimators based on Gedamke and Hoenig (2006) <doi:10.1577/T05-153.1>.

This project extends R with a mechanism for efficient parallel data access by utilizing C++ shared memory. Large data objects can be accessed and manipulated directly from R without redundant copying, providing both speed and memory efficiency.

This package provides functions to perform sensitivity analysis on a model with multivariate output.

Monolix is a tool for running mixed effects model using saem'. This tool allows you to convert Monolix models to rxode2 (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) using the form compatible with nlmixr2 (Fidler et al (2019) <doi:10.1002/psp4.12445>). If available, the rxode2 model will read in the Monolix data and compare the simulation for the population model individual model and residual model to immediately show how well the translation is performing. This saves the model development time for people who are creating an rxode2 model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a rxode2 model. This is complementary to the babelmixr2 package that translates nlmixr2 models to Monolix and can convert the objects converted from monolix2rx to a full nlmixr2 fit. While not required, you can get/install the lixoftConnectors package in the Monolix installation, as described at the following url <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When lixoftConnectors is available, Monolix can be used to load its model library instead manually setting up text files (which only works with old versions of Monolix').

Find dark genes. These genes are often disregarded due to no detected mutation or differential expression, but are important in coordinating the functionality in cancer networks.

Citrus is a computational technique developed for the analysis of high dimensional cytometry data sets. This package extracts, statistically analyzes, and visualizes marker expression from citrus data. This code was used to generate data for Figures 3 and 4 in the forthcoming manuscript: Throm et al. â Identification of Enhanced Interferon-Gamma Signaling in Polyarticular Juvenile Idiopathic Arthritis with Mass Cytometryâ , JCI-Insight. For more information on Citrus, please see: Bruggner et al. (2014) <doi:10.1073/pnas.1408792111>. To download the citrus package, please see <https://github.com/nolanlab/citrus>.

This package provides a guidance system for analysis with missing data. It incorporates expert, up-to-date methodology to help researchers choose the most appropriate analysis approach when some data are missing. You provide the available data and the assumed causal structure, including the likely causes of missing data. midoc will advise which analysis approaches can be used, and how best to perform them. midoc follows the framework for the treatment and reporting of missing data in observational studies (TARMOS). Lee et al (2021). <doi:10.1016/j.jclinepi.2021.01.008>.

This package provides a lavaan'-like syntax for OpenMx models. The syntax supports definition variables, bounds, and parameter transformations. This allows for latent growth curve models with person-specific measurement occasions, moderated nonlinear factor analysis and much more.

Analysis of experimental multi-parent populations to detect regions of the genome (called quantitative trait loci, QTLs) influencing phenotypic traits measured in unique and multiple environments. The population must be composed of crosses between a set of at least three parents (e.g. factorial design, diallel', or nested association mapping). The functions cover data processing, QTL detection, and results visualization. The implemented methodology is described in Garin, Wimmer, Mezmouk, Malosetti and van Eeuwijk (2017) <doi:10.1007/s00122-017-2923-3>, in Garin, Malosetti and van Eeuwijk (2020) <doi: 10.1007/s00122-020-03621-0>, and in Garin, Diallo, Tekete, Thera, ..., and Rami (2024) <doi: 10.1093/genetics/iyae003>.

Play and record games of minesweeper using a graphics device that supports event handling. Replay recorded games and save GIF animations of them. Based on classic minesweeper as detailed by Crow P. (1997) <https://minesweepergame.com/math/a-mathematical-introduction-to-the-game-of-minesweeper-1997.pdf>.