Implements an iterative mean-variance panel regression estimator that allows both the mean and variance of the dependent variable to be functions of covariates. The method alternates between estimating a mean equation (using generalized linear models with Gaussian family) and a variance equation (using generalized linear models with Gamma family on squared within-group residuals) until convergence. Based on the methodology in Mooi-Reci and Liao (2025) &lt;<a href="https://doi.org/10.1093%2Fesr%2Fjcae052" target="_top">doi:10.1093/esr/jcae052</a>&gt;.

A macro language for 'R' programs, which provides a macro facility similar to 'SAS®'. This package contains basic macro capabilities like defining macro variables, executing conditional logic, and defining macro functions.

Detect and test for changes in covariance structures of functional data, as well as changepoint detection for multivariate data more generally. Method for detecting non-stationarity in resting state functional Magnetic Resonance Imaging (fMRI) scans as seen in Ramsay, K., &amp; Chenouri, S. (2025) &lt;<a href="https://doi.org/10.1080%2F10485252.2025.2503891" target="_top">doi:10.1080/10485252.2025.2503891</a>&gt; is implemented in fmri_changepoints(). Also includes depth- and rank-based implementation of the wild binary segmentation algorithm for detecting multiple changepoints in multivariate data.

Provides a high-performance 'R' interface to the 'kuzu' graph database. It uses the 'reticulate' package to wrap the official 'Python' client ('kuzu', 'pandas', and 'networkx'), allowing users to interact with 'kuzu' seamlessly from within 'R'. Key features include managing database connections, executing 'Cypher' queries, and efficiently loading data from 'R' data frames. It also provides seamless integration with the 'R' ecosystem by converting query results directly into popular 'R' data structures, including 'tibble', 'igraph', 'tidygraph', and 'g6R' objects, making 'kuzu's powerful graph computation capabilities readily available for data analysis and visualization workflows in 'R'. The 'kuzu' documentation can be found at &lt;<a href="https://kuzudb.github.io/docs/" target="_top">https://kuzudb.github.io/docs/</a>&gt;.

https://cran.r-project.org/package=RPesto

An easy-to-use tool for implementing Neural Ordinary Differential Equations (NODEs) in pharmacometric software such as 'Monolix', 'NONMEM', and 'nlmixr2', see Bräm et al. (2024) &lt;<a href="https://doi.org/10.1007%2Fs10928-023-09886-4" target="_top">doi:10.1007/s10928-023-09886-4</a>&gt; and Bräm et al. (2025) &lt;<a href="https://doi.org/10.1002%2Fpsp4.13265" target="_top">doi:10.1002/psp4.13265</a>&gt;. The main functionality is to automatically generate structural model code describing computations within a neural network. Additionally, parameters and software settings can be initialized automatically. For using these additional functionalities with 'Monolix', 'pmxNODE' interfaces with 'MonolixSuite' via the 'lixoftConnectors' package. The 'lixoftConnectors' package is distributed with 'MonolixSuite' (&lt;<a href="https://monolixsuite.slp-software.com/r-functions/2024R1/package-lixoftconnectors" target="_top">https://monolixsuite.slp-software.com/r-functions/2024R1/package-lixoftconnectors</a>&gt;) and is not available from public repositories.

Provides a suite of methods for detecting influential subjects in longitudinal datasets, particularly when observations occur at irregular time points. The methods identify individuals whose response trajectories deviate significantly from the population pattern, enabling detection of anomalies or subjects exerting undue influence on model outcomes.