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.

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.

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;.

Implements diversification analyses using the phylogenetic birth-death-shift model. It leverages belief propagation techniques to calculate branch-specific diversification rates, see Kopperud &amp; Hoehna (2025) &lt;<a href="https://doi.org/10.1093%2Fsysbio%2Fsyaf041" target="_top">doi:10.1093/sysbio/syaf041</a>&gt;.

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.

The Open Data Format (ODF) is a new, non-proprietary, multilingual, metadata enriched, and zip-compressed data format with metadata structured in the Data Documentation Initiative (DDI) Codebook standard. This package allows reading and writing of data files in the Open Data Format (ODF) in R, and displaying metadata in different languages. For further information on the Open Data Format, see &lt;<a href="https://opendataformat.github.io/" target="_top">https://opendataformat.github.io/</a>&gt;.

A framework used to detect and handle outliers during data analysis workflows. Outlier detection is a statistical concept with applications in data analysis workflows, highlighting records that are suspiciously high or low. Outlier detection in distribution models was initiated by Chapman (1991) (available at &lt;<a href="https://www.researchgate.net/publication/332537800_Quality_control_and_validation_of_point-sourced_environmental_resource_data" target="_top">https://www.researchgate.net/publication/332537800_Quality_control_and_validation_of_point-sourced_environmental_resource_data</a>&gt;), who developed the reverse jackknifing method. The concept was further developed and incorporated into different R packages, including 'flexsdm' (Velazco et al., 2022, &lt;<a href="https://doi.org/10.1111%2F2041-210X.13874" target="_top">doi:10.1111/2041-210X.13874</a>&gt;) and 'biogeo' (Robertson et al., 2016 &lt;<a href="https://doi.org/10.1111%2Fecog.02118" target="_top">doi:10.1111/ecog.02118</a>&gt;). We compiled various outlier detection methods obtained from the literature, including those elaborated in Dastjerdy et al. (2023) &lt;<a href="https://doi.org/10.3390%2Fgeotechnics3020022" target="_top">doi:10.3390/geotechnics3020022</a>&gt; and Liu et al. (2008) &lt;<a href="https://doi.org/10.1109%2FICDM.2008.17" target="_top">doi:10.1109/ICDM.2008.17</a>&gt;. In this package, we introduced the ensembling aspect, where multiple outlier detection methods are used to flag the record as either an absolute outlier. The concept can also be applied in general data analysis, as well as during the development of species distribution models.

Extends the 'SelectBoost' approach to Generalized Additive Models for Location, Scale and Shape (GAMLSS). Implements bootstrap stability-selection across parameter-specific formulas (mu, sigma, nu, tau) via gamlss::stepGAIC(). Includes optional standardization of predictors and helper functions for corrected AIC calculation. More details can be found in Bertrand and Maumy (2024) &lt;<a href="https://hal.science/hal-05352041" target="_top">https://hal.science/hal-05352041</a>&gt; that highlights correlation-aware resampling to improve variable selection for GAMLSS and quantile regression when predictors are numerous and highly correlated.

Trains per-horizon probabilistic ensembles from a univariate time series. It supports 'rpart', 'glmnet', and 'kNN' engines with flexible residual distributions and heteroscedastic scale models, weighting variants by calibration-aware scores. A Gaussian/t copula couples the marginals to simulate joint forecast paths, returning quantiles, means, and step increments across horizons.

The tools provide an interface for inter-process communication ('IPC') between 'R' and 'Go' using the 'Nanomsg' library. It vendors on the 'Go' side the 'mangos' library &lt;<a href="https://github.com/nanomsg/mangos" target="_top">https://github.com/nanomsg/mangos</a>&gt; and 'arrow-go' &lt;<a href="https://github.com/apache/arrow-go" target="_top">https://github.com/apache/arrow-go</a>&gt; and uses 'nanonext' and 'nanoarrow' on the 'R' side.

An implementation of generalized linear models (GLMs) for studying relationships among attributes in connected populations, where responses of connected units can be dependent, as introduced by Fritz et al. (2025) &lt;<a href="https://doi.org/10.1080%2F01621459.2025.2565851" target="_top">doi:10.1080/01621459.2025.2565851</a>&gt;. 'igml' extends GLMs for independent responses to dependent responses and can be used for studying spillover in connected populations and other network-mediated phenomena.

A faster implementation of Bayesian Causal Forests (BCF; Hahn et al. (2020) &lt;<a href="https://doi.org/10.1214%2F19-BA1195" target="_top">doi:10.1214/19-BA1195</a>&gt;), which uses regression tree ensembles to estimate the conditional average treatment effect of a binary treatment on a scalar output as a function of many covariates. This implementation avoids many redundant computations and memory allocations present in the original BCF implementation, allowing the model to be fit to larger datasets. The implementation was originally developed for the 2022 American Causal Inference Conference's Data Challenge. See Kokandakar et al. (2023) &lt;<a href="https://doi.org/10.1353%2Fobs.2023.0024" target="_top">doi:10.1353/obs.2023.0024</a>&gt; for more details.

Companion package of Arnaud Barat, Andreu Sansó, Maite Arilla-Osuna, Ruth Blasco, Iñaki Pérez-Fernández, Gabriel Cifuentes-Alcobenda, Rubén Llorente, Daniel Vivar-Ríos, Ella Assaf, Ran Barkai, Avi Gopher, &amp; Jordi Rosell-Ardèvol (2025), "Quantifying Diversity through Entropy Decomposition. Insights into Hominin Occupation and Carcass Processing at Qesem cave".

Implements algorithms for analyzing Cayley graphs of permutation groups, with a focus on the TopSpin puzzle and similar permutation-based combinatorial puzzles. Provides methods for cycle detection, state space exploration, and finding optimal operation sequences in permutation groups generated by shift and reverse operations.