Provides functions to compute standardized differences for numeric, binary, and categorical variables on Apache Spark DataFrames using 'sparklyr'. The implementation mirrors the methods used in the 'stddiff' package but operates on distributed data. See Zhicheng Du, Yuantao Hao (2022) &lt;<a href="https://doi.org/10.32614%2FCRAN.package.stddiff" target="_top">doi:10.32614/CRAN.package.stddiff</a>&gt; for reference.

An implementation of statistical tools for the analysis of rotation-valued time series and functional data. It relies on pre-existing quaternion data structure provided by the 'Eigen' 'C++' library.

High-quality real-world data can be transformed into scientific real-world evidence for regulatory and healthcare decision-making using proven analytical methods and techniques. For example, propensity score (PS) methodology can be applied to select a subset of real-world data containing patients that are similar to those in the current clinical study in terms of baseline covariates, and to stratify the selected patients together with those in the current study into more homogeneous strata. Then, statistical methods such as the power prior approach or composite likelihood approach can be applied in each stratum to draw inference for the parameters of interest. This package provides functions that implement the PS-integrated real-world evidence analysis methods such as Wang et al. (2019) &lt;<a href="https://doi.org/10.1080%2F10543406.2019.1657133" target="_top">doi:10.1080/10543406.2019.1657133</a>&gt;, Wang et al. (2020) &lt;<a href="https://doi.org/10.1080%2F10543406.2019.1684309" target="_top">doi:10.1080/10543406.2019.1684309</a>&gt;, and Chen et al. (2020) &lt;<a href="https://doi.org/10.1080%2F10543406.2020.1730877" target="_top">doi:10.1080/10543406.2020.1730877</a>&gt;.

Location- and scale-invariant Box-Cox and Yeo-Johnson power transformations allow for transforming variables with distributions distant from 0 to normality. Transformers are implemented as S4 objects. These allow for transforming new instances to normality after optimising fitting parameters on other data. A test for central normality allows for rejecting transformations that fail to produce a suitably normal distribution, independent of sample number.

Provides standardised functions for quantifying plant disease intensity and disease development over time. The package implements Percent Disease Index (PDI) for assessing overall disease severity based on categorical ratings, Area Under the Disease Progress Curve (AUDPC) for summarizing disease progression using trapezoidal integration, and Relative AUDPC (rAUDPC) for expressing disease development relative to the maximum possible severity over the observation period. These indices are widely used in plant pathology and epidemiology for comparing treatments, cultivars, and environments.

Provides functions to retrieve public data from ORCID (Open Researcher and Contributor ID) records via the ORCID public API. Fetches employment history, education, works (publications, datasets, preprints), funding, peer review activities, and other public information. Returns data as structured data.table objects for easy analysis and manipulation. Replaces the discontinued 'rorcid' package with a modern, CRAN-compliant implementation.

Provides estimation and leave-one-cluster-out jackknife standard errors for four longitudinal cluster-randomized trial estimands: horizontal individual average treatment effect (h-iATE), horizontal cluster average treatment effect (h-cATE), vertical individual average treatment effect (v-iATE), and vertical cluster-period average treatment effect (v-cATE), using unadjusted and augmented (model-robust standardization) estimators. The working model may be fit using linear mixed models for continuous outcomes or generalized estimating equations and generalized linear mixed models for binary outcomes. Period inclusion for aggregation is determined automatically: only periods with both treated and control clusters are included in the construction of the marginal means and treatment effect contrasts. See Fang et al. (2025) &lt;<a href="https://doi.org/10.48550%2FarXiv.2507.17190" target="_top">doi:10.48550/arXiv.2507.17190</a>&gt;.

Analyses the stability and structural behaviour of export and import patterns across multiple countries using a Markov chain modelling framework. Constructs transition probability matrices to quantify changes in trade shares between successive periods, thereby capturing persistence, structural shifts, and inter-country interdependence in trade performance. By iteratively generating expected trade distributions over time, the approach facilitates assessment of stability, long-run equilibrium tendencies, and comparative dynamics in longitudinal trade data, providing a rigorous tool for empirical analysis of export–import behaviour. Methodological foundations follow standard Markov chain theory as described in Gagniuc (2017) &lt;Doi:10.1002/9781119387596&gt;.

The goal of 'dynamicpv' is to provide a simple way to calculate (net) present values and outputs from health economic models (especially cost-effectiveness and budget impact) in discrete time that reflect dynamic pricing and dynamic uptake. Dynamic pricing is also known as life cycle pricing; dynamic uptake is also known as multiple or stacked cohorts, or dynamic disease prevalence. Shafrin (2024) &lt;<a href="https://doi.org/10.1515%2Ffhep-2024-0014" target="_top">doi:10.1515/fhep-2024-0014</a>&gt; provides an explanation of dynamic value elements, in the context of Generalized Cost Effectiveness Analysis, and Puls (2024) &lt;<a href="https://doi.org/10.1016%2Fj.jval.2024.03.006" target="_top">doi:10.1016/j.jval.2024.03.006</a>&gt; reviews challenges of incorporating such dynamic value elements. This package aims to reduce those challenges.

Gaussian mixture modeling of one- and two-dimensional data, provided in original or binned form, with an option to estimate the number of model components. The method uses Gaussian Mixture Models (GMM) with initial parameters determined by a dynamic programming algorithm, leading to stable and reproducible model fitting.

Automated data quality auditing using unsupervised machine learning. Provides AI-driven anomaly detection for data quality assessment, primarily designed for Electronic Health Records (EHR) data, with benchmarking capabilities for validation and publication. Methods based on: 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;, Breunig et al. (2000) &lt;<a href="https://doi.org/10.1145%2F342009.335388" target="_top">doi:10.1145/342009.335388</a>&gt;.

Provides a systematic framework for neural network–based model selection and forecasting using single hidden layer feed-forward networks. It evaluates all possible combinations of predictor variables and hidden layer configurations, selecting the optimal model based on predictive accuracy criteria such as root mean squared error (RMSE) and mean absolute percentage error (MAPE). Predictors are automatically standardized, and model performance is assessed using out-of-sample validation. The package is designed for empirical modelling and forecasting in economics, agriculture, trade, climate, and related applied research domains where nonlinear relationships and robust predictive performance are of primary interest.

Visualize the partitions of simple decision trees, involving one or two predictors, on the scale of the original data. Provides an intuitive alternative to traditional tree diagrams, by visualizing how a decision tree divides the predictor space in a simple 2D plot alongside the original data. The 'parttree' package supports both classification and regression trees from 'rpart' and 'partykit', as well as trees produced by popular frontend systems like 'tidymodels' and 'mlr3'. Visualization methods are provided for both base R graphics and 'ggplot2'.

Generates interactive plots for analysing and visualising three-class high dimensional data. It is particularly suited to visualising differences in continuous attributes such as gene/protein/biomarker expression levels between three groups. Differential gene/biomarker expression analysis between two classes is typically shown as a volcano plot. However, with three groups this type of visualisation is particularly difficult to interpret. This package generates 3D volcano plots and 3-way polar plots for easier interpretation of three-class data.

Implementations of the k-means, hierarchical agglomerative and DBSCAN clustering methods for functional data which allows for jointly aligning and clustering curves. It supports functional data defined on one-dimensional domains but possibly evaluating in multivariate codomains. It supports functional data defined in arrays but also via the 'fd' and 'funData' classes for functional data defined in the 'fda' and 'funData' packages respectively. It currently supports shift, dilation and affine warping functions for functional data defined on the real line and uses the SRVF framework to handle boundary-preserving warping for functional data defined on a specific interval. Main reference for the k-means algorithm: Sangalli L.M., Secchi P., Vantini S., Vitelli V. (2010) "k-mean alignment for curve clustering" &lt;<a href="https://doi.org/10.1016%2Fj.csda.2009.12.008" target="_top">doi:10.1016/j.csda.2009.12.008</a>&gt;. Main reference for the SRVF framework: Tucker, J. D., Wu, W., &amp; Srivastava, A. (2013) "Generative models for functional data using phase and amplitude separation" &lt;<a href="https://doi.org/10.1016%2Fj.csda.2012.12.001" target="_top">doi:10.1016/j.csda.2012.12.001</a>&gt;.