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;.

Fits linear difference-in-differences models in scenarios where intervention roll-outs are staggered over time. The package implements a version of an approach proposed by Sun and Abraham (2021) &lt;<a href="https://doi.org/10.1016%2Fj.jeconom.2020.09.006" target="_top">doi:10.1016/j.jeconom.2020.09.006</a>&gt; to estimate cohort- and time-since-treatment specific difference-in-differences parameters, and it provides convenience functions both for specifying the model and for flexibly aggregating coefficients to answer a variety of research questions.

Read raw and processed data from acoustic ejection mass spectrometry (AEMS) files produced by the Sciex EchoMS instrument. Includes functions to create interactive reader objects, extract raw intensity measurements, mass spectra, and fully-processed mass-transition intensity areas. Methods for data processing and analysis are described in Rimmer et al. (2025) &lt;<a href="https://doi.org/10.1021%2Facs.analchem.5c03730" target="_top">doi:10.1021/acs.analchem.5c03730</a>&gt;. Supports both multiple reaction monitoring (MRM) and full-scan (neutral loss and precursor ion) data formats.

Provides methods for recursive partitioning based on the 'Graded Response Model' ('GRM'), extending the 'MOB' algorithm from the 'partykit' package. The package allows for fitting 'GRM' trees that partition the population into homogeneous subgroups based on item response patterns and covariates. Includes specialized plotting functions for visualizing 'GRM' trees with different terminal node displays (threshold regions, parameter profiles, and factor score distributions). For more details on the methods, see Samejima (1969) &lt;<a href="https://doi.org/10.1002%2FJ.2333-8504.1968.TB00153.X" target="_top">doi:10.1002/J.2333-8504.1968.TB00153.X</a>&gt;, Komboz et al. (2018) &lt;<a href="https://doi.org/10.1177%2F0013164416664394" target="_top">doi:10.1177/0013164416664394</a>&gt; and Arimoro et al. (2025) &lt;<a href="https://doi.org/10.1007%2Fs11136-025-04018-6" target="_top">doi:10.1007/s11136-025-04018-6</a>&gt;.

Extracts and parses structured metadata ('YAML' or 'TOML') from the beginning of text documents. Front matter is a common pattern in 'Quarto' documents, 'R Markdown' documents, static site generators, documentation systems, content management tools and even 'Python' and 'R' scripts where metadata is placed at the top of a document, separated from the main content by delimiter fences.

Implements a system of linear equations to recover unreported diagnostic test accuracy cell counts from commonly reported measures such as sensitivity, specificity, predictive values, prevalence, and sample size. The package is intended for applied researchers who require complete 2x2 table counts for downstream analyses.

Estimates Boltzmann–Lotka–Volterra (BLV) interaction model efficiently. Enables programmatic and graphical exploration of the solution space of BLV models when parameters are varied. See Wilson, A. (2008) &lt;dx.doi.org/10.1098/rsif.2007.1288&gt;.

Description: Provides streamlined tools for retrieving sequences from NCBI, performing sequence alignments (pairwise and multiple), and building phylogenetic trees. Implements the Needleman-Wunsch algorithm for global alignment (Needleman &amp; Wunsch (1970) &lt;<a href="https://doi.org/10.1016%2F0022-2836%2870%2990057-4" target="_top">doi:10.1016/0022-2836(70)90057-4</a>&gt;), Smith-Waterman for local alignment (Smith &amp; Waterman (1981) &lt;<a href="https://doi.org/10.1016%2F0022-2836%2881%2990087-5" target="_top">doi:10.1016/0022-2836(81)90087-5</a>&gt;), and Neighbor-Joining for tree construction (Saitou &amp; Nei (1987) &lt;<a href="https://doi.org/10.1093%2Foxfordjournals.molbev.a040454" target="_top">doi:10.1093/oxfordjournals.molbev.a040454</a>&gt;).

Provides a variety of tools for assessing dose response curves, with an emphasis on toxicity test data. The main feature of this package are modular functions which can be combined through the namesake pipeline, 'runtoxdrc', to automate the analysis for large and complex datasets. This includes optional data preprocessing steps, like outlier detection, solvent effects, blank correction, averaging technical replicates, and much more. Additionally, this pipeline is adaptable to any long form dataset, and does not require specific column or group naming to work.

Provides deterministic approximation methods for use with the 'nimble' package. These include Laplace approximation and higher-order extension of Laplace approximation using adaptive Gauss-Hermite quadrature (AGHQ), plus nested deterministic approximation methods related to the 'INLA' approach. Additional information is available in the NIMBLE User Manual and a 'nimbleQuad' tutorial, both available at &lt;<a href="https://r-nimble.org/documentation.html" target="_top">https://r-nimble.org/documentation.html</a>&gt;.