A Tidy Grouping Set Aggregation

A Tidy implementation of 'grouping sets', 'rollup' and 'cube' - extensions of the 'group_by' clause that allow for computing multiple 'group_by' clauses in a single statement. For more detailed information on these functions, please refer to "Enhanced Aggregation, Cube, Grouping and Rollup" < https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C+Grouping+and+Rollup>.

Tidy Multilocus Amplicon Genotypes

Variant determination and genotyping from high throughput sequences from multilocus amplicon libraries, typically sequenced in Illumina MiSeq or similar. It provides a set of core functions for the central steps: demultiplex by locus, truncate reads, variant calling, and genotype calling. Additionally, it provides a set of functions for diagnosis and estimation of best running parameters and multiple extensions for genotype/variants manipulation and reformatting. Output variants and genotypes are output in 'tidy' format, thus facilitating reformatting, manipulation and potential connection to other R packages.

Tidy Temporal Network Analysis

Building, analysis and visualisation of temporal networks. Supports several formats of relational data: interval data with explicit start and end times, contact data of instantaneous events, threaded interactions such as forum or chat data, and co-presence logs. Every analysis returns a tidy data frame carrying proper vertex labels. Metrics cover time-varying centrality, graph-level structure, edge formation and dissolution, burstiness, time-respecting paths, reachability and mixing. Offers a wide variety of network visualisations and animations, as well as plots of temporal network metrics across time.

Tools for Tidy Vowel Normalization

An implementation of tidy speaker vowel normalization. This includes generic functions for defining new normalization methods for points, formant tracks, and Discrete Cosine Transform coefficients, as well as convenience functions implementing established normalization methods. References for the implemented methods are: Johnson, Keith (2020) Lobanov, Boris (1971) Nearey, Terrance M. (1978) < https://sites.ualberta.ca/~tnearey/Nearey1978_compressed.pdf> Syrdal, Ann K., and Gopal, H. S. (1986) Watt, Dominic, and Fabricius, Anne (2002) < https://www.latl.leeds.ac.uk/article/evaluation-of-a-technique-for-improving-the-mapping-of-multiple-speakers-vowel-spaces-in-the-f1-f2-plane/>.

Core Tools for Packages in the 'fable' Framework

Provides tools, helpers and data structures for developing models and time series functions for 'fable' and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis.

Tidy Modelling for Nested Data

A modelling framework for nested data using the 'tidymodels' ecosystem. Specify how to nest data using the 'recipes' package, create testing and training splits using 'rsample', and fit models to this data using the 'parsnip' and 'workflows' packages. Allows any model to be fit to nested data.

Tidy, Integrated Gene Annotation

A framework for intuitive, multi-source gene and protein annotation, with a focus on integrating functional genomics with disease and drug data for translational insights. Methods used include g:Profiler (Raudvere et al. (2019) ), biomaRt (Durinck et al. (2009) ), and the Open Targets Platform (Koscielny et al. (2017) ).

Tidy and Geospatial Kernel Smoothing

Extensions of the kernel smoothing functions from the 'ks' package for compatibility with the tidyverse and geospatial ecosystems .

Tidy Tools for Paleoenvironmental Archives

Provides a set of functions with a common framework for age-depth model management, stratigraphic visualization, and common statistical transformations. The focus of the package is stratigraphic visualization, for which 'ggplot2' components are provided to reproduce the scales, geometries, facets, and theme elements commonly used in publication-quality stratigraphic diagrams. Helpers are also provided to reproduce the exploratory statistical summaries that are frequently included on stratigraphic diagrams. See Dunnington et al. (2021) .

Tidy Presentation of Clinical Reporting

Streamlined statistical reporting in 'Rmarkdown' environments. Facilitates the automated reporting of descriptive statistics, multiple univariate models, multivariable models and tables combining these outputs. Plotting functions include customisable survival curves, forest plots from logistic and ordinal regression and bivariate comparison plots.