Tidy 'STAC' Workflows for R

Wraps the 'rstac' package with a pipe-friendly, tidy API. All results return 'tibbles' instead of nested lists. Ships with a catalog registry of known 'STAC' endpoints including Planetary Computer, Earth Search, and 'USGS', while supporting any 'STAC' API URL.

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/>.

Tidy Fault Tree Analysis

Fault tree analysis with tidy data. Represent a fault tree as two tables, one of nodes (the top event, gates and basic events) and one of edges, so the whole workflow stays in ordinary data frames. Derive the Boolean equation of the top event, enumerate the truth table, and find minimal cut sets with the Method of Obtaining Cut Sets (MOCUS) algorithm of Fussell and Vesely (1972), implemented in 'R' and, by default, in C++ through 'Rcpp'. Evaluate thousands of binary or probabilistic scenarios in one vectorized call, put uncertainty intervals around the top-event probability, follow it over time, and rank which component fix lowers it most. Lay out and draw the tree with 'tidygraph', 'igraph' and 'ggplot2'. Methods follow the "Fault Tree Handbook" of Vesely, Goldberg, Roberts and Haasl (1981) < https://www.nrc.gov/docs/ML1007/ML100780465.pdf>. A 'Python' port with the same functions ships in the same repository.

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 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 Tools for 'Raster' Data

Facilities to work with vector and raster data in efficient repeatable and systematic work flow. Missing functionality in existing packages is included here to allow extraction from raster data with 'simple features' and 'Spatial' types and to make extraction consistent and straightforward. Extract cell numbers from raster data and return the cells as a data frame rather than as lists of matrices or vectors. The functions here allow spatial data to be used without special handling for the format currently in use.