This package provides tools to deploy TensorFlow <https://www.tensorflow.org/> models across multiple services. Currently, it provides a local server for testing cloudml compatible services.

Uses the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and van der Linden (2018) <doi:10.1201/9781315117430> to construct fixed, adaptive, and parallel tests. Supports the following mixed-integer programming (MIP) solver packages: Rsymphony', highs', gurobi', lpSolve', and Rglpk'. The gurobi package is not available from CRAN; see <https://www.gurobi.com/downloads/>.

This package provides tools for performing Transition Network Analysis (TNA) to study relational dynamics, including functions for building and plotting TNA models, calculating centrality measures, and identifying dominant events and patterns. TNA statistical techniques (e.g., bootstrapping and permutation tests) ensure the reliability of observed insights and confirm that identified dynamics are meaningful. See (Saqr et al., 2025) <doi:10.1145/3706468.3706513> for more details on TNA.

Tri-hierarchical incomplete block design is defined as an arrangement of v treatments each replicated r times in a three system of blocks if, each block of the first system contains m_1 blocks of second system and each block of the second system contains m_2 blocks of the third system. Ignoring the first and second system of blocks, it leaves an incomplete block design with b_3 blocks of size k_3i units; ignoring first and third system of blocks, it leaves an incomplete block design with b_2 blocks each of size k_2i units and ignoring the second and third system of blocks, it leaves an incomplete block design with b_1 blocks each of size k_1 units. For dealing with experimental circumstances where there are three nested sources of variation, a tri-hierarchical incomplete block design can be adopted. Tri - hierarchical incomplete block designs can find application potential in obtaining mating-environmental designs for breeding trials. To know more about nested block designs one can refer Preece (1967) <doi:10.1093/biomet/54.3-4.479>. This package includes series1(), series2(), series3() and series4() functions. This package generates tri-hierarchical designs with six component designs under certain parameter restrictions.

Efficient tabulation with Stata-like output. For each unique value of the variable, it shows the number of observations with that value, proportion of observations with that value, and cumulative proportion, in descending order of frequency. Accepts data.table, tibble, or data.frame as input. Efficient with big data: if you give it a data.table, tab() uses data.table syntax.

Test your data! An extension of the testthat unit testing framework with a family of functions and reporting tools for checking and validating data frames.

The best ANN structure for time series data analysis is a demanding need in the present era. This package will find the best-fitted ANN model based on forecasting accuracy. The optimum size of the hidden layers was also determined after determining the number of lags to be included. This package has been developed using the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.

Differentiate client errors (4xx) from server errors (5xx) for the plumber and RestRserve HTTP API frameworks. The package also includes a built-in logging mechanism to standard output (STDOUT) or standard error (STDERR) depending on the log level.

This package provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure, and support for tibbles and methods for dplyr'.

Pre-process for discrete time series data set which is not continuous at the column of date'. Refilling records of missing date and other columns to the hollow data set so that final data set is able to be dealt with time series analysis.

Generates a game of 2048 that can be played in the console. Supports grids of arbitrary sizes, undoing the last move, and resuming a game that was exited during the current session.

This package provides ggplot2 geoms for drawing treemaps.

Builds tables with customizable rows. Users can specify the type of data to use for each row, as well as how to handle missing data and the types of comparison tests to run on the table columns.

Offers a TableContainer() function to create tables enriched with row, column, and table annotations. This package is similar to SummarizedExperiment in Bioconductor <doi:10.18129/B9.bioc.SummarizedExperiment>, but designed to work independently of Bioconductor, it ensures annotations are automatically updated when the table is subset. Additionally, it includes format_tbl() methods for enhanced table formatting and display.

Documentation for commonly-used objects included in the base distribution of R. Note that tldrDocs does not export any functions itself, its purpose is to write .Rd files during its installation for tldr() to find.

Time Series Segmented Residual Trends is a method for the automated detection of land degradation from remotely sensed vegetation and climate datasets. TSS-RESTREND incorporates aspects of two existing degradation detection methods: RESTREND which is used to control for climate variability, and BFAST which is used to look for structural changes in the ecosystem. The full details of the testing and justification of the TSS-RESTREND method (version 0.1.02) are published in Burrell et al., (2017). <doi:10.1016/j.rse.2017.05.018>. The changes to the method introduced in version 0.2.03 focus on the inclusion of temperature as an additional climate variable. This allows for land degradation assessment in temperature limited drylands. A paper that details this work is currently under review. There are also a number of bug fixes and speed improvements. Version 0.3.0 introduces additional attribution for eCO2, climate change and climate variability the details of which are in press in Burrell et al., (2020). The version under active development and additional example scripts showing how the package can be applied can be found at <https://github.com/ArdenB/TSSRESTREND>.

Simple trustworthy utility functions to use TauDEM (Terrain Analysis Using Digital Elevation Models <https://hydrology.usu.edu/taudem/taudem5/>) command-line interface. This package provides a guide to installation of TauDEM and its dependencies GDAL (Geopatial Data Abstraction Library) and MPI (Message Passing Interface) for different operating systems. Moreover, it checks that TauDEM and its dependencies are correctly installed and included to the PATH, and it provides wrapper commands for calling TauDEM methods from R.

Evaluate inline or chunks of R code in template files and replace with their output modifying the resulting template.

This package implements the TabNet model by Sercan O. Arik et al. (2019) <doi:10.48550/arXiv.1908.07442> with Coherent Hierarchical Multi-label Classification Networks by Giunchiglia et al. <doi:10.48550/arXiv.2010.10151> and provides a consistent interface for fitting and creating predictions. It's also fully compatible with the tidymodels ecosystem.

User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions. A detailed documentation is available in Heck, Arnold, & Arnold (2018) <DOI:10.3758/s13428-017-0869-7> and a tutorial on MPT modeling can be found in Schmidt, Erdfelder, & Heck (2023) <DOI:10.1037/met0000561>.

Statistical interpretation of forensic glass transfer (Simulation of the probability distribution of recovered glass fragments).

Text categorization based on n-grams.

This package provides a set of functions to implement Time Series Cointegrated System (TSCS) spatial interpolation and relevant data visualization.

The typicality and eccentricity data analysis (TEDA) framework was put forward by Angelov (2013) <DOI:10.14313/JAMRIS_2-2014/16>. It has been further developed into multiple different techniques since, and provides a non-parametric way of determining how similar an observation, from a process that is not purely random, is to other observations generated by the process. This package provides code to use the batch and recursive TEDA methods that have been published.