Collection of functions that allow to export data frames to excel workbook.

Create publication quality plots and tables for Item Response Theory and Classical Test theory based item analysis, exploratory and confirmatory factor analysis.

Perform and Runtime statistical comparisons between models. This package aims at choosing the best model for a particular dataset, regarding its discriminant power and runtime.

This package provides functions to access the database of 217 data-frames with aggregate study-level characteristics (that may act as effect modifiers) extracted from published systematic reviews with network meta-analysis. The database shall only be used for developing and appraising the methodology to assess the transitivity assumption quantitatively.

This package contains functions for calculating the Federal Highway Administration (FHWA) Transportation Performance Management (TPM) performance measures. Currently, the package provides methods for the System Reliability and Freight (PM3) performance measures calculated from travel time data provided by The National Performance Management Research Data Set (NPMRDS), including Level of Travel Time Reliability (LOTTR), Truck Travel Time Reliability (TTTR), and Peak Hour Excessive Delay (PHED) metric scores for calculating statewide reliability performance measures. Implements <https://www.fhwa.dot.gov/tpm/guidance/pm3_hpms.pdf>.

This package performs transformation discrimination analysis and non-transformation discrimination analysis. It also includes functions for Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Mixture Discriminant Analysis. In the context of mixture discriminant analysis, it offers options for both common covariance matrix (common sigma) and individual covariance matrices (uncommon sigma) for the mixture components.

Features include the ability to extract tabled content from NISO-JATS-coded XML, any native HTML or HML file, DOCX, and PDF documents, and then collapse it into a text format that is readable by humans by mimicking the actions of a screen reader. As tables within PDF documents are extracted with the tabulapdf package, and the table captions and footnotes cannot be extracted, the results on tables within PDF documents have to be considered less precise. The function table2matrix() returns a list of the tables within a document as character matrices. table2text() collapses the matrix content into a list of character strings by imitating the behavior of a screen reader. The textual representation of characters and numbers can be unified with unifyMatrix() before parsing. The function table2stats() extracts the tabled statistical test results from the collapsed text with the function standardStats() from the JATSdecoder package and, if activated, checks the reported and coded p-values for consistency. Due to the great variability and potential complexity of table structures, parsing accuracy may vary. A detailed description of how tableParser works is provided here: <doi:10.48550/arXiv.2603.19756>.

Calculate Expert Team on Climate Change Detection and Indices (ETCCDI) <-- (acronym) climate indices from daily or hourly temperature and precipitation data. Provides flexible data handling.

This package provides functions for estimating natural direct and indirect effects for mediation analysis. It uses weighting where the weights are functions of estimates of the probability of exposure or treatment assignment (Hong, G (2010). <https://cepa.stanford.edu/sites/default/files/workshops/GH_JSM%20Proceedings%202010.pdf> Huber, M. (2014). <doi:10.1002/jae.2341>). Estimation of probabilities can use generalized boosting or logistic regression. Additional functions provide diagnostics of the model fit and weights. The vignette provides details and examples.

An implementation of the Thornley transport resistance plant growth model. The package can be used to simulate plant growth as forced by climate system variables. The package provides methods for formatting forcing variables, simulating growth dynamics and calibrating model parameters. For more information see Higgins et al. (2025) TTR.PGM: An R package for modelling the distributions and dynamics of plants using the Thornley transport resistance plant growth model. Methods in Ecology and Evolution. in press.

Measuring angles between points in a landscape is much easier than measuring distances. When the location of three points is known the position of the observer can be determined based solely on the angles between these points as seen by the observer. This task (known as triangulation) however requires onerous calculations - these calculations are automated by this package.

Table 1 is the classical way to describe the patients in a clinical study. The amount of splits in the data in such a table is limited. Table1Heatmap draws a heatmap of all crosstables that can be generated with the data. Users can choose between showing the actual crosstables or direction of effect of associations, and highlight associations by number of patients or p-values. v1.2 - fixed "missing "no visible global function definition for ..".

Identifies clusters of individual longitudinal trajectories. In the spirit of Leffondre et al. (2004), the procedure involves identifying each trajectory to a point in the space of measures. In this context, a measure is a quantity meant to capture a certain characteristic feature of the trajectory. The points in the space of measures are then clustered using a version of the Spectral Clustering algorithm.

Useful functions to connect to TM1 <https://www.ibm.com/uk-en/products/planning-and-analytics> instance from R via REST API. With the functions in the package, data can be imported from TM1 via mdx view or native view, data can be sent to TM1', processes and chores can be executed, and cube and dimension metadata information can be taken.

The companion package that provides all the datasets used in the book "Data Integration, Manipulation and Visualization of Phylogenetic Trees" by Guangchuang Yu (2022, ISBN:9781032233574).

Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see for example Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E>. This package provides an interface for fitting MMRM within the tern <https://cran.r-project.org/package=tern> framework by Zhu et al. (2023) and tabulate results easily using rtables <https://cran.r-project.org/package=rtables> by Becker et al. (2023). It builds on mmrm <https://cran.r-project.org/package=mmrm> by Sabanés Bové et al. (2023) for the actual MMRM computations.

Computation and visualization of Taxicab Correspondence Analysis, Choulakian (2006) <doi:10.1007/s11336-004-1231-4>. Classical correspondence analysis (CA) is a statistical method to analyse 2-dimensional tables of positive numbers and is typically applied to contingency tables (Benzecri, J.-P. (1973). L'Analyse des Donnees. Volume II. L'Analyse des Correspondances. Paris, France: Dunod). Classical CA is based on the Euclidean distance. Taxicab CA is like classical CA but is based on the Taxicab or Manhattan distance. For some tables, Taxicab CA gives more informative results than classical CA.

This package provides a plug-in for the tm text mining framework providing mail handling functionality.

Set of functions designed to help in the analysis of TDP sensors. Features includes dates and time conversion, weather data interpolation, daily maximum of tension analysis and calculations required to convert sap flow density data to sap flow rates at the tree and plot scale (For more information see : Granier (1985) <DOI:10.1051/forest:19850204> & Granier (1987) <DOI:10.1093/treephys/3.4.309>).

Calculates total survey error (TSE) for one or more surveys, using both scale-dependent and scale-independent metrics. Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTIND and its documentation), properly input column names (see functions documentation), and run your functions. For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc.

Tensor-train is a compact representation for higher-order tensors. Some algorithms for performing tensor-train decomposition are available such as TT-SVD, TT-WOPT, and TT-Cross. For the details of the algorithms, see I. V. Oseledets (2011) <doi:10.1137/090752286>, Yuan Longao, et al (2017) <doi:10.48550/arXiv.1709.02641>, I. V. Oseledets (2010) <doi:10.1016/j.laa.2009.07.024>.

This package provides access to datasets, models and preprocessing facilities for deep learning with images. Integrates seamlessly with the torch package and its API borrows heavily from the PyTorch vision package.

There are some experimental scenarios where each experimental unit receives a sequence of treatments across multiple periods, and treatment effects persist beyond the period of application. It focuses on the construction and calculation of the parametric value of the residual effect designs balanced for carryover effects, also referred to as crossover designs, change-over designs, or repeated measurements designs (Aggarwal and Jha, 2010<doi:10.1080/15598608.2010.10412013>). The primary objective of the package is to generate a new class of Balanced Ternary Residual Effect Designs (BTREDs), balanced for carryover effects tailored explicitly for situations where the number of periods is less than or equal to the number of treatments. In addition, the package provides four new classes of Partially Balanced Ternary Residual Effect Designs (PBTREDs), constructed using incomplete block designs, initial sequences, and rectangular association scheme. In addition, one extra function is included to help study the parametric properties of a given residual effect design.

Computes correlation matrices as tidy data frames and creates publication-ready correlograms with ggplot2'. The package is designed for teaching and exploratory analysis workflows where users want one consistent interface for selecting numeric variables, calculating pairwise correlations, optionally estimating p-values, reordering variables, and drawing tile, point, or mixed correlograms.