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.

Fitting models for, and simulation of, trend locally stationary wavelet (TLSW) time series models, which take account of time-varying trend and dependence structure in a univariate time series. The TLSW model, and its estimation, is described in McGonigle, Killick and Nunes (2022a) <doi:10.1111/jtsa.12643>, (2022b) <doi:10.1214/22-EJS2044>. Further information regarding the use of the package, along with detailed examples, can be found in McGonigle, Killick and Nunes (2025) <doi:10.18637/jss.v115.i10>. New users will likely want to start with the TLSW function.

Estimate and return either the traffic speed or the car entries in the city of Thessaloniki using historical traffic data. It's used in transport pilot of the BigDataEurope project. There are functions for processing these data, training a neural network, select the most appropriate model and predict the traffic speed or the car entries for a selected time date.

Statistics students often have problems understanding the relation between a random variable's true scale and its z-values. To allow instructors to better better visualize histograms for these students, the package provides histograms with two horizontal axis containing z-values and the true scale of the variable. The function TeachHistDens() provides a density histogram with two axis. TeachHistCounts() and TeachHistRelFreq() are variations for count and relative frequency histograms, respectively. TeachConfInterv() and TeachHypTest() help instructors to visualize confidence levels and the results of hypothesis tests.

This package provides a collection of functions for automatically creating Stan code for transition diagnostic classification models (TDCMs) as they are defined by Madison and Bradshaw (2018) <DOI:10.1007/s11336-018-9638-5>. This package supports automating the creation of Stan code for TDCMs, fungible TDCMs (i.e., TDCMs with item parameters constrained to be equal across all items), and multi-threaded TDCMs.

Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) <https://www.degruyter.com/view/title/575978> "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis.

This package provides a pipeline for short tandem repeat instability analysis from fragment analysis data. Inputs of fsa files or peak tables, and a user supplied metadata data-frame. The package identifies ladders, calls peaks, identifies the modal peaks, calls repeats, then calculates repeat instability metrics (e.g. expansion index from Lee et al. (2010) <doi:10.1186/1752-0509-4-29>).

The maximum likelihood classifier (MLC) is one of the most common classifiers used for remote sensing imagery. This package uses RcppArmadillo to provide a fast implementation of the MLC to train and predict over tabular data (data.frame). The algorithms were based on Mather (1985) <doi:10.1080/01431168508948456> method.

An R shiny app designed for diverse text analysis tasks, offering a wide range of methodologies tailored to Natural Language Processing (NLP) needs. It is a versatile, general-purpose tool for analyzing textual data. tall features a comprehensive workflow, including data cleaning, preprocessing, statistical analysis, and visualization, all integrated for effective text analysis.

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.

This package implements the template ICA (independent components analysis) model proposed in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638> and the spatial template ICA model proposed in proposed in Mejia et al. (2022) <doi:10.1080/10618600.2022.2104289>. Both models estimate subject-level brain as deviations from known population-level networks, which are estimated using standard ICA algorithms. Both models employ an expectation-maximization algorithm for estimation of the latent brain networks and unknown model parameters. Includes direct support for CIFTI', GIFTI', and NIFTI neuroimaging file formats.

Estimation of group-based trajectory models, including finite mixture models for longitudinal data, supporting censored normal, zero-inflated Poisson, logit, and beta distributions, using expectation-maximization and quasi-Newton methods, with tools for model selection, diagnostics, and visualization of latent trajectory groups, <doi:10.4159/9780674041318>, Nagin, D. (2005). Group-Based Modeling of Development. Cambridge, MA: Harvard University Press. and Noel (2022), <https://orbilu.uni.lu/>, thesis.

This package provides triangulations of regular height fields, based on the methods described in "Fast Polygonal Approximation of Terrains and Height Fields" Michael Garland and Paul S. Heckbert (1995) <https://www.mgarland.org/files/papers/scape.pdf> using code from the hmm library written by Michael Fogleman <https://github.com/fogleman/hmm>.

This package implements Time-Weighted Dynamic Time Warping (TWDTW), a measure for quantifying time series similarity. The TWDTW algorithm, described in Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and Maus et al. (2019) <doi:10.18637/jss.v088.i05>, is applicable to multi-dimensional time series of various resolutions. It is particularly suitable for comparing time series with seasonality for environmental and ecological data analysis, covering domains such as remote sensing imagery, climate data, hydrology, and animal movement. The twdtw package offers a user-friendly R interface, efficient Fortran routines for TWDTW calculations, flexible time weighting definitions, as well as utilities for time series preprocessing and visualization.

Prebuilt shiny modules containing tools for viewing data, visualizing data, understanding missing and outlier values within your data and performing simple data analysis. This extends teal framework that supports reproducible research and analysis.

This package provides the "r, q, p, and d" distribution functions for the triangle distribution. Also includes maximum likelihood estimation of parameters.

This package provides a tidy approach to analysis of biological sequences. All processing and data-storage functions are heavily optimized to allow the fastest and most efficient data storage.

To make it easy to generate random numbers based upon the underlying stats distribution functions. All data is returned in a tidy and structured format making working with the data simple and straight forward. Given that the data is returned in a tidy tibble it lends itself to working with the rest of the tidyverse'.

Language specific cardinal to ordinal number conversion.

This package creates some WebGL shaders. They can be used as the background of a Shiny app. They also can be visualized in the RStudio viewer pane or included in Rmd documents, but this is pretty useless, besides contemplating them.

This package provides a new measure of similarity between a pair of mass spectrometry (MS) experiments, called truncated rank correlation (TRC). To provide a robust metric of similarity in noisy high-dimensional data, TRC uses truncated top ranks (or top m-ranks) for calculating correlation. Truncated rank correlation as a robust measure of test-retest reliability in mass spectrometry data. For more details see Lim et al. (2019) <doi:10.1515/sagmb-2018-0056>.

Non-parametric trend comparison of two independent samples with sequential subsamples. For more details, please refer to Wang, Stapleton, and Chen (2018) <doi:10.1080/00949655.2018.1482492>.

This package provides functions that compute predictions from Generalised Additive Models (GAMs) fitted with mgcv and return them as a tibble. These can be plotted with a generic plot()-method that uses ggplot2 or plotted as any other data frame. The main function is predict_gam().

To facilitate the analysis of positron emission tomography (PET) time activity curve (TAC) data, and to encourage open science and replicability, this package supports data loading and analysis of multiple TAC file formats. Functions are available to analyze loaded TAC data for individual participants or in batches. Major functionality includes weighted TAC merging by region of interest (ROI), calculating models including standardized uptake value ratio (SUVR) and distribution volume ratio (DVR, Logan et al. 1996 <doi:10.1097/00004647-199609000-00008>), basic plotting functions and calculation of cut-off values (Aizenstein et al. 2008 <doi:10.1001/archneur.65.11.1509>). Please see the walkthrough vignette for a detailed overview of tacmagic functions.