Matching terminal restriction fragment length polymorphism ('TRFLP') profiles between unknown samples and a database of known samples. TRAMPR facilitates analysis of many unknown profiles at once, and provides tools for working directly with electrophoresis output through to generating summaries suitable for community analyses with R's rich set of statistical functions. TRAMPR also resolves the issues of multiple TRFLP profiles within a species, and shared TRFLP profiles across species.

This package provides methods for handling the missing values outliers are introduced in this package. The recognized missing values and outliers are replaced using a model-based approach. The model may consist of both autoregressive components and external regressors. The methods work robust and efficient, and they are fully tunable. The primary motivation for writing the package was preprocessing of the energy systems data, e.g. power plant production time series, but the package could be used with any time series data. For details, see Narajewski et al. (2021) <doi:10.1016/j.softx.2021.100809>.

The main objective of cooperative Transferable-Utility games (TU-games) is to allocate a good among the agents involved. The package implements major solution concepts including the Shapley value, Banzhaf value, and egalitarian rules, alongside their extensions for structured games: the Owen value and Banzhaf-Owen value for games with a priori unions, and the Myerson value for communication games on networks. To address the inherent exponential computational complexity of exact evaluation, the package offers both exact algorithms and linear approximation methods based on sampling, enabling the analysis of large-scale games. Additionally, it supports core set-based solutions, allowing computation of the vertices and the centroid of the core.

Accurate calculation of nucleic acid melting temperature (Tm) is fundamental to many molecular biology applications, and this software scales Tm analysis from individual sequences to genome-wide thermodynamic profiling. This package extends Tm analysis from simple sequence level computation to comprehensive genome-wide thermodynamic profiling. It takes four input sources: sequence strings, a FASTA file, an installed BSgenome package named by string, or a GRanges carrying sequences. A regions argument selects what to cover and window and slide set the resolution at which it is tiled. The implementation provides three Tm calculation methods: the Wallace rule (Thein & Wallace, 1986), empirical GC-content formulas (Marmur, 1962; Schildkraut, 1965; Wetmur, 1991; Untergasser, 2012; von Ahsen, 2001), and nearest-neighbor thermodynamics (Breslauer, 1986; Sugimoto, 1996; Allawi, 1997, 1998; SantaLucia, 2004; Freier, 1986; Xia, 1998; Chen, 2012; Bommarito, 2000; Turner, 2010; Sugimoto, 1995; Peyret, 1999; SantaLucia & Peyret, 2001; Watkins & SantaLucia, 2005; Zuber, 2022; Ghosh, 2020, 2023). Nearest-neighbor parameter sets are provided for DNA, RNA and RNA/DNA hybrid duplexes. These include sets obtained by melting-temperature optimization that are fitted directly at a stated sodium concentration (Weber, 2015; Ferreira, 2019; Basilio Barbosa, 2019; Banerjee, 2020), which replace salt correction rather than being corrected; salt correction is skipped automatically when the requested condition matches the one a set was fitted at. The Zuber (2022) set additionally replaces the single terminal-AU penalty with end terms that depend on the penultimate base pair, applied automatically at both duplex ends. Parameter sets measured under molecular crowding (Ghosh, 2020, 2023) are also provided for DNA and RNA duplexes, so that duplex stability can be evaluated under cell-like rather than dilute-solution conditions. Salt corrections are otherwise applied to the nearest-neighbor model (SantaLucia, 1996, 1998; Owczarzy, 2004, 2008), while each empirical GC-content formula carries the salt term it was published with; corrections for chemical conditions such as dimethyl sulfoxide and formamide apply to both. A compiled C++ core, and task partitioning by region across BiocParallel workers through a BPPARAM argument, profile the human genome in 3 minutes on a six-core laptop. This package returns result as a GRanges object for interoperability with Bioconductor workflows and downstream multi-omics analyses. Data-level integration reconciles Tm windows with external multi-omics GRanges objects through overlap, nearest-feature, windowed-count, and binned-average strategies, returning a single unified GRanges object ready for downstream analysis. Visualization-level integration renders multiple feature layers as independent concentric tracks on a shared genomic axis, each retaining its native coordinate resolution. Group comparison supports Wilcoxon rank-sum and Student's t-tests with multiple available correction methods for contrasting Tm and other features across region classes.

This package provides a framework for statistical analysis in content analysis. In addition to a pipeline for preprocessing text corpora and linking to the latent Dirichlet allocation from the lda package, plots are offered for the descriptive analysis of text corpora and topic models. In addition, an implementation of Chang's intruder words and intruder topics is provided. Sample data for the vignette is included in the toscaData package, which is available on gitHub: <https://github.com/Docma-TU/toscaData>.

Today's public transit vehicles produce a large amount of automatic vehicle location (AVL) data. This data is very useful for planning and performance studies, but can be noisy, error-prone, and sparse. This package provides tools for cleaning AVL point data and turning it into continuous, differentiable, monotonic, and invertible vehicle trajectory functions, based on the work of Robbennolt et al. (2025) <doi:10.48550/arXiv.2509.00119> and Huang et al. (2023) <doi:10.1109/ITSC57777.2023.10422524>.

This package provides comprehensive tools for conducting research using the Thinking Grid framework, a psychological measurement approach for understanding the stream of thought. Includes functions for generating Qualtrics surveys with the thinking grid, processing survey responses, calculating quadrant depths, and creating various visualization types including heatmaps, animations, and statistical plots. See Irving, Z. C., Murray, S., Kuvar, V., Urena, M., and Mills, C. (2025) "Consciousness, Just in Time: Fluctuations in the Stream of Consciousness during Tasks and Rest" (manuscript under review, draft available from zci7c@virginia.edu).

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

This package provides a tidy-style interface for applying differential privacy to data frames. Provides pipe-friendly functions to add calibrated noise, compute private statistics, and track privacy budgets using the epsilon-delta differential privacy framework. Implements the Laplace mechanism (Dwork et al. 2006 <doi:10.1007/11681878_14>) and the Gaussian mechanism for achieving differential privacy as described in Dwork and Roth (2014) <doi:10.1561/0400000042>.

Implementation and forecasting univariate time series data using the Support Vector Machine model. Support Vector Machine is one of the prominent machine learning approach for non-linear time series forecasting. For method details see Kim, K. (2003) <doi:10.1016/S0925-2312(03)00372-2>.

It provides generic methods that are used by more than one package, avoiding conflicts. This package will be imported by tidySingleCellExperiment and tidyseurat'.

Download taxonomic databases, convert them into SQLite format, and query them locally for fast, reliable, and reproducible access to taxonomic data.

Calculates the number of true positives and false positives from a dataset formatted for Jackknife alternative free-response receiver operating characteristic which is used for statistical analysis which is explained in the book Chakraborty DP (2017), "Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples", Taylor-Francis <https://www.crcpress.com/9781482214840>.

This package provides geomorphology-based hydrological modelling for transferring streamflow measurements from gauged to ungauged catchments. Inverse modelling makes it possible to estimate net rainfall from streamflow measurements following Boudhraâ et al. (2018) <doi:10.1080/02626667.2018.1425801>. The resulting net rainfall is then interpolated to ungauged catchments to simulate streamflow following de Lavenne et al. (2016) <doi:10.1002/2016WR018716>.

This package implements harmonic analysis of tidal and sea-level data. Over 400 harmonic tidal constituents can be estimated, all with daily nodal corrections. Time-varying mean sea-levels can also be used.

TEMPoral TEnsor Decomposition (TEMPTED), is a dimension reduction method for multivariate longitudinal data with varying temporal sampling. It formats the data into a temporal tensor and decomposes it into a summation of low-dimensional components, each consisting of a subject loading vector, a feature loading vector, and a continuous temporal loading function. These loadings provide a low-dimensional representation of subjects or samples and can be used to identify features associated with clusters of subjects or samples. TEMPTED provides the flexibility of allowing subjects to have different temporal sampling, so time points do not need to be binned, and missing time points do not need to be imputed.

Tide analysis and prediction of predominantly semi-diurnal tides with two high waters and two low waters during one lunar day (~24.842 hours, ~1.035 days). The analysis should preferably cover an observation period of at least 19 years. For shorter periods, for example, the nodal cycle can not be taken into account, which particularly affects the height calculation. The main objective of this package is to produce tide tables.

This package provides tools to calculate trait probability density functions (TPD) at any scale (e.g. populations, species, communities). TPD functions are used to compute several indices of functional diversity, as well as its partition across scales. These indices constitute a unified framework that incorporates the underlying probabilistic nature of trait distributions into uni- or multidimensional functional trait-based studies. See Carmona et al. (2016) <doi:10.1016/j.tree.2016.02.003> for further information.

Handles truncated members from the exponential family of probability distributions. Contains functions such as rtruncnorm() and dtruncpois(), which are truncated versions of rnorm() and dpois() from the stats package that also offer richer output containing, for example, the distribution parameters. It also provides functions to retrieve the original distribution parameters from a truncated sample by maximum-likelihood estimation.

This package provides a synthetic control offers a way of evaluating the effect of an intervention in comparative case studies. The package makes a number of improvements when implementing the method in R. These improvements allow users to inspect, visualize, and tune the synthetic control more easily. A key benefit of a tidy implementation is that the entire preparation process for building the synthetic control can be accomplished in a single pipe.

Graphic interface for text analysis, implement a few methods such as biplots, correspondence analysis, co-occurrence, clustering, topic models, correlations and sentiments.

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

This package provides functions to scale, log-transform and fit linear models within a tidyverse'-style R code framework. Intended to smooth over inconsistencies in output of base R statistical functions, allowing ease of teaching, learning and daily use. Inspired by the tidy principles used in broom Robinson (2017) <doi:10.21105/joss.00341>.

The United Nations Sustainable Development Goals (SDGs) have become an important guideline for organisations to monitor and plan their contributions to social, economic, and environmental transformations. The text2sdg package is an open-source analysis package that identifies SDGs in text using scientifically developed query systems, opening up the opportunity to monitor any type of text-based data, such as scientific output or corporate publications. For more information see Meier, Mata & Wulff (2025) <doi:10.32614/RJ-2024-005> and Wulff, Meier & Mata (2024) <doi:10.1007/s11625-024-01516-3>.