Finds the most likely originating tissue(s) and developmental stage(s) of tissue-specific RNA sequencing data. The package identifies both pure transcriptomes and mixtures of transcriptomes. The most likely identity is found through comparisons of the sequencing data with high-throughput in situ hybridisation patterns. Typical uses are the identification of cancer cell origins, validation of cell culture strain identities, validation of single-cell transcriptomes, and validation of identity and purity of flow-sorting and dissection sequencing products.

This package implements fast Monte Carlo simulations for goodness-of-fit (GOF) tests for discrete distributions. This includes tests based on the Chi-squared statistic, the log-likelihood-ratio (G^2) statistic, the Freeman-Tukey (Hellinger-distance) statistic, the Kolmogorov-Smirnov statistic, the Cramer-von Mises statistic as described in Choulakian, Lockhart and Stephens (1994) <doi:10.2307/3315828>, and the root-mean-square statistic, see Perkins, Tygert, and Ward (2011) <doi:10.1016/j.amc.2011.03.124>.

An implementation of several functions for feature extraction in ordinal time series datasets. Specifically, some of the features proposed by Weiss (2019) <doi:10.1080/01621459.2019.1604370> can be computed. These features can be used to perform inferential tasks or to feed machine learning algorithms for ordinal time series, among others. The package also includes some interesting datasets containing financial time series. Practitioners from a broad variety of fields could benefit from the general framework provided by otsfeatures'.

Parse, tokenize, validate, format, analyze and translate SQL between more than 30 dialects ('PostgreSQL', MySQL', BigQuery', Snowflake', DuckDB', T-SQL', and others) using the polyglot-sql Rust crate <https://github.com/tobilg/polyglot>, a Rust port of the SQLGlot Python library. All processing happens locally in the R session; no database connection, Python runtime or external service is required. Includes column-level lineage, structural query analysis, query optimization, AST diffing and OpenLineage facet generation.

This package provides tools to conduct interpretable sensitivity analyses for weighted estimators, introduced in Huang (2024) <doi:10.1093/jrsssa/qnae012> and Hartman and Huang (2024) <doi:10.1017/pan.2023.12>. The package allows researchers to generate the set of recommended sensitivity summaries to evaluate the sensitivity in their underlying weighting estimators to omitted moderators or confounders. The tools can be flexibly applied in causal inference settings (i.e., in external and internal validity contexts) or survey contexts.

Using Gaussian graphical models we propose a novel approach to perform pathway analysis using gene expression. Given the structure of a graph (a pathway) we introduce two statistical tests to compare the mean and the concentration matrices between two groups. Specifically, these tests can be performed on the graph and on its connected components (cliques). The package is based on the method described in Massa M.S., Chiogna M., Romualdi C. (2010) <doi:10.1186/1752-0509-4-121>.

This package contains functions for a variational Bayesian method for sparse PCA proposed by Ning (2020) <arXiv:2102.00305>. There are two algorithms: the PX-CAVI algorithm (if assuming the loadings matrix is jointly row-sparse) and the batch PX-CAVI algorithm (if without this assumption). The outputs of the main function, VBsparsePCA(), include the mean and covariance of the loadings matrix, the score functions, the variable selection results, and the estimated variance of the random noise.

A tandem repeat in DNA is two or more adjacent, approximate copies of a pattern of nucleotides. Tandem Repeats Finder is a program to locate and display tandem repeats in DNA sequences. In order to use the program, the user submits a sequence in FASTA format. The output consists of two files: a repeat table file and an alignment file. Submitted sequences may be of arbitrary length. Repeats with pattern size in the range from 1 to 2000 bases are detected.

The package CellBarcode performs Cellular DNA Barcode analysis. It can handle all kinds of DNA barcodes, as long as the barcode is within a single sequencing read and has a pattern that can be matched by a regular expression. \codeCellBarcode can handle barcodes with flexible lengths, with or without UMI (unique molecular identifier). This tool also can be used for pre-processing some amplicon data such as CRISPR gRNA screening, immune repertoire sequencing, and metagenome data.

Routines to handle family data with a Pedigree object. The initial purpose was to create correlation structures that describe family relationships such as kinship and identity-by-descent, which can be used to model family data in mixed effects models, such as in the coxme function. Also includes a tool for Pedigree drawing which is focused on producing compact layouts without intervention. Recent additions include utilities to trim the Pedigree object with various criteria, and kinship for the X chromosome.

SpectralTAD is an R package designed to identify Topologically Associated Domains (TADs) from Hi-C contact matrices. It uses a modified version of spectral clustering that uses a sliding window to quickly detect TADs. The function works on a range of different formats of contact matrices and returns a bed file of TAD coordinates. The method does not require users to adjust any parameters to work and gives them control over the number of hierarchical levels to be returned.

SpatialCPie is an R package designed to facilitate cluster evaluation for spatial transcriptomics data by providing intuitive visualizations that display the relationships between clusters in order to guide the user during cluster identification and other downstream applications. The package is built around a shiny "gadget" to allow the exploration of the data with multiple plots in parallel and an interactive UI. The user can easily toggle between different cluster resolutions in order to choose the most appropriate visual cues.

This package provides functions to estimate a factor model using discrete and continuous proxy variables. The function dproxyme estimates a factor model of discrete proxy variables using an EM algorithm (Dempster, Laird, Rubin (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x>; Hu (2008) <doi:10.1016/j.jeconom.2007.12.001>; Hu(2017) <doi:10.1016/j.jeconom.2017.06.002> ). The function cproxyme estimates a linear factor model (Cunha, Heckman, and Schennach (2010) <doi:10.3982/ECTA6551>).

The fusion learning method uses a model selection algorithm to learn from multiple data sets across different experimental platforms through group penalization. The responses of interest may include a mix of discrete and continuous variables. The responses may share the same set of predictors, however, the models and parameters differ across different platforms. Integrating information from different data sets can enhance the power of model selection. Package is based on Xin Gao, Raymond J. Carroll (2017) <arXiv:1610.00667v1>.

This package provides a curated taxonomic backbone of mammal species recorded in Peru, based on the checklist published by Pacheco and collaborators (2021) <doi:10.15381/rpb.v28i4.21019>. The package includes standardized species data, occurrence records by ecological regions, endemic status, and tools for validating and matching scientific names through exact and approximate string procedures. It is designed as a lightweight and reliable reference for ecological, environmental, biogeographical, and conservation workflows that require verified species information for Peruvian mammals.

This package provides functions that support a broad range of common tasks in physical activity research, including but not limited to creation of Bland-Altman plots (<doi:10.1136/bmj.313.7049.106>), metabolic calculations such as basal metabolic rate predictions (<https://europepmc.org/article/med/4044297/reloa>), demographic calculations such as age-for-body-mass-index percentile (<https://www.cdc.gov/growthcharts/cdc_charts.htm>), and analysis of bout detection algorithm performance (<https://pubmed.ncbi.nlm.nih.gov/34258524/>).

An API wrapper around the ProPublica API <https://projects.propublica.org/api-docs/congress-api/> for U.S. Congressional Bills. Users can include their API key, U.S. Congress, branch, and offset ranges, to return a dataframe of all results within those parameters. This package is different from the RPublica package because it is for the ProPublica U.S. Congress data API, and the RPublica package is for the Nonprofit Explorer, Forensics, and Free the Files data APIs.

This package provides a complete analysis pipeline for the WHO STEPwise Approach to NCD Risk Factor Surveillance (STEPS) as described in Riley et al. (2016) <doi:10.2105/AJPH.2015.302962>. Imports raw survey data ('CSV', Excel', Stata', SPSS'), applies WHO-standard cleaning and recoding, sets up complex survey designs, computes all standard NCD indicators (tobacco, alcohol, diet, physical activity, anthropometry, blood pressure, biochemical), and generates publication-ready tables, visualisations, and Word'/'HTML reports (fact sheet, data book, country report).

This package contains functions for analysis and summary of tidal datasets. Also provides access to tidal data collected by the National Oceanic and Atmospheric Administration's Center for Operational Oceanographic Products and Services and the Permanent Service for Mean Sea Level. For detailed description and application examples, see Hill, T.D. and S.C. Anisfeld (2021) <doi:10.6084/m9.figshare.14161202.v1> and Hill, T.D. and S.C. Anisfeld (2015) <doi:10.1016/j.ecss.2015.06.004>.

Programming oncology specific Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R'. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team (2021), <https://www.cdisc.org/standards/foundational/adam>). The package is an extension package of the admiral package.

This package provides functions to compute distances between probability measures or any other data object than can be posed in this way, entropy measures for samples of curves, distances and depth measures for functional data, and the Generalized Mahalanobis Kernel distance for high dimensional data. For further details about the metrics please refer to Martos et al (2014) <doi:10.3233/IDA-140706>; Martos et al (2018) <doi:10.3390/e20010033>; Hernandez et al (2018, submitted); Martos et al (2018, submitted).

The four-gamete test is based on the infinite-sites model which assumes that the probability of the same mutation occurring twice (recurrent or parallel mutations) and the probability of a mutation back to the original state (reverse mutations) are close to zero. Without these types of mutations, the only explanation for observing the four dilocus genotypes (example below) is recombination (Hudson and Kaplan 1985, Genetics 111:147-164). Thus, the presence of all four gametes is also called phylogenetic incompatibility.

This package implements commonly used Indian farm cost concepts for agricultural economic analysis, including Cost A1, A2, B1, B2, C1, C2, and C3. Tools are provided to calculate cost concepts from farm-level input data, aggregate costs across groups, summarize distributions, compute returns and benefit-cost ratios, estimate cost of production and break-even values, decompose cost shares, conduct one-way sensitivity analysis, and visualize concept-wise costs. The implementation is designed for reproducible farm management and cost-of-cultivation studies.

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>.