Allows the user to convert PDF tables to formats more amenable to analysis ('.csv', .xml', or .xlsx') by wrapping the PDFTables API. In order to use the package, the user needs to sign up for an API account on the PDFTables website (<https://pdftables.com/pdf-to-excel-api>). The package works by taking a PDF file as input, uploading it to PDFTables, and returning a file with the extracted data.

This package provides an object type and associated tools for storing and wrangling panel data. Implements several methods for creating regression models that take advantage of the unique aspects of panel data. Among other capabilities, automates the "within-between" (also known as "between-within" and "hybrid") panel regression specification that combines the desirable aspects of both fixed effects and random effects econometric models and fits them as multilevel models (Allison, 2009 <doi:10.4135/9781412993869.d33>; Bell & Jones, 2015 <doi:10.1017/psrm.2014.7>). These models can also be estimated via generalized estimating equations (GEE; McNeish, 2019 <doi:10.1080/00273171.2019.1602504>) and Bayesian estimation is (optionally) supported via Stan'. Supports estimation of asymmetric effects models via first differences (Allison, 2019 <doi:10.1177/2378023119826441>) as well as a generalized linear model extension thereof using GEE.

This package provides functions for simulating from and fitting the latent hidden Markov models for response process data (Tang, 2024) <doi:10.1007/s11336-023-09938-1>. It also includes functions for simulating from and fitting ordinary hidden Markov models.

Compute bending energies, principal warps, partial warp scores, and the non-affine component of shape variation for 2D landmark configurations, as well as Mardia-Dryden distributions and self-similar distributions of landmarks, as described in Mitteroecker et al. (2020) <doi:10.1093/sysbio/syaa007>. Working examples to decompose shape variation into small-scale and large-scale components, and to decompose the total shape variation into outline and residual shape components are provided. Two landmark datasets are provided, that quantify skull morphology in humans and papionin primates, respectively from Mitteroecker et al. (2020) <doi:10.5061/dryad.j6q573n8s> and Grunstra et al. (2020) <doi:10.5061/dryad.zkh189373>.

Improves genotype inference and downstream Adaptive Immune Receptor Repertoire Sequence data analysis. Inference of allele similarity clusters, an alternative naming scheme and genotype inference for immunoglobulin heavy chain repertoires. The main tools are allele similarity clusters, and allele based genotype. The first tool is designed to reduce the ambiguity within the immunoglobulin heavy chain V alleles. The ambiguity is caused by duplicated or similar alleles which are shared among different genes. The second tool is an allele based genotype, that determined the presence of an allele based on a threshold derived from a naive population. See Peres et al. (2023) <doi:10.1093/nar/gkad603>.

This package implements univariate polynomial operations in R, including polynomial arithmetic, finding zeros, plotting, and some operations on lists of polynomials.

Prism <https://prismjs.com/> is a lightweight, extensible syntax highlighter, built with modern web standards in mind. This package provides server-side rendering in R using V8 such that no JavaScript library is required in the resulting HTML documents. Over 400 languages are supported.

This package provides tools to import, clean, filter, and prepare Project FeederWatch data for analysis. Includes functions for taxonomic rollup, easy filtering, zerofilling, merging in site metadata, and more. Project FeederWatch data comes from <https://feederwatch.org/explore/raw-dataset-requests/>.

This package implements two-stage pseudo maximum likelihood estimation (PMLE) for copula-based regression models with semi-competing risks data. The marginal distributions are modeled by semiparametric transformation regression models, and the dependence between bivariate event times is specified by a parametric copula function. See Arachchige, Chen and Zhou (2025) <doi:10.1007/s10985-024-09640-z> for details.

Analyzing genetic data obtained from pooled samples. This package can read in Fragment Analysis output files, process the data, and score peaks, as well as facilitate various analyses, including cluster analysis, calculation of genetic distances and diversity indices, as well as bootstrap resampling for statistical inference. Specifically tailored to handle genetic data efficiently, researchers can explore population structure, genetic differentiation, and genetic relatedness among samples. We updated some functions from Covarrubias-Pazaran et al. (2016) <doi:10.1186/s12863-016-0365-6> to allow for the use of new file formats and referenced the following to write our genetic analysis functions: Long et al. (2022) <doi:10.1038/s41598-022-04776-0>, Jost (2008) <doi:10.1111/j.1365-294x.2008.03887.x>, Nei (1973) <doi:10.1073/pnas.70.12.3321>, Foulley et al. (2006) <doi:10.1016/j.livprodsci.2005.10.021>, Chao et al. (2008) <doi:10.1111/j.1541-0420.2008.01010.x>.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Women (age 15-49 years) questionnaire data for Punjab, Pakistan.

Use the paged media properties in CSS and the JavaScript library paged.js to split the content of an HTML document into discrete pages. Each page can have its page size, page numbers, margin boxes, and running headers, etc. Applications of this package include books, letters, reports, papers, business cards, resumes, and posters.

Fast exponentiation when the exponent is an integer.

This package provides programmatic access to GitHub API with a focus on project management. Key functionality includes setting up issues and milestones from R objects or YAML configurations, querying outstanding or completed tasks, and generating progress updates in tables, charts, and RMarkdown reports. Useful for those using GitHub in personal, professional, or academic settings with an emphasis on streamlining the workflow of data analysis projects.

This package implements recently developed projection pursuit algorithms for finding optimal linear cluster separators. The clustering algorithms use optimal hyperplane separators based on minimum density, Pavlidis et. al (2016) <http://jmlr.org/papers/volume17/15-307/15-307.pdf>; minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>; and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.

Implementation of the pattern recognition technique Principal Component Pursuit tailored to environmental health data, as described in Gibson et al (2022) <doi:10.1289/EHP10479>.

This package provides a dataset containing properties for chemical elements. Helper functions are also provided to access some atomic properties.

This package provides functions to measure Alpha, Beta and Gamma Proximity to Irreplaceability. The methods for Alpha and Beta irreplaceability were first described in: Baisero D., Schuster R. & Plumptre A.J. Redefining and Mapping Global Irreplaceability. Conservation Biology 2021;1-11. <doi:10.1111/cobi.13806>.

This work is an extension of the state space model for Poisson count data, Poisson-Gamma model, towards a semiparametric specification. Just like the generalized additive models (GAM), cubic splines are used for covariate smoothing. The semiparametric models are fitted by an iterative process that combines maximization of likelihood and backfitting algorithm.

Reads in multi-part parquet files. Will read in parquet files that have not been previously coalesced into one file. Convenient for reading in moderately sized, but split files.

This package provides a framework for quality-aware analysis of ground-based phenological data from the PEP725 Pan-European Phenology Database (Templ et al. (2018) <doi:10.1007/s00484-018-1512-8>; Templ et al. (2026) <doi:10.1111/nph.70869>) and similar observation networks. Implements station-level data quality grading, outlier detection, phenological normals (climate baselines), anomaly detection, elevation and latitude gradient estimation with robust regression, spatial synchrony quantification, partial least squares (PLS) regression for identifying temperature-sensitive periods, and sequential Mann-Kendall trend analysis. Supports data import from PEP725 files, conversion of user-supplied data, and downloadable synthetic datasets for teaching without barriers of registration. All analysis outputs provide print', summary', and plot methods. Interactive spatial visualization is available via leaflet'.

This package provides tools for the test for the comparison of survival curves, the evaluation of the goodness-of-fit and the predictive capacity of the proportional hazards model.

This package provides a C++ reimplementation of poLCA - latent class analysis and latent class regression models for polytomous outcome variables, also known as latent structure analysis. It attempts to reproduce results and be as similar as possible to the original code, while running faster, especially with multiple repetitions, by utilising multiple threads. Further reading is available on the Queen Mary, University of London, IT Services Research blog <https://blog.hpc.qmul.ac.uk/speeding_up_r_packages/>.

This package provides tools for analyzing data generated from conjoint survey experiments, a method widely used in the social sciences for studying multidimensional preferences. The package implements estimation of marginal means (MMs) and average marginal component effects (AMCEs), with corrections for measurement error. Methods include profile-level and choice-level estimators, bias correction using intra-respondent reliability (IRR), and visualization utilities. For details on the methodology, see Clayton, Horiuchi, Kaufman, King, and Komisarchik (2025) <https://gking.harvard.edu/conjointE>.