This package provides tools for managing nested, multi-level configuration systems with runtime mutability, type validation, and default value management. Supports creating hierarchical options managers with customizable validators for scalar and vector types (numeric, character, logical), enumerated values, bounded ranges, and complex structures like XY pairs. Options can be dynamically modified at runtime while maintaining type safety through validator functions, and easily reset to their default values when needed.

This package provides a modified version of alternating logistic regressions (ALR) with estimation based on orthogonalized residuals (ORTH) is implemented, which use paired estimating equations to jointly estimate parameters in marginal mean and within-association models. The within-cluster association between ordinal responses is modeled by global pairwise odds ratios (POR). A finite-sample bias correction is provided to POR parameter estimates based on matrix multiplicative adjusted orthogonalized residuals (MMORTH) for correcting estimating equations, and different bias-corrected variance estimators such as BC1, BC2, and BC3.

This package provides functions to access and download data from the Open Case Studies <https://www.opencasestudies.org/> repositories on GitHub <https://github.com/opencasestudies>. Different functions enable users to grab the data they need at different sections in the case study, as well as download the whole case study repository. All the user needs to do is input the name of the case study being worked on. The package relies on the httr::GET() function to access files through the GitHub API. The functions usethis::use_zip() and usethis::create_from_github() are used to clone and/or download the case study repositories. To cite an individual case study, please see the respective README file at <https://github.com/opencasestudies/>. <https://github.com/opencasestudies/ocs-bp-rural-and-urban-obesity> <https://github.com/opencasestudies/ocs-bp-air-pollution> <https://github.com/opencasestudies/ocs-bp-vaping-case-study> <https://github.com/opencasestudies/ocs-bp-opioid-rural-urban> <https://github.com/opencasestudies/ocs-bp-RTC-wrangling> <https://github.com/opencasestudies/ocs-bp-RTC-analysis> <https://github.com/opencasestudies/ocs-bp-youth-disconnection> <https://github.com/opencasestudies/ocs-bp-youth-mental-health> <https://github.com/opencasestudies/ocs-bp-school-shootings-dashboard> <https://github.com/opencasestudies/ocs-bp-co2-emissions> <https://github.com/opencasestudies/ocs-bp-diet>.

Robust multi-criteria land-allocation optimization that explicitly accounts for the uncertainty of the indicators in the objective function. Solves the problem of allocating scarce land to various land-use options with regard to multiple, coequal indicators. The method aims to find the land allocation that represents the indicator composition with the best possible trade-off under uncertainty. optimLanduse includes the actual optimization procedure as described by Knoke et al. (2016) <doi:10.1038/ncomms11877> and the post-hoc calculation of the portfolio performance as presented by Gosling et al. (2020) <doi:10.1016/j.jenvman.2020.110248>.

Represents the basis functions for B-splines in a simple matrix formulation that facilitates, taking integrals, derivatives, and making orthogonal the basis functions.

This package provides implementations of some of the most important outlier detection algorithms. Includes a tutorial mode option that shows a description of each algorithm and provides a step-by-step execution explanation of how it identifies outliers from the given data with the specified input parameters. References include the works of Azzedine Boukerche, Lining Zheng, and Omar Alfandi (2020) <doi:10.1145/3381028>, Abir Smiti (2020) <doi:10.1016/j.cosrev.2020.100306>, and Xiaogang Su, Chih-Ling Tsai (2011) <doi:10.1002/widm.19>.

Clinical reports generated by Oncomine Reporter software contain critical data in unstructured PDF format, making manual extraction time-consuming and error-prone. ORscraper provides a coherent suite of functions to automate this process, allowing researchers to parse reports, identify key biomarkers, extract genetic variant tables, and filter results. It also integrates with the NCBI ClinVar API <https://www.ncbi.nlm.nih.gov/clinvar/> to enrich extracted data.

Interface with the One Health VBD (vector-borne disease) Hub <https://vbdhub.org/> and related repositories (VectorByte <https://www.vectorbyte.org>, GBIF <https://www.gbif.org> and AREAdata <https://pearselab.github.io/areadata/>) directly to find, download, and subset vector-borne disease data.

Bindings, methods, and tuners for using ordinal classification models with the parsnip and dials packages. These include the regularized elastic net ordinal regression of Wurm, Hanlon, and Rathouz (2021) <doi:10.18637/jss.v099.i06> in ordinalNet', the ordinal classification trees of Galimberti, Soffritti, and Di Maso (2012) <doi:10.18637/jss.v047.i10> in rpartScore', and the latent variable ordinal forests of Hornung (2020) <doi:10.1007/s00357-018-9302-x> in ordinalForest'.

The popular population genetic software Treemix by Pickrell and Pritchard (2012) <DOI:10.1371/journal.pgen.1002967> estimates the number of migration edges on a population tree. However, it can be difficult to determine the number of migration edges to include. Previously, it was customary to stop adding migration edges when 99.8% of variation in the data was explained, but OptM automates this process using an ad hoc statistic based on the second-order rate of change in the log likelihood. OptM also has added functionality for various threshold modeling to compare with the ad hoc statistic.

Data on the most popular baby names by sex and year, and for each state in Australia, as provided by the state and territory governments. The quality and quantity of the data varies with the state.

The log-rank test is performed to assess the survival outcomes between two group. When there is no proper control group or obtaining such data is cumbersome, one sample log-rank test can be applied. This package performs one sample log-rank test as described in Finkelstein et al. (2003)<doi:10.1093/jnci/djt227> and variation of the test for small sample sizes which is detailed in FD Liddell (1984)<doi:10.1136/jech.38.1.85> paper. Visualization function in the package generates Kaplan-Meier Curve comparing survival curve of the general population against that of the population of interest.

Estimates ordered probit switching regression models - a Heckman type selection model with an ordinal selection and continuous outcomes. Different model specifications are allowed for each treatment/regime. For more details on the method, see Wang & Mokhtarian (2024) <doi:10.1016/j.tra.2024.104072> or Chiburis & Lokshin (2007) <doi:10.1177/1536867X0700700202>.

Match, download, convert and import Open Street Map data extracts obtained from several providers.

Programs for detecting and cleaning outliers in single time series and in time series from homogeneous and heterogeneous databases using an Orthogonal Greedy Algorithm (OGA) for saturated linear regression models. The programs implement the procedures presented in the paper entitled "Efficient Outlier Detection for Large Time Series Databases" by Pedro Galeano, Daniel Peña and Ruey S. Tsay (2026), working paper, Universidad Carlos III de Madrid. Version 1.1.2 fixes one bug.

This package provides clean, tidy access to data published by the Office for Budget Responsibility ('OBR'), the UK's independent fiscal watchdog. Covers the Public Finances Databank (outturn for PSNB, PSND, receipts, and expenditure since 1946), the Historical Official Forecasts Database (every OBR forecast since 2010), the Economic and Fiscal Outlook detailed forecast tables (five-year projections from the latest Budget), the Welfare Trends Report (incapacity benefit spending and caseloads), and the Fiscal Risks and Sustainability Report (50-year state pension projections). Data is downloaded from the OBR on first use and cached locally for subsequent calls. Data is sourced from the OBR website <https://obr.uk>.

Visualise results obtained from analysing data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model using shiny applications.

This package provides a framework for the optimization of breeding programs via optimum contribution selection and mate allocation. An easy to use set of function for computation of optimum contributions of selection candidates, and of the population genetic parameters to be optimized. These parameters can be estimated using pedigree or genotype information, and include kinships, kinships at native haplotype segments, and breed composition of crossbred individuals. They are suitable for managing genetic diversity, removing introgressed genetic material, and accelerating genetic gain. Additionally, functions are provided for computing genetic contributions from ancestors, inbreeding coefficients, the native effective size, the native genome equivalent, pedigree completeness, and for preparing and plotting pedigrees. The methods are described in:\n Wellmann, R., and Pfeiffer, I. (2009) <doi:10.1017/S0016672309000202>.\n Wellmann, R., and Bennewitz, J. (2011) <doi:10.2527/jas.2010-3709>.\n Wellmann, R., Hartwig, S., Bennewitz, J. (2012) <doi:10.1186/1297-9686-44-34>.\n de Cara, M. A. R., Villanueva, B., Toro, M. A., Fernandez, J. (2013) <doi:10.1111/mec.12560>.\n Wellmann, R., Bennewitz, J., Meuwissen, T.H.E. (2014) <doi:10.1017/S0016672314000196>.\n Wellmann, R. (2019) <doi:10.1186/s12859-018-2450-5>.

Trains per-horizon probabilistic ensembles from a univariate time series. It supports rpart', glmnet', and kNN engines with flexible residual distributions and heteroscedastic scale models, weighting variants by calibration-aware scores. A Gaussian/t copula couples the marginals to simulate joint forecast paths, returning quantiles, means, and step increments across horizons.

This package provides a regression framework for response variables which are continuous self-rating scales such as the Visual Analog Scale (VAS) used in pain assessment, or the Linear Analog Self-Assessment (LASA) scales in quality of life studies. These scales measure subjects perception of an intangible quantity, and cannot be handled as ratio variables because of their inherent non-linearity. We treat them as ordinal variables, measured on a continuous scale. A function (the g function) connects the scale with an underlying continuous latent variable. The link function is the inverse of the CDF of the assumed underlying distribution of the latent variable. A variety of link functions are currently implemented. Such models are described in Manuguerra et al (2020) <doi:10.18637/jss.v096.i08>.

This package implements multiple existing open-source algorithms for coding cause of death from verbal autopsies. The methods implemented include InterVA4 by Byass et al (2012) <doi:10.3402/gha.v5i0.19281>, InterVA5 by Byass at al (2019) <doi:10.1186/s12916-019-1333-6>, InSilicoVA by McCormick et al (2016) <doi:10.1080/01621459.2016.1152191>, NBC by Miasnikof et al (2015) <doi:10.1186/s12916-015-0521-2>, and a replication of Tariff method by James et al (2011) <doi:10.1186/1478-7954-9-31> and Serina, et al. (2015) <doi:10.1186/s12916-015-0527-9>. It also provides tools for data manipulation tasks commonly used in Verbal Autopsy analysis and implements easy graphical visualization of individual and population level statistics. The NBC method is implemented by the nbc4va package that can be installed from <https://github.com/rrwen/nbc4va>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist in the implementation of the Tariff method.

Algorithm of online regularized k-means to deal with online multi(single) view data. The philosophy of the package is described in Guo G. (2024) <doi:10.1016/j.ins.2024.121133>.

This package implements orbit counting using a fast combinatorial approach. Counts orbits of nodes and edges from edge matrix or data frame, or a graph object from the graph package.

Calculates ordinated diet breadth with some plotting functions.