This database contains necessary data relevant to medical costs on obesity throughout the United States. This database, in form of an R package, could output necessary data frames relevant to obesity costs, where the clients could easily manipulate the output using difference parameters, e.g. relative risks for each illnesses. This package contributes to parts of our published journal named "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis". Please use the following citation for the journal: Woods Thomas, Tatjana Miljkovic (2022) "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis" <doi:10.3390/risks10100197>. The database is composed of the following main tables: 1. Relative_Risks: (constant) Relative risks for a given disease group with a risk factor of obesity; 2. Disease_Cost: (obesity_cost_disease) Supplementary output with all variables related to individual disease groups in a given state and year; 3. Full_Cost: (obesity_cost_full) Complete output with all variables used to make cost calculations, as well as cost calculations in a given state and year; 4. National_Summary: (obesity_cost_national_summary) National summary cost calculations in a given year. Three functions are included to assist users in calling and adjusting the mentioned tables and they are data_load(), data_produce(), and rel_risk_fun().

Combine the air quality data analysis methods of openair with the JavaScript Leaflet (<https://leafletjs.com/>) library. Functionality includes plotting site maps, "directional analysis" figures such as polar plots, and air mass trajectories.

This package provides tools for easy exploration of the world ocean atlas of the US agency National Oceanic and Atmospheric Administration (NOAA). It includes functions to extract NetCDF data from the repository and code to visualize several physical and chemical parameters of the ocean. A Shiny app further allows interactive exploration of the data. The methods for data collecting and quality checks are described in several papers, which can be found here: <https://www.ncei.noaa.gov/products/world-ocean-atlas>.

Calculates ordinated diet breadth with some plotting functions.

It makes an objective Bayesian analysis of the spatial regression model using both the normal (NSR) and student-T (TSR) distributions. The functions provided give prior and posterior objective densities and allow default Bayesian estimation of the model regression parameters. Details can be found in Ordonez et al. (2020) <arXiv:2004.04341>.

This package provides a collection of functions that aid in calculating the optimum time to stock hatchery reared fish into a body of water given the growth, mortality and cost of raising a particular number of individuals to a certain length.

This package provides a collection of aesthetically appealing color palettes for effective data visualization with ggplot2'. Palettes support both discrete and continuous data.

Access data and processing functionalities of openEO compliant back-ends in R.

Calculates autoecological data (optima and tolerance ranges) of a biological species given an environmental matrix. The package calculates by weighted averaging, using the number of occurrences to adjust the tolerance assigned to each taxon to estimate optima and tolerance range in cases where taxa have unequal occurrences. See the detailed methodology by Birks et al. (1990) <doi:10.1098/rstb.1990.0062>, and a case example by Potapova and Charles (2003) <doi:10.1046/j.1365-2427.2003.01080.x>.

Defines thresholds for breaking data into a number of discrete levels, minimizing the (mean) squared error within all bins.

Implement a new stopping rule to detect anomaly in the covariance structure of high-dimensional online data. The detection procedure can be applied to Gaussian or non-Gaussian data with a large number of components. Moreover, it allows both spatial and temporal dependence in data. The dependence can be estimated by a data-driven procedure. The level of threshold in the stopping rule can be determined at a pre-selected average run length. More detail can be seen in Li, L. and Li, J. (2020) "Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks." <arXiv:1911.07762>.

This package provides a visualization tool for multivariate data. This package maintains the original functionality of a radar chart and avoids potential misuse of its connected regions, with newly added features to better assist multi-criteria decision-making.

Interact seamlessly with Open Target GraphQL endpoint to query and retrieve tidy data tables, facilitating the analysis of gene, disease, drug, and genetic data. For more information about the Open Target API (<https://platform.opentargets.org/api>).

This package provides functions to estimate the optimal threshold of diagnostic markers or treatment selection markers. The optimal threshold is the marker value that maximizes the utility of the marker based-strategy (for diagnostic or treatment selection) in a given population. The utility function depends on the type of marker (diagnostic or treatment selection), but always takes into account the preferences of the patients or the physician in the decision process. For estimating the optimal threshold, ones must specify the distributions of the marker in different groups (defined according to the type of marker, diagnostic or treatment selection) and provides data to estimate the parameters of these distributions. Ones must also provide some features of the target populations (disease prevalence or treatment efficacies) as well as the preferences of patients or physicians. The functions rely on Bayesian inference which helps producing several indicators derived from the optimal threshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and Subtil, F (2019) <doi:10.1177/0962280218821394> for the original article that describes the estimation method for treatment selection markers and Subtil, F, and Rabilloud, M (2019) <doi:10.1002/bimj.200900242> for diagnostic markers.

This package contains data from the May 2021 Occupational Employment and Wage Statistics data release from the U.S. Bureau of Labor Statistics. The dataset covers employment and wages across occupations, industries, states, and at the national level. Metropolitan data is not included.

Sample from the limiting distributions of empirical Wasserstein distances under the null hypothesis and under the alternative. Perform a two-sample test on multivariate data using these limiting distributions and binning.

Calculate the optimal sample size allocation that uses the minimum resources to achieve targeted statistical power in experiments. Perform power analyses with and without accommodating costs and budget. The designs cover single-level and multilevel experiments detecting main, mediation, and moderation effects (and some combinations). The references for the proposed methods include: (1) Shen, Z., & Kelcey, B. (2020). Optimal sample allocation under unequal costs in cluster-randomized trials. Journal of Educational and Behavioral Statistics, 45(4): 446-474. <doi:10.3102/1076998620912418>. (2) Shen, Z., & Kelcey, B. (2022b). Optimal sample allocation for three-level multisite cluster-randomized trials. Journal of Research on Educational Effectiveness, 15 (1), 130-150. <doi:10.1080/19345747.2021.1953200>. (3) Shen, Z., & Kelcey, B. (2022a). Optimal sample allocation in multisite randomized trials. The Journal of Experimental Education, 90(3), 693-711. <doi:10.1080/00220973.2020.1830361>. (4) Shen, Z., Leite, W., Zhang, H., Quan, J., & Kuang, H. (2025). Using ant colony optimization to identify optimal sample allocations in cluster-randomized trials. The Journal of Experimental Education, 93(1), 167-185. <doi:10.1080/00220973.2024.2306392>. (5) Shen, Z., Li, W., & Leite, W. (in press). Statistical power and optimal design for randomized controlled trials investigating mediation effects. Psychological Methods. <doi:10.1037/met0000698>. (6) Champely, S. (2020). pwr: Basic functions for power analysis (Version 1.3-0) [Software]. Available from <https://CRAN.R-project.org/package=pwr>.

Construct and evaluate directed tree structures that model the process of occurrence of genetic alterations during carcinogenesis as described in Szabo, A. and Boucher, K (2002) <doi:10.1016/S0025-5564(02)00086-X>.

Allows production of Microsoft corporate documents from R Markdown by reusing formatting defined in Microsoft Word documents. You can reuse table styles, list styles but also add column sections, landscape oriented pages. Table and image captions as well as cross-references are transformed into Microsoft Word fields, allowing documents edition and merging without issue with references; the syntax conforms to the bookdown cross-reference definition. Objects generated by the officer package are also supported in the knitr chunks. Microsoft PowerPoint presentations also benefit from this as well as the ability to produce editable vector graphics in PowerPoint and also to define placeholder where content is to be added.

Maps of Australian coastline and administrative regions. Data can be drawn or accessed directly as simple features objects. Includes simple functions for country or state maps of Australia and in-built data sets of administrative regions from the Australian Bureau of Statistics <https://www.abs.gov.au/>. Layers include electoral divisions and local government areas, simplified from the original sources but with sufficient detail to allow mapping of a local municipality.

Generate systems of ordinary differential equations (ODE) and integrate them, using a domain specific language (DSL). The DSL uses R's syntax, but compiles to C in order to efficiently solve the system. A solver is not provided, but instead interfaces to the packages deSolve and dde are generated. With these, while solving the differential equations, no allocations are done and the calculations remain entirely in compiled code. Alternatively, a model can be transpiled to R for use in contexts where a C compiler is not present. After compilation, models can be inspected to return information about parameters and outputs, or intermediate values after calculations. odin is not targeted at any particular domain and is suitable for any system that can be expressed primarily as mathematical expressions. Additional support is provided for working with delays (delay differential equations, DDE), using interpolated functions during interpolation, and for integrating quantities that represent arrays.

Two-part system for first collecting then managing direct observation data, as described by Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.

Fast, optimal, and reproducible clustering algorithms for circular, periodic, or framed data. The algorithms introduced here are based on a core algorithm for optimal framed clustering the authors have developed (Debnath & Song 2021) <doi:10.1109/TCBB.2021.3077573>. The runtime of these algorithms is O(K N log^2 N), where K is the number of clusters and N is the number of circular data points. On a desktop computer using a single processor core, millions of data points can be grouped into a few clusters within seconds. One can apply the algorithms to characterize events along circular DNA molecules, circular RNA molecules, and circular genomes of bacteria, chloroplast, and mitochondria. One can also cluster climate data along any given longitude or latitude. Periodic data clustering can be formulated as circular clustering. The algorithms offer a general high-performance solution to circular, periodic, or framed data clustering.

This package provides a set of tools to extract bibliographic content from OpenAlex database using API <https://docs.openalex.org>.