Calculates ordinated diet breadth with some plotting functions.

An approach to outlier detection in RNA-seq and related data based on five statistics. OutSeekR implements an outlier test by comparing the distributions of these statistics in observed data with those of simulated null data.

The ordinal forest (OF) method allows ordinal regression with high-dimensional and low-dimensional data. After having constructed an OF prediction rule using a training dataset, it can be used to predict the values of the ordinal target variable for new observations. Moreover, by means of the (permutation-based) variable importance measure of OF, it is also possible to rank the covariates with respect to their importance in the prediction of the values of the ordinal target variable. OF is presented in Hornung (2020). NOTE: Starting with package version 2.4, it is also possible to obtain class probability predictions in addition to the class point predictions. Moreover, the variable importance values can also be based on the class probability predictions. Preliminary results indicate that this might lead to a better discrimination between influential and non-influential covariates. The main functions of the package are: ordfor() (construction of OF) and predict.ordfor() (prediction of the target variable values of new observations). References: Hornung R. (2020) Ordinal Forests. Journal of Classification 37, 4â 17. <doi:10.1007/s00357-018-9302-x>.

This package provides functions to design and simulate optimal two-stage randomized controlled trials (RCTs) with ordered categorical outcomes, supporting rank-based tests and group-sequential decision rules. Methods build on classical and modern rank tests and two-stage/Group-Sequential designs, e.g., Park (2025) <doi: 10.1371/journal.pone.0318211>. Please see the package reference manual and vignettes for details.

Primarily devoted to implementing the Univariate Bootstrap (as well as the Traditional Bootstrap). In addition there are multiple functions for DeFries-Fulker behavioral genetics models. The univariate bootstrapping functions, DeFries-Fulker functions, regression and traditional bootstrapping functions form the original core. Additional features may come online later, however this software is a work in progress. For more information about univariate bootstrapping see: Lee and Rodgers (1998) and Beasley et al (2007) <doi:10.1037/1082-989X.12.4.414>.

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

This package provides a programmatic interface to the OpenM++ microsimulation platform (<https://openmpp.org>). The primary goal of this package is to wrap the OpenM++ Web Service (OMS) to provide OpenM++ users a programmatic interface for the R language.

Outcome-dependent sampling (ODS) schemes are cost-effective ways to enhance study efficiency. In ODS designs, one observes the exposure/covariates with a probability that depends on the outcome variable. Popular ODS designs include case-control for binary outcome, case-cohort for time-to-event outcome, and continuous outcome ODS design (Zhou et al. 2002) <doi: 10.1111/j.0006-341X.2002.00413.x>. Because ODS data has biased sampling nature, standard statistical analysis such as linear regression will lead to biases estimates of the population parameters. This package implements four statistical methods related to ODS designs: (1) An empirical likelihood method analyzing the primary continuous outcome with respect to exposure variables in continuous ODS design (Zhou et al., 2002). (2) A partial linear model analyzing the primary outcome in continuous ODS design (Zhou, Qin and Longnecker, 2011) <doi: 10.1111/j.1541-0420.2010.01500.x>. (3) Analyze a secondary outcome in continuous ODS design (Pan et al. 2018) <doi: 10.1002/sim.7672>. (4) An estimated likelihood method analyzing a secondary outcome in case-cohort data (Pan et al. 2017) <doi: 10.1111/biom.12838>.

Empirical or simulated disease outbreak data, provided either as RData or as text files.

This package provides dates for public and school holidays for a number of countries and their subdivisions through the OpenHolidays API at <https://www.openholidaysapi.org/en/>.

Shiny Application to visualize Olympic Data. From 1896 to 2016. Even Winter Olympics events are included. Data is from Kaggle at <https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results>.

This package provides a user-friendly R-based software package for gene clustering. Clusters are given by genes matched to prespecified profiles across various ordered treatment groups. It is particularly useful for analyzing data obtained from short time-course or dose-response microarray experiments.

This package contains data from the May 2020 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.

Obtaining Bayes Expected A Posteriori (EAP) individual score estimates based on linear and non-linear extended Exploratoy Factor Analysis solutions that include a correlated-residual structure.

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.

This package provides a client that grants access to the power of the ohsome API from R. It lets you analyze the rich data source of the OpenStreetMap (OSM) history. You can retrieve the geometry of OSM data at specific points in time, and you can get aggregated statistics on the evolution of OSM elements and specify your own temporal, spatial and/or thematic filters.

Introduces optional types with some() and none, as well as match_with() from functional languages.

An interface between R and the OSRM API. OSRM is a routing service based on OpenStreetMap data. See <http://project-osrm.org/> for more information. This package enables the computation of routes, trips, isochrones and travel distances matrices (travel time and kilometric distance).

This package provides a simple wrapper for the Octopus Energy API <https://developer.octopus.energy/docs/api/>. It handles authentication, by storing a provided API key and meter details. Implemented endpoints include products for viewing tariff details and consumption for viewing meter consumption data.

Summarises key information in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Assess suitability to perform specific epidemiological studies and explore the different domains to obtain feasibility counts and trends.

After develop a ODK <https://opendatakit.org/> frame, we can link the frame to Google Sheets <https://www.google.com/sheets/about/> and collect data through Android <https://www.android.com/>. This data uploaded to a Google sheets'. odk2spss() function help to convert the odk frame into SPSS <https://www.ibm.com/analytics/us/en/technology/spss/> frame. Also able to add downloaded Google sheets data or read data from Google sheets by using ODK frame submission_url'.

This package provides a set of commands to manage an abstract optimization method. The goal is to provide a building block for a large class of specialized optimization methods. This package manages: the number of variables, the minimum and maximum bounds, the number of non linear inequality constraints, the cost function, the logging system, various termination criteria, etc...

Computes confidence regions on the location of response surface optima. Response surface models can be up to cubic polynomial models in up to 5 controllable factors, or Thin Plate Spline models in 2 controllable factors.

This package provides a system for calculating the minimum total sample size needed to achieve a prespecified power or the optimal allocation for each treatment group with a fixed total sample size to maximize the power.