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>.

The oblique decision tree (ODT) uses linear combinations of predictors as partitioning variables in a decision tree. Oblique Decision Random Forest (ODRF) is an ensemble of multiple ODTs generated by feature bagging. Oblique Decision Boosting Tree (ODBT) applies feature bagging during the training process of ODT-based boosting trees to ensemble multiple boosting trees. All three methods can be used for classification and regression, and ODT and ODRF serve as supplements to the classical CART of Breiman (1984) <DOI:10.1201/9781315139470> and Random Forest of Breiman (2001) <DOI:10.1023/A:1010933404324> respectively.

Provide functionality for cancer subtyping using nearest centroids or machine learning methods based on TCGA data.

The supplied code allows for the generation of discrete time series of oscillating species. General shapes can be selected by means of individual functions, which are widely customizable by means of function arguments. All code was developed in the Biological Information Processing Group at the BioQuant Center at Heidelberg University, Germany.

The classical and extended occupancy distributions occur in cases where balls are randomly allocated to bins. The PDF, CDF, quantile functions, generation of random variates, and calculating the first four central moments of the distributions are implemented as described in Oâ Neill (2019) <doi:10.1080/00031305.2019.1699445>.

Optimal scaling of a data vector, relative to a set of targets, is obtained through a least-squares transformation subject to appropriate measurement constraints. The targets are usually predicted values from a statistical model. If the data are nominal level, then the transformation must be identity-preserving. If the data are ordinal level, then the transformation must be monotonic. If the data are discrete, then tied data values must remain tied in the optimal transformation. If the data are continuous, then tied data values can be untied in the optimal transformation.

Automatically adding pkg:: to a function, i.e. mutate() becomes dplyr::mutate(). It is up to the user to determine which packages should be used explicitly, whether to include base R packages or use the functionality on selected text, a file, or a complete directory. User friendly logging is provided in the RStudio Markers pane. Lives in the spirit of lintr and styler'. Can also be used for checking which packages are actually used in a project.

Anomaly detection in dynamic, temporal networks. The package oddnet uses a feature-based method to identify anomalies. First, it computes many features for each network. Then it models the features using time series methods. Using time series residuals it detects anomalies. This way, the temporal dependencies are accounted for when identifying anomalies (Kandanaarachchi, Hyndman 2022) <arXiv:2210.07407>.

Fits two-dimensional data by means of orthogonal nonlinear least-squares using Levenberg-Marquardt minimization and provides functionality for fit diagnostics and plotting. Delivers the same results as the ODRPACK Fortran implementation described in Boggs et al. (1989) <doi:10.1145/76909.76913>, but is implemented in pure R.

An integrated R interface to the Overture API (<https://docs.overturemaps.org/>). Allows R users to return Overture data as dbplyr data frames or materialized sf spatial data frames.

This package provides a derivative-based optimization framework that allows users to combine eight convergence criteria. Unlike standard optimization functions, this package includes a built-in mechanism to verify the positive definiteness of the Hessian matrix at the point of convergence. This additional check helps prevent the solver from falsely identifying non-optimal solutions, such as saddle points, as valid minima.

Objects and methods to handle and solve the min-sum location problem, also known as Fermat-Weber problem. The min-sum location problem search for a point such that the weighted sum of the distances to the demand points are minimized. See "The Fermat-Weber location problem revisited" by Brimberg, Mathematical Programming, 1, pg. 71-76, 1995. <DOI:10.1007/BF01592245>. General global optimization algorithms are used to solve the problem, along with the adhoc Weiszfeld method, see "Sur le point pour lequel la Somme des distances de n points donnes est minimum", by Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pg. 355-386, 1937 or "On the point for which the sum of the distances to n given points is minimum", by E. Weiszfeld and F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. <DOI:10.1007/s10479-008-0352-z>.

Analyses of OTU tables produced by 16S rRNA gene amplicon sequencing, as well as example data. It contains the data and scripts used in the paper Linz, et al. (2017) "Bacterial community composition and dynamics spanning five years in freshwater bog lakes," <doi: 10.1128/mSphere.00169-17>.

This package provides tools analyzing regression models for ordinal compositional data using Wasserstein-based distances. The package includes linear programming solvers under simplex constraints, tensor product constructions and performance metrics.

Users can build a single shiny app for exploring population characterization, population-level causal effect estimation, and patient-level prediction results generated via the R analyses packages in HADES (see <https://ohdsi.github.io/Hades/>). Learn more about OhdsiShinyAppBuilder at <https://ohdsi.github.io/OhdsiShinyAppBuilder/>.

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.

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>.

Picks the suitable cell types in spatial and scRNA-seq data using shrinkage methods. The package includes curated reference gene expression profiles for human and mouse cell types, facilitating immediate application to common spatial transcriptomics or scRNA datasets. Additionally, users can input custom reference data to support tissue- or experiment-specific analyses.

This package provides a suite of functions for the design of case-control and two-phase studies, and the analysis of data that arise from them. Functions in this packages provides Monte Carlo based evaluation of operating characteristics such as powers for estimators of the components of a logistic regression model. For additional detail see: Haneuse S, Saegusa T and Lumley T (2011)<doi:10.18637/jss.v043.i11>.

Streamlines the setup and execution of network studies using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Creates standardised project structures with template code, manages dependencies with renv', provides code review utilities, and supports containerised execution with Docker for reproducible multi-site studies. Includes GitHub integration for collaboration and version control.

Use health data in the Observational Medical Outcomes Partnership Common Data Model format in Spark'. Functionality includes creating all required tables and fields and creation of a single reference to the data. Native Spark functionality is supported.

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 method for the quantitative prediction using omics data. This package provides functions to construct the quantitative prediction model using omics data.

This package creates a client with queries for the UK Open Banking ('Open Data') API.