This package provides a collection of numerical optimization algorithms. One is a simple implementation of the primitive grid search algorithm, the other is an extension of the simulated annealing algorithm that can take custom boundaries into account. The methodology for this bounded simulated annealing algorithm is due to Haario and Saksman (1991), <doi:10.2307/1427681>.

This package provides the setup and calculations needed to run a likelihood-based continual reassessment method (CRM) dose finding trial and performs simulations to assess design performance under various scenarios. 3 dose finding designs are included in this package: ordinal proportional odds model (POM) CRM, ordinal continuation ratio (CR) model CRM, and the binary 2-parameter logistic model CRM. These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose-limiting toxicity (DLT) rates, discrete or continuous dose levels, combining ordinal grades 0 and 1 into one category, and incorporate safety and/or stopping rules. For POM and CR model designs, ordinal toxicity grades are specified by common terminology criteria for adverse events (CTCAE) version 4.0. Function pseudodata creates the necessary starting models for these 3 designs, and function nextdose estimates the next dose to test in a cohort of patients for a target DLT rate. We also provide the function crmsimulations to assess the performance of these 3 dose finding designs under various scenarios.

Estimate location-shift models or rating-scale models accounting for response styles (RSRS) for the regression analysis of ordinal responses.

This package provides a mutable Signal object can report changes to its state, clients could register functions so that they are called whenever the signal is emitted. The signal could be emitted, disconnected, blocked, unblocked, and buffered.

This contains functions and data used by the Open Visualization Academy classes on data processing and visualization. The tutorial included with this package requires the gradethis package which can be installed using "remotes::install_github('rstudio/gradethis')".

An implementation of DuMouchel's (1999) <doi:10.1080/00031305.1999.10474456> Bayesian data mining method for the market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and posterior quantile scores using the Gamma-Poisson Shrinker (GPS) model to find unusually large cell counts in large, sparse contingency tables. Can be used to find unusually high reporting rates of adverse events associated with products. In general, can be used to mine any database where the co-occurrence of two variables or items is of interest. Also calculates relative and proportional reporting ratios. Builds on the work of the PhViD package, from which much of the code is derived. Some of the added features include stratification to adjust for confounding variables and data squashing to improve computational efficiency. Includes an implementation of the EM algorithm for hyperparameter estimation loosely derived from the mederrRank package.

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.

Seamlessly build and manipulate graph structures, leveraging its high-performance methods for filtering, joining, and mutating data. Ensures that mutations and changes to the graph are performed in place, streamlining your workflow for optimal productivity.

This package provides tools for annotating characters (character matrices) with anatomical and phenotype ontologies. Includes functions for visualising character annotations and creating simple queries using ontological relationships.

Growing collection of helper functions for point pattern analysis. Most functions are designed to work with the spatstat (<http://spatstat.org>) package. The focus of most functions are either null models or summary functions for spatial point patterns. For a detailed description of all null models and summary functions, see Wiegand and Moloney (2014, ISBN:9781420082548).

Retrieve data from the Our World in Data (OWID) Chart API <https://docs.owid.io/projects/etl/api/>. OWID provides public access to more than 5,000 charts focusing on global problems such as poverty, disease, hunger, climate change, war, existential risks, and inequality.

This package provides a wrapper for the OpenTripPlanner <http://www.opentripplanner.org/> REST API. Queries are submitted to the relevant OpenTripPlanner API resource, the response is parsed and useful R objects are returned.

This package provides definitions of core classes and methods used by analytic pipelines that query the OMOP (Observational Medical Outcomes Partnership) common data model.

Computes the routing distribution, the expectation of the number of broadcasts, transmissions and receptions considering an Opportunistic transport model. It provides theoretical results and also estimated values based on Monte Carlo simulations.

Offers a gene-based meta-analysis test with filtering to detect gene-environment interactions (GxE) with association data, proposed by Wang et al. (2018) <doi:10.1002/gepi.22115>. It first conducts a meta-filtering test to filter out unpromising SNPs by combining all samples in the consortia data. It then runs a test of omnibus-filtering-based GxE meta-analysis (ofGEM) that combines the strengths of the fixed- and random-effects meta-analysis with meta-filtering. It can also analyze data from multiple ethnic groups.

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

This package provides a function for fitting various penalized Bayesian cumulative link ordinal response models when the number of parameters exceeds the sample size. These models have been described in Zhang and Archer (2021) <doi:10.1186/s12859-021-04432-w>.

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.

Fits community site occupancy models to environmental DNA metabarcoding data collected using spatially-replicated survey design. Model fitting results can be used to evaluate and compare the effectiveness of species detection to find an efficient survey design. Reference: Fukaya et al. (2022) <doi:10.1111/2041-210X.13732>, Fukaya and Hasebe (2025) <doi:10.1002/1438-390X.12219>.

This package performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform distribution covering the whole Euclidean space. Parameters are estimated by (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2016) <doi:10.1080/01621459.2015.1100996>, and Coretto and Hennig (2017) <https://jmlr.org/papers/v18/16-382.html>.

This package provides a tool for visualizing numerical data (e.g., gene expression, protein abundance) on predefined anatomical maps of human/mouse organs and subcellular organelles. It supports customization of color schemes, filtering by organ systems (for organisms) or organelle types, and generation of optional bar charts for quantitative comparison. The package integrates coordinate data for organs and organelles to plot anatomical/subcellular contours, mapping data values to specific structures for intuitive visualization of biological data distribution.The underlying method was described in the preprint by Zhou et al. (2022) <doi:10.1101/2022.09.07.506938>.

This package provides an end-to-end workflow for integrative analysis of two omics layers using sparse canonical correlation analysis (sCCA), including sample alignment, feature selection, network edge construction, and visualization of gene-metabolite relationships. The underlying methods are based on penalized matrix decomposition and sparse CCA (Witten, Tibshirani and Hastie (2009) <doi:10.1093/biostatistics/kxp008>), with design principles inspired by multivariate integrative frameworks such as mixOmics (Rohart et al. (2017) <doi:10.1371/journal.pcbi.1005752>).

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.

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>.