This package implements the objective Bayesian methodology proposed in Consonni and Deldossi in order to choose the optimal experiment that better discriminate between competing models, see Deldossi and Nai Ruscone (2020) <doi:10.18637/jss.v094.i02>.

Install and control Open Source Routing Machine ('OSRM') backend executables to prepare routing data and run/stop a local OSRM server. For computations with the running server use the osrm R package (<https://cran.r-project.org/package=osrm>).

Transactions occurring for a UK-based and registered, non-store online retail between 01/12/2010 and 09/12/2011 (Chen et. al., 2012, <doi:10.1145/1835804.1835882>). This dataset is included in this package with the donor's permission, Dr. Daqing Chen.

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.

The aim of od is to provide tools and example datasets for working with origin-destination ('OD') datasets of the type used to describe aggregate urban mobility patterns (Carey et al. 1981) <doi:10.1287/trsc.15.1.32>. The package builds on functions for working with OD data in the package stplanr', (Lovelace and Ellison 2018) <doi:10.32614/RJ-2018-053> with a focus on computational efficiency and support for the sf class system (Pebesma 2018) <doi:10.32614/RJ-2018-009>. With few dependencies and a simple class system based on data frames, the package is intended to facilitate efficient analysis of OD datasets and to provide a place for developing new functions. The package enables the creation and analysis of geographic entities representing large scale mobility patterns, from daily travel between zones in cities to migration between countries.

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...

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.

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

Several Oceanographic data sets are provided for use by the oce package and for other purposes.

This package provides a client for the open-source monitoring and alerting toolkit, Prometheus', that emits metrics in the OpenMetrics format. Allows users to automatically instrument Plumber and Shiny applications, collect standard process metrics, as well as define custom counter, gauge, and histogram metrics of their own.

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>.

Fit a variety of models to two-way tables with ordered categories. Most of the models are appropriate to apply to tables of that have correlated ordered response categories. There is a particular interest in rater data and models for rescore tables. Some utility functions (e.g., Cohen's kappa and weighted kappa) support more general work on rater agreement. Because the names of the models are very similar, the functions that implement them are organized by last name of the primary author of the article or book that suggested the model, with the name of the function beginning with that author's name and an underscore. This may make some models more difficult to locate if one doesn't have the original sources. The vignettes and tests can help to locate models of interest. For more dertaiils see the following references: Agresti, A. (1983) <doi:10.1016/0167-7152(83)90051-2> "A Simple Diagonals-Parameter Symmetry And Quasi-Symmetry Model", Agrestim A. (1983) <doi:10.2307/2531022> "Testing Marginal Homogeneity for Ordinal Categorical Variables", Agresti, A. (1988) <doi:10.2307/2531866> "A Model For Agreement Between Ratings On An Ordinal Scale", Agresti, A. (1989) <doi:10.1016/0167-7152(89)90104-1> "An Agreement Model With Kappa As Parameter", Agresti, A. (2010 ISBN:978-0470082898) "Analysis Of Ordinal Categorical Data", Bhapkar, V. P. (1966) <doi:10.1080/01621459.1966.10502021> "A Note On The Equivalence Of Two Test Criteria For Hypotheses In Categorical Data", Bhapkar, V. P. (1979) <doi:10.2307/2530344> "On Tests Of Marginal Symmetry And Quasi-Symmetry In Two And Three-Dimensional Contingency Tables", Bowker, A. H. (1948) <doi:10.2307/2280710> "A Test For Symmetry In Contingency Tables", Clayton, D. G. (1974) <doi:10.2307/2335638> "Some Odds Ratio Statistics For The Analysis Of Ordered Categorical Data", Cliff, N. (1993) <doi:10.1037/0033-2909.114.3.494> "Dominance Statistics: Ordinal Analyses To Answer Ordinal Questions", Cliff, N. (1996 ISBN:978-0805813333) "Ordinal Methods For Behavioral Data Analysis", Goodman, L. A. (1979) <doi:10.1080/01621459.1979.10481650> "Simple Models For The Analysis Of Association In Cross-Classifications Having Ordered Categories", Goodman, L. A. (1979) <doi:10.2307/2335159> "Multiplicative Models For Square Contingency Tables With Ordered Categories", Ireland, C. T., Ku, H. H., & Kullback, S. (1969) <doi:10.2307/2286071> "Symmetry And Marginal Homogeneity Of An r à r Contingency Table", Ishi-kuntz, M. (1994 ISBN:978-0803943766) "Ordinal Log-linear Models", McCullah, P. (1977) <doi:10.2307/2345320> "A Logistic Model For Paired Comparisons With Ordered Categorical Data", McCullagh, P. (1978) <doi:10.2307/2335224> A Class Of Parametric Models For The Analysis Of Square Contingency Tables With Ordered Categories", McCullagh, P. (1980) <doi:10.1111/j.2517-6161.1980.tb01109.x> "Regression Models For Ordinal Data", Penn State: Eberly College of Science (undated) <https://online.stat.psu.edu/stat504/lesson/11> "Stat 504: Analysis of Discrete Data, 11. Advanced Topics I", Schuster, C. (2001) <doi:10.3102/10769986026003331> "Kappa As A Parameter Of A Symmetry Model For Rater Agreement", Shoukri, M. M. (2004 ISBN:978-1584883210). "Measures Of Interobserver Agreement", Stuart, A. (1953) <doi:10.2307/2333101> "The Estimation Of And Comparison Of Strengths Of Association In Contingency Tables", Stuart, A. (1955) <doi:10.2307/2333387> "A Test For Homogeneity Of The Marginal Distributions In A Two-Way Classification", von Eye, A., & Mun, E. Y. (2005 ISBN:978-0805849677) "Analyzing Rater Agreement: Manifest Variable Methods".

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>.

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>.

Get operating characteristics of one-arm clinical trial designs for time-to-event endpoint through simulation and perform analysis with time-to-event data.

Efficient Monte Carlo Algorithms for the price and the sensitivities of Asian and European Options under Geometric Brownian Motion.

Supports the definition of sets of properties on objects. Observers can listen to changes on individual properties or the set as a whole. The properties are meant to be fully self-describing. In support of this, there is a framework for defining enumerated types, as well as other bounded types, as S4 classes.

R Interface to ONNX - Open Neural Network Exchange <https://onnx.ai/>. ONNX provides an open source format for machine learning models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.

Given a certain coverage level, obtains simultaneous confidence bands for the survival and cumulative hazard functions such that the area between is minimized. Produces an approximate solution based on local time arguments.

Supports the analysis of Oceanographic data, including ADCP measurements, measurements made with argo floats, CTD measurements, sectional data, sea-level time series, coastline and topographic data, etc. Provides specialized functions for calculating seawater properties such as potential temperature in either the UNESCO or TEOS-10 equation of state. Produces graphical displays that conform to the conventions of the Oceanographic literature. This package is discussed extensively by Kelley (2018) "Oceanographic Analysis with R" <doi:10.1007/978-1-4939-8844-0>.

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.

Helps to create ggplot2 charts in the style used by the National Road Safety Observatory (ONSV). The package includes functions to customize ggplot2 objects with new theme and colors.

This package implements a simulation study to assess the strengths and weaknesses of causal inference methods for estimating policy effects using panel data. See Griffin et al. (2021) <doi:10.1007/s10742-022-00284-w> and Griffin et al. (2022) <doi:10.1186/s12874-021-01471-y> for a description of our methods.

Identifies the optimal transformation of a surrogate marker and estimates the proportion of treatment explained (PTE) by the optimally-transformed surrogate at an earlier time point when the primary outcome of interest is a censored time-to-event outcome; details are described in Wang et al (2021) <doi:10.1002/sim.9185>.