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>.

This package provides a comprehensive set of helpers that streamline data transmission and processing, making it effortless to interact with the OpenAI API.

This package implements ordered beta regression models, which are for modeling continuous variables with upper and lower bounds, such as survey sliders, dose-response relationships and indexes. For more information, see Kubinec (2023) <doi:10.31235/osf.io/2sx6y>. The package is a front-end to the R package brms', which facilitates a range of regression specifications, including hierarchical, dynamic and multivariate modeling.

The Sequence of Physical Processes (SPP) framework is a way of interpreting the transient data derived from oscillatory rheological tests. It is designed to allow both the linear and non-linear deformation regimes to be understood within a single unified framework. This code provides a convenient way to determine the SPP framework metrics for a given sample of oscillatory data. It will produce a text file containing the SPP metrics, which the user can then plot using their software of choice. It can also produce a second text file with additional derived data (components of tangent, normal, and binormal vectors), as well as pre-plotted figures if so desired. It is the R version of the Package SPP by Simon Rogers Group for Soft Matter (Simon A. Rogers, Brian M. Erwin, Dimitris Vlassopoulos, Michel Cloitre (2011) <doi:10.1122/1.3544591>).

This package provides functionalities and data structures to retrieve, analyze and visualize aviation data. It includes a client interface to the OpenSky API <https://opensky-network.org>. It allows retrieval of flight information, as well as aircraft state vectors.

Interface to OpenStreetMap API for fetching and saving data from/to the OpenStreetMap database (<https://wiki.openstreetmap.org/wiki/API_v0.6>).

The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. The details are covered in Kim and Choi (2020) <doi:10.1111/abac.12172>, and Kim (2021) <doi:10.1080/00031305.2020.1750484>.

This package performs one-way tests in independent groups designs including homoscedastic and heteroscedastic tests. These are one-way analysis of variance (ANOVA), Welch's heteroscedastic F test, Welch's heteroscedastic F test with trimmed means and Winsorized variances, Brown-Forsythe test, Alexander-Govern test, James second order test, Kruskal-Wallis test, Scott-Smith test, Box F test, Johansen F test, Generalized tests equivalent to Parametric Bootstrap and Fiducial tests, Alvandi's F test, Alvandi's generalized p-value, approximate F test, B square test, Cochran test, Weerahandi's generalized F test, modified Brown-Forsythe test, adjusted Welch's heteroscedastic F test, Welch-Aspin test, Permutation F test. The package performs pairwise comparisons and graphical approaches. Also, the package includes Student's t test, Welch's t test and Mann-Whitney U test for two samples. Moreover, it assesses variance homogeneity and normality of data in each group via tests and plots (Dag et al., 2018, <https://journal.r-project.org/archive/2018/RJ-2018-022/RJ-2018-022.pdf>).

Obtain optimum block from Non-overlapping Block Bootstrap method.

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 for checking that the output of an optimization algorithm is indeed at a local mode of the objective function. This is accomplished graphically by calculating all one-dimensional "projection plots" of the objective function, i.e., varying each input variable one at a time with all other elements of the potential solution being fixed. The numerical values in these plots can be readily extracted for the purpose of automated and systematic unit-testing of optimization routines.

This package provides a unified object-oriented framework for numerical optimizers in R. Allows for both minimization and maximization with any optimizer, optimization over more than one function argument, measuring of computation time, setting a time limit for long optimization tasks.

This package provides tools to process raster data and apply Otsu-based thresholding for burned area mapping and other image segmentation tasks. Implements the method described by Otsu (1979) <doi:10.1109/TSMC.1979.4310076>, a data-driven technique that determines an optimal threshold by maximizing the inter-class variance of pixel intensities. It includes validation functions to assess segmentation accuracy against reference data using standard accuracy metrics such as precision, recall, and F1-score.

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.

Represents the basis functions for B-splines in a simple matrix formulation that facilitates, taking integrals, derivatives, and making orthogonal the basis functions.

Collects a list of your third party R packages, and scans them with the OSS Index provided by Sonatype', reporting back on any vulnerabilities that are found in the third party packages you use.

Match, download, convert and import Open Street Map data extracts obtained from several providers.

Estimate the positron emission tomography (PET) neuroreceptor occupancies from the total volumes of distribution of a set of regions of interest. Fitting methods include the simple reference region', ordinary least squares (sometimes known as occupancy plot), and restricted maximum likelihood estimation'.

An algorithm for fitting interpretable additive neural networks for identifiable and visualizable feature effects using post hoc orthogonalization. Fit custom neural networks intuitively using established R formula notation, including interaction effects of arbitrary order while preserving identifiability to enable a functional decomposition of the prediction function. For more details see Koehler et al. (2025) <doi:10.1038/s44387-025-00033-7>.

This package provides tools for multivariate outlier detection based on geometric properties of multivariate data using random directional projections. Observation-level outlier scores are computed by jointly probing radial magnitude and angular alignment through repeated projections onto random directions, with optional robust centering and covariance adjustment. In addition to global outlier scoring, the method produces dimension-level contribution measures to support interpretation of detected anomalies. Visualization utilities are included to summarize directional contributions for extreme observations.

Different measures which can be used to quantify similarities between regions. These measures are isonymy, isonymy between, Lasker distance, coefficients of Hedrick and Nei. In addition, it calculates biodiversity indices such as Margalef, Menhinick, Simpson, Shannon, Shannon-Wiener, Sheldon, Heip, Hill Numbers, Geometric Mean and Cressie and Read statistics.

Machine learning estimator specifically optimized for predictive modeling of ordered non-numeric outcomes. ocf provides forest-based estimation of the conditional choice probabilities and the covariatesâ marginal effects. Under an "honesty" condition, the estimates are consistent and asymptotically normal and standard errors can be obtained by leveraging the weight-based representation of the random forest predictions. Please reference the use as Di Francesco (2025) <doi:10.1080/07474938.2024.2429596>.

We introduce an R function one_two_sample() which can deal with one and two (normal) samples, Ying-Ying Zhang, Yi Wei (2012) <doi:10.2991/asshm-13.2013.29>. For one normal sample x, the function reports descriptive statistics, plot, interval estimation and test of hypothesis of x. For two normal samples x and y, the function reports descriptive statistics, plot, interval estimation and test of hypothesis of x and y, respectively. It also reports interval estimation and test of hypothesis of mu1-mu2 (the difference of the means of x and y) and sigma1^2 / sigma2^2 (the ratio of the variances of x and y), tests whether x and y are from the same population, finds the correlation coefficient of x and y if x and y have the same length.

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".