Supplemental functions and data for OpenIntro resources, which includes open-source textbooks and resources for introductory statistics (<https://www.openintro.org/>). The package contains datasets used in our open-source textbooks along with custom plotting functions for reproducing book figures. Note that many functions and examples include color transparency; some plotting elements may not show up properly (or at all) when run in some versions of Windows operating system.

This package provides a collection of functions to facilitate analysis of proteomic data from Olink, primarily NPX data that has been exported from Olink Software. The functions also work on QUANT data from Olink by log- transforming the QUANT data. The functions are focused on reading data, facilitating data wrangling and quality control analysis, performing statistical analysis and generating figures to visualize the results of the statistical analysis. The goal of this package is to help users extract biological insights from proteomic data run on the Olink platform.

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 a novel method to implement cancer subtyping and subtype specific drug targets identification via non-negative matrix tri-factorization. To improve the interpretability, we introduce orthogonal constraint to the row coefficient matrix and column coefficient matrix. To meet the prior knowledge that each subtype should be strongly associated with few gene sets, we introduce sparsity constraint to the association sub-matrix. The average residue was introduced to evaluate the row and column cluster numbers. This is part of the work "Liver Cancer Analysis via Orthogonal Sparse Non-Negative Matrix Tri- Factorization" which will be submitted to BBRC.

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.

An R autograding extension for Otter-Grader (<https://otter-grader.readthedocs.io>). It supports grading R scripts, R Markdown documents, and R Jupyter Notebooks.

Quantifies hypothesis to data fit for repeated measures and longitudinal data, as described by Thorngate (1987) <doi:10.1016/S0166-4115(08)60083-7> and Grice et al., (2015) <doi:10.1177/2158244015604192>. Hypothesis and data are encoded as pairwise relative orderings which are then compared to determine the percentage of orderings in the data that are matched by the hypothesis.

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

Algorithms for ordinal causal discovery. This package aims to enable users to discover causality for observational ordinal categorical data with greedy and exhaustive search. See Ni, Y., & Mallick, B. (2022) <https://proceedings.mlr.press/v180/ni22a/ni22a.pdf> "Ordinal Causal Discovery. Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, (UAI 2022), PMLR 180:1530â 1540".

This package provides tools for the analysis of land use and cover (LUC) time series. It includes support for loading spatiotemporal raster data and synthesized spatial plotting. Several LUC change (LUCC) metrics in regular or irregular time intervals can be extracted and visualized through one- and multistep sankey and chord diagrams. A complete intensity analysis according to Aldwaik and Pontius (2012) <doi:10.1016/j.landurbplan.2012.02.010> is implemented, including tools for the generation of standardized multilevel output graphics.

This is a tool to find the optimal rerandomization threshold in non-sequential experiments. We offer three procedures based on assumptions made on the residuals distribution: (1) normality assumed (2) excess kurtosis assumed (3) entire distribution assumed. Illustrations are included. Also included is a routine to unbiasedly estimate Frobenius norms of variance-covariance matrices. Details of the method can be found in "Optimal Rerandomization via a Criterion that Provides Insurance Against Failed Experiments" Adam Kapelner, Abba M. Krieger, Michael Sklar and David Azriel (2020) <arXiv:1905.03337>.

This package provides a wrapper for optim for nonlinear regression problems; see Nocedal J and Write S (2006, ISBN: 978-0387-30303-1). Performs ordinary least squares (OLS), iterative re-weighted least squares (IRWLS), and maximum likelihood (MLE). Also includes the robust outlier detection (ROUT) algorithm; see Motulsky, H and Brown, R (2006) <doi:10.1186/1471-2105-7-123>.

This package provides simple crosstab output with optional statistics (e.g., Goodman-Kruskal Gamma, Somers d, and Kendall's tau-b) as well as two-way and one-way tables. The package is used within the statistics component of the Masters of Science (MSc) in Social Science of the Internet at the Oxford Internet Institute (OII), University of Oxford, but the functions should be useful for general data analysis and especially for analysis of categorical and ordinal data.

This package provides an interface to connect R with the <https://github.com/IDEMSInternational/open-app-builder> OpenAppBuilder platform, enabling users to retrieve and work with user and notification data for analysis and processing. It is designed for developers and analysts to seamlessly integrate data from OpenAppBuilder into R workflows via a Postgres database connection, allowing direct querying and import of app data into R.

Determine the sea area where the fishing boat operates. The latitude and longitude of geographic coordinates are used to match oceanic areas and economic sea areas. You can plot the distribution map with dotplot() function. Please refer to Flanders Marine Institute (2020) <doi:10.14284/403>.

An implementation of optimal weight exchange algorithm Yang(2013) <doi:10.1080/01621459.2013.806268> for three models. They are Crossover model with subject dropout, crossover model with proportional first order residual effects and interference model. You can use it to find either A-opt or D-opt approximate designs. Exact designs can be automatically rounded from approximate designs and relative efficiency is provided as well.

In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators.

Raman and (FT)IR spectral analysis tool for plastic particles and other environmental samples (Cowger et al. 2021, <doi:10.1021/acs.analchem.1c00123>). With read_any(), Open Specy provides a single function for reading individual, batch, or map spectral data files like .asp, .csv, .jdx, .spc, .spa, .0, and .zip. process_spec() simplifies processing spectra, including smoothing, baseline correction, range restriction and flattening, intensity conversions, wavenumber alignment, and min-max normalization. Spectra can be identified in batch using an onboard reference library (Cowger et al. 2020, <doi:10.1177/0003702820929064>) using match_spec(). A Shiny app is available via run_app() or online at <https://www.openanalysis.org/openspecy/>.

Quickly create numeric matrices for machine learning algorithms that require them. It converts factor columns into onehot vectors.

Representations, conversions and display of orientation SO(3) data. See the orientlib help topic for details.

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>.

Fits ordinal regression models with elastic net penalty. Supported model families include cumulative probability, stopping ratio, continuation ratio, and adjacent category. These families are a subset of vector glm's which belong to a model class we call the elementwise link multinomial-ordinal (ELMO) class. Each family in this class links a vector of covariates to a vector of class probabilities. Each of these families has a parallel form, which is appropriate for ordinal response data, as well as a nonparallel form that is appropriate for an unordered categorical response, or as a more flexible model for ordinal data. The parallel model has a single set of coefficients, whereas the nonparallel model has a set of coefficients for each response category except the baseline category. It is also possible to fit a model with both parallel and nonparallel terms, which we call the semi-parallel model. The semi-parallel model has the flexibility of the nonparallel model, but the elastic net penalty shrinks it toward the parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2021) <doi:10.18637/jss.v099.i06>.

This package provides a set of tools that enables using OxCal from within R. OxCal (<https://c14.arch.ox.ac.uk/oxcal.html>) is a standard archaeological tool intended to provide 14C calibration and analysis of archaeological and environmental chronological information. OxcAAR allows simple calibration with Oxcal and plotting of the results as well as the execution of sophisticated ('OxCal') code and the import of the results of bulk analysis and complex Bayesian sequential calibration.

Computes A-, MV-, D- and E-optimal or near-optimal row-column designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all pairwise treatment contrasts. The algorithms used in this package are based on the array exchange and treatment exchange algorithms adopted from Debusho, Gemechu and Haines (2018) <doi:10.1080/03610918.2018.1429617> algorithms after adjusting for the row-column designs setup. The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly.