The identity provider ['OneLogin']<http://onelogin.com> is used for authentication via Single Sign On (SSO). This package provides an R interface to their API.

This package provides a generalised data structure for fast and efficient loading and data munching of sparse omics data. The OmicFlow requires an up-front validated metadata template from the user, which serves as a guide to connect all the pieces together by aligning them into a single object that is defined as an omics class. Once this unified structure is established, users can perform manual subsetting, visualisation, and statistical analysis, or leverage the automated autoFlow method to generate a comprehensive report.

Detect the number and locations of change points. The locations can be either exact or in terms of ranges, depending on the available computational resource. The method is based on Jie Ding, Yu Xiang, Lu Shen, Vahid Tarokh (2017) <doi:10.1109/TSP.2017.2711558>.

This package provides functions for optimal policy learning in socioeconomic applications helping users to learn the most effective policies based on data in order to maximize empirical welfare. Specifically, OPL allows to find "treatment assignment rules" that maximize the overall welfare, defined as the sum of the policy effects estimated over all the policy beneficiaries. Documentation about OPL is provided by several international articles via Athey et al (2021, <doi:10.3982/ECTA15732>), Kitagawa et al (2018, <doi:10.3982/ECTA13288>), Cerulli (2022, <doi:10.1080/13504851.2022.2032577>), the paper by Cerulli (2021, <doi:10.1080/13504851.2020.1820939>) and the book by Gareth et al (2013, <doi:10.1007/978-1-4614-7138-7>).

This package provides a wrapper for the Onboard Data building data API <https://api.onboarddata.io/swagger>. Along with streamlining access to the API, this package simplifies access to sensor time series data, metadata (sensors, equipment, and buildings), and details about the Onboard data model/ontology.

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.

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>.

Convenient download functions enabling access Open Source Asset Pricing (OpenAP) data. This package enables users to download predictor portfolio returns (over 200 cross-sectional predictors with multiple portfolio construction methods) and firm characteristics (over 200 characteristics replicated from the academic asset pricing literature). Center for Research in Security Prices (CRSP)-based variables such as Price, Size, and Short-term Reversal can be downloaded with a Wharton Research Data Services (WRDS, <https://wrds-www.wharton.upenn.edu/>) subscription. For a full list of what is available, see <https://www.openassetpricing.com/>.

Model mixed integer linear programs in an algebraic way directly in R. The model is solver-independent and thus offers the possibility to solve a model with different solvers. It currently only supports linear constraints and objective functions. See the ompr website <https://dirkschumacher.github.io/ompr/> for more information, documentation and examples.

Create regression tables for publication. Currently supports lm', glm', survreg', and ivreg outputs.

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>.

For the problem of indirect treatment comparison with limited subject-level data, this package provides tools for model-based standardisation with several different computation approaches. See Remiroâ Azócar A, Heath A, Baio G (2022) "Parametric Gâ computation for compatible indirect treatment comparisons with limited individual patient data", Res. Synth. Methods, 1â 31. ISSN 1759-2879, <doi:10.1002/jrsm.1565>.

The online principal component method can process the online data set. The philosophy of the package is described in Guo G. (2018) <doi:10.1080/10485252.2018.1531130>.

Density, distribution function, quantile function and random generation for the Odd Log-Logistic Generalized Gamma proposed in Prataviera, F. et al (2017) <doi:10.1080/00949655.2016.1238088>.

Computes optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained.

O-statistics, or overlap statistics, measure the degree of community-level trait overlap. They are estimated by fitting nonparametric kernel density functions to each speciesâ trait distribution and calculating their areas of overlap. For instance, the median pairwise overlap for a community is calculated by first determining the overlap of each species pair in trait space, and then taking the median overlap of each species pair in a community. This median overlap value is called the O-statistic (O for overlap). The Ostats() function calculates separate univariate overlap statistics for each trait, while the Ostats_multivariate() function calculates a single multivariate overlap statistic for all traits. O-statistics can be evaluated against null models to obtain standardized effect sizes. Ostats is part of the collaborative Macrosystems Biodiversity Project "Local- to continental-scale drivers of biodiversity across the National Ecological Observatory Network (NEON)." For more information on this project, see the Macrosystems Biodiversity Website (<https://neon-biodiversity.github.io/>). Calculation of O-statistics is described in Read et al. (2018) <doi:10.1111/ecog.03641>, and a teaching module for introducing the underlying biological concepts at an undergraduate level is described in Grady et al. (2018) <http://tiee.esa.org/vol/v14/issues/figure_sets/grady/abstract.html>.

Assessment and diagnostics for comparing competing clustering solutions, using predictive models. The main intended use is for comparing clustering/classification solutions of ecological data (e.g. presence/absence, counts, ordinal scores) to 1) find an optimal partitioning solution, 2) identify characteristic species and 3) refine a classification by merging clusters that increase predictive performance. However, in a more general sense, this package can do the above for any set of clustering solutions for i observations of j variables.

An interface to the search API of HAL <https://hal.science/>, the French open archive for scholarly documents from all academic fields. This package provides programmatic access to the API <https://api.archives-ouvertes.fr/docs> and allows to search for records and download documents.

Algorithms for D-, A-, I-, and c-optimal designs. For more details, see the package description. Some of the functions in this package require the gurobi software and its accompanying R package. For their installation, please follow the instructions at <https://www.gurobi.com> and the file gurobi_inst.txt, respectively.

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

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 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.

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 provides functions for implementing different versions of the OSCV method in the kernel regression and density estimation frameworks. The package mainly supports the following articles: (1) Savchuk, O.Y., Hart, J.D. (2017). Fully robust one-sided cross-validation for regression functions. Computational Statistics, <doi:10.1007/s00180-017-0713-7> and (2) Savchuk, O.Y. (2017). One-sided cross-validation for nonsmooth density functions, <arXiv:1703.05157>.