Aims to support all features of the system credential store, including non-portable ones. Supports Keychain on macOS', and Credential Manager on Windows'. See the keyring package if you need a portable API'.

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.

Automatically adding pkg:: to a function, i.e. mutate() becomes dplyr::mutate(). It is up to the user to determine which packages should be used explicitly, whether to include base R packages or use the functionality on selected text, a file, or a complete directory. User friendly logging is provided in the RStudio Markers pane. Lives in the spirit of lintr and styler'. Can also be used for checking which packages are actually used in a project.

This package provides a client that grants access to the power of the ohsome API from R. It lets you analyze the rich data source of the OpenStreetMap (OSM) history. You can retrieve the geometry of OSM data at specific points in time, and you can get aggregated statistics on the evolution of OSM elements and specify your own temporal, spatial and/or thematic filters.

The classical and extended occupancy distributions occur in cases where balls are randomly allocated to bins. The PDF, CDF, quantile functions, generation of random variates, and calculating the first four central moments of the distributions are implemented as described in Oâ Neill (2019) <doi:10.1080/00031305.2019.1699445>.

Algorithm of online regularized k-means to deal with online multi(single) view data. The philosophy of the package is described in Guo G. (2024) <doi:10.1016/j.ins.2024.121133>.

An assortment of helper functions for managing data (e.g., rotating values in matrices by a user-defined angle, switching from row- to column-indexing), dates (e.g., intuiting year from messy date strings), handling missing values (e.g., removing elements/rows across multiple vectors or matrices if any have an NA), text (e.g., flushing reports to the console in real-time); and combining data frames with different schema (copying, filling, or concatenating columns or applying functions before combining).

Facilitates the creation of intuitive figures to describe metabolomics data by utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) hierarchy data, and gathers functional orthology and gene data from the KEGG-REST API.

This package provides a collection of functions to construct sets of orthogonal polynomials and their recurrence relations. Additional functions are provided to calculate the derivative, integral, value and roots of lists of polynomial objects.

This package provides methods for determining optimum plot size and shape in field experiments using Fairfield-Smith's variance law approach. It will evaluate field variability, determine optimum plot size and shape and study fertility trends across the field.

We consider the problem of estimating two isotonic regression curves g1* and g2* under the constraint that they are ordered, i.e. g1* <= g2*. Given two sets of n data points y_1, ..., y_n and z_1, ..., z_n that are observed at (the same) deterministic design points x_1, ..., x_n, the estimates are obtained by minimizing the Least Squares criterion L(a, b) = sum_i=1^n (y_i - a_i)^2 w1(x_i) + sum_i=1^n (z_i - b_i)^2 w2(x_i) over the class of pairs of vectors (a, b) such that a and b are isotonic and a_i <= b_i for all i = 1, ..., n. We offer two different approaches to compute the estimates: a projected subgradient algorithm where the projection is calculated using a PAVA as well as Dykstra's cyclical projection algorithm.

DNA methylation is an important epigenetic process that regulates gene activity through chemical modifications of DNA without changing its sequence. OpEnCAST is a plant-specific ensemble-based prediction package that identifies 4mC, 5mC and 6mA methylation sites directly from DNA sequences. It combines multiple machine learning algorithms trained on monocot (Oryza sp.) and dicot (Arabidopsis sp.) reference models to deliver accurate predictions. This methodology is being inspired by the ensemble algorithm for methylation prediction developed by Wang et al. (2022) <doi:10.1186/s12859-022-04756-1>.

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.

Aids practitioners to optimally design experiments that measure the slope divided by the intercept and provides confidence intervals for the ratio.

The restricted optimal design method is implemented to optimally allocate a set of items that require calibration to a group of examinees. The optimization process is based on the method described in detail by Ul Hassan and Miller in their works published in (2019) <doi:10.1177/0146621618824854> and (2021) <doi:10.1016/j.csda.2021.107177>. To use the method, preliminary item characteristics must be provided as input. These characteristics can either be expert guesses or based on previous calibration with a small number of examinees. The item characteristics should be described in the form of parameters for an Item Response Theory (IRT) model. These models can include the Rasch model, the 2-parameter logistic model, the 3-parameter logistic model, or a mixture of these models. The output consists of a set of rules for each item that determine which examinees should be assigned to each item. The efficiency or gain achieved through the optimal design is quantified by comparing it to a random allocation. This comparison allows for an assessment of how much improvement or advantage is gained by using the optimal design approach. This work was supported by the Swedish Research Council (Vetenskapsrådet) Grant 2019-02706.

Flexible optimizer with numerous input specifications for detailed parameterisation. Designed for complex loss functions with state and parameter space constraints. Visualization tools for validation and analysis of the convergence are included.

Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of RcppArmadillo to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the findHOGFeatures function of the SimpleCV computer vision platform, the average_hash(), dhash() and phash() functions are based on the ImageHash python library. The Gabor Feature Extraction functions are based on Matlab code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, <doi:10.1016/j.eswa.2015.06.025>. The SLIC and SLICO superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, <doi:10.1109/TPAMI.2012.120> and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.

Facilitates the automatic detection of acoustic signals, providing functions to diagnose and optimize the performance of detection routines. Detections from other software can also be explored and optimized. This package has been peer-reviewed by rOpenSci. Araya-Salas et al. (2022) <doi:10.1101/2022.12.13.520253>.

This package provides an interface to OpenCL, allowing R to leverage computing power of GPUs and other HPC accelerator devices.

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>.

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 tools to analyse, interpret and understand air pollution data. Data are typically regular time series and air quality measurement, meteorological data and dispersion model output can be analysed. The package is described in Carslaw and Ropkins (2012, <doi:10.1016/j.envsoft.2011.09.008>) and subsequent papers.

It implements functions for simulation and estimation of the ordinal latent block model (OLBM), as described in Corneli, Bouveyron and Latouche (2019).

This package provides a user-friendly R-based software package for gene clustering. Clusters are given by genes matched to prespecified profiles across various ordered treatment groups. It is particularly useful for analyzing data obtained from short time-course or dose-response microarray experiments.