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 a general framework for the application of cross-validation schemes to particular functions. By allowing arbitrary lists of results, origami accommodates a range of cross-validation applications. This implementation was first described by Coyle and Hejazi (2018) <doi:10.21105/joss.00512>.

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.

Defines thresholds for breaking data into a number of discrete levels, minimizing the (mean) squared error within all bins.

This package implements the orthogonal reparameterization approach recommended by Lancaster (2002) to estimate dynamic panel models with fixed effects (and optionally: panel specific intercepts). The approach uses a likelihood-based estimator and produces estimates that are asymptotically unbiased as N goes to infinity, with a T as low as 2.

Estimates win ratio or Mann-Whitney parameter for two group comparisons using ordered composite endpoints with right censoring as described in Follmann, Fay, Hamasaki, and Evans (2020)<doi:10.1002/sim.7890>.

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>.

We provide two algorithms for monitoring change points with online matrix-valued time series, under the assumption of a two-way factor structure. The algorithms are based on different calculations of the second moment matrices. One is based on stacking the columns of matrix observations, while another is by a more delicate projected approach. A well-known fact is that, in the presence of a change point, a factor model can be rewritten as a model with a larger number of common factors. In turn, this entails that, in the presence of a change point, the number of spiked eigenvalues in the second moment matrix of the data increases. Based on this, we propose two families of procedures - one based on the fluctuations of partial sums, and one based on extreme value theory - to monitor whether the first non-spiked eigenvalue diverges after a point in time in the monitoring horizon, thereby indicating the presence of a change point. This package also provides some simple functions for detecting and removing outliers, imputing missing entries and testing moments. See more details in He et al. (2021)<doi:10.48550/arXiv.2112.13479>.

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.

R bindings to odiff', a blazing-fast pixel-by-pixel image comparison tool <https://github.com/dmtrKovalenko/odiff>. Supports PNG, JPEG, WEBP, and TIFF with configurable thresholds, antialiasing detection, and region ignoring. Requires system installation of odiff'. Ideal for visual regression testing in automated workflows.

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.

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>.

Microarray probe ID is not convenient for further enrichment analysis and target gene selection. The package is created for the rice microarray probe ID conversion. This package can convert microarray probe ID from GPL6864 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL6864>, GPL8852 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL8852>, and GPL2025 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> platforms to RAP-DB ID. RAP-DB "The Rice Annotation Project Database" <https://rapdb.dna.affrc.go.jp> is a well-known database for rice Oryza sativa, and the gene ID in this database is widely used in many areas related to rice research. For multiple probes representing a single gene, This package can merge them by taking the mean, max, or min value of these probes. Or we can keep multiple probes by appending sequence numbers to duplicate the RAP-DB ID.

Implementation of a likelihood ratio test of differential onset of senescence between two groups. Given two groups with measures of age and of an individual trait likely to be subjected to senescence (e.g. body mass), OnAge provides an asymptotic p-value for the null hypothesis that senescence starts at the same age in both groups. The package implements the procedure used in Douhard et al. (2017) <doi:10.1111/oik.04421>.

Solver for linear, quadratic, and rational programs with linear, quadratic, and rational constraints. A unified interface to different R packages is provided. Optimization problems are transformed into equivalent formulations and solved by the respective package. For example, quadratic programming problems with linear, quadratic and rational constraints can be solved by augmented Lagrangian minimization using package alabama', or by sequential quadratic programming using solver slsqp'. Alternatively, they can be reformulated as optimization problems with second order cone constraints and solved with package cccp'.

This package creates mock data for testing and package development for the Observational Medical Outcomes Partnership common data model. The package offers functions crafted with pipeline-friendly implementation, enabling users to effortlessly include only the necessary tables for their testing needs.

This package implements the algorithm in Chen, Wang and Samworth (2020) <arxiv:2003.03668> for online detection of sudden mean changes in a sequence of high-dimensional observations. It also implements methods by Mei (2010) <doi:10.1093/biomet/asq010>, Xie and Siegmund (2013) <doi:10.1214/13-AOS1094> and Chan (2017) <doi:10.1214/17-AOS1546>.

Help and demo in Spanish of the orloca package. Ayuda y demo en espanol del paquete orloca. Objetos y metodos para manejar y resolver el problema de localizacion de suma minima, tambien conocido como problema de Fermat-Weber. El problema de localizacion de suma minima busca un punto tal que la suma ponderada de las distancias a los puntos de demanda se minimice. Vease "The Fermat-Weber location problem revisited" por Brimberg, Mathematical Programming, 1, pag. 71-76, 1995. <DOI: 10.1007/BF01592245>. Se usan algoritmos generales de optimizacion global para resolver el problema, junto con el metodo especifico Weiszfeld, vease "Sur le point pour lequel la Somme des distance de n points donnes est minimum", por Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pag. 355-386, 1937 o "On the point for which the sum of the distances to n given points is minimum", por E. Weiszfeld y F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. <DOI:10.1007/s10479-008-0352-z>.

This package provides functions to perform subspace clustering and classification.

Estimates out-of-sample R² through bootstrap or cross-validation as a measure of predictive performance. In addition, a standard error for this point estimate is provided, and confidence intervals are constructed.

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.

Simultaneously evaluate multiple ordinal outcome measures. Applied data analysts in particular are faced with uncertainty in choosing appropriate statistical tests for ordinal data. The included shiny application allows users to simulate outcomes given different ordinal data distributions.

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.

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.