An implementation of the Harris Corner Detection as described in the paper "An Analysis and Implementation of the Harris Corner Detector" by Sánchez J. et al (2018) available at <doi:10.5201/ipol.2018.229>. The package allows to detect relevant points in images which are characteristic to the digital image.

This package implements some item response models for multiple ratings, including the hierarchical rater model, conditional maximum likelihood estimation of linear logistic partial credit model and a wrapper function to the commercial FACETS program. See Robitzsch and Steinfeld (2018) for a description of the functionality of the package. See Wang, Su and Qiu (2014; <doi:10.1111/jedm.12045>) for an overview of modeling alternatives.

Convert between Irish grid references and Irish Grid coordinates. Irish grid references can also be converted to or from an sf object in any coordinate reference system. Precisions from 1 m to 100 km including 2 km (tetrads) are supported, as are datasets with mixed precision. Conversion to sf polygons is precision-aware.

This package provides a collection of statistical tests for genetic association studies and summary data based Mendelian randomization.

Most existing approaches for network reconstruction can only infer an overall network and, also, fail to capture a complete set of network properties. To address these issues, a new model has been developed, which converts static data into their dynamic form. idopNetwork is an R interface to this model, it can inferring informative, dynamic, omnidirectional and personalized networks. For more information on functional clustering part, see Kim et al. (2008) <doi:10.1534/genetics.108.093690>, Wang et al. (2011) <doi:10.1093/bib/bbr032>. For more information on our model, see Chen et al. (2019) <doi:10.1038/s41540-019-0116-1>, and Cao et al. (2022) <doi:10.1080/19490976.2022.2106103>.

This package provides a suite of convenient tools for social network analysis geared toward students, entry-level users, and non-expert practitioners. â ideanetâ features unique functions for the processing and measurement of sociocentric and egocentric network data. These functions automatically generate node- and system-level measures commonly used in the analysis of these types of networks. Outputs from these functions maximize the ability of novice users to employ network measurements in further analyses while making all users less prone to common data analytic errors. Additionally, â ideanetâ features an R Shiny graphic user interface that allows novices to explore network data with minimal need for coding.

This package provides a collection of functions for creating color schemes. Used to support packages and scripts written by researchers at the United States Geological Survey (USGS) Idaho National Laboratory Project Office.

This package provides functions to to compute a continuum of information-based measures for quantifying the temporal stability of populations, communities, and ecosystems, as well as their associated synchrony, based on species (or species assemblage) biomass or other key variables. When biodiversity data are available, the package also enables the assessment of the corresponding diversityâ stability relationships. All measures are applicable in both temporal and spatial contexts. The theoretical and methodological background is detailed in Chao et al. (2025) <doi:10.1101/2025.08.20.671203>.

Offers modeling the association between gene-expression and bioassay data, taking care of the effect due to a fingerprint feature and helps with several plots to better understand the analysis.

This package provides functions to conduct a model-agnostic asymptotic hypothesis test for the identification of interaction effects in black-box machine learning models. The null hypothesis assumes that a given set of covariates does not contribute to interaction effects in the prediction model. The test statistic is based on the difference of variances of partial dependence functions (Friedman (2008) <doi:10.1214/07-AOAS148> and Welchowski (2022) <doi:10.1007/s13253-021-00479-7>) with respect to the original black-box predictions and the predictions under the null hypothesis. The hypothesis test can be applied to any black-box prediction model, and the null hypothesis of the test can be flexibly specified according to the research question of interest. Furthermore, the test is computationally fast to apply as the null distribution does not require resampling or refitting black-box prediction models.

Multiple Imputation for Informative Censoring. This package implements two methods. Gamma Imputation described in <DOI:10.1002/sim.6274> and Risk Score Imputation described in <DOI:10.1002/sim.3480>.

Infix functions in R are those that comes between its arguments such as %in%, +, and *. These are useful in R programming when manipulating data, performing logical operations, and making new functions. infixit extends the infix functions found in R to simplify frequent tasks, such as finding elements that are NOT in a set, in-line text concatenation, augmented assignment operations, additional logical and control flow operators, and identifying if a number or date lies between two others.

An imprecise inference presented in the study of Walley (1996) <doi:10.1111/j.2517-6161.1996.tb02065.x> is one of the statistical reasoning methods when prior information is unavailable. Functions and utils needed for illustrating this inferential paradigm are implemented for classroom teaching and further comprehensive research. Two imprecise models are demonstrated using multinomial data and 2x2 contingency table data. The concepts of prior ignorance and imprecision are discussed in lower and upper probabilities. Representation invariance principle, hypothesis testing, decision-making, and further generalization are also illustrated.

Simple handling of survey data. Smart handling of meta-information like e.g. variable-labels value-labels and scale-levels. Easy access and validation of meta-information. Useage of value labels and values respectively for subsetting and recoding data.

Improve optical character recognition by binarizing images. The package focuses primarily on local adaptive thresholding algorithms. In English, this means that it has the ability to turn a color or gray scale image into a black and white image. This is particularly useful as a preprocessing step for optical character recognition or handwritten text recognition.

This package implements the integrative conditional autoregressive horseshoe model discussed in Jendoubi, T., Ebbels, T.M. Integrative analysis of time course metabolic data and biomarker discovery. BMC Bioinformatics 21, 11 (2020) <doi:10.1186/s12859-019-3333-0>. The model consists in three levels: Metabolic pathways level modeling interdependencies between variables via a conditional auto-regressive (CAR) component, integrative analysis level to identify potential associations between heterogeneous omic variables via a Horseshoe prior and experimental design level to capture experimental design conditions through a mixed-effects model. The package also provides functions to simulate data from the model, construct pathway matrices, post process and plot model parameters.

This package performs hypothesis testing using the interval estimates (e.g., confidence intervals). The non-overlapping interval estimates indicates the statistical significance. References to these procedures can be found at Noguchi and Marmolejo-Ramos (2016) <doi:10.1080/00031305.2016.1200487>, Bonett and Seier (2003) <doi:10.1198/0003130032323>, and Lemm (2006) <doi:10.1300/J082v51n02_05>.

Generate interactive volcano plots for exploring gene expression data. Built with ggplot2', the plots are rendered interactive using ggiraph', enabling users to hover over points to display detailed information or click to trigger custom actions.

This package provides user-friendly and configurable print debugging via a single function, ic(). Wrap an expression in ic() to print the expression, its value and (where available) its source location. Debugging output can be toggled globally without modifying code.

Collection of functions for IO Psychologists.

Calculation of informative simultaneous confidence intervals for graphical described multiple test procedures and given information weights. Bretz et al. (2009) <doi:10.1002/sim.3495> and Brannath et al. (2024) <doi:10.48550/arXiv.2402.13719>. Furthermore, exploration of the behavior of the informative bounds in dependence of the information weights. Comparisons with compatible bounds are possible. Strassburger and Bretz (2008) <doi:10.1002/sim.3338>.

This package provides functions to help with analysis of longitudinal data featuring irregular observation times, where the observation times may be associated with the outcome process. There are functions to quantify the degree of irregularity, fit inverse-intensity weighted Generalized Estimating Equations (Lin H, Scharfstein DO, Rosenheck RA (2004) <doi:10.1111/j.1467-9868.2004.b5543.x>), perform multiple outputation (Pullenayegum EM (2016) <doi:10.1002/sim.6829>) and fit semi-parametric joint models (Liang Y (2009) <doi: 10.1111/j.1541-0420.2008.01104.x>).

An R implementation of Matthew Thomas's Python library inteq'. First, this solves Fredholm integral equations of the first kind ($f(s) = \int_a^b K(s, y) g(y) dy$) using methods described by Twomey (1963) <doi:10.1145/321150.321157>. Second, this solves Volterra integral equations of the first kind ($f(s) = \int_0^s K(s,y) g(t) dt$) using methods from Betto and Thomas (2021) <doi:10.48550/arXiv.2106.08496>. Third, this solves Voltera integral equations of the second kind ($g(s) = f(s) + \int_a^s K(s,y) g(y) dy$) using methods from Linz (1969) <doi:10.1137/0706034>.

This package implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist.