Versatile tools and data for graph matching analysis with various forms of prior information that supports working with igraph objects, matrix objects, or lists of either.

Suite of functions to study animal incubation. At the core of incR lies an algorithm that allows for the scoring of incubation behaviour. Additionally, several functions extract biologically relevant metrics of incubation such as off-bout number and off-bout duration - for a review of avian incubation studies, see Nests, Eggs, and Incubation: New ideas about avian reproduction (2015) edited by D. Charles Deeming and S. James Reynolds <doi:10.1093/acprof:oso/9780198718666.001.0001>.

Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data.

This package provides a set of functions for the modeling of data derived from the Minidisc Infiltrometer device. It calculates cumulative infiltration and square root of time. Also, it calculates the A parameter based on soil physical properties.

This package implements a wide range of metrics for measuring glucose control and glucose variability based on continuous glucose monitoring data. The list of implemented metrics is summarized in Rodbard (2009) <doi:10.1089/dia.2009.0015>. Additional visualization tools include time-series plots, lasagna plots and ambulatory glucose profile report.

We consider the non-parametric maximum likelihood estimation of the underlying distribution function, assuming log-concavity, based on mixed-case interval-censored data. The algorithm implemented is base on Chi Wing Chu, Hok Kan Ling and Chaoyu Yuan (2024, <doi:10.48550/arXiv.2411.19878>).

This package provides a user-friendly toolbox for doing the statistical analysis of interval-valued responses in questionnaires measuring intrinsically imprecise human attributes or features (attitudes, perceptions, opinions, feelings, etc.). In particular, this package provides S4 classes, methods, and functions in order to compute basic arithmetic and statistical operations with interval-valued data; prepare customized plots; associate each interval-valued response to its equivalent Likert-type and visual analogue scales answers through the minimum theta-distance and the mid-point criteria; analyze the reliability of respondents answers from the internal consistency point of view by means of Cronbach's alpha coefficient; and simulate interval-valued responses in this type of questionnaires. The package also incorporates some real-life data that can be used to illustrate its working with several non-trivial reproducible examples. The methodology used in this package is based in many theoretical and applied publications from SMIRE+CoDiRE (Statistical Methods with Imprecise Random Elements and Comparison of Distributions of Random Elements) Research Group (<https://bellman.ciencias.uniovi.es/smire+codire/>) from the University of Oviedo (Spain).

This package provides a pair of functions for getting and setting the IEEE rounding mode for floating point computations.

This package provides functions for computing the global and local Gaussian density estimates based on the ICV bandwidth. See the article of Savchuk, O.Y., Hart, J.D., Sheather, S.J. (2010). Indirect cross-validation for density estimation. Journal of the American Statistical Association, 105(489), 415-423 <doi:10.1198/jasa.2010.tm08532>.

Training datasets for iC10; which implements the classifier described in the paper Genome-driven integrated classification of breast cancer validated in over 7,500 samples (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group. Genomic annotation for the training dataset has been obtained from Mark Dunning's lluminaHumanv3.db package.

Multivariate Expectation-Maximization (EM) based imputation framework that offers several different algorithms. These include regularisation methods like Lasso and Ridge regression, tree-based models and dimensionality reduction methods like PCA and PLS.

This package provides composable invertible transforms for (sparse) matrices.

The ISA is a biclustering algorithm that finds modules in an input matrix. A module or bicluster is a block of the reordered input matrix.

This package provides advanced functions for image processing based on the package imager'.

Given two unbiased samples of patient level data on cost and effectiveness for a pair of treatments, make head-to-head treatment comparisons by (i) generating the bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with specified confidence fraction within [0.50, 0.99] that is equivariant with respect to changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge with economic preferences from an ICE map with specified values of lambda, beta and gamma parameters, (iv) display VAGR and ALICE acceptability curves, and (v) illustrate variation in ICE preferences by displaying potentially non-linear indifference(iso-preference) curves from an ICE map with specified values of lambda, beta and either gamma or eta parameters.

Classical Ising Model is a land mark system in statistical physics.The model explains the physics of spin glasses and magnetic materials, and cooperative phenomenon in general, for example phase transitions and neural networks.This package provides utilities to simulate one dimensional Ising Model with Metropolis and Glauber Monte Carlo with single flip dynamics in periodic boundary conditions. Utility functions for exact solutions are provided. Such as transfer matrix for 1D. Utility functions for exact solutions are provided. Example use cases are as follows: Measuring effective ergodicity and power-laws in so called functional-diffusion.

Generates random numbers corresponding to the events on a Poisson point process with changing event rates. This includes the possibility to incorporate additional information such as the number of events occurring or the location of an already known event. It can also generate the probability density functions of specific events in the cases where additional information is available. Based on Hohmann (2019) <arXiv:1901.10754>.

Neural network has potential in forestry modelling. This package is designed to create and assess Artificial Intelligence based Neural Networks with varying architectures for prediction of volume of forest trees using two input features: height and diameter at breast height, as they are the key factors in predicting volume, therefore development and validation of efficient volume prediction neural network model is necessary. This package has been developed using the algorithm of Tabassum et al. (2022) <doi:10.18805/ag.D-5555>.

This package provides a toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550> and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.

This package provides a model that provides researchers with a powerful tool for the classification and study of native corn by aiding in the identification of racial complexes which are fundamental to Mexico's agriculture and culture. This package has been developed based on data collected by "Proyecto Global de Maà ces Nativos México", which has conducted exhaustive surveys across the country to document the qualitative and quantitative characteristics of different types of native maize. The trained model uses a robust and diverse dataset, enabling it to achieve an 80% accuracy in classifying maize racial complexes. The characteristics included in the analysis comprise geographic location, grain and cob colors, as well as various physical measurements, such as lengths and widths.

This package provides convenient access to the German modification of the International Classification of Diagnoses, 10th revision (ICD-10-GM). It provides functionality to aid in the identification, specification and historisation of ICD-10 codes. Its intended use is the analysis of routinely collected data in the context of epidemiology, medical research and health services research. The underlying metadata are released by the German Institute for Medical Documentation and Information <https://www.dimdi.de>, and are redistributed in accordance with their license.

This package provides a set of functions to estimate interactions flexibly in the face of possibly many controls. Implements the procedures described in Blackwell and Olson (2022) <doi:10.1017/pan.2021.19>.

The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen from a set of optional candidates by cross-validation or alternatively generated from the input data.

Two functions for running and then post-estimating an Interrupted Time Series Analysis model. This is a solution for running time series analyses on temporally short data. See English (2019) The its.analysis R package - Modelling short time series data <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3398189> for an overview of the method.