An R package for inferring cell-type specific gene regulatory network from single-cell RNA-seq data.

The functions compute the double-entry intraclass correlation, which is an index of profile similarity (Furr, 2010; McCrae, 2008). The double-entry intraclass correlation is a more precise index of the agreement of two empirically observed profiles than the often-used intraclass correlation (McCrae, 2008). Profiles comprising correlations are automatically transformed according to the Fisher z-transformation before the double-entry intraclass correlation is calculated. If the profiles comprise scores such as sum scores from various personality scales, it is recommended to standardize each individual score prior to computation of the double-entry intraclass correlation (McCrae, 2008). See Furr (2010) <doi:10.1080/00223890903379134> or McCrae (2008) <doi:10.1080/00223890701845104> for details.

Implementation of some Individual Based Models (IBMs, sensu Grimm and Railsback 2005) and methods to create new ones, particularly for population dynamics models (reproduction, mortality and movement). The basic operations for the simulations are implemented in Rcpp for speed.

This package provides S4 classes for Internet Protocol (IP) versions 4 and 6 addresses and efficient methods for IP addresses comparison, arithmetic, bit manipulation and lookup. Both IPv4 and IPv6 arbitrary ranges are also supported as well as internationalized ('IDN') domain lookup with and whois query.

This package performs Invariant Coordinate Selection (ICS) (Tyler, Critchley, Duembgen and Oja (2009) <doi:10.1111/j.1467-9868.2009.00706.x>) and especially ICS for multivariate outlier detection with application to quality control (Archimbaud, Nordhausen, Ruiz-Gazen (2018) <doi:10.1016/j.csda.2018.06.011>) using a shiny app.

This package contains techniques for mining large and high-dimensional data sets by using the concept of Intrinsic Dimension (ID). Here the ID is not necessarily an integer. It is extended to fractal dimensions. And the Morisita estimator is used for the ID estimation, but other tools are included as well.

Calibration and risk-set calibration methods for fitting Cox proportional hazard model when a binary covariate is measured intermittently. Methods include functions to fit calibration models from interval-censored data and modified partial likelihood for the proportional hazard model, Nevo et al. (2018+) <arXiv:1801.01529>.

Fit parametric models for time-to-event data that show an initial incubation period', i.e., a variable delay phase where the hazard is zero. The delayed Weibull distribution serves as foundational data model. The specific method of MPSE (maximum product of spacings estimation) and MLE-based methods are used for parameter estimation. Bootstrap confidence intervals for parameters and significance tests in a two group setting are provided.

R interface to access the Vocabularies REST API of the ICES (International Council for the Exploration of the Sea) Vocabularies database <https://vocab.ices.dk/services/>.

This package provides a general-purpose workflow for image segmentation using TensorFlow models based on the U-Net architecture by Ronneberger et al. (2015) <arXiv:1505.04597> and the U-Net++ architecture by Zhou et al. (2018) <arXiv:1807.10165>. We provide pre-trained models for assessing canopy density and understory vegetation density from vegetation photos. In addition, the package provides a workflow for easily creating model input and model architectures for general-purpose image segmentation based on grayscale or color images, both for binary and multi-class image segmentation.

This package provides a variety of improved shrinkage estimators in the area of statistical analysis: unrestricted; restricted; preliminary test; improved preliminary test; Stein; and positive-rule Stein. More details can be found in chapter 7 of Saleh, A. K. Md. E. (2006) <ISBN: 978-0-471-56375-4>.

The wiDB...() functions provide an interface to the public API of the wiDB <https://github.com/SPATIAL-Lab/isoWater/blob/master/Protocol.md>: build, check and submit queries, and receive and unpack responses. Data analysis functions support Bayesian inference of the source and source isotope composition of water samples that may have experienced evaporation. Algorithms adapted from Bowen et al. (2018, <doi:10.1007/s00442-018-4192-5>).

Mining informative genes with certain biological meanings are important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package selects the informative genes related to a specific trait using gene expression dataset. These trait specific genes are considered as informative genes. This package returns the informative gene set from the high dimensional gene expression data using a combination of methods SVM and MRMR (for feature selection) with bootstrapping procedure.

Calculates insulin secretion rates from C-peptide values based on the methods described in Van Cauter et al. (1992) <doi:10.2337/diab.41.3.368>. Includes functions to calculate estimated insulin secretion rates using linear or cubic spline interpolation of c-peptide values (see Eaton et al., 1980 <doi:10.1210/jcem-51-3-520> and Polonsky et al., 1986 <doi:10.1172/JCI112308>) and to calculate estimates of input coefficients (volume of distribution, short half life, long half life, and fraction attributed to short half life) as described by Van Cauter. Although the generated coefficients are specific to insulin secretion, the two-compartment secretion model used here is useful for certain applications beyond insulin.

This package implements imputation methods using EM and Data Augmentation for multinomial data following the work of Schafer 1997 <ISBN: 978-0-412-04061-0>.

Spatial interpolation toolkit designed for environmental and geospatial applications. It includes a range of methods, from traditional techniques to advanced machine learning approaches, ensuring accurate and efficient estimation of values in unobserved locations.

Finds optimal designs for nonlinear models using a metaheuristic algorithm called Imperialist Competitive Algorithm (ICA). See, for details, Masoudi et al. (2022) <doi:10.32614/RJ-2022-043>, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014> and Masoudi et al. (2019) <doi:10.1080/10618600.2019.1601097>.

An interface to the algorithms of Interpretable AI <https://www.interpretable.ai> from the R programming language. Interpretable AI provides various modules, including Optimal Trees for classification, regression, prescription and survival analysis, Optimal Imputation for missing data imputation and outlier detection, and Optimal Feature Selection for exact sparse regression. The iai package is an open-source project. The Interpretable AI software modules are proprietary products, but free academic and evaluation licenses are available.

Automatically detects Copy Number Variations (CNV) from Next Generation Sequencing data using a machine learning algorithm, Isolation forest. More details about the method can be found in the paper by Cabello-Aguilar (2022) <doi:10.1101/2022.01.03.474771>.

Create and view tickets in gitea', a self-hosted git service <https://gitea.io>, using an RStudio addin, and use helper functions to publish documentation and use git.

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.

This package implements inequality constrained inference. This includes parameter estimation in normal (linear) models under linear equality and inequality constraints, as well as normal likelihood ratio tests involving inequality-constrained hypotheses. For inequality-constrained linear models, averaging over R-squared for different orderings of regressors is also included.

Helper functions and example data sets to facilitate the estimation of IRTree models from data with different shape and using different software.

Allows direct access to the macroeconomic, financial and regional database maintained by Brazilian Institute for Applied Economic Research ('Ipea'). This R package uses the Ipeadata API. For more information, see <http://www.ipeadata.gov.br/>.