This package implements the instruments for complex-valued modelling, including time series analysis and forecasting. This is based on the monograph by Svetunkov & Svetunkov (2024) <doi: 10.1007/978-3-031-62608-1>.

Estimates sugar beet canopy closure with remotely sensed leaf area index and estimates when action might be needed to protect the crop from a Leaf Spot epidemic with a negative prognosis model based on published models.

This package provides a new method for identification of clusters of genomic regions within chromosomes. Primarily, it is used for calling clusters of cis-regulatory elements (COREs). CREAM uses genome-wide maps of genomic regions in the tissue or cell type of interest, such as those generated from chromatin-based assays including DNaseI, ATAC or ChIP-Seq. CREAM considers proximity of the elements within chromosomes of a given sample to identify COREs in the following steps: 1) It identifies window size or the maximum allowed distance between the elements within each CORE, 2) It identifies number of elements which should be clustered as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which does not pass the threshold considered in the approach.

This package provides a simulation model and accompanying functions that support assessing silvicultural concepts on the forest estate level with a focus on the CO2 uptake by wood growth and CO2 emissions by forest operations. For achieving this, a virtual forest estate area is split into the areas covered by typical phases of the silvicultural concept of interest. Given initial area shares of these phases, the dynamics of these areas is simulated. The typical carbon stocks and flows which are known for all phases are attributed post-hoc to the areas and upscaled to the estate level. CO2 emissions by forest operations are estimated based on the amounts and dimensions of the harvested timber. Probabilities of damage events are taken into account.

Search across R files with contextual results, highlights and clickable links. Includes an add-in for further workflow enhancement.

Changing the name of an existing R package is annoying but common task especially in the early stages of package development. This package (mostly) automates this task.

This package provides a database of Chinese surnames and given names (1930-2008). This database contains nationwide frequency statistics of 1,806 Chinese surnames and 2,614 Chinese characters used in given names, covering about 1.2 billion Han Chinese population (96.8 percent of the Han Chinese household-registered population born from 1930 to 2008 and still alive in 2008). This package also contains a function for computing multiple indices of Chinese surnames and given names for social science research (e.g., name uniqueness, name gender, name valence, and name warmth/competence). Details are provided at <https://psychbruce.github.io/ChineseNames/>.

Allows you to connect to data sources across the crypto ecosystem. This data can enable a range of activity such as portfolio tracking, programmatic trading, or industry analysis. The package is described in French (2024) <https://github.com/TrevorFrench/cryptotrackr/wiki>.

This package provides a robust constrained L1 minimization method for estimating a large sparse inverse covariance matrix (aka precision matrix), and recovering its support for building graphical models. The computation uses linear programming. The method was published in TT Cai, W Liu, X Luo (2011) <doi:10.1198/jasa.2011.tm10155>.

Produce forest plots to visualize covariate effects using either the command line or an interactive Shiny application.

Significance test of Spearman's Rho or Kendall's Tau between short-range dependent random variables.

Implementation of the empirical method to derive log2 counts per million (CPM) cutoff to filter out lowly expressed genes using ERCC spike-ins as described in Goll and Bosinger et.al (2022)<doi:10.1101/2022.06.23.497396>. This package utilizes the synthetic mRNA control pairs developed by the External RNA Controls Consortium (ERCC) (ERCC 1 / ERCC 2) that are spiked into sample pairs at known ratios at various absolute abundances. The relationship between the observed and expected fold changes is then used to empirically determine an optimal log2 CPM cutoff for filtering out lowly expressed genes.

An efficient cross-validated approach for covariance matrix estimation, particularly useful in high-dimensional settings. This method relies upon the theory of high-dimensional loss-based covariance matrix estimator selection developed by Boileau et al. (2022) <doi:10.1080/10618600.2022.2110883> to identify the optimal estimator from among a prespecified set of candidates.

Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites.

Augment clinical data with metadata to create output used in conventional publications and reports.

Accuracy metrics are commonly used to assess the discriminating ability of diagnostic tests or biomarkers. Among them, metrics based on the ROC framework are particularly popular. When classification involves subclasses, the package CompClassMetrics includes functions that can provide the point estimate, confidence interval as well as true values if a parametric setting is known. For more details see Nan and Tian (2025) <doi:10.1177/09622802251343600>, Nan and Tian (2023) <doi:10.1002/sim.9908>, Feng and Tian (2020) <doi:10.1177/0962280220938077> and Wang et al (2016) <doi:10.1002/sim.6843>.

Providing a set of functions to easily generate and iterate complex networks. The functions can be used to generate realistic networks with a wide range of different clustering, density, and average path length. For more information consult research articles by Amiyaal Ilany and Erol Akcay (2016) <doi:10.1093/icb/icw068> and Ilany and Erol Akcay (2016) <doi:10.1101/026120>, which have inspired many methods in this package.

This package provides a tool for matching ICD-10 codes to corresponding Clinical Classification Software Refined (CCSR) codes. The main function, CCSRfind(), identifies each CCSR code that applies to an individual given their diagnosis codes. It also provides a summary of CCSR codes that are matched to a dataset. The package contains 3 datasets: DXCCSR (mapping of ICD-10 codes to CCSR codes), Legend (conversion of DXCCSR to CCSRfind-usable format for CCSR codes with less than or equal to 1000 ICD-10 diagnosis codes), and LegendExtend (conversion of DXCCSR to CCSRfind-usable format for CCSR codes with more than 1000 ICD-10 dx codes). The disc() function applies grepl() ('base') to multiple columns and is used in CCSRfind().

Generate random numbers from the Cryptographically Secure Pseudorandom Number Generator (CSPRNG) provided by the underlying operating system. System CSPRNGs are seeded internally by the OS with entropy it gathers from the system hardware. The following system functions are used: arc4random_buf() on macOS and BSD; BCryptgenRandom() on Windows; Sys_getrandom() on Linux.

Analysis of configuration frequencies for simple and repeated measures, multiple-samples CFA, hierarchical CFA, bootstrap CFA, functional CFA, Kieser-Victor CFA, and Lindner's test using a conventional and an accelerated algorithm.

This package performs the cross-match test that is an exact, distribution free test of equality of 2 high dimensional multivariate distributions. The input is a distance matrix and the labels of the two groups to be compared, the output is the number of cross-matches and a p-value. See Rosenbaum (2005) <doi:10.1111/j.1467-9868.2005.00513.x>.

This package provides a comprehensive reproducibility framework designed for R and bioinformatics workflows. Automatically captures the entire analysis environment including R session info, package versions, external tool versions ('Samtools', STAR', BWA', etc.), conda environments, reference genomes, data provenance with smart checksumming for large files, parameter choices, random seeds, and hardware specifications. Generates executable scripts with Docker', Singularity', and renv configurations. Integrates with workflow managers ('Nextflow', Snakemake', WDL', CWL') to ensure complete reproducibility of computational research workflows.

Terrestrial maps with simplified topologies for Census Divisions, Agricultural Regions, Economic Regions, Federal Electoral Divisions and Provinces.

This package implements various estimators for average treatment effects - an inverse probability weighted (IPW) estimator, an augmented inverse probability weighted (AIPW) estimator, and a standard regression estimator - that make use of generalized additive models for the treatment assignment model and/or outcome model. See: Glynn, Adam N. and Kevin M. Quinn. 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator." Political Analysis. 18: 36-56.