This package provides a set of function for clustering data observation with hybrid method Fuzzy ART and K-Means by Sengupta, Ghosh & Dan (2011) <doi:10.1080/0951192X.2011.602362>.

Procedures for calculating variance components, study variation, percent study variation, and percent tolerance for gauge repeatability and reproducibility study. Methods included are ANOVA and Average / Range methods. Requires balanced study.

This package implements a geographically weighted partial correlation which is an extension from gwss() function in the GWmodel package (Percival and Tsutsumida (2017) <doi:10.1553/giscience2017_01_s36>).

Facilitates the analysis and evaluation of hydrologic model output and time-series data with functions focused on comparison of modeled (simulated) and observed data, period-of-record statistics, and trends.

Graphics device routing all graphics commands to a Java program. The actual functionality of the JavaGD depends on the Java-side implementation. Simple AWT and Swing implementations are included.

Location and scale hypothesis testing using the LePage test and variants of its as proposed by Hussain A. and Tsagris M. (2025), <doi:10.48550/arXiv.2509.19126>.

Image-based color matching using the "Mycological Colour Chart" by Rayner (1970, ISBN:9780851980263) and its associated fungal pigments. This package will assist mycologists in identifying color during morphological analysis.

Prepare pharmacokinetic/pharmacodynamic (PK/PD) data for PK/PD analyses. This package provides functions to standardize infusion and bolus dose data while linking it to drug level or concentration data.

This package provides a collection of functions for reading soil data from U.S. Department of Agriculture Natural Resources Conservation Service (USDA-NRCS) and National Cooperative Soil Survey (NCSS) databases.

This package implements an ensemble machine learning approach to predict the sporulation potential of metagenome-assembled genomes (MAGs) from uncultivated Firmicutes based on the presence/absence of sporulation-associated genes.

This package provides functions for evaluating tournament predictions, simulating results from individual soccer matches and tournaments. See <http://sandsynligvis.dk/2018/08/03/world-cup-prediction-winners/> for more information.

Applies re-sampled kernel density method to detect vote fraud. It estimates the proportion of coarse vote-shares in the observed data relative to the null hypothesis of no fraud.

Data frames with time information are subset and flagged with period information. Data frames with times are dealt as timeDF objects and periods are represented as periodDF objects.

This package provides a simplified version of the Portal Project Database designed for teaching. It provides a real world example of life-history, population, and ecological data, with sufficient complexity to teach many aspects of data analysis and management, but with many complexities removed to allow students to focus on the core ideas and skills being taught. The full database (which should be used for research) is available at <https://github.com/weecology/PortalData>.

The main purpose of this package is to perform simulation-based estimation of stochastic actor-oriented models for longitudinal network data collected as panel data. Dependent variables can be single or multivariate networks, which can be directed, non-directed, or two-mode; and associated actor variables. There are also functions for testing parameters and checking goodness of fit. An overview of these models is given in Snijders (2017), <doi:10.1146/annurev-statistics-060116-054035>.

For any two way feature-set from a pair of pre-processed omics data, 3 different true discovery proportions (TDP), namely pairwise-TDP, column-TDP and row-TDP are calculated. Due to embedded closed testing procedure, the choice of feature-sets can be changed infinite times and even after seeing the data without any change in type I error rate. For more details refer to Ebrahimpoor et al., (2024) <doi:10.48550/arXiv.2410.19523>.

Interface to the ChEA3 transcription factor enrichment API. ChEA3 integrates evidence from ChIP-seq, co-expression, and literature resources to prioritize transcription factors regulating a given set of genes. This package provides convenient R functions to query the API, retrieve ranked results across collections (including integrated scores), and standardize output for downstream analysis in R/Bioconductor workflows. See <https://maayanlab.cloud/chea3/> or Keenan (2019) <doi:10.1093/nar/gkz446> for further details.

Network-based regularization has achieved success in variable selection for high-dimensional biological data due to its ability to incorporate correlations among genomic features. This package provides procedures of network-based variable selection for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and Ren et al.(2019) <doi:10.1002/gepi.22194>). Continuous, binary, and survival response are supported. Robust network-based methods are available for continuous and survival responses.

The CSV library provides a complete interface to CSV files and data. It offers tools to enable you to read and write to and from Strings or IO objects, as needed.

This package provides functions to estimate variance-mean dependence in count data from high-throughput nucleotide sequencing assays and test for differential expression based on a model using the negative binomial distribution.

This package implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. It provides a robust toolbox supporting over 70 methods for semantic similarity analysis.

This package is designed for visualization of RNA-related genomic features with respect to the landmarks of RNA transcripts, i.e., transcription starting site, start codon, stop codon and transcription ending site.

This package extends the ggplot2 plotting system which implements a grammar of graphics. ggtree is designed for visualization and annotation of phylogenetic trees and other tree-like structures with their annotation data.

This package conducts batch effects removal from a taxa read count table by a conditional quantile regression method. The distributional attributes of microbiome data - zero-inflation and over-dispersion, are simultaneously considered.