Sends queries to a specified Neo4J graph database, capturing results in a dataframe where appropriate. Other useful functions for the importing and management of data on the Neo4J server and basic local server admin.

This package infers species associations from community matrices. Uses local and (optional) regional-scale co-occurrence data by comparing observed partial correlation coefficients between species to those estimated from regional species distributions. Extends Gaussian graphical models to a null modeling framework. Provides interface to a variety of inverse covariance matrix estimation methods.

This package provides quality control (QC), normalization, and batch effect correction operations for NanoString nCounter data, Talhouk et al. (2016) <doi:10.1371/journal.pone.0153844>. Various metrics are used to determine which samples passed or failed QC. Gene expression should first be normalized to housekeeping genes, before a reference-based approach is used to adjust for batch effects. Raw NanoString data can be imported in the form of Reporter Code Count (RCC) files.

Dealing with neutrosophic data of the form N=D+I(where N is a Neutrosophic number ,D is the determinant part of the number and I is the indeterminacy part) using the neutrosophic two way anova test keeps the type I error low. This algorithm calculates the fisher statistics when we have a neutrosophic data, also tests two hypothesizes, first is to test differences between treatments, and second is to test differences between sectors. For more information see Miari, Mahmoud; Anan, Mohamad Taher; Zeina, Mohamed Bisher(2022) <https://www.americaspg.com/articleinfo/21/show/1058>.

Calculate the precision in mean differences (raw or Cohen's D) and correlation coefficients for different sample sizes. Uses permutations of the collected functional magnetic resonance imaging (fMRI) region of interest data. Method described in Klapwijk, Jongerling, Hoijtink and Crone (2024) <doi:10.31234/osf.io/cz32t>.

Implementation of the two error variance estimation methods in high-dimensional linear models of Yu, Bien (2017) <arXiv:1712.02412>.

Utilities and kinship information for behavior genetics and developmental research using the National Longitudinal Survey of Youth (NLSY; <https://www.nlsinfo.org/>).

This package provides a tool for drawing sassy UML (Unified Modeling Language) diagrams based on a simple syntax, see <https://www.nomnoml.com>. Supports styling, R Markdown and exporting diagrams in the PNG format. Note: you need a chromium based browser installed on your system.

Optimizing regular numeric problems in optically stimulated luminescence dating, such as: equivalent dose calculation, dose rate determination, growth curve fitting, decay curve decomposition, statistical age model optimization, and statistical plot visualization.

An adaptation of Non-dominated Sorting Genetic Algorithm III for multi objective feature selection tasks. Non-dominated Sorting Genetic Algorithm III is a genetic algorithm that solves multiple optimization problems simultaneously by applying a non-dominated sorting technique. It uses a reference points based selection operator to explore solution space and preserve diversity. See the original paper by K. Deb and H. Jain (2014) <DOI:10.1109/TEVC.2013.2281534> for a detailed description.

Optimization for nonlinear objective and constraint functions. Linear or nonlinear equality and inequality constraints are allowed. It accepts the input parameters as a constrained matrix.

Estimate the non-linear odds ratio and plot it against a continuous exposure.

This package provides tools for drawing Statistical Process Control (SPC) charts. This package supports the NHS Making Data Count programme, and allows users to draw XmR charts, use change points and apply rules with summary indicators for when rules are breached.

This package performs analysis of one-way multivariate data, for small samples using Nonparametric techniques. Using approximations for ANOVA Type, Wilks Lambda, Lawley Hotelling, and Bartlett Nanda Pillai Test statics, the package compares the multivariate distributions for a single explanatory variable. The comparison is also performed using a permutation test for each of the four test statistics. The package also performs an all-subsets algorithm regarding variables and regarding factor levels.

Predicting the structure of a graph including new nodes and edges using a time series of graphs. Flux balance analysis, a linear and integer programming technique used in biochemistry is used with time series prediction methods to predict the graph structure at a future time point Kandanaarachchi (2025) <doi:10.48550/arXiv.2507.05806>.

This package contains data, code, and figures from Hill et al. 2018a (Journal of Experimental Marine Biology and Ecology; <DOI: 10.1016/j.jembe.2018.07.006>) and Hill et al. 2018b (Data In Brief <DOI: 10.1016/j.dib.2018.09.133>). Datasets document plant allometry, stem heights, nutrient and stable isotope content, and sediment denitrification enzyme assays. The data and analysis offer an examination of nitrogen uptake and allocation in two salt marsh plant species.

This package provides a collection of common univariate bounded probability distributions transformed to the unbounded real line, for the purpose of increased MCMC efficiency.

Nested Partially Balanced Bipartite Block (NPBBB) designs involve two levels of blocking: (i) The block design (ignoring sub-block classification) serves as a partially balanced bipartite block (PBBB) design, and (ii) The sub-block design (ignoring block classification) also serves as a PBBB design. More details on constructions of the PBBB designs and their characterization properties are available in Vinayaka et al.(2023) <doi:10.1080/03610926.2023.2251623>. This package calculates A-efficiency values for both block and sub-block structures, along with all parameters of a given NPBBB design.

Due to Rstudio's status as open source software, we believe it will be utilized frequently for future data analysis by users whom lack formal training or experience with R'. The NMVANOVA (Novice Model Variation ANOVA) a streamlined variation of experimental design functions that allows novice Rstudio users to perform different model variations one-way analysis of variance without downloading multiple libraries or packages. Users can easily manipulate the data block, and needed inputs so that users only have to plugin the four designed variables/values.

Fits conditional naive Bayes survival models for right-censored outcomes using inverse-probability of censoring weighting. The package provides model fitting, prediction, resampling-based evaluation, cross-validation, hyper-parameter tuning, and permutation variable importance utilities for horizon-specific survival prediction. The model is the censored naive Bayes classifier of Wolfson et al. (2015) <doi:10.1002/sim.6526>, which combines the marginal Kaplan-Meier survivor function with horizon-specific class-conditional covariate densities and inverse-probability-of-censoring weights. Resampling evaluation uses the inverse-probability-of-censoring-weighted Brier score of Gerds and Schumacher (2006) <doi:10.1002/bimj.200610301>.

This package provides a collection of tools that allow users to perform critical steps in the process of assessing ecological niche evolution over phylogenies, with uncertainty incorporated explicitly in reconstructions. The method proposed here for ancestral reconstruction of ecological niches characterizes species niches using a bin-based approach that incorporates uncertainty in estimations. Compared to other existing methods, the approaches presented here reduce risk of overestimation of amounts and rates of ecological niche evolution. The main analyses include: initial exploration of environmental data in occurrence records and accessible areas, preparation of data for phylogenetic analyses, executing comparative phylogenetic analyses of ecological niches, and plotting for interpretations. Details on the theoretical background and methods used can be found in: Owens et al. (2020) <doi:10.1002/ece3.6359>, Peterson et al. (1999) <doi:10.1126/science.285.5431.1265>, Soberón and Peterson (2005) <doi:10.17161/bi.v2i0.4>, Peterson (2011) <doi:10.1111/j.1365-2699.2010.02456.x>, Barve et al. (2011) <doi:10.1111/ecog.02671>, Machado-Stredel et al. (2021) <doi:10.21425/F5FBG48814>, Owens et al. (2013) <doi:10.1016/j.ecolmodel.2013.04.011>, Saupe et al. (2018) <doi:10.1093/sysbio/syx084>, and Cobos et al. (2021) <doi:10.1111/jav.02868>.

Network Pre-Processing and normalization. Methods for normalizing graphs, including Chua normalization, Laplacian normalization, Binary magnification, min-max normalization and others. Methods to sparsify adjacency matrices. Methods for graph pre-processing and for filtering edges of the graph.

This package creates collapsible, expandable HTML tables from hierarchical data. Supports data frame input with multi-level grouping, custom column formatters, bottom-up rollup aggregation, and CSS-variable-based theming. Works in Shiny applications, R Markdown, Quarto', and the RStudio Viewer.

Simulates the extinction of species in ecological networks and it analyzes its cascading effects, described in Dunne et al. (2002) <doi:10.1073/pnas.192407699>.