Create a hexagon tile map display from spatial polygons. Each polygon is represented by a hexagon tile, placed as close to it's original centroid as possible, with a focus on maintaining spatial relationship to a focal point. Developed to aid visualisation and analysis of spatial distributions across Australia, which can be challenging due to the concentration of the population on the coast and wide open interior.

This package provides tools for converting data from complex or irregular layouts to a columnar structure. For example, tables with multilevel column or row headers, or spreadsheets. Header and data cells are selected by their contents and position, as well as formatting and comments where available, and are associated with one other by their proximity in given directions. Functions for data frames and HTML tables are provided.

SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size.

Dalli is a high performance pure Ruby client for accessing memcached servers. Dalli supports:

• Simple and complex memcached configurations

• Fail-over between memcached instances

• Fine-grained control of data serialization and compression

• Thread-safe operation

• SSL/TLS connections to memcached

• SASL authentication.

The name is a variant of Salvador Dali for his famous painting The Persistence of Memory.

This package provides functions for radiation safety, also known as "radiation protection" and "radiological control". The science of radiation protection is called "health physics" and its engineering functions are called "radiological engineering". Functions in this package cover many of the computations needed by radiation safety professionals. Examples include: obtaining updated calibration and source check values for radiation monitors to account for radioactive decay in a reference source, simulating instrument readings to better understand measurement uncertainty, correcting instrument readings for geometry and ambient atmospheric conditions. Many of these functions are described in Johnson and Kirby (2011, ISBN-13: 978-1609134198). Utilities are also included for developing inputs and processing outputs with radiation transport codes, such as MCNP, a general-purpose Monte Carlo N-Particle code that can be used for neutron, photon, electron, or coupled neutron/photon/electron transport (Werner et. al. (2018) <doi:10.2172/1419730>).

Query functions to the GPlates <https://www.gplates.org/> Desktop Application and the GPlates Web Service <https://gws.gplates.org/> allow users to reconstruct past positions of geographic entities based on user-selected rotation models without leaving the R running environment. The online method (GPlates Web Service) makes the rotation of static plates, coastlines, and a low number of geographic coordinates available using nothing but an internet connection. The offline method requires an external installation of the GPlates Desktop Application, but allows the efficient batch rotation of thousands of coordinates, Simple Features (sf) and Spatial (sp) objects with custom reconstruction trees and partitioning polygons. Examples of such plate tectonic models are accessible via the chronosphere <https://cran.r-project.org/package=chronosphere>. This R extension is developed under the umbrella of the DFG (Deutsche Forschungsgemeinschaft) Research Unit TERSANE2 (For 2332, TEmperature Related Stressors in ANcient Extinctions).

It calculates the Air Pollution Tolerance Index (APTI) of plant species using biochemical parameters such as chlorophyll content, leaf extract pH, relative water content, and ascorbic acid content. It helps in identifying tolerant species for greenbelt development and pollution mitigation studies. It includes a shiny app for interactive APTI calculation and visualisation. For method details see, Sahu et al. (2020).<DOI:10.1007/s42452-020-3120-6>.

An R wrapper for agena.ai <https://www.agena.ai> which provides users capabilities to work with agena.ai using the R environment. Users can create Bayesian network models from scratch or import existing models in R and export to agena.ai cloud or local API for calculations. Note: running calculations requires a valid agena.ai API license (past the initial trial period of the local API).

An iterative implementation of a recursive binary partitioning algorithm to measure pairwise dependence with a modular design that allows user specification of the splitting logic and stop criteria. Helper functions provide suggested versions of both and support visualization and the computation of summary statistics on final binnings. For a complete description of the functionality and algorithm, see Salahub and Oldford (2023) <doi:10.48550/arXiv.2311.08561>.

Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications).

The Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012) can be used to estimate the causal effect of a continuous exposure on a continuous outcome. This package provides an approximate sensitivity analysis of BAC with regards to the hyperparameter omega. BACprior also provides functions to guide the user in their choice of an appropriate omega value. The method is based on Lefebvre, Atherton and Talbot (2014).

Calculates and visualises cumulative percent decay curves, which are typically calculated from metagenomic taxonomic profiles. These can be used to estimate the level of expected endogenous taxa at different abundance levels retrieved from metagenomic samples, when comparing to samples of known sampling site or source. Method described in Fellows Yates, J. A. et. al. (2021) Proceedings of the National Academy of Sciences USA <doi:10.1073/pnas.2021655118>.

One way to choose the number of principal components is via the reconstruction error. This package is designed mainly for this purpose. Graphical representation is also supported, plus some other principal component analysis related functions. References include: Jolliffe I.T. (2002). Principal Component Analysis. <doi:10.1007/b98835> and Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press.

Conducts conditional random sampling on observed values in sparse matrices. Useful for training and test set splitting sparse matrices prior to model fitting in cross-validation procedures and estimating the predictive accuracy of data imputation methods, such as matrix factorization or singular value decomposition (SVD). Although designed for applications with sparse matrices, CRASSMAT can also be applied to complete matrices, as well as to those containing missing values.

Estimate prior variable weights for Bayesian Additive Regression Trees (BART). These weights correspond to the probabilities of the variables being selected in the splitting rules of the sum-of-trees. Weights are estimated using empirical Bayes and external information on the explanatory variables (co-data). BART models are fitted using the dbarts R package. See Goedhart and others (2023) <doi:10.48550/arXiv.2311.09997> for details.

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.

This package provides a multi-layered untargeted pipeline for high-throughput LC/HRMS data processing to extract signals of organic small molecules. The package performs ion pairing, peak detection, peak table alignment, retention time correction, aligned peak table gap filling, peak annotation and visualization of extracted ion chromatograms (EICs) and total ion chromatograms (TICs). The IDSL.IPA package was introduced in <doi:10.1021/acs.jproteome.2c00120> .

This package provides methods to extract information on pathways, genes and various single-nucleotid polymorphisms (SNPs) from online databases. It provides functions for data preparation and evaluation of genetic influence on a binary outcome using the logistic kernel machine test (LKMT). Three different kernel functions are offered to analyze genotype information in this variance component test: A linear kernel, a size-adjusted kernel and a network-based kernel).

This package performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals (Beath, 2014 <doi:10.1002/jrsm.1114>).

Function pip3d() tests whether a point in 3D space is within, exactly on, or outside an enclosed surface defined by a triangular mesh. Function pip2d() tests whether a point in 2D space is within, exactly on, or outside a polygon. For a reference, see: Liu et al., A new point containment test algorithm based on preprocessing and determining triangles, Computer-Aided Design 42(12):1143-1150.

Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al.(2001) <isbn:9781566705783>. The ssdtools package uses Maximum Likelihood to fit distributions such as the gamma, log-logistic, log-normal and log-normal log-normal mixture. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by bootstrapping.

This package performs parametric synthesis of sounds with harmonic and noise components such as animal vocalizations or human voice. Also offers tools for audio manipulation and acoustic analysis, including pitch tracking, spectral analysis, audio segmentation, pitch and formant shifting, etc. Includes four interactive web apps for synthesizing and annotating audio, manually correcting pitch contours, and measuring formant frequencies. Reference: Anikin (2019) <doi:10.3758/s13428-018-1095-7>.

The goal of safejoin is to guarantee that when performing joins extra rows are not added to your data. safejoin provides a wrapper around dplyr::left_join that will raise an error when extra rows are unexpectedly added to your data. This can be useful when working with data where you expect there to be a many to one relationship but you are not certain the relationship holds.

This package provides functions to estimate a strategic selection estimator. A strategic selection estimator is an agent error model in which the two random components are not assumed to be orthogonal. In addition this package provides generic functions to print and plot objects of its class as well as the necessary functions to create tables for LaTeX. There is also a function to create dyadic data sets.