Fits Emax models to pharmacokinetic/pharmacodynamic (PK/PD) data, estimate key parameters, and visualise model fits for multiple PK/PD indices. Methods are described in Macdougall J (2006) &lt;<a href="https://doi.org/10.1007%2F0-387-33706-7_9" target="_top">doi:10.1007/0-387-33706-7_9</a>&gt;, Spiess AN, Neumeyer N (2010) &lt;<a href="https://doi.org/10.1186%2F1471-2210-10-6" target="_top">doi:10.1186/1471-2210-10-6</a>&gt;, and Burnham KP, Anderson DR (2004) &lt;<a href="https://doi.org/10.1177%2F0049124104268644" target="_top">doi:10.1177/0049124104268644</a>&gt;.

An interface for 'NetLogo' &lt;<a href="https://www.netlogo.org" target="_top">https://www.netlogo.org</a>&gt; that enables programmatic setup and execution of simulations. Designed to facilitate integrating 'NetLogo' models into reproducible workflows by creating and running 'BehaviorSpace' experiments and retrieving their results.

Generates (U,W) mixture graphs where U is a line graph graphon and W is a dense graphon. Graphons are graph limits and graphon U can be written as sequence of positive numbers adding to 1. Graphs are sampled from U and W and joined randomly to obtain the mixture graph. Given a mixture graph, U can be inferred. Kandanaarachchi and Ong (2025) &lt;<a href="https://doi.org/10.48550%2FarXiv.2505.13864" target="_top">doi:10.48550/arXiv.2505.13864</a>&gt;.

Subset and download data from EU Copernicus Climate Data Service: &lt;<a href="https://cds.climate.copernicus.eu/" target="_top">https://cds.climate.copernicus.eu/</a>&gt;. Import information about the Earth's past, present and future climate from Copernicus into R without the need of external software.

Offers a set of objects tailored to simplify working with choice data. It enables the computation of choice probabilities and the likelihood of various types of choice models based on given data.

There is limited native support for external pointers in the R interface. This package provides some basic tools to verify, create and modify 'externalptr' objects.

A Variational Bayesian algorithm for high-dimensional multi-source heterogeneous linear models. More details have been written up in a paper submitted to the journal Statistics in Medicine, and the details of variational Bayesian methods can be found in Ray and Szabo (2021) &lt;<a href="https://doi.org/10.1080%2F01621459.2020.1847121" target="_top">doi:10.1080/01621459.2020.1847121</a>&gt;. It simultaneously performs parameter estimation and variable selection. The algorithm supports two model settings: (1) local models, where variable selection is only applied to homogeneous coefficients, and (2) global models, where variable selection is also performed on heterogeneous coefficients. Two forms of Spike-and-Slab priors are available: the Laplace distribution and the Gaussian distribution as the Slab component.

Detects values imported from spreadsheets that were auto-converted to Excel date serials and reconstructs the originally intended day.month decimals (for example, '30.3' that Excel displayed as '30/03/2025'). The functions work in a vectorized manner, preserve non-serial values, and support both the 1900 and 1904 date systems.

Provides functions to construct two-phase design layouts, compute treatment- and block-incidence matrices, derive C-matrices for residual, direct, and interaction effects, and calculate the efficiency factor for two-phase experimental designs with factorial treatment structure.

Calculate geolocations by light using template matching. The routine uses a calibration free optimization of a sky illuminance model to determine locations robustly using a template matching approach, as described by Ekstrom (2004) &lt;<a href="https://nipr.repo.nii.ac.jp/records/2496" target="_top">https://nipr.repo.nii.ac.jp/records/2496</a>&gt;, and behaviourly informed constraints (step-selection).

Enable users to measure and record the execution time of pipe operations (using |&gt;) with optional logging to dataframes and output to the console.

Creates a spectroscopy guideline with a highly accurate prediction model for soil properties using machine learning or deep learning algorithms such as LASSO, Random Forest, Cubist, etc., and decide which algorithm generates the best model for different soil types.

Implementation of the FVIBES, the Fuzzy Variable-Importance Based Eigenspace Separation algorithm as described in the paper by Ghashti, J.S., Hare, W., and J.R.J. Thompson (2025). Variable-Weighted Adjacency Constructions for Fuzzy Spectral Clustering. Submitted.

Automatically fetch, transform and arrange subsets of multidimensional data sets (collections of files) stored in local and/or remote file systems or servers, using multicore capabilities where possible. This tool provides an interface to perceive a collection of data sets as a single large multidimensional data array, and enables the user to request for automatic retrieval, processing and arrangement of subsets of the large array. Wrapper functions to add support for custom file formats can be plugged in/out, making the tool suitable for any research field where large multidimensional data sets are involved.

Simulate a virtual population of subjects that has demographic distributions (height, weight, and BMI) and correlations (height and weight), by sex and age, which mimic those reported in real-world anthropometric growth charts (CDC, WHO, or Fenton).

Collection of tools for the analysis of the resilience of dynamic networks. Created as a classroom project.

Performs partial verification bias (PVB) correction for binary diagnostic tests, where PVB arises from selective patient verification in diagnostic accuracy studies. Supports correction of important accuracy measures &ndash; sensitivity, specificity, positive predictive values and negative predictive value &ndash; under missing-at-random and missing-not-at-random missing data mechanisms. Available methods and references are "Begg and Greenes' methods" in Alonzo &amp; Pepe (2005) &lt;<a href="https://doi.org/10.1111%2Fj.1467-9876.2005.00477.x" target="_top">doi:10.1111/j.1467-9876.2005.00477.x</a>&gt; and deGroot et al. (2011) &lt;<a href="https://doi.org/10.1016%2Fj.annepidem.2010.10.004" target="_top">doi:10.1016/j.annepidem.2010.10.004</a>&gt;; "Multiple imputation" in Harel &amp; Zhou (2006) &lt;<a href="https://doi.org/10.1002%2Fsim.2494" target="_top">doi:10.1002/sim.2494</a>&gt;, "EM-based logistic regression" in Kosinski &amp; Barnhart (2003) &lt;<a href="https://doi.org/10.1111%2F1541-0420.00019" target="_top">doi:10.1111/1541-0420.00019</a>&gt;; "Inverse probability weighting" in Alonzo &amp; Pepe (2005) &lt;<a href="https://doi.org/10.1111%2Fj.1467-9876.2005.00477.x" target="_top">doi:10.1111/j.1467-9876.2005.00477.x</a>&gt;; "Inverse probability bootstrap sampling" in Nahorniak et al. (2015) &lt;<a href="https://doi.org/10.1371%2Fjournal.pone.0131765" target="_top">doi:10.1371/journal.pone.0131765</a>&gt; and Arifin &amp; Yusof (2022) &lt;<a href="https://doi.org/10.3390%2Fdiagnostics12112839" target="_top">doi:10.3390/diagnostics12112839</a>&gt;; "Scaled inverse probability resampling methods" in Arifin &amp; Yusof (2025) &lt;<a href="https://doi.org/10.1371%2Fjournal.pone.0321440" target="_top">doi:10.1371/journal.pone.0321440</a>&gt;.

Implements three complementary pipelines for causal analysis on macroeconomic time series: (1) Error-Correction Models with Multivariate Adaptive Regression Splines (ECM-MARS), (2) Bayesian Structural Time Series (BSTS), and (3) Bayesian GLM with AR(1) errors validated with Leave-Future-Out (LFO). Heavy backends (Stan) are optional and never used in examples or tests.