This package implements Multivariate Quantile-on-Quantile Regression (m-QQR) of Sinha, Ghosh, Hussain, Nguyen and Das (2023) <doi:10.1016/j.eneco.2023.107021>, extending the bivariate Quantile-on-Quantile regression of Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013> to include exogenous moderators and controls with optional interaction terms. For each pair of quantile levels (theta of the response and tau of the regressor) the package fits a locally-weighted quantile regression of y on the principal regressor x, a lagged dependent variable, moderators Z and the x*Z interaction terms, using Gaussian kernel weights on the empirical cumulative distribution function (CDF) distance. Bootstrap standard errors and Koenker-Machado pseudo R-squared are reported. Visualisations include MATLAB'-style Parula and Jet 3D surfaces, heatmaps and contour plots through plotly'.

This package provides methods for detecting signals related to (adverse event, medical product e.g. drugs, vaccines) pairs, a data generation function for simulating pharmacovigilance datasets, and various utility functions. For more details please see Liu A., Mukhopadhyay R., and Markatou M. <doi:10.48550/arXiv.2410.01168>.

Implementation of two tools to merge Hardware Event Monitors (HEMs) from different subexperiments. Hardware Reading and Merging (HRM), which uses order statistics to merge; and MUlti-Correlation HEM (MUCH) which merges using a multivariate normal distribution. The reference paper for HRM is: S. Vilardell, I. Serra, R. Santalla, E. Mezzetti, J. Abella and F. J. Cazorla, "HRM: Merging Hardware Event Monitors for Improved Timing Analysis of Complex MPSoCs," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 11, pp. 3662-3673, Nov. 2020, <doi:10.1109/TCAD.2020.3013051>. For MUCH: S. Vilardell, I. Serra, E. Mezzetti, J. Abella, and F. J. Cazorla. 2021. "MUCH: exploiting pairwise hardware event monitor correlations for improved timing analysis of complex MPSoCs". In Proceedings of the 36th Annual ACM Symposium on Applied Computing (SAC 21). Association for Computing Machinery. <doi:10.1145/3412841.3441931>. This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773).

Mask ranges based on expert knowledge or remote sensing layers. These tools can be combined to quantitatively and reproducibly generate a new map or to update an existing map. Methods include expert opinion and data-driven tools to generate thresholds for binary masks.

Takes QC signal for each day and normalize metabolomic data that has been acquired in a certain period of time. At least three QC per day are required.

Apply the marginal classification method to achieve the purpose of providing the point and interval estimates for the minimal clinically important difference based on the classical anchor-based method. For more details of the methodology, please see Zehua Zhou, Leslie J. Bisson and Jiwei Zhao (2021) <arXiv:2108.11589>.

Simulates Blockchain-Enabled Maritime Transportation Systems (BMTS) by integrating asynchronous, great-circle vessel kinematics with decentralised edge-computing consensus layers. The package provides a fully self-contained cyber-range running completely offline, permitting the risk-free injection of spatial and telemetry threat vectors (e.g., geofence breaches, coordinate modification, and node impersonation). It includes an interactive workspace engine built using shinydashboard and leaflet map layers for monitoring vessel states and ledger updates. Implemented consensus structures are modeled on a lightweight Proof-of-Authentication framework optimized for resource-constrained distributed nodes.

Model based clustering using the multivariate multiple Scaled t (MST) and multivariate multiple scaled contaminated normal (MSCN) distributions. The MST is an extension of the multivariate Student-t distribution to include flexible tail behaviors, Forbes, F. & Wraith, D. (2014) <doi:10.1007/s11222-013-9414-4>. The MSCN represents a heavy-tailed generalization of the multivariate normal (MN) distribution to model elliptical contoured scatters in the presence of mild outliers (also referred to as "bad" points) and automatically detect bad points, Punzo, A. & Tortora, C. (2021) <doi:10.1177/1471082X19890935>.

This package provides a collection of functions for processing and analyzing metabolite data. The namesake function mrbin() converts 1D or 2D Nuclear Magnetic Resonance data into a matrix of values suitable for further data analysis and performs basic processing steps in a reproducible way. Negative values, a common issue in such data, can be replaced by positive values (<doi:10.1021/acs.jproteome.0c00684>). All used parameters are stored in a readable text file and can be restored from that file to enable exact reproduction of the data at a later time. The function fia() ranks features according to their impact on classifier models, especially artificial neural network models.

Counting process structure is fundamental to model time varying covariates. This package restructures dataframes in the counting process format for one or more variables. F. W. Dekker, et al. (2008) <doi:10.1038/ki.2008.328>.

This package provides an interactive shiny application for teaching and applied analysis of geostatistical data. Users can explore spatial data, assess spatial correlation through the empirical variogram, fit model-based geostatistical models for continuous, prevalence and count outcomes, produce spatial predictions, and download reports. The methodology follows the model-based geostatistics framework of Diggle and Giorgi (2019, ISBN:9781138732353).

Fits Bayesian time-course models for model-based network meta-analysis (MBNMA) that allows inclusion of multiple time-points from studies. Repeated measures over time are accounted for within studies by applying different time-course functions, following the method of Pedder et al. (2019) <doi:10.1002/jrsm.1351>. The method allows synthesis of studies with multiple follow-up measurements that can account for time-course for a single or multiple treatment comparisons. Several general time-course functions are provided; others may be added by the user. Various characteristics can be flexibly added to the models, such as correlation between time points and shared class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects models and/or by node-splitting.

This package provides a comprehensive toolkit for missing person identification combining genetic and non-genetic evidence within a Bayesian framework. Computes likelihood ratios (LRs) for DNA profiles, biological sex, age, hair color, and birthdate evidence. Provides decision analysis tools including optimal LR thresholds, error rate calculations, and ROC curve visualization. Includes interactive Shiny applications for exploring evidence combinations. For methodological details see Marsico et al. (2023) <doi:10.1016/j.fsigen.2023.102891> and Marsico, Vigeland et al. (2021) <doi:10.1016/j.fsigen.2021.102519>.

This package implements an MCMC sampler for the posterior distribution of arbitrary time-homogeneous multivariate stochastic differential equation (SDE) models with possibly latent components. The package provides a simple entry point to integrate user-defined models directly with the sampler's C++ code, and parallelizes large portions of the calculations when compiled with OpenMP'.

This package provides tools to generate HTML interfaces for adaptive and non-adaptive tests using the shiny package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). Suitable for applying unidimensional and multidimensional computerized adaptive tests (CAT) using item response theory methodology and for creating simple questionnaires forms to collect response data directly in R. Additionally, optimal test designs (e.g., "shadow testing") are supported for tests that contain a large number of item selection constraints. Finally, package contains tools useful for performing Monte Carlo simulations for studying test item banks.

Simple tools to perform mixture optimization based on the desirability package by Max Kuhn. It also provides a plot routine using ggplot2 and patchwork'.

Bending non-positive-definite (symmetric) matrices to positive-definite, using weighted and unweighted methods. Jorjani, H., et al. (2003) <doi:10.3168/jds.S0022-0302(03)73646-7>. Schaeffer, L. R. (2014) <http://animalbiosciences.uoguelph.ca/~lrs/ELARES/PDforce.pdf>.

Addressing a central challenge encountered in Mendelian randomization (MR) studies, where MR primarily focuses on discerning the effects of individual exposures on specific outcomes and establishes causal links between them. Using a network-based methodology, the intricacy involving interdependent outcomes due to numerous factors has been tackled through this routine. Based on Ni et al. (2018) <doi:10.1214/17-BA1087>, MR.RGM extends to a broader exploration of the causal landscape by leveraging on network structures and involves the construction of causal graphs that capture interactions between response variables and consequently between responses and instrument variables. The resulting Graph visually represents these causal connections, showing directed edges with effect sizes labeled. MR.RGM facilitates the navigation of various data availability scenarios effectively by accommodating three input formats, i.e., individual-level data and two types of summary-level data. The method also optionally incorporates measured covariates (when available) and allows flexible modeling of the error variance structure, including correlated errors that may reflect unmeasured confounding among responses. In the process, causal effects, adjacency matrices, and other essential parameters of the complex biological networks, are estimated. Besides, MR.RGM provides uncertainty quantification for specific network structures among response variables. Parts of the Inverse Wishart sampler are adapted from the econ722 repository by DiTraglia (GPL-2.0).

This package provides essential tools for the pre-processing techniques of matching and weighting multiply imputed datasets. The package includes functions for matching within and across multiply imputed datasets using various methods, estimating weights for units in the imputed datasets using multiple weighting methods, calculating causal effect estimates in each matched or weighted dataset using parametric or non-parametric statistical models, and pooling the resulting estimates according to Rubin's rules (please see <https://journal.r-project.org/archive/2021/RJ-2021-073/> for more details).

Implementations of various robust and flexible model-based clustering methods for data sets with missing values at random (Tong and Tortora, 2025, <doi:10.18637/jss.v115.i03>). Two main models are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, <doi:10.1007/s11634-021-00476-1>) and Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>). Mixtures via some special or limiting cases of the multivariate generalized hyperbolic distribution are also included: Normal-Inverse Gaussian, Symmetric Normal-Inverse Gaussian, Skew-Cauchy, Cauchy, Skew-t, Student's t, Normal, Symmetric Generalized Hyperbolic, Hyperbolic Univariate Marginals, Hyperbolic, and Symmetric Hyperbolic. Funding: This work was partially supported by the National Science foundation NSF Grant NO. 2209974.

Uses the metadata information stored in metacore objects to check and build metadata associated columns.

Approximate node interaction parameters of Markov Random Fields graphical networks. Models can incorporate additional covariates, allowing users to estimate how interactions between nodes in the graph are predicted to change across covariate gradients. The general methods implemented in this package are described in Clark et al. (2018) <doi:10.1002/ecy.2221>.

Bindings for hierarchical regression models for use with the parsnip package. Models include longitudinal generalized linear models (Liang and Zeger, 1986) <doi:10.1093/biomet/73.1.13>, and mixed-effect models (Pinheiro and Bates) <doi:10.1007/978-1-4419-0318-1_1>.

Computes multiple correlation coefficient when the data matrix is given and tests its significance.