PDF is a standard file format for laying out text and images in documents. At its core, these documents are sequences of objects defined in plain text. This package allows for the creation of PDF documents at a very low level without any library or graphics device dependencies.

An implementation of the mixed neighbourhood selection (MNS) algorithm. The MNS algorithm can be used to estimate multiple related precision matrices. In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis.

Simulation and visualization of complex models for longitudinal data. The models are encoded using the model coding language Mlxtran and automatically converted into C++ codes. That allows one to implement very easily complex ODE-based models and complex statistical models, including mixed effects models, for continuous, count, categorical, and time-to-event data.

Procedures to simulate, estimate and diagnose MGARCH processes of BEKK and multivariate GJR (bivariate asymmetric GARCH model) specification.

This package provides data about morphemes, the smallest units of meaning in a language.

The algorithms implemented here are used to detect the community structure of a network. These algorithms follow different approaches, but are all based on the concept of modularity maximization.

Core functions as well as diagnostic and calibration tools for combining matching and linear regression for causal inference in observational studies.

This package performs maximum a posteriori Bayesian estimation of individual pharmacokinetic parameters from a model defined in mrgsolve', typically for model-based therapeutic drug monitoring. Internally computes an objective function value from model and data, performs optimization and returns predictions in a convenient format. The performance of the package was described by Le Louedec et al (2021) <doi:10.1002/psp4.12689>.

Create minimal, responsive, and style-agnostic HTML documents with the lightweight CSS frameworks such as sakura', Water.css', and spcss'. Powerful features include table of contents floating as a sidebar, folding codes and results, and more.

This is the core package offering a portal to the many packages universe. It includes functions to help researchers access, work across, and maintain ensembles of datasets on global governance called datacubes.

Code to support a systems biology research program from inception through publication. The methods focus on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations. The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques. As a result, we encourage suggested additions to the package, both methodological and graphical.

This package provides implementations of functions that can be used to test multivariate integration routines. The package covers six different integration domains (unit hypercube, unit ball, unit sphere, standard simplex, non-negative real numbers and R^n). For each domain several functions with different properties (smooth, non-differentiable, ...) are available. The functions are available in all dimensions n >= 1. For each function the exact value of the integral is known and implemented to allow testing the accuracy of multivariate integration routines. Details on the available test functions can be found at on the development website.

This package performs multi-omic differential network analysis by revealing differential interactions between molecular entities (genes, proteins, transcription factors, or other biomolecules) across the omic datasets provided. For each omic dataset, a differential network is constructed where links represent statistically significant differential interactions between entities. These networks are then integrated into a comprehensive visualization using distinct colors to distinguish interactions from different omic layers. This unified display allows interactive exploration of cross-omic patterns, such as differential interactions present at both transcript and protein levels. For each link, users can access differential statistical significance metrics (p values or adjusted p values, calculated via robust or traditional linear regression with interaction term) and differential regression plots. The methods implemented in this package are described in Sciacca et al. (2023) <doi:10.1093/bioinformatics/btad192>.

This package provides an extensive collection of datasets related to medicine, diseases, treatments, drugs, and public health. This package covers topics such as drug effectiveness, vaccine trials, survival rates, infectious disease outbreaks, and medical treatments. The included datasets span various health conditions, including AIDS, cancer, bacterial infections, and COVID-19, along with information on pharmaceuticals and vaccines. These datasets are sourced from the R ecosystem and other R packages, remaining unaltered to ensure data integrity. This package serves as a valuable resource for researchers, analysts, and healthcare professionals interested in conducting medical and public health data analysis in R.

Automated calculation and visualization of survey data statistics on a color-coded (choropleth) map.

This package provides a GUI with which users can construct and interact with Multibiplot Analysis.

Gibbs sampler for fitting multivariate Bayesian linear regression with shrinkage priors (MBSP), using the three parameter beta normal family. The method is described in Bai and Ghosh (2018) <doi:10.1016/j.jmva.2018.04.010>.

Two distinct but related statistical approaches to the problem of identifying the combinations of medication error characteristics that are more likely to result in harm are implemented in this package: 1) a Bayesian hierarchical model with optimal Bayesian ranking on the log odds of harm, and 2) an empirical Bayes model that estimates the ratio of the observed count of harm to the count that would be expected if error characteristics and harm were independent. In addition, for the Bayesian hierarchical model, the package provides functions to assess the sensitivity of results to different specifications of the random effects distributions.

This package implements Gibbs sampling and Bayes factors for multinomial models with linear inequality constraints on the vector of probability parameters. As special cases, the model class includes models that predict a linear order of binomial probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture models assuming that the parameter vector p must be inside the convex hull of a finite number of predicted patterns (i.e., vertices). A formal definition of inequality-constrained multinomial models and the implemented computational methods is provided in: Heck, D.W., & Davis-Stober, C.P. (2019). Multinomial models with linear inequality constraints: Overview and improvements of computational methods for Bayesian inference. Journal of Mathematical Psychology, 91, 70-87. <doi:10.1016/j.jmp.2019.03.004>. Inequality-constrained multinomial models have applications in the area of judgment and decision making to fit and test random utility models (Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011). Transitivity of preferences. Psychological Review, 118, 42â 56, <doi:10.1037/a0021150>) or to perform outcome-based strategy classification to select the decision strategy that provides the best account for a vector of observed choice frequencies (Heck, D.W., Hilbig, B.E., & Moshagen, M. (2017). From information processing to decisions: Formalizing and comparing probabilistic choice models. Cognitive Psychology, 96, 26â 40. <doi:10.1016/j.cogpsych.2017.05.003>).

This package provides methods for estimating and utilizing the multivariate generalized propensity score (mvGPS) for multiple continuous exposures described in Williams, J.R, and Crespi, C.M. (2020) <arxiv:2008.13767>. The methods allow estimation of a dose-response surface relating the joint distribution of multiple continuous exposure variables to an outcome. Weights are constructed assuming a multivariate normal density for the marginal and conditional distribution of exposures given a set of confounders. Confounders can be different for different exposure variables. The weights are designed to achieve balance across all exposure dimensions and can be used to estimate dose-response surfaces.

Allows users to simulate matrix population models with particular characteristics based on aspects of life history such as mortality trajectories and fertility trajectories. Also allows the exploration of sampling error due to small sample size.

This package provides functions for adaptive parallel tempering (APT) with NIMBLE models. Adapted from Lacki & Miasojedow (2016) <DOI:10.1007/s11222-015-9579-0> and Miasojedow, Moulines and Vihola (2013) <DOI:10.1080/10618600.2013.778779>.

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

Helps a clinical trial team discuss the clinical goals of a well-defined biomarker with a diagnostic, staging, prognostic, or predictive purpose. From this discussion will come a statistical plan for a (non-randomized) validation trial. Both prospective and retrospective trials are supported. In a specific focused discussion, investigators should determine the range of "discomfort" for the NNT, number needed to treat. The meaning of the discomfort range, [NNTlower, NNTupper], is that within this range most physicians would feel discomfort either in treating or withholding treatment. A pair of NNT values bracketing that range, NNTpos and NNTneg, become the targets of the study's design. If the trial can demonstrate that a positive biomarker test yields an NNT less than NNTlower, and that a negative biomarker test yields an NNT less than NNTlower, then the biomarker may be useful for patients. A highlight of the package is visualization of a "contra-Bayes" theorem, which produces criteria for retrospective case-controls studies.