Allows the user to access functionality in the CDK', a Java framework for cheminformatics. This allows the user to load molecules, evaluate fingerprints, calculate molecular descriptors and so on. In addition, the CDK API allows the user to view structures in 2D.

This package implements simple Hamiltonian Monte Carlo routines in R for sampling from any desired target distribution which is continuous and smooth. See Neal (2017) <arXiv:1701.02434> for further details on Hamiltonian Monte Carlo. Automatic parameter selection is not supported.

This package provides a wrapper for running the bundled Open-WBO Maximum Satisfiability (MaxSAT) solver (<https://github.com/sat-group/open-wbo>). Users can pass command-line arguments to the solver and capture its output as a character string or file.

This package provides a simple interface to and data from the Human Protein Atlas project.

This package provides tools to fit and compare Gaussian linear and nonlinear mixed-effects models.

This package provides tools for the analysis and visualization of bilateral asymmetry in parasitic infections.

The package is designed to detect marker genes from Microarray gene expression data sets.

This package can optimize the parameter in S-system models given time series data.

Estimating the average causal effect based on the Bayesian Adjustment for Confounding (BAC) algorithm.

Compute multivariate location, scale, and correlation estimates based on Tukey's biweight M-estimator.

Capture code evaluations and script executions by expressions, outputs, and condition calls for logging.

Rudimentary functions for sampling and calculating density from the matrix-variate variance-gamma distribution.

Data sets for the Panel Data Econometrics with R <doi:10.1002/9781119504641> book.

ML and GM estimation and diagnostic testing of econometric models for spatial panel data.

Make it easy to use vue in R with helper dependency functions and examples.

Empirical best linear unbiased prediction (EBLUP) and robust prediction of the area-level means under the basic unit-level model. The model can be fitted by maximum likelihood or a (robust) M-estimator. Mean square prediction error is computed by a parametric bootstrap.

This package provides a novel sufficient-dimension reduction method is robust against outliers using alpha-distance covariance and manifold-learning in dimensionality reduction problems. Please refer Hsin-Hsiung Huang, Feng Yu & Teng Zhang (2024) <doi:10.1080/10485252.2024.2313137> for the details.

An R-shiny application to visualize bio-loggers time series at a microsecond precision as Acceleration, Temperature, Pressure, Light intensity. It is possible to link behavioral labels extracted from BORIS software <http://www.boris.unito.it> or manually written in a csv file.

This package provides a simple implementation of Binary Indexed Tree by R. The BinaryIndexedTree class supports construction of Binary Indexed Tree from a vector, update of a value in the vector and query for the sum of a interval of the vector.

Graphics for statistics on a sphere, as applied to geological fault data, crystallography, earthquake focal mechanisms, radiation patterns, ternary plots and geographical/geological maps. Non-double couple plotting of focal spheres and source type maps are included for statistical analysis of moment tensors.

This package provides a collection of implementations of semi-supervised classifiers and methods to evaluate their performance. The package includes implementations of, among others, Implicitly Constrained Learning, Moment Constrained Learning, the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic Likelihood estimation, S4VM and WellSVM.

An R interface to KEA (Version 5.0). KEA (for Keyphrase Extraction Algorithm) allows for extracting keyphrases from text documents. It can be either used for free indexing or for indexing with a controlled vocabulary. For more information see <http://www.nzdl.org/Kea/>.

This package provides a robust Partial Least-Squares (PLS) method is implemented that is robust to outliers in the residuals as well as to leverage points. A specific weighting scheme is applied which avoids iterations, and leads to a highly efficient robust PLS estimator.

Systematically transform immunoassay data, evaluate if the data is normally distributed, and pick the right method for cut point determination based on that evaluation. This package can also produce plots that are needed for reports, so data analysis and visualization can be done easily.