Estimation, inference and forecasting using the Bayesian approach for multivariate threshold autoregressive (TAR) models in which the distribution used to describe the noise process belongs to the class of Gaussian variance mixtures.

Estimates out-of-sample R² through bootstrap or cross-validation as a measure of predictive performance. In addition, a standard error for this point estimate is provided, and confidence intervals are constructed.

Enables the removal of training data from fitted R models while retaining predict functionality. The purged models are more portable as their memory footprints do not scale with the training sample size.

This package provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson. 2014. An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC.

This package provides functionality for simulating data generation processes across various spatial regression models, conceptually aligned with the dgp module of the Python library spreg <https://pysal.org/spreg/api.html#dgp>.

This package provides a suite of plots for displaying variable importance and two-way variable interaction jointly. Can also display partial dependence plots laid out in a pairs plot or zenplots style.

This package provides a toolkit to set up an R data package in a consistent structure. Automates tasks like tidy data export, data dictionary documentation, README and website creation, and citation management.

This R/Bioconductor package provides an interface between HDF5 and R. HDF5's main features are the ability to store and access very large and/or complex datasets and a wide variety of metadata on mass storage (disk) through a completely portable file format. The rhdf5 package is thus suited for the exchange of large and/or complex datasets between R and other software package, and for letting R applications work on datasets that are larger than the available RAM.

The Kolmogorov-Smirnov (K-S) statistic is a standard method to measure the model strength for credit risk scoring models. This package calculates the Kâ S statistic and plots the true-positive rate and false-positive rate to measure the model strength. This package was written with the credit marketer, who uses risk models in conjunction with his campaigns. The users could read more details from Thrasher (1992) <doi:10.1002/dir.4000060408> and pyks <https://pypi.org/project/pyks/>.

This package provides methods for model building and model evaluation of mixed effects models using Monolix <https://monolix.lixoft.com>. Monolix is a software tool for nonlinear mixed effects modeling that must have been installed in order to use Rsmlx'. Among other tasks, Rsmlx provides a powerful tool for automatic PK model building, performs statistical tests for model assessment, bootstrap simulation and likelihood profiling for computing confidence intervals. Rsmlx also proposes several automatic covariate search methods for mixed effects models.

This package provides a common framework for optimization of black-box functions for other packages, e.g. mlr3. It offers various optimization methods e.g. grid search, random search and generalized simulated annealing.

This package provides a simple interface to lat/long projection and datum transformation of the PROJ.4 cartographic projections library. It allows transformation of geographic coordinates from one projection and/or datum to another.

This package provides tools to generate a violin point plot, a combination of a violin/histogram plot and a scatter plot by offsetting points within a category based on their density using quasirandom noise.

LIGER is a package for integrating and analyzing multiple single-cell datasets, developed and maintained by the Macosko lab. It relies on integrative non-negative matrix factorization to identify shared and dataset-specific factors.

The rcshist utility displays the complete revision history of a set of RCS files including log messages and patches. It can also display the patch associated with a particular revision of an RCS file.

iSEEu (the iSEE universe) contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels, or modes allowing easy configuration of iSEE applications.

The PLIER (Probe Logarithmic Error Intensity Estimate) method produces an improved signal by accounting for experimentally observed patterns in probe behavior and handling error at the appropriately at low and high signal values.

Data sets are referred to in the text "Applied Survival Analysis Using R" by Dirk F. Moore, Springer, 2016, ISBN: 978-3-319-31243-9, <DOI:10.1007/978-3-319-31245-3>.

This package provides a collection of datasets on the Alone survival TV series in tidy format. Included in the package are 4 datasets detailing the survivors, their loadouts, episode details and season information.

Bayesian Hierarchical beta-binomial models for modeling cell population to predictors/exposures. This package utilizes runjags to run Gibbs sampling, parallelizing the chains. Options for different covariances/relationship structures between parameters of interest.

Connect to the California Irrigation Management Information System (CIMIS) Web API. See the CIMIS main page <https://cimis.water.ca.gov> and web API documentation <https://et.water.ca.gov> for more information.

Calculate the R-squared, aka explained randomness, based on the partial likelihood ratio statistic under the Cox Proportional Hazard model [J O'Quigley, R Xu, J Stare (2005) <doi:10.1002/sim.1946>].

Allows to visualize high-density electroencephalography (HD-EEG) data through interactive plots and animations, enabling exploratory and communicative analysis of temporal-spatial brain signals. Funder: Masaryk University (Grant No. MUNI/A/1457/2023).

Fit a fractional binomial regression model and extended zero-inflated negative binomial regression model to count data with excess zeros using maximum likelihood estimation. Compare zero-inflated regression models via Vuong closeness test.