This package provides a companion to the Chinese book ``Modern Statistical Graphics''.

This package provides a collection of functions for conducting a meta-analysis with mean differences data. It uses recommended procedures as described in The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).

Extends the mlr3 package with a connector to the package batchtools'. This allows to run large-scale benchmark experiments on scheduled high-performance computing clusters.

Colour palettes and helper functions for visualising Mycobacterium tuberculosis genomic and epidemiological data with ggplot2 and ggtree'. The package provides predefined palettes, scale functions, tree/cladogram helpers, and convenient preview tools to ensure consistent branding in pathogen-omics visualisations. The palettes were developed as part of the mycolorsTB project <https://github.com/PathoGenOmics-Lab/mycolorsTB>.

This package contains the MultiFractal Detrended Fluctuation Analysis (MFDFA), MultiFractal Detrended Cross-Correlation Analysis (MFXDFA), and the Multiscale Multifractal Analysis (MMA). The MFDFA() function proposed in this package was used in Laib et al. (<doi:10.1016/j.chaos.2018.02.024> and <doi:10.1063/1.5022737>). See references for more information. Interested users can find a parallel version of the MFDFA() function on GitHub.

Statistical inference for quadratic functional of the moderate-dimensional linear model in Guo and Cheng (2021) <DOI:10.1080/01621459.2021.1893177>.

Correct identification and handling of missing data is one of the most important steps in any analysis. To aid this process, mde provides a very easy to use yet robust framework to quickly get an idea of where the missing data lies and therefore find the most appropriate action to take. Graham WJ (2009) <doi:10.1146/annurev.psych.58.110405.085530>.

Information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru.

An implementation for multivariate functional additive mixed models (multiFAMM), see Volkmann et al. (2021, <arXiv:2103.06606>). It builds on developed methods for univariate sparse functional regression models and multivariate functional principal component analysis. This package contains the function to run a multiFAMM and some convenience functions useful when working with large models. An additional package on GitHub contains more convenience functions to reproduce the analyses of the corresponding paper (<https://github.com/alexvolkmann/multifammPaper>).

The mlrMBO package can ordinarily not be used for optimization within mlr3', because of incompatibilities of their respective class systems. mlrintermbo offers a compatibility interface that provides mlrMBO as an mlr3tuning Tuner object, for tuning of machine learning algorithms within mlr3', as well as a bbotk Optimizer object for optimization of general objective functions using the bbotk black box optimization framework. The control parameters of mlrMBO are faithfully reproduced as a paradox ParamSet'.

This package implements the Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge ('MOC-GaPBK') proposed by Parraga-Alava and others (2018) <doi:10.1186/s13040-018-0178-4>. The algorithm performs gene clustering using NSGA-II as the underlying multi-objective evolutionary engine, together with Path-Relinking and Pareto Local Search as intensification and diversification strategies. Two versions of the Xie-Beni validity index are used as objective functions, one per distance matrix, so that prior biological knowledge can be incorporated through the second matrix.

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>.

Climate-sensitive, single-tree forest simulator based on data-driven machine learning. It simulates the main forest processesâ radial growth, height growth, mortality, crown recession, regeneration, and harvestingâ so users can assess stand development under climate and management scenarios. The height model is described by Skudnik and JevÅ¡enak (2022) <doi:10.1016/j.foreco.2022.120017>, the basal-area increment model by JevÅ¡enak and Skudnik (2021) <doi:10.1016/j.foreco.2020.118601>, and an overview of the MLFS package, workflow, and applications is provided by JevÅ¡enak, ArniÄ , Krajnc, and Skudnik (2023), Ecological Informatics <doi:10.1016/j.ecoinf.2023.102115>.

This package provides tools for likelihood-based inference in one-dimensional stochastic differential equations with mixed effects using expectationâ maximization (EM) algorithms. The package supports Wiener and Ornsteinâ Uhlenbeck diffusion processes with user-specified drift functions, allowing flexible parametric forms including polynomial, exponential, and trigonometric structures. Estimation is performed via Markov chain Monte Carlo EM.

This package provides a standalone package combining several stop-word lists for 65 languages with a median of 329 stop words for language and over 1,000 entries for English, Breton, Latin, Slovenian, and Ancient Greek! The user automatically gets access to all the unique stop words contained in: the StopwordISO repository; the Natural Language Toolkit for python'; the Snowball stop-word list; the R package quanteda'; the marimo repository; the Perseus project; and A. Berra's list of stop words for Ancient Greek and Latin.

Dimension-reduction methods aim at defining a score that maximizes signal diversity. Three approaches, tree weight, maximum entropy weights, and maximum variance weights are provided. These methods are described in He and Fong (2019) <DOI:10.1002/sim.8212>.

This package provides functions for carrying out nonparametric hypothesis tests of the MCAR hypothesis based on the theory of Frechet classes and compatibility. Also gives functions for computing halfspace representations of the marginal polytope and related geometric objects.

This package contains a collection of datasets for working with machine learning tasks. It will contain datasets for supervised machine learning Jiang (2020)<doi:10.1016/j.beth.2020.05.002> and will include datasets for classification and regression. The aim of this package is to use data generated around health and other domains.

This package provides a suite of tools for transforming an existing workflow into a self-documenting pipeline with very minimal upfront costs. Segments of the pipeline are specified in much the same way a Make rule is, by declaring an executable recipe (which might be an R script), along with the corresponding targets and dependencies. When the entire pipeline is run through, only those recipes that need to be executed will be. Meanwhile, execution metadata is captured behind the scenes for later inspection.

This package contains functions for converting existing HTML/JavaScript source into equivalent shiny functions. Bootstraps the process of making new shiny functions by allowing us to turn HTML snippets directly into R functions.

This package provides a simple way to memoize function results to improve performance by eliminating unnecessary computation or data retrieval activities.

The mycobacrvR package contains utilities to provide detailed information for B cell and T cell epitopes for predicted adhesins from various servers such as ABCpred, Bcepred, Bimas, Propred, NetMHC and IEDB. Please refer the URL below to download data files (data_mycobacrvR.zip) used in functions of this package.

This is the very popular mine sweeper game! The game requires you to find out tiles that contain mines through clues from unmasking neighboring tiles. Each tile that does not contain a mine shows the number of mines in its adjacent tiles. If you unmask all tiles that do not contain mines, you win the game; if you unmask any tile that contains a mine, you lose the game. For further game instructions, please run `help(run_game)` and check details. This game runs in X11-compatible devices with `grDevices::x11()`.

Multi-criteria design of experiments algorithm that simultaneously optimizes up to six different criteria ('I', Id', D', Ds', A and As'). The algorithm finds the optimal Pareto front and, if requested, selects a possible symmetrical design on it. The symmetrical design is selected based on two techniques: minimum distance with the Utopia point or the TOPSIS approach.