Uses three different correlation coefficients to calculate measurement-level adequate correlations in a feature matrix: Pearson product-moment correlation coefficient, Intraclass correlation and Cramer's V.

Diagnostic plots for optimisation, with a focus on projection pursuit. These show paths the optimiser takes in the high-dimensional space in multiple ways: by reducing the dimension using principal component analysis, and also using the tour to show the path on the high-dimensional space. Several botanical colour palettes are included, reflecting the name of the package. A paper describing the methodology can be found at <https://journal.r-project.org/articles/RJ-2021-105/index.html>.

Maximum Likelihood Estimation of Stochastic Frontier Production and Cost Functions. Two specifications are available: the error components specification with time-varying efficiencies (Battese and Coelli, 1992, <doi:10.1007/BF00158774>) and a model specification in which the firm effects are directly influenced by a number of variables (Battese and Coelli, 1995, <doi:10.1007/BF01205442>).

This package implements the Mode Jumping Markov Chain Monte Carlo algorithm described in <doi:10.1016/j.csda.2018.05.020> and its Genetically Modified counterpart described in <doi:10.1613/jair.1.13047> as well as the sub-sampling versions described in <doi:10.1016/j.ijar.2022.08.018> for flexible Bayesian model selection and model averaging.

It implements many univariate and multivariate permutation (and rotation) tests. Allowed tests: the t one and two samples, ANOVA, linear models, Chi Squared test, rank tests (i.e. Wilcoxon, Mann-Whitney, Kruskal-Wallis), Sign test and Mc Nemar. Test on Linear Models are performed also in presence of covariates (i.e. nuisance parameters). The permutation and the rotation methods to get the null distribution of the test statistics are available. It also implements methods for multiplicity control such as Westfall & Young minP procedure and Closed Testing (Marcus, 1976) and k-FWER. Moreover, it allows to test for fixed effects in mixed effects models.

Simulates plot data in multi-environment field trials with one or more traits. Its core function generates plot errors that capture spatial trend, random error (noise), and extraneous variation, which are combined at a user-defined ratio. Phenotypes can be generated by combining the plot errors with simulated genetic values that capture genotype-by-environment (GxE) interaction using wrapper functions for the R package `AlphaSimR`.

Useful functions to standardize software outputs from ProteomeDiscoverer, Spectronaut, DIA-NN and MaxQuant on precursor, modified peptide and proteingroup level and to trace software differences for identifications such as varying proteingroup denotations for common precursor.

The free algebra in R with non-commuting indeterminates. Uses disordR discipline (Hankin, 2022, <doi:10.48550/ARXIV.2210.03856>). To cite the package in publications please use Hankin (2022) <doi:10.48550/ARXIV.2211.04002>.

The ability to tune models is important. finetune enhances the tune package by providing more specialized methods for finding reasonable values of model tuning parameters. Two racing methods described by Kuhn (2014) <doi:10.48550/arXiv.1405.6974> are included. An iterative search method using generalized simulated annealing (Bohachevsky, Johnson and Stein, 1986) <doi:10.1080/00401706.1986.10488128> is also included.

This package provides an implementation of finite mixture regression models for censored data under four distributional families: Normal (FM-NCR), Student t (FM-TCR), skew-Normal (FM-SNCR), and skew-t (FM-STCR). The package enables flexible modeling of skewness and heavy tails often observed in real-world data, while explicitly accounting for censoring. Functions are included for parameter estimation via the Expectation-Maximization (EM) algorithm, computation of standard errors, and model comparison criteria such as the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the Efficient Determination Criterion (EDC). The underlying methodology is described in Park et al. (2024) <doi:10.1007/s00180-024-01459-4>.

Transformations that allow obtaining a flat table from reports in text or Excel format that contain data in the form of pivot tables. They can be defined for a single report and applied to a set of reports.

This package provides a shiny design of experiments (DOE) app that aids in the creation of traditional, un-replicated, augmented and partially-replicated designs applied to agriculture, plant breeding, forestry, animal and biological sciences.

Perform robust inference based on applying Fast and Robust Bootstrap on robust estimators (Van Aelst and Willems (2013) <doi:10.18637/jss.v053.i03>). This method constitutes an alternative to ordinary bootstrap or asymptotic inference. procedures when using robust estimators such as S-, MM- or GS-estimators. The available methods are multivariate regression, principal component analysis and one-sample and two-sample Hotelling tests. It provides both the robust point estimates and uncertainty measures based on the fast and robust bootstrap.

Fast functions for timestamp manipulation that avoid system calls and take shortcuts to facilitate operations on very large data.

Allows prophet models from the prophet package to be used in a tidy workflow with the modelling interface of fabletools'. This extends prophet to provide enhanced model specification and management, performance evaluation methods, and model combination tools.

Single unified interface for end-to-end modelling of regression, categorical and time-to-event (survival) outcomes. Models created using familiar are self-containing, and their use does not require additional information such as baseline survival, feature clustering, or feature transformation and normalisation parameters. Model performance, calibration, risk group stratification, (permutation) variable importance, individual conditional expectation, partial dependence, and more, are assessed automatically as part of the evaluation process and exported in tabular format and plotted, and may also be computed manually using export and plot functions. Where possible, metrics and values obtained during the evaluation process come with confidence intervals.

The CRAN check results and where your package stands in the CRAN submission queue in your R terminal.

This package provides high-level access to neuroimaging data from standard software packages like FreeSurfer <http://freesurfer.net/> on the level of subjects and groups. Load morphometry data, surfaces and brain parcellations based on atlases. Mask data using labels, load data for specific atlas regions only, and visualize data and statistical results directly in R'.

This package provides tools and features for "Exploratory Landscape Analysis (ELA)" of single-objective continuous optimization problems. Those features are able to quantify rather complex properties, such as the global structure, separability, etc., of the optimization problems.

Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) <doi:10.1198/TECH.2009.08104>.

This package provides functions to automate the detection and resolution of taxonomic and stratigraphic errors in fossil occurrence datasets. Functions were developed using data from the Paleobiology Database.

Recent technological advances have enable the simultaneous collection of multi-omics data i.e., different types or modalities of molecular data, presenting challenges for integrative prediction modeling due to the heterogeneous, high-dimensional nature and possible missing modalities of some individuals. We introduce this package for late integrative prediction modeling, enabling modality-specific variable selection and prediction modeling, followed by the aggregation of the modality-specific predictions to train a final meta-model. This package facilitates conducting late integration predictive modeling in a systematic, structured, and reproducible way.

This package performs dose assignment and trial simulation for the FBCRM (Fully Bayesian Continual Reassessment Method) and MFBCRM (Mixture Fully Bayesian Continual Reassessment Method) phase I clinical trial designs. These trial designs extend the Continual Reassessment Method (CRM) and Bayesian Model Averaging Continual Reassessment Method (BMA-CRM) by allowing the prior toxicity skeleton itself to be random, with posterior distributions obtained from Markov Chain Monte Carlo. On average, the FBCRM and MFBCRM methods outperformed the CRM and BMA-CRM methods in terms of selecting an optimal dose level across thousands of randomly generated simulation scenarios. Details on the methods and results of this simulation study are available on request, and the manuscript is currently under review.

Classical (bottom-up and top-down), optimal combination and heuristic point (Di Fonzo and Girolimetto, 2023 <doi:10.1016/j.ijforecast.2021.08.004>) and probabilistic (Girolimetto et al. 2024 <doi:10.1016/j.ijforecast.2023.10.003>) forecast reconciliation procedures for linearly constrained time series (e.g., hierarchical or grouped time series) in cross-sectional, temporal, or cross-temporal frameworks.