Compute the coefficient of determination for outcomes in n-dimensions. May be useful for multidimensional predictions (such as a multinomial model) or calculating goodness of fit from latent variable models such as probabilistic topic models like latent Dirichlet allocation or deterministic topic models like latent semantic analysis. Based on Jones (2019) <doi:10.48550/arXiv.1911.11061>.

Fitting multivariate response models with random effects on one or two levels; whereby the (one-dimensional) random effect represents a latent variable approximating the multivariate space of outcomes, after possible adjustment for covariates. The method is particularly useful for multivariate, highly correlated outcome variables with unobserved heterogeneities. Applications include regression with multivariate responses, as well as multivariate clustering or ranking problems. See Zhang and Einbeck (2024) <doi:10.1007/s42519-023-00357-0>.

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 a comprehensive suite for assessing multivariate normality using six statistical tests (Mardia, Henzeâ Zirkler, Henzeâ Wagner, Royston, Doornikâ Hansen, Energy). Also includes univariate diagnostics, bivariate density visualization, robust outlier detection, power transformations (e.g., Boxâ Cox, Yeoâ Johnson), and imputation strategies ("mean", "median", "mice") for handling missing data. Bootstrap resampling is supported for selected tests to improve p-value accuracy in small samples. Diagnostic plots are available via both ggplot2 and interactive plotly visualizations. See Korkmaz et al. (2014) <https://journal.r-project.org/articles/RJ-2014-031/RJ-2014-031.pdf>.

Generalized Egger tests for detecting publication bias in meta-analysis for diagnostic accuracy test (Noma (2020) <doi:10.1111/biom.13343>, Noma (2022) <doi:10.48550/arXiv.2209.07270>). These publication bias tests are generally more powerful compared with the conventional univariate publication bias tests and can incorporate correlation information between the outcome variables.

This package provides helper functions to access datasets from the Metropolitan Transportation Authority (MTA) portion of the New York State Open Data platform <https://data.ny.gov/>. Returns results as tidy tibbles with support for optional filtering, sorting, and row limits through the Socrata API.

Implementation of the Marine Predators Algorithm (MPA) in R. MPA is a nature-inspired optimization algorithm that follows the rules governing optimal foraging strategy and encounter rate policy between predator and prey in marine ecosystems. Based on the paper by Faramarzi et al. (2020) <doi:10.1016/j.eswa.2020.113377>.

Fit data from a continuous population with a smooth density on finite interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author of this package published in the Journal of Nonparametric Statistics: Guan (2016) <doi:10.1080/10485252.2016.1163349> and Guan (2017) <doi:10.1080/10485252.2017.1374384>. For data with covariates, under some semiparametric regression models such as Cox proportional hazards model and the accelerated failure time model, the baseline survival function can be estimated smoothly based on general interval censored data.

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.

Density computation, random matrix generation, maximum likelihood estimation, and regression for the matrix normal distribution. References: Pocuca N., Gallaugher M. P., Clark K. M. & McNicholas P. D. (2019). Assessing and Visualizing Matrix Variate Normality. <doi:10.48550/arXiv.1910.02859> and the relevant wikipedia page.

Supports matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry workflows from raw Bruker spectra to cohort-level peak matrices. Provides spectrum loading, Savitzky-Golay smoothing, baseline correction (SNIP and TopHat), Gaussian kernel-regression-based peak detection including shoulder peaks, peak-quality assessment, filtering, and cohort feature analysis. Computationally intensive routines are implemented in C++ using Rcpp'. The implemented signal-processing methods include those described by Savitzky and Golay (1964) <doi:10.1021/ac60214a047>, Ryan et al. (1988) <doi:10.1016/0168-583X(88)90063-8>, Stanford, Bagley and Solomon (2016) <doi:10.1186/s12953-016-0107-8>, and Nadaraya-Watson kernel regression (Nadaraya (1964) <doi:10.1137/1109020>; Watson (1964) <https://www.jstor.org/stable/25049340>).

Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) <doi:10.31234/osf.io/qbdjt>.

The primary aim of MasterBayes is to use Markov chain Monte Carlo (MCMC) techniques to integrate over uncertainty in pedigree configurations estimated from molecular markers and phenotypic data (Hadfield et al. (2006) <doi:10.1111/j.1365-294X.2006.03050.x>). Emphasis is put on the marginal distribution of parameters that relate the phenotypic data to the pedigree. All simulation is done in compiled C++ for efficiency.

Using this package, one can determine the minimum sample size required so that the mean square error of the sample mean and the population mean of a distribution becomes less than some pre-determined epsilon, i.e. it helps the user to determine the minimum sample size required to attain the pre-fixed precision level by minimizing the difference between the sample mean and population mean.

Multivariable Fractional Polynomial algorithm for model-building. Fractional polynomials are used to represent curvature in regression models. A key reference is Royston and Altman, 1994.

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'.

Computing the Mann-Whitney effect based on copula models. Estimation of the association parameter in survival copula models. A description of the underlying methods is described in Nakazono et al. (2024) <doi:10.3390/math12101453> and Nakazono et al. (accepted for publication in Statistical Papers).

This package provides a toolbox for modeling manifest and latent group differences and moderation effects in various statistical network models.

Following the common types of measures of uncertainty for parameter estimation, two measures of uncertainty were proposed for model selection, see Liu, Li and Jiang (2020) <doi:10.1007/s11749-020-00737-9>. The first measure is a kind of model confidence set that relates to the variation of model selection, called Mac. The second measure focuses on error of model selection, called LogP. They are all computed via bootstrapping. This package provides functions to compute these two measures. Furthermore, a similar model confidence set adapted from Bayesian Model Averaging can also be computed using this package.

Perform a mail merge (mass email) using the message defined in markdown, the recipients in a csv file, and gmail as the mailing engine. With this package you can parse markdown documents as the body of email, and the yaml header to specify the subject line of the email. Any braces in the email will be encoded with glue::glue()'. You can preview the email in the RStudio viewer pane, and send (draft) email using gmailr'.

Test for independence of two random vectors, learn and report the dependency structure. For more information, see Gorsky, Shai and Li Ma, Multiscale Fisher's Independence Test for Multivariate Dependence, Biometrika, accepted, January 2022.

Multi-Dimensional Analysis (MDA) is an adaptation of factor analysis developed by Douglas Biber (1992) <doi:10.1007/BF00136979>. Its most common use is to describe language as it varies by genre, register, and use. This package contains functions for carrying out the calculations needed to describe and plot MDA results: dimension scores, dimension means, and factor loadings.

Many tools for making, manipulating, and modifying many different types of networks. All functions operate with matrices, edge lists, and igraph', network', and tidygraph objects, on directed, multiplex, multimodal, signed, and other networks. The package includes functions for importing and exporting, creating and generating networks, modifying networks and node and tie attributes, and describing networks with sensible defaults.

Computes densities, probabilities, and random deviates of the Matrix Normal (Pocuca et al. (2019) <doi:10.48550/arXiv.1910.02859>). Also includes simple but useful matrix functions. See the vignette for more information.