Simulating and estimating (regime-switching) Markov chain Gaussian fields with covariance functions of the Gneiting class (Gneiting 2002) <doi:10.1198/016214502760047113>. It supports parameter estimation by weighted least squares and maximum likelihood methods, and produces Kriging forecasts and intervals for existing and new locations.

This package provides tools for multivariate analyses of morphological data, wrapped in one package, to make the workflow convenient and fast. Statistical and graphical tools provide a comprehensive framework for checking and manipulating input data, statistical analyses, and visualization of results. Several methods are provided for the analysis of raw data, to make the dataset ready for downstream analyses. Integrated statistical methods include hierarchical classification, principal component analysis, principal coordinates analysis, non-metric multidimensional scaling, and multiple discriminant analyses: canonical, stepwise, and classificatory (linear, quadratic, and the non-parametric k nearest neighbours). The philosophy of the package is described in Å lenker et al. 2022.

This package contains functions intended to facilitate the production of plant taxonomic monographs. The package includes functions to convert tables into taxonomic descriptions, lists of collectors, examined specimens, identification keys (dichotomous and interactive), and can generate a monograph skeleton. Additionally, wrapper functions to batch the production of phenology histograms and distributional and diversity maps are also available.

Extends the mlr3 ecosystem to functional analysis by adding support for irregular and regular functional data as defined in the tf package. The package provides PipeOps for preprocessing functional columns and for extracting scalar features, thereby allowing standard machine learning algorithms to be applied afterwards. Available operations include simple functional features such as the mean or maximum, smoothing, interpolation, flattening, and functional PCA'.

Calculate the maximal fat oxidation, the exercise intensity that elicits the maximal fat oxidation and the SIN model to represent the fat oxidation kinetics. Three variables can be obtained from the SIN model: dilatation, symmetry and translation. Examples of these methods can be found in Montes de Oca et al (2021) <doi:10.1080/17461391.2020.1788650> and Chenevière et al. (2009) <doi:10.1249/MSS.0b013e31819e2f91>.

This package provides tools and functions to fit a multilevel index of dissimilarity.

Package for combined miRNA- and mRNA-testing.

Automate the explanatory analysis of machine learning predictive models. Generate advanced interactive model explanations in the form of a serverless HTML site with only one line of code. This tool is model-agnostic, therefore compatible with most of the black-box predictive models and frameworks. The main function computes various (instance and model-level) explanations and produces a customisable dashboard, which consists of multiple panels for plots with their short descriptions. It is possible to easily save the dashboard and share it with others. modelStudio facilitates the process of Interactive Explanatory Model Analysis introduced in Baniecki et al. (2023) <doi:10.1007/s10618-023-00924-w>.

The Markov Decision Processes (MDP) toolbox proposes functions related to the resolution of discrete-time Markov Decision Processes: finite horizon, value iteration, policy iteration, linear programming algorithms with some variants and also proposes some functions related to Reinforcement Learning.

It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2023) <doi:10.18637/jss.v106.i03>).

This package provides a comprehensive package for comparing multiple Structural Equation Models (SEM). Supports both nested and non-nested model comparisons, chi-square difference tests, and extraction of multiple fit indices including AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), CFI (Comparative Fit Index), TLI (Tucker-Lewis Index), RMSEA (Root Mean Square Error of Approximation), and SRMR (Standardized Root Mean Square Residual). Built on top of the lavaan package for seamless SEM model comparison workflows. The Vuong test (Vuong, 1989) for non-nested models is used as the statistical test.

Some basic math calculators for finding angles for triangles and for finding the greatest common divisor of two numbers and so on.

Estimation and comparison of the performances of diagnostic tests in multi-reader multi-case studies where true case statuses (or ground truths) are known and one or more readers provide test ratings for multiple cases. Reader performance metrics are provided for area under and expected utility of ROC curves, likelihood ratio of positive or negative tests, and sensitivity and specificity. ROC curves can be estimated empirically or with binormal or binormal likelihood-ratio models. Statistical comparisons of diagnostic tests are based on the ANOVA model of Obuchowski-Rockette and the unified framework of Hillis (2005) <doi:10.1002/sim.2024>. The ANOVA can be conducted with data from a full factorial, nested, or partially paired study design; with random or fixed readers or cases; and covariances estimated with the DeLong method, jackknifing, or an unbiased method. Smith and Hillis (2020) <doi:10.1117/12.2549075>.

Fits mixed membership models with discrete multivariate data (with or without repeated measures) following the general framework of Erosheva et al (2004). This package uses a Variational EM approach by approximating the posterior distribution of latent memberships and selecting hyperparameters through a pseudo-MLE procedure. Currently supported data types are Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al (2014) for other examples of mixed membership models.

Data and examples from a multilevel modelling software review as well as other well-known data sets from the multilevel modelling literature.

Convenient wrapper functions for the analysis of matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) spectra data in order to select only representative spectra (also called cherry-pick). The package covers the preprocessing and dereplication steps (based on Strejcek, Smrhova, Junkova and Uhlik (2018) <doi:10.3389/fmicb.2018.01294>) needed to cluster MALDI-TOF spectra before the final cherry-picking step. It enables the easy exclusion of spectra and/or clusters to accommodate complex cherry-picking strategies. Alternatively, cherry-picking using taxonomic identification MALDI-TOF data is made easy with functions to import inconsistently formatted reports.

Palettes Inspired by Works at the Metropolitan Museum of Art in New York. Currently contains over 50 color schemes and checks for colorblind-friendliness of palettes. Colorblind accessibility checked using the colorblindcheck package by Jakub Nowosad'<https://jakubnowosad.com/colorblindcheck/>.

This package provides a flexible framework for fitting multivariate ordinal regression models with composite likelihood methods. Methodological details are given in Hirk, Hornik, Vana (2020) <doi:10.18637/jss.v093.i04>.

Given a CSV file with titles and abstracts, the package creates a document-term matrix that is lemmatized and stemmed and can directly be used to train machine learning methods for automatic title-abstract screening in the preparation of a meta analysis.

This package implements likelihood-based estimation and diagnostics for multi-type recurrent event data with dynamic risk that depends on prior events and accommodates terminating events. Methods are described in Ghosh, Chan, Younes and Davis (2023) "A Dynamic Risk Model for Multitype Recurrent Events" <doi:10.1093/aje/kwac213>.

Estimation of marginal hazard ratios in clustered failure time data. It implements the weighted generalized estimating equation approach based on a semiparametric marginal proportional hazards model (See Niu, Y. Peng, Y.(2015). "A new estimating equation approach for marginal hazard ratio estimation"), accounting for within-cluster correlations. 5 different correlation structures are supported. The package is designed for researchers in biostatistics and epidemiology who require accurate and efficient estimation methods for survival analysis in clustered data settings.

Multi Calculator of different scores to measure adherence to Mediterranean Diet, to compute them in nutriepidemiological data. Additionally, a sample dataset of this kind of data is provided, and some other minor tools useful in epidemiological studies.

The routine twosample_test() in this package runs the two-sample test using various test statistic for multivariate data. The user can also run several tests and then find a p value adjusted for simultaneous inference. The p values are found via permutation or via the parametric bootstrap. The routine twosample_power() allows the estimation of the power of the tests. The routine run.studies() allows a user to quickly study the power of a new method and how it compares to those included in the package. For details of the methods and references see the included vignettes.

This package provides functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure. For details see Kourentzes et al. (2014) <doi:10.1016/j.ijforecast.2013.09.006>.