Use probability theory under the Bayesian framework for calculating the risk of selecting candidates in a multi-environment context. Contained are functions used to fit a Bayesian multi-environment model (based on the available presets), extract posterior values and maximum posterior values, compute the variance components, check the modelâ s convergence, and calculate the probabilities. For both across and within-environments scopes, the package computes the probability of superior performance and the pairwise probability of superior performance. Furthermore, the probability of superior stability and the pairwise probability of superior stability across environments is estimated. A joint probability of superior performance and stability is also provided.

This package provides tools for calculating and viewing topological properties of phylogenetic trees.

Allows for data to be transformed before using it to construct models. Builds structures to allow functions in the PMML package to output transformation details in addition to the model in the resulting PMML file. The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://www.dmg.org>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms.

Given an arbitrary set of spatial regions and road networks, generate a set of representative points, or pseudohouseholds, that can be used for travel burden analysis. Parallel processing is supported.

Bivariate additive categorical regression via penalized maximum likelihood. Under a multinomial framework, the method fits bivariate models where both responses are nominal, ordinal, or a mix of the two. Partial proportional odds models are supported, with flexible (non-)uniform association structures. Various logit types and parametrizations can be specified for both marginals and the association, including Daleâ s model. The association structure can be regularized using polynomial-type penalty terms. Additive effects are modeled using P-splines. Standard methods such as summary(), residuals(), and predict() are available.

The Piece-wise exponential (Additive Mixed) Model (PAMM; Bender and others (2018) <doi: 10.1177/1471082X17748083>) is a powerful model class for the analysis of survival (or time-to-event) data, based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2019)), as well as support for left-truncated data as well as competing risks, recurrent events and multi-state settings. pammtools provides tidy workflow for survival analysis with PAMMs, including data simulation, transformation and other functions for data preprocessing and model post-processing as well as visualization.

Enhanced RTF wrapper written in R for use with existing R tables packages such as Huxtable or GT'. This package fills a gap where tables in certain packages can be written out to RTF, but cannot add certain metadata or features to the document that are required/expected in a report for a regulatory submission, such as multiple levels of titles and footnotes, making the document landscape, and controlling properties such as margins.

Levels and changes of productivity and profitability are measured with various indices. The package contains the multiplicatively complete Färe-Primont, Fisher, Hicks-Moorsteen, Laspeyres, Lowe, and Paasche indices, as well as the classic Malmquist productivity index. Färe-Primont and Lowe indices verify the transitivity property and can therefore be used for multilateral or multitemporal comparison. Fisher, Hicks-Moorsteen, Laspeyres, Malmquist, and Paasche indices are not transitive and are only to be used for binary comparison. All indices can also be decomposed into different components, providing insightful information on the sources of productivity and profitability changes. In the use of Malmquist productivity index, the technological change index can be further decomposed into bias technological change components. The package also allows to prohibit technological regression (negative technological change). In the case of the Fisher, Hicks-Moorsteen, Laspeyres, Paasche and the transitive Färe-Primont and Lowe indices, it is furthermore possible to rule out technological change. Deflated shadow prices can also be obtained. Besides, the package allows parallel computing as an option, depending on the user's computer configuration. All computations are carried out with the nonparametric Data Envelopment Analysis (DEA), and several assumptions regarding returns to scale are available. All DEA linear programs are implemented using lp_solve'.

The calculation of p-variation of the finite sample data. This package is a realisation of the procedure described in Butkus, V. & Norvaisa, R. Lith Math J (2018). <doi: 10.1007/s10986-018-9414-3> The formal definitions and reference into literature are given in vignette.

This package provides a function for estimating the transition probabilities in an illness-death model. The transition probabilities can be estimated from the unsmoothed landmark estimators developed by de Una-Alvarez and Meira-Machado (2015) <doi:10.1111/biom.12288>. Presmoothed estimates can also be obtained through the use of a parametric family of binary regression curves, such as logit, probit or cauchit. The additive logistic regression model and nonparametric regression are also alternatives which have been implemented. The idea behind the presmoothed landmark estimators is to use the presmoothing techniques developed by Cao et al. (2005) <doi:10.1007/s00180-007-0076-6> in the landmark estimation of the transition probabilities.

Extracts features from amplification curve data of quantitative Polymerase Chain Reactions (qPCR) according to Pabinger et al. 2014 <doi:10.1016/j.bdq.2014.08.002> for machine learning purposes. Helper functions prepare the amplification curve data for processing as functional data (e.g., Hausdorff distance) or enable the plotting of amplification curve classes (negative, ambiguous, positive). The hookreg() and hookregNL() functions of Burdukiewicz et al. (2018) <doi:10.1016/j.bdq.2018.08.001> can be used to predict amplification curves with an hook effect-like curvature. The pcrfit_single() function can be used to extract features from an amplification curve.

Visualize the partitions of simple decision trees, involving one or two predictors, on the scale of the original data. Provides an intuitive alternative to traditional tree diagrams, by visualizing how a decision tree divides the predictor space in a simple 2D plot alongside the original data. The parttree package supports both classification and regression trees from rpart and partykit', as well as trees produced by popular frontend systems like tidymodels and mlr3'. Visualization methods are provided for both base R graphics and ggplot2'.

This package implements the Bi-objective Lexicographical Classification method and Performance Assessment Ratio at 10% metric for algorithm classification. Constructs matrices representing algorithm performance under multiple criteria, facilitating decision-making in algorithm selection and evaluation. Analyzes and compares algorithm performance based on various metrics to identify the most suitable algorithms for specific tasks. This package includes methods for algorithm classification and evaluation, with examples provided in the documentation. Carvalho (2019) presents a statistical evaluation of algorithmic computational experimentation with infeasible solutions <doi:10.48550/arXiv.1902.00101>. Moreira and Carvalho (2023) analyze power in preprocessing methodologies for datasets with missing values <doi:10.1080/03610918.2023.2234683>.

It enables sparklyr to integrate with Spark Connect', and Databricks Connect by providing a wrapper over the PySpark python library.

This package provides a collection of methods for commonly undertaken analytical tasks, primarily developed for Public Health Scotland (PHS) analysts, but the package is also generally useful to others working in the healthcare space, particularly since it has functions for working with Community Health Index (CHI) numbers. The package can help to make data manipulation and analysis more efficient and reproducible.

This package creates PRISMA <http://prisma-statement.org/> diagram from a minimal dataset of included and excluded studies and allows for more custom diagrams. PRISMA diagrams are used to track the identification, screening, eligibility, and inclusion of studies in a systematic review.

This package provides tools for simplifying the creation and management of data structures suitable for dealing with policy portfolios, that is, two-dimensional spaces of policy instruments and policy targets. The package also allows to generate measures of portfolio characteristics and facilitates their visualization.

This package provides a collection of tools to explore the phylogenetic signal in univariate and multivariate data. The package provides functions to plot traits data against a phylogenetic tree, different measures and tests for the phylogenetic signal, methods to describe where the signal is located and a phylogenetic clustering method.

Conduct dsep tests (piecewise SEM) of a directed, or mixed, acyclic graph without latent variables (but possibly with implicitly marginalized or conditioned latent variables that create dependent errors) based on linear, generalized linear, or additive modelswith or without a nesting structure for the data. Also included are functions to do desp tests step-by-step,exploratory path analysis, and Monte Carlo X2 probabilities. This package accompanies Shipley, B, (2026).Cause and Correlation in Biology: A User's Guide to Path Analysis, StructuralEquations and Causal Inference (3rd edition). Cambridge University Press.

This package implements a unified interface for benchmarking meta-analytic publication bias correction methods through simulation studies (see BartoÅ¡ et al., 2025, <doi:10.48550/arXiv.2510.19489>). It provides 1) predefined data-generating mechanisms from the literature, 2) functions for running meta-analytic methods on simulated data, 3) pre-simulated datasets and pre-computed results for reproducible benchmarks, 4) tools for visualizing and comparing method performance.

This package provides functions for testing phylogenetic niche conservatism, a key prerequisite in community assembly studies. The package integrates global functional trait data across major taxonomic groups and implements methods such as Pagel's Lambda and Blomberg's K to quantify phylogenetic signals in ecological communities. Methods are described in Münkemüller et al. (2012) <doi:10.1111/j.2041-210X.2012.00196.x>.

This package provides tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.

This package provides functions and example data to teach and increase the reproducibility of the methods and code underlying the Propensity to Cycle Tool (PCT), a research project and web application hosted at <https://www.pct.bike/>. For an academic paper on the methods, see Lovelace et al (2017) <doi:10.5198/jtlu.2016.862>.

It estimates power and sample size for Partial Least Squares-based methods described in Andreella, et al., (2024), <doi:10.48550/arXiv.2403.10289>.