Allows users to derive multi-objective weights from pairwise comparisons, which research shows is more repeatable, transparent, and intuitive other techniques. These weights can be rank existing alternatives or to define a multi-objective utility function for optimization.

Multi-state models are essential tools in longitudinal data analysis. One primary goal of these models is the estimation of transition probabilities, a critical metric for predicting clinical prognosis across various stages of diseases or medical conditions. Traditionally, inference in multi-state models relies on the Aalen-Johansen (AJ) estimator which is consistent under the Markov assumption. However, in many practical applications, the Markovian nature of the process is often not guaranteed, limiting the applicability of the AJ estimator in more complex scenarios. This package extends the landmark Aalen-Johansen estimator (Putter, H, Spitoni, C (2018) <doi:10.1177/0962280216674497>) incorporating presmoothing techniques described by Soutinho, Meira-Machado and Oliveira (2020) <doi:10.1080/03610918.2020.1762895>, offering a robust alternative for estimating transition probabilities in non-Markovian multi-state models with multiple states and potential reversible transitions.

Evaluate the predictive performance of an existing (i.e. previously developed) prediction/ prognostic model given relevant information about the existing prediction model (e.g. coefficients) and a new dataset. Provides a range of model updating methods that help tailor the existing model to the new dataset; see Su et al. (2018) <doi:10.1177/0962280215626466>. Techniques to aggregate multiple existing prediction models on the new data are also provided; see Debray et al. (2014) <doi:10.1002/sim.6080> and Martin et al. (2018) <doi:10.1002/sim.7586>).

This package provides methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.

R interface to PRIMME <https://www.cs.wm.edu/~andreas/software/>, a C library for computing a few eigenvalues and their corresponding eigenvectors of a real symmetric or complex Hermitian matrix, or generalized Hermitian eigenproblem. It can also compute singular values and vectors of a square or rectangular matrix. PRIMME finds largest, smallest, or interior singular/eigenvalues and can use preconditioning to accelerate convergence. General description of the methods are provided in the papers Stathopoulos (2010, <doi:10.1145/1731022.1731031>) and Wu (2017, <doi:10.1137/16M1082214>). See citation("PRIMME") for details.

An implementation of the van Westendorp Price Sensitivity Meter in R, which is a survey-based approach to analyze consumer price preferences and sensitivity (van Westendorp 1976, isbn:9789283100386).

This package provides tools for reshaping, plotting, and manipulating matrices of orthogonal polynomials.

This package provides a reliable and flexible toolbox to score patient-reported outcome (PRO), Quality of Life (QOL), and other psychometric measures. The guiding philosophy is that scoring errors can be eliminated by using a limited number of well-tested, well-behaved functions to score PRO-like measures. The workhorse of the package is the scoreScale function, which can be used to score most single-scale measures. It can reverse code items that need to be reversed before scoring and pro-rate scores for missing item data. Currently, three different types of scores can be output: summed item scores, mean item scores, and scores scaled to range from 0 to 100. The PROscorerTools functions can be used to write new functions that score more complex measures. In fact, PROscorerTools functions are the building blocks of the scoring functions in the PROscorer package (which is a repository of functions that score specific commonly-used instruments). Users are encouraged to use PROscorerTools to write scoring functions for their favorite PRO-like instruments, and to submit these functions for inclusion in PROscorer (a tutorial vignette will be added soon). The long-term vision for the PROscorerTools and PROscorer packages is to provide an easy-to-use system to facilitate the incorporation of PRO measures into research studies in a scientifically rigorous and reproducible manner. These packages and their vignettes are intended to help establish and promote "best practices" for scoring and describing PRO-like measures in research.

Quickly and easily generate plots of acoustic data aligned with transcriptions similar to those made in Praat using either derived signals generated directly in R with wrassp or imported derived signals from Praat'. Provides easy and fast out-of-the-box solutions but also a high extent of flexibility. Also provides options for embedding audio in figures and animating figures.

Send push notifications to mobile devices or the desktop using Pushover <https://pushover.net>. These notifications can display things such as results, job status, plots, or any other text or numeric data.

Inspired by Moreira and Gamerman (2022) <doi:10.1214/21-AOAS1569>, this methodology expands the idea by including Marks in the point process. Using efficient C++ code, the estimation is possible and made faster with OpenMP <https://www.openmp.org/> enabled computers. This package was developed under the project PTDC/MAT-STA/28243/2017, supported by Portuguese funds through the Portuguese Foundation for Science and Technology (FCT).

This package provides some easy-to-use functions for spatial analyses of (plant-) phenological data sets and satellite observations of vegetation.

Estimation, prediction, thresholding, transformation, and plotting for partially linear additive quantile regression. Intuitive functions for fitting and plotting partially linear additive quantile regression models. Uses and works with functions from the quantreg package.

This package provides tools for downloading, reading and analyzing the Continuous National Household Sample Survey - PNADC, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website <https://www.ibge.gov.br/>. Further analysis must be made using package survey'.

Algorithms to implement various Bayesian penalized survival regression models including: semiparametric proportional hazards models with lasso priors (Lee et al., Int J Biostat, 2011 <doi:10.2202/1557-4679.1301>) and three other shrinkage and group priors (Lee et al., Stat Anal Data Min, 2015 <doi:10.1002/sam.11266>); parametric accelerated failure time models with group/ordinary lasso prior (Lee et al. Comput Stat Data Anal, 2017 <doi:10.1016/j.csda.2017.02.014>).

The primary goal of phase I clinical trials is to find the maximum tolerated dose (MTD). To reach this objective, we introduce a new design for phase I clinical trials, the posterior predictive (PoP) design. The PoP design is an innovative model-assisted design that is as simply as the conventional algorithmic designs as its decision rules can be pre-tabulated prior to the onset of trial, but is of more flexibility of selecting diverse target toxicity rates and cohort sizes. The PoP design has desirable properties, such as coherence and consistency. Moreover, the PoP design provides better empirical performance than the BOIN and Keyboard design with respect to high average probabilities of choosing the MTD and slightly lower risk of treating patients at subtherapeutic or overly toxic doses.

This package provides programmatic access to GitHub API with a focus on project management. Key functionality includes setting up issues and milestones from R objects or YAML configurations, querying outstanding or completed tasks, and generating progress updates in tables, charts, and RMarkdown reports. Useful for those using GitHub in personal, professional, or academic settings with an emphasis on streamlining the workflow of data analysis projects.

Facilitates analysis of paleontological sequences of trait values. Functions are provided to fit, using maximum likelihood, simple evolutionary models (including unbiased random walks, directional evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and complex models (punctuation, mode shifts).

An implementation of the pediatric complex chronic conditions (CCC) classification system using R and C++.

Calculate and optimize dynamic performance ratings of association football teams competing in matches, in accordance with the method used in the research paper "Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries", by Constantinou and Fenton (2013) <doi:10.1515/jqas-2012-0036> This dynamic rating system has proven to provide superior results for predicting association football outcomes.

Run population simulations using an Individual-Based Model (IBM) compiled in C.

Extends the Heckman selection framework to panel data with individual random effects. The first stage models participation via a panel Probit specification, while the second stage can take a panel linear, Probit, Poisson, or Poisson log-normal form. Model details are provided in Bailey and Peng (2025) <doi:10.2139/ssrn.5475626> and Peng and Van den Bulte (2024) <doi:10.1287/mnsc.2019.01897>.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Household Listing questionnaire data for Punjab, Pakistan.

This package provides a multiple testing procedure for testing several groups of hypotheses is implemented. Linear dependency among the hypotheses within the same group is modeled by using hidden Markov Models. It is noted that a smaller p value does not necessarily imply more significance due to the dependency. A typical application is to analyze genome wide association studies datasets, where SNPs from the same chromosome are treated as a group and exhibit strong linear genomic dependency. See Wei Z, Sun W, Wang K, Hakonarson H (2009) <doi:10.1093/bioinformatics/btp476> for more details.