The purpose of PH1XBAR is to build a Phase I Shewhart control chart for the basic Shewhart, the variance components and the ARMA models in R for subgrouped and individual data. More details can be found: Yao and Chakraborti (2020) <doi: 10.1002/qre.2793>, Yao and Chakraborti (2021) <doi: 10.1080/08982112.2021.1878220>, and Yao et al. (2023) <doi: 10.1080/00224065.2022.2139783>.

Calculate and compare the prediction probability (PK) values for Anesthetic Depth Indicators. The PK values are widely used for measuring the performance of anesthetic depth and were first proposed by the group of Dr. Warren D. Smith in the paper Warren D. Smith; Robert C. Dutton; Ty N. Smith (1996) <doi:10.1097/00000542-199601000-00005> and Warren D. Smith; Robert C. Dutton; Ty N. Smith (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1199::AID-SIM218%3E3.0.CO;2-Y>. The authors provided two Microsoft Excel files in xls format for calculating and comparing PK values. This package provides an easy-to-use API for calculating and comparing PK values in R.

This package provides a shiny application for visualizing high-dimensional data using non-linear dimensionality reduction (NLDR) techniques such as t-SNE and UMAP. It provides an interactive platform to explore high-dimensional datasets, diagnose the quality of the embeddings using the quollr package, and compare different NLDR methods.

Reproducible cleaning of biomedical laboratory data using visualization, error correction, and transformation methods implemented as interactive R notebooks. A detailed description of the methods ca ben found in Malkusch, S., Hahnefeld, L., Gurke, R. and J. Lotsch. (2021) <doi:10.1002/psp4.12704>.

This package provides functions to perform the peer performance analysis of funds returns as described in Ardia and Boudt (2018) <doi:10.1016/j.jbankfin.2017.10.014>.

Allows biomechanical pressure data from a range of systems to be imported and processed in a reproducible manner. Automatic and manual tools are included to let the user define regions (masks) to be analyzed. Also includes functions for visualizing and animating pressure data. Example methods are described in Shi et al., (2022) <doi:10.1038/s41598-022-19814-0>, Lee et al., (2014) <doi:10.1186/1757-1146-7-18>, van der Zward et al., (2014) <doi:10.1186/1757-1146-7-20>, Najafi et al., (2010) <doi:10.1016/j.gaitpost.2009.09.003>, Cavanagh and Rodgers (1987) <doi:10.1016/0021-9290(87)90255-7>.

An interface to compute poverty and inequality indicators for more than 160 countries and regions from the World Bank's database of household surveys, through the Poverty and Inequality Portal (PIP).

This package provides functions to estimate and plot smooth or linear population trends, or population indices, from animal or plant count survey data.

In causal mediation analysis with multiple causally ordered mediators, a set of path-specific effects are identified under standard ignorability assumptions. This package implements an imputation approach to estimating these effects along with a set of bias formulas for conducting sensitivity analysis (Zhou and Yamamoto <doi:10.31235/osf.io/2rx6p>). It contains two main functions: paths() for estimating path-specific effects and sens() for conducting sensitivity analysis. Estimation uncertainty is quantified using the nonparametric bootstrap.

This package provides a comprehensive and easy to use R implementation of confirmatory phylogenetic path analysis as described by Von Hardenberg and Gonzalez-Voyer (2012) <doi:10.1111/j.1558-5646.2012.01790.x>.

Pharmacokinetics is the study of drug absorption, distribution, metabolism, and excretion. The pharmacokinetics model explains that how the drug concentration change as the drug moves through the different compartments of the body. For pharmacokinetic modeling and analysis, it is essential to understand the basic pharmacokinetic parameters. All parameters are considered, but only some of parameters are used in the model. Therefore, we need to convert the estimated parameters to the other parameters after fitting the specific pharmacokinetic model. This package is developed to help this converting work. For more detailed explanation of pharmacokinetic parameters, see "Gabrielsson and Weiner" (2007), "ISBN-10: 9197651001"; "Benet and Zia-Amirhosseini" (1995) <DOI: 10.1177/019262339502300203>; "Mould and Upton" (2012) <DOI: 10.1038/psp.2012.4>; "Mould and Upton" (2013) <DOI: 10.1038/psp.2013.14>.

Based on different statistical definitions of discrimination, several methods have been proposed to detect and mitigate social inequality in machine learning models. This package aims to provide an alternative to fairness treatment in predictive models. The ROC method implemented in this package is described by Kamiran, Karim and Zhang (2012) <https://ieeexplore.ieee.org/document/6413831/>.

This package provides a suite of Propensity Score Predictive Inference (PSPI) methods to generalize treatment effects in trials to target populations. The package includes an existing model Bayesian Causal Forest (BCF) and four PSPI models (BCF-PS, FullBART, SplineBART, DSplineBART). These methods leverage Bayesian Additive Regression Trees (BART) to adjust for high-dimensional covariates and nonlinear associations, while SplineBART and DSplineBART further use propensity score based splines to address covariate shift between trial data and target population.

Fits Parametric Frailty Models by maximum marginal likelihood. Possible baseline hazards: exponential, Weibull, inverse Weibull (Fréchet), Gompertz, lognormal, log-skew-normal, and loglogistic. Possible Frailty distributions: gamma, positive stable, inverse Gaussian and lognormal.

This package provides a shiny app that supports merging of PDF and/or image files with page selection, removal, or rotation options. It is a fast, free, and secure alternative to commercial software or various online websites which require users to sign-up, and it avoids any potential risks associated with uploading files elsewhere.

Analysis of terms in linear, generalized and mixed linear models, on the basis of multiple comparisons of factor contrasts. Specially suited for the analysis of interaction terms.

Create, transform, and summarize custom random variables with distribution functions (analogues of p*()', d*()', q*()', and r*() functions from base R). Two types of distributions are supported: "discrete" (random variable has finite number of output values) and "continuous" (infinite number of values in the form of continuous random variable). Functions for distribution transformations and summaries are available. Implemented approaches often emphasize approximate and numerical solutions: all distributions assume finite support and finite values of density function; some methods implemented with simulation techniques.

This package implements the estimator of Tugan (2021) <doi:10.1093/ectj/utaa021> for panel vector autoregression (VAR) models with interactive fixed effects. Provides joint estimation of VAR coefficients, latent common factors, and factor loadings via an iterative algorithm that alternates between principal component estimation of the factors and least squares estimation of the VAR coefficients, following the approach of Bai (2009) <doi:10.3982/ECTA6135>. Supports impulse response functions under recursive (Cholesky) identification, parametric confidence bands from the joint asymptotic distribution of the estimator (Theorem 2.3), and a classical residual bootstrap for robustness checks.

Recent years have seen an increased interest in novel methods for analyzing quantitative data from experimental psychology. Currently, however, they lack an established and accessible software framework. Many existing implementations provide no guidelines, consisting of small code snippets, or sets of packages. In addition, the use of existing packages often requires advanced programming experience. PredPsych is a user-friendly toolbox based on machine learning predictive algorithms. It comprises of multiple functionalities for multivariate analyses of quantitative behavioral data based on machine learning models.

Learn optimal policies via doubly robust empirical welfare maximization over trees. Given doubly robust reward estimates, this package finds a rule-based treatment prescription policy, where the policy takes the form of a shallow decision tree that is globally (or close to) optimal.

Spatial estimation of a prevalence surface or a relative risks surface, using data from a Demographic and Health Survey (DHS) or an analog survey, see Larmarange et al. (2011) <doi:10.4000/cybergeo.24606>.

Reconcile species names across datasets and phylogenetic trees for comparative biology workflows. Identifies mismatches due to formatting differences, taxonomic synonymy, and spelling errors. Produces detailed reports documenting how each name was resolved, which taxonomic authority was used, and what remains unresolved. Supports exact matching, name normalisation, synonym resolution via local taxonomic databases, and fuzzy matching for likely typos. Detects taxonomic splits and lumps. For methodological context, see Nakagawa et al. (2026) <doi:10.32942/X2468Z>.

Fits Bayesian mixture models to estimate marker dosage for dominant markers in autopolyploids using JAGS (1.0 or greater) as outlined in Baker et al "Bayesian estimation of marker dosage in sugarcane and other autopolyploids" (2010, <doi:10.1007/s00122-010-1283-z>). May be used in conjunction with polySegratio for simulation studies and comparison with standard methods.

This package provides a progression model for repeated measures (PMRM) is a continuous-time nonlinear mixed-effects model for longitudinal clinical trials in progressive diseases. Unlike mixed models for repeated measures (MMRMs), which estimate treatment effects as linear combinations of additive effects on the outcome scale, PMRMs characterize treatment effects in terms of the underlying disease trajectory. This framing yields clinically interpretable quantities such as average time saved and percent reduction in decline due to treatment. This package implements frequentist PMRMs by Raket (2022) <doi:10.1002/sim.9581> using RTMB by Kristensen (2016) <doi:10.18637/jss.v070.i05>.