This package provides tools to decompose (transformed) spatial connectivity matrices and perform supervised or unsupervised semiparametric spatial filtering in a regression framework. The package supports unsupervised spatial filtering in standard linear as well as some generalized linear regression models.

Seed vigor is defined as the sum total of those properties of the seed which determine the level of activity and performance of the seed or seed lot during germination and seedling emergence. Testing for vigor becomes more important for carryover seeds, especially if seeds were stored under unknown conditions or under unfavorable storage conditions. Seed vigor testing is also used as indicator of the storage potential of a seed lot and in ranking various seed lots with different qualities. The vigour index is calculated using the equation given by (Ling et al. 2014) <doi:10.1038/srep05859>.

Conduct latent trajectory class analysis with longitudinal data. Our method supports longitudinal continuous, binary and count data. For more methodological details, please refer to Hart, K.R., Fei, T. and Hanfelt, J.J. (2020), Scalable and robust latent trajectory class analysis using artificial likelihood. Biometrics <doi:10.1111/biom.13366>.

This package provides a metric expressing the quality of a UMAP layout. This is a package that contains the Saturn_coefficient() function that reads an input matrix, its dimensionality reduction produced by UMAP, and evaluates the quality of this dimensionality reduction by producing a real value in the [0; 1] interval. We call this real value Saturn coefficient. A higher value means better dimensionality reduction; a lower value means worse dimensionality reduction. Reference: Davide Chicco et al. "The Saturn coefficient for evaluating the quality of UMAP dimensionality reduction results" (2025, in preparation).

Extension to the spatstat package, enabling the user to fit point process models to point pattern data by local composite likelihood ('geographically weighted regression').

Provide various functions and tools to help fit models for estimating treatment effects in stepped wedge cluster randomized trials. Implements methods described in Kenny, Voldal, Xia, and Heagerty (2022) "Analysis of stepped wedge cluster randomized trials in the presence of a time-varying treatment effect", <doi:10.1002/sim.9511>.

Builds regression trees and random forests for longitudinal or functional data using a spline projection method. Implements and extends the work of Yu and Lambert (1999) <doi:10.1080/10618600.1999.10474847>. This method allows trees and forests to be built while considering either level and shape or only shape of response trajectories.

To calculate the standard error of measurement (SEM) to assess the observer variability (inter- and intra-observer variation). The methods used in this package are referenced from Zoran B. PopoviÄ (2017) <doi:10.21037/cdt.2017.03.12>.

An analytic framework for the calculation of norm- and criterion-referenced academic growth estimates using large scale, longitudinal education assessment data as developed in Betebenner (2009) <doi:10.1111/j.1745-3992.2009.00161.x>.

Create sets of variables based on a mutual information approach. In this context, a set is a collection of distinct elements (e.g., variables) that can also be treated as a single entity. Mutual information, a concept from probability theory, quantifies the dependence between two variables by expressing how much information about one variable can be gained from observing the other. Furthermore, you can analyze, and visualize these sets in order to better understand the relationships among variables.

This package provides an efficient framework for high-dimensional linear and diagonal discriminant analysis with variable selection. The classifier is trained using James-Stein-type shrinkage estimators and predictor variables are ranked using correlation-adjusted t-scores (CAT scores). Variable selection error is controlled using false non-discovery rates or higher criticism.

This package provides a unique dataset of historical forest cover across all states in the United States, spanning from 1907 to 2017, along with 1630 as a reference year. This dataset is important for understanding environmental changes and land use trends over time. It includes functionality for easy access of the data.

Incorporate various statistics and layout customization options to enhance the efficiency and adaptability of the Kaplan-Meier plots.

This package provides the necessary sample size for a longitudinal study with binary outcome in order to attain a pre-specified power while strictly maintaining the Type I error rate. Kapur K, Bhaumik R, Tang XC, Hur K, Reda DJ, Bhaumik D (2014) <doi:10.1002/sim.6203>.

This package provides additional convenience functions for gtsummary (Sjoberg et al. (2021) <doi:10.32614/RJ-2021-053>) & gt tables, including automatic variable labeling from dictionaries, standardized missing value display, and consistent formatting helpers for streamlined table styling workflows.

Simultaneous/joint diagonalization of local autocovariance matrices to estimate spatio-temporally uncorrelated random fields.

Estimation of an S-shaped function and its corresponding inflection point via a least squares approach. A sequential mixed primal-dual based algorithm is implemented for the fast computation. Details can be found in Feng et al. (2022) <doi:10.1111/rssb.12481>.

The sparse principal component regression is computed. The regularization parameters are optimized by cross-validation.

Complementary indexes calculation to the Outlying Mean Index analysis to explore niche shift of a community and biological constraint within an Euclidean space, with graphical displays. For details see Karasiewicz et al. (2017) <doi:10.7717/peerj.3364>.

This tool is designed to analyze up to 5 Fraud Detection Questions integrated into a survey, focusing on potential fraudulent participants to clean the survey dataset from potential fraud. Fraud Detection Questions and further information available at <https://surveydefense.org>.

This package provides a select control widget for Shiny'. It is easily customizable, and one can easily use HTML in the items and KaTeX to type mathematics.

Example clinical trial data sets formatted for easy use in R.

Standardized accuracy (staccuracy) is a framework for expressing accuracy scores such that 50% represents a reference level of performance and 100% is a perfect prediction. The staccuracy package provides tools for creating staccuracy functions as well as some recommended staccuracy measures. It also provides functions for some classic performance metrics such as mean absolute error (MAE), root mean squared error (RMSE), and area under the receiver operating characteristic curve (AUCROC), as well as their winsorized versions when applicable.

Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)<doi:10.1111/j.1467-9868.2008.00674.x>) and all of its variants in generalized linear models (Fan and Song (2009)<doi:10.1214/10-AOS798>) and the Cox proportional hazards model (Fan, Feng and Wu (2010)<doi:10.1214/10-IMSCOLL606>).