Implementation of the structural model for variances in order to detect differentially expressed genes from gene expression data.

In a clinical trial with repeated measures designs, outcomes are often taken from subjects at fixed time-points. The focus of the trial may be to compare the mean outcome in two or more groups at some pre-specified time after enrollment. In the presence of missing data auxiliary assumptions are necessary to perform such comparisons. One commonly employed assumption is the missing at random assumption (MAR). The samon package allows the user to perform a (parameterized) sensitivity analysis of this assumption. In particular it can be used to examine the sensitivity of tests in the difference in outcomes to violations of the MAR assumption. The sensitivity analysis can be performed under two scenarios, a) where the data exhibit a monotone missing data pattern (see the samon() function), and, b) where in addition to a monotone missing data pattern the data exhibit intermittent missing values (see the samonIM() function).

This package implements the Smoothness-Penalized Deconvolution method for estimating a probability density under measurement error of Kent and Ruppert (2023) <doi:10.1080/01621459.2023.2259028>. The estimator is formed by computing a histogram of the error-contaminated data, and then finding an estimate that minimizes a reconstruction error plus a smoothness-inducing penalty term. The primary function, sped(), takes the data and error distribution, and returns the estimator as a function.

Determines networks of significant synchronization between the discrete states of nodes; see Tumminello et al <doi:10.1371/journal.pone.0017994>.

Privacy protected raster maps can be created from spatial point data. Protection methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) <doi:10.1007/978-3-319-45381-1_9>, continuous variables by de Wolf and de Jonge (2018) <doi:10.1007/978-3-319-99771-1_23>, suppressing revealing values and a generalization of the quad tree method by Suñé, Rovira, Ibáñez and Farré (2017) <doi:10.2901/EUROSTAT.C2017.001>.

This package provides basic functions that support an implementation of (discrete) choice experiments (CEs). CEs is a question-based survey method measuring people's preferences for goods/services and their characteristics. Refer to Louviere et al. (2000) <doi:10.1017/CBO9780511753831> for details on CEs, and Aizaki (2012) <doi:10.18637/jss.v050.c02> for the package.

Stock-and-flow models are a computational method from the field of system dynamics. They represent how systems change over time and are mathematically equivalent to ordinary differential equations. sdbuildR (system dynamics builder) provides an intuitive interface for constructing stock-and-flow models without requiring extensive domain knowledge. Models can quickly be simulated and revised, supporting iterative development. sdbuildR simulates models in R and Julia', where Julia offers unit support and large-scale ensemble simulations. Additionally, sdbuildR can import models created in Insight Maker (<https://insightmaker.com/>).

Generates a random quotation from a database of quotes on topics in statistics, data visualization and science. Other functions allow searching the quotes database by key term tags, or authors or creating a word cloud. The output is designed to be suitable for use at the console, in Rmarkdown and LaTeX.

This package provides confidence intervals in least-squares regressions when the variable of interest has a shift-share structure, and in instrumental variables regressions when the instrument has a shift-share structure. The confidence intervals implement the AKM and AKM0 methods developed in Adão, Kolesár, and Morales (2019) <doi:10.1093/qje/qjz025>.

This package provides tools for setting up ("design"), conducting, and evaluating large-scale simulation studies with graphics and tables, including parallel computations.

The developed function is designed for the generation of spatial grids based on user-specified longitude and latitude coordinates. The function first validates the input longitude and latitude values, ensuring they fall within the appropriate geographic ranges. It then creates a polygon from the coordinates and determines the appropriate Universal Transverse Mercator zone based on the provided hemisphere and longitude values. Subsequently, transforming the input Shapefile to the Universal Transverse Mercator projection when necessary. Finally, a spatial grid is generated with the specified interval and saved as a Shapefile. For method details see, Brus,D.J.(2022).<DOI:10.1201/9781003258940>. The function takes into account crucial parameters such as the hemisphere (north or south), desired grid interval, and the output Shapefile path. The developed function is an efficient tool, simplifying the process of empty spatial grid generation for applications such as, geo-statistical analysis, digital soil mapping product generation, etc. Whether for environmental studies, urban planning, or any other geo-spatial analysis, this package caters to the diverse needs of users working with spatial data, enhancing the accessibility and ease of spatial data processing and visualization.

This package provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after finding good starting values via a K-means clustering of the genetic data. Details can be found in: Foster, S. D.; Feutry, P.; Grewe, P. M.; Berry, O.; Hui, F. K. C. & Davies (2020) <doi:10.1111/1755-0998.12920>.

This package provides functions to calculate exact critical values, statistical power, expected time to signal, and required sample sizes for performing exact sequential analysis. All these calculations can be done for either Poisson or binomial data, for continuous or group sequential analyses, and for different types of rejection boundaries. In case of group sequential analyses, the group sizes do not have to be specified in advance and the alpha spending can be arbitrarily settled. For regression versions of the methods, Monte Carlo and asymptotic methods are used.

Many of the models encountered in applications of point process methods to the study of spatio-temporal phenomena are covered in stpp'. This package provides statistical tools for analyzing the global and local second-order properties of spatio-temporal point processes, including estimators of the space-time inhomogeneous K-function and pair correlation function. It also includes tools to get static and dynamic display of spatio-temporal point patterns. See Gabriel et al (2013) <doi:10.18637/jss.v053.i02>.

This package provides methods for computing spatial, temporal, and spatiotemporal statistics as described in Gouhier and Guichard (2014) <doi:10.1111/2041-210X.12188>. These methods include empirical univariate, bivariate and multivariate variograms; fitting variogram models; phase locking and synchrony analysis; generating autocorrelated and cross-correlated matrices.

This package provides a spectral framework to map quantitative trait loci (QTLs) affecting joint differential networks of gene co-Expression. Test the equivalence among multiple biological networks via spectral statistics. See reference Hu, J., Weber, J. N., Fuess, L. E., Steinel, N. C., Bolnick, D. I., & Wang, M. (2025) <doi:10.1371/journal.pcbi.1012953>.

This package provides ggplot2 graphics for analysing time series data. It aims to fit into the tidyverse and grammar of graphics framework for handling temporal data.

This package implements named semaphores from the boost C++ library <https://www.boost.org/> for interprocess communication. Multiple R sessions on the same host can block (with optional timeout) on a semaphore until it becomes positive, then atomically decrement it and unblock. Any session can increment the semaphore.

This package provides a multidimensional dataset of students performance assessment in high school physics. The SPHERE dataset was collected from 497 students in four public high schools specifically measuring their conceptual understanding, scientific ability, and attitude toward physics [see Santoso et al. (2024) <doi:10.17632/88d7m2fv7p.1>]. The data collection was conducted using some research based assessments established by the physics education research community. They include the Force Concept Inventory, the Force and Motion Conceptual Evaluation, the Rotational and Rolling Motion Conceptual Survey, the Fluid Mechanics Concept Inventory, the Mechanical Waves Conceptual Survey, the Thermal Concept Evaluation, the Survey of Thermodynamic Processes and First and Second Laws, the Scientific Abilities Assessment Rubrics, and the Colorado Learning Attitudes about Science Survey. Students attributes related to gender, age, socioeconomic status, domicile, literacy, physics identity, and test results administered using teachers developed items are also reported in this dataset.

Reliability and agreement analyses often have limited software support. Therefore, this package was created to make agreement and reliability analyses easier for the average researcher. The functions within this package include simple tests of agreement, agreement analysis for nested and replicate data, and provide robust analyses of reliability. In addition, this package contains a set of functions to help when planning studies looking to assess measurement agreement.

Fitting of non-parametric production frontiers for use in efficiency analysis. Methods are provided for both a smooth analogue of Data Envelopment Analysis (DEA) and a non-parametric analogue of Stochastic Frontier Analysis (SFA). Frontiers are constructed for multiple inputs and a single output using constrained kernel smoothing as in Racine et al. (2009), which allow for the imposition of monotonicity and concavity constraints on the estimated frontier.

Builds, evaluates and validates a nomogram with survey data and right-censored outcomes. As described in Capanu (2015) <doi:10.18637/jss.v064.c01>, the package contains functions to create the nomogram, validate it using bootstrap, as well as produce the calibration plots.

Sample size estimation and blinded sample size reestimation in Adaptive Study Design.

Estimates a covariance matrix using Stein's isotonized covariance estimator, or a related estimator suggested by Haff.