This package provides a rich set of UI components for building Shiny applications, including inputs, containers, overlays, menus, and various utilities. All components from Fluent UI (the underlying JavaScript library) are available and have usage examples in R.

This package provides a sparse covariance estimator based on different thresholding operators.

Obtain parameters of Svensson's Method, including percentage agreement, systematic change and individual change. Also, the contingency table can be generated. Svensson's Method is a rank-invariant nonparametric method for the analysis of ordered scales which measures the level of change both from systematic and individual aspects. For the details, please refer to Svensson E. Analysis of systematic and random differences between paired ordinal categorical data [dissertation]. Stockholm: Almqvist & Wiksell International; 1993.

This package provides a set of functions for obtaining positional parameters and magnitude difference between components of binary and multiple stellar systems from series of speckle images.

Streamlines geographic data transformation, storage and publication, simplifying data preparation and enhancing interoperability across formats and platforms.

This package contains functions that help to determine event boundaries in event segmentation experiments by bootstrapping a critical segmentation magnitude under the null hypothesis that all key presses were randomly distributed across the experiment. Segmentation magnitude is defined as the sum of Gaussians centered at the times of the segmentation key presses performed by the participants. Within a participant, the maximum of the overlaid Gaussians is used to prevent an excessive influence of a single participant on the overall outcome (e.g. if a participant is pressing the key multiple times in succession). Further functions are included, such as plotting the results.

Efficient implementation of sparse group lasso with optional bound constraints on the coefficients; see <doi:10.18637/jss.v110.i06>. It supports the use of a sparse design matrix as well as returning coefficient estimates in a sparse matrix. Furthermore, it correctly calculates the degrees of freedom to allow for information criteria rather than cross-validation with very large data. Finally, the interface to compiled code avoids unnecessary copies and allows for the use of long integers.

This package provides a collection of functions for symbolic computation using the caracas package for structural equation models and other statistical analyses. Among its features is the ability to calculate the model-implied covariance (and correlation) matrix and the sampling covariance matrix of variable functions using the delta method.

This package provides a graphical user interface for cross-sectional network modeling with the statnet software suite <https://github.com/statnet>.

Get sun position, sunlight phases (times for sunrise, sunset, dusk, etc.), moon position and lunar phase for the given location and time. Most calculations are based on the formulas given in Astronomy Answers articles about position of the sun and the planets : <https://www.aa.quae.nl/en/reken/zonpositie.html>.

Generalised additive P-spline regression models estimation using the separation of overlapping precision matrices (SOP) method. Estimation is based on the equivalence between P-splines and linear mixed models, and variance/smoothing parameters are estimated based on restricted maximum likelihood (REML). The package enables users to estimate P-spline models with overlapping penalties. Based on the work described in Rodriguez-Alvarez et al. (2015) <doi:10.1007/s11222-014-9464-2>; Rodriguez-Alvarez et al. (2019) <doi:10.1007/s11222-018-9818-2>, and Eilers and Marx (1996) <doi:10.1214/ss/1038425655>.

This package creates a wrapper for the SuiteSparse routines that execute the Takahashi equations. These equations compute the elements of the inverse of a sparse matrix at locations where the its Cholesky factor is structurally non-zero. The resulting matrix is known as a sparse inverse subset. Some helper functions are also implemented. Support for spam matrices is currently limited and will be implemented in the future. See Rue and Martino (2007) <doi:10.1016/j.jspi.2006.07.016> and Zammit-Mangion and Rougier (2018) <doi:10.1016/j.csda.2018.02.001> for the application of these equations to statistics.

This package provides functions for computing test subscores using different methods in both classical test theory (CTT) and item response theory (IRT). This package enables three types of subscoring methods within the framework of CTT and IRT, including (1) Wainer's augmentation method (Wainer et. al., 2001) <doi:10.4324/9781410604729>, (2) Haberman's subscoring methods (Haberman, 2008) <doi:10.3102/1076998607302636>, and (3) Yen's objective performance index (OPI; Yen, 1987) <https://www.ets.org/research/policy_research_reports/publications/paper/1987/hrap>. It also includes functions to compute Proportional Reduction of Mean Squared Errors (PRMSEs) in Haberman's methods which are used to examine whether test subscores are of added value. In addition, the package includes a function to assess the local independence assumption of IRT with Yen's Q3 statistic (Yen, 1984 <doi:10.1177/014662168400800201>; Yen, 1993 <doi:10.1111/j.1745-3984.1993.tb00423.x>).

This package implements a method for fitting a bounded probability distribution to quantiles (for example stated by an expert), see Bornkamp and Ickstadt (2009) for details. For this purpose B-splines are used, and the density is obtained by penalized least squares based on a Brier entropy penalty. The package provides methods for fitting the distribution as well as methods for evaluating the underlying density and cdf. In addition methods for plotting the distribution, drawing random numbers and calculating quantiles of the obtained distribution are provided.

Spatial versions of Regression Discontinuity Designs (RDDs) are becoming increasingly popular as tools for causal inference. However, conducting state-of-the-art analyses often involves tedious and time-consuming steps. This package offers comprehensive functionalities for executing all required spatial and econometric tasks in a streamlined manner. Moreover, it equips researchers with tools for performing essential placebo and balancing checks comprehensively. The fact that researchers do not have to rely on APIs of external GIS software ensures replicability and raises the standard for spatial RDDs.

Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers.

It provides cumulative distribution function (CDF), quantile, p-value, statistical power calculator and random number generator for a collection of group-testing procedures, including the Higher Criticism tests, the one-sided Kolmogorov-Smirnov tests, the one-sided Berk-Jones tests, the one-sided phi-divergence tests, etc. The input are a group of p-values. The null hypothesis is that they are i.i.d. Uniform(0,1). In the context of signal detection, the null hypothesis means no signals. In the context of the goodness-of-fit testing, which contrasts a group of i.i.d. random variables to a given continuous distribution, the input p-values can be obtained by the CDF transformation. The null hypothesis means that these random variables follow the given distribution. For reference, see [1]Hong Zhang, Jiashun Jin and Zheyang Wu. "Distributions and power of optimal signal-detection statistics in finite case", IEEE Transactions on Signal Processing (2020) 68, 1021-1033; [2] Hong Zhang and Zheyang Wu. "The general goodness-of-fit tests for correlated data", Computational Statistics & Data Analysis (2022) 167, 107379.

This package provides functions to extract citation data from Google Scholar. Convenience functions are also provided for comparing multiple scholars and predicting future h-index values.

Spatial stratified heterogeneity (SSH) denotes the coexistence of within-strata homogeneity and between-strata heterogeneity. Information consistency-based methods provide a rigorous approach to quantify SSH and evaluate its role in spatial processes, grounded in principles of geographical stratification and information theory (Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>; Wang, J. et al. (2024) <doi:10.1080/24694452.2023.2289982>).

Cluster-independent method based on topology structure of gene co-expression network for identifying feature gene sets, extracting cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Without prior cell clustering, SifiNet circumvents potential inaccuracies in clustering that may influence subsequent analyses. This method is introduced in Qi Gao, Zhicheng Ji, Liuyang Wang, Kouros Owzar, Qi-Jing Li, Cliburn Chan, Jichun Xie "SifiNet: a robust and accurate method to identify feature gene sets and annotate cells" (2024) <doi:10.1093/nar/gkae307>.

It helps in determination of sample size for estimation of population mean and proportion based upon the availability of prior information on coefficient of variation (CV) of the population under Simple Random Sampling (SRS) with or without replacement sampling design. If there is no prior information on the population CV, then a small preliminary sample of size is selected to estimate the population CV which is then used for determination of final sample size. If the final sample size is more than the preliminary sample size, then the preliminary sample is augmented by drawing additional units from the remaining population units so that the size of the augmented sample is equal to the final sample size. On the other hand, if the preliminary sample size is larger than the final sample size, then the preliminary sample is considered as the final sample.

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.

This package provides a collection of Radix Tree and Trie algorithms for finding similar sequences and calculating sequence distances (Levenshtein and other distance metrics). This work was inspired by a trie implementation in Python: "Fast and Easy Levenshtein distance using a Trie." Hanov (2011) <https://stevehanov.ca/blog/index.php?id=114>.

Supplementary functions for item response models aiming to complement existing R packages. The functionality includes among others multidimensional compensatory and noncompensatory IRT models (Reckase, 2009, <doi:10.1007/978-0-387-89976-3>), MCMC for hierarchical IRT models and testlet models (Fox, 2010, <doi:10.1007/978-1-4419-0742-4>), NOHARM (McDonald, 1982, <doi:10.1177/014662168200600402>), Rasch copula model (Braeken, 2011, <doi:10.1007/s11336-010-9190-4>; Schroeders, Robitzsch & Schipolowski, 2014, <doi:10.1111/jedm.12054>), faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011, <doi:10.1111/j.1745-3984.2011.00143.x>), ordinal IRT model (ISOP; Scheiblechner, 1995, <doi:10.1007/BF02301417>), DETECT statistic (Stout, Habing, Douglas & Kim, 1996, <doi:10.1177/014662169602000403>), local structural equation modeling (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016, <doi:10.1080/00273171.2016.1142856>).