This package provides functions to access data from public RESTful APIs including Nager.Date and World Bank API', retrieving real-time or historical data related to China, such as holidays and economic indicators. Additionally, the package includes one of the largest curated collections of open datasets focused on China and Hong Kong, covering topics such as air quality, demographics, input-output tables, epidemiology, political structure, names, and social indicators. The package supports reproducible research and teaching by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: Nager.Date <https://date.nager.at/Api>, World Bank API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>.

Processes survey data and displays estimation results along with the relative standard error in a table, including the number of samples and also uses a t-distribution approach to compute confidence intervals, similar to SPSS (Statistical Package for the Social Sciences) software.

This package performs a Correspondence Analysis (CA) on a contingency table and creates a scatterplot of the row and column points on the selected dimensions. Optionally, the function can add segments to the plot to visualize significant associations between row and column categories on the basis of positive (unadjusted) standardized residuals larger than a given threshold.

This package provides functions to check whether a vector of p-values respects the assumptions of FDR (false discovery rate) control procedures and to compute adjusted p-values.

This package implements higher order likelihood-based inference for logistic and loglinear models.

Clustering, or cluster analysis, is a widely used technique in bioinformatics to identify groups of similar biological data points. Consensus clustering is an extension to clustering algorithms that aims to construct a robust result from those clustering features that are invariant under different sources of variation. For the reference, please cite the following paper: Yousefi, Melograna, et. al., (2023) <doi:10.3389/fmicb.2023.1170391>.

This package provides functions to produce some circular plots for circular data, in a height- or area-proportional manner. They include bar plots, smooth density plots, stacked dot plots, histograms, multi-class stacked smooth density plots, and multi-class stacked histograms.

This is an opinionated wrapper around the python-chess package. It allows users to read and write PGN files as well as create and explore game trees such as the ones seen in chess books.

Decorate functions to make them return enhanced output. The enhanced output consists in an object of type chronicle containing the result of the function applied to its arguments, as well as a log detailing when the function was run, what were its inputs, what were the errors (if the function failed to run) and other useful information. Tools to handle decorated functions are included, such as a forward pipe operator that makes chaining decorated functions possible.

The goal of cvsem is to provide functions that allow for comparing Structural Equation Models (SEM) using cross-validation. Users can specify multiple SEMs using lavaan syntax. cvsem computes the Kullback Leibler (KL) Divergence between 1) the model implied covariance matrix estimated from the training data and 2) the sample covariance matrix estimated from the test data described in Cudeck, Robert & Browne (1983) <doi:10.18637/jss.v048.i02>. The KL Divergence is computed for each of the specified SEMs allowing for the models to be compared based on their prediction errors.

Extracts colors from various image types, returns customized reports and plots treemaps and 3D scatterplots of image compositions. Color palettes can also be created.

This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default.

This package implements the natural capital asset pricing for nature (CAPN) approach, using a collocation or iteration method and associated functions for dynamic programs. Development of this package was supported by the Knobloch Family Foundation and Lenfest Ocean Program Contract ID 00029728.

Set chunk hooks for R Markdown documents <https://rmarkdown.rstudio.com/>, and improve user experience. For example, change units of figure sizes, benchmark chunks, and number lines on code blocks.

Estimation of quantile regression models for survival data.

Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data.

This package implements the board game CamelUp for use in introductory statistics classes using a Shiny app.

Retail shopping transactions for 2,469 households over one year. Originates from the 84.51° Complete Journey 2.0 source files <https://www.8451.com/area51> which also includes useful metadata on products, coupons, campaigns, and promotions.

Wrapper of .Call() that runs exit handlers to clean up C resources. Helps managing C (non-R) resources while using the R API.

Facilitate Pharmacokinetic (PK) and Pharmacodynamic (PD) modeling and simulation with powerful tools for Nonlinear Mixed-Effects (NLME) modeling. The package provides access to the same advanced Maximum Likelihood algorithms used by the NLME-Engine in the Phoenix platform. These tools support a range of analyses, from parametric methods to individual and pooled data, and support integrated use within the Pirana pharmacometric workbench <doi:10.1002/psp4.70067>. Execution is supported both locally or on remote machines.

Constructs co-occurrence networks from several types of input data, such as delimited fields, long/bipartite tables, binary matrices, or wide sequences. Returns tidy edge data frames and supports optional scaling, splitting into several networks, thresholding, and subsetting. Provides eight similarity measures, including Jaccard, cosine, and association strength. Supports export to several network and file formats. Network construction and analysis methods follow Saqr, Lopez-Pernas, Conde, and Hernandez-Garcia (2024, <doi:10.1007/978-3-031-54464-4_15>).

The Genetic Algorithm (GA) is used to perform changepoint analysis in time series data. The package also includes an extended island version of GA, as described in Lu, Lund, and Lee (2010, <doi:10.1214/09-AOAS289>). By mimicking the principles of natural selection and evolution, GA provides a powerful stochastic search technique for solving combinatorial optimization problems. In changepointGA', each chromosome represents a changepoint configuration, including the number and locations of changepoints, hyperparameters, and model parameters. The package employs genetic operatorsâ selection, crossover, and mutationâ to iteratively improve solutions based on the given fitness (objective) function. Key features of changepointGA include encoding changepoint configurations in an integer format, enabling dynamic and simultaneous estimation of model hyperparameters, changepoint configurations, and associated parameters. The detailed algorithmic implementation can be found in the package vignettes and in the paper of Li and Lu (2024, <doi:10.48550/arXiv.2410.15571>).

Automated assessment and selection of weighting factors for accurate quantification using linear calibration curve. In addition, a shiny App is provided, allowing users to analyze their data using an interactive graphical user interface, without any programming requirements.

Evaluates the stability and significance of clusters on igraph graphs. Supports weighted and unweighted graphs. Implements the cluster evaluation methods defined by Arratia A, Renedo M (2021) <doi:10.7717/peerj-cs.600>. Also includes an implementation of the Reduced Mutual Information introduced by Newman et al. (2020) <doi:10.1103/PhysRevE.101.042304>.