This package provides functions to parse glycan structure text representations into glyrepr glycan structures. Currently, it supports StrucGP-style, pGlyco-style, IUPAC-condensed, IUPAC-extended, IUPAC-short, IUPAC-compact, WURCS, LINUCS, Linear Code, GlycoCT, KCF, and GlycoWorkbench formats. It also provides an automatic parser to detect the format and parse the structure string.

This package provides methods from the paper: Pena, EA and Slate, EH, "Global Validation of Linear Model Assumptions," J. American Statistical Association, 101(473):341-354, 2006.

It implements a hybrid spatial model for improved spatial prediction by combining the variable selection capability of LASSO (Least Absolute Shrinkage and Selection Operator) with the Geographically Weighted Regression (GWR) model that captures the spatially varying relationship efficiently. For method details see, Wheeler, D.C.(2009).<DOI:10.1068/a40256>. The developed hybrid model efficiently selects the relevant variables by using LASSO as the first step; these selected variables are then incorporated into the GWR framework, allowing the estimation of spatially varying regression coefficients at unknown locations and finally predicting the values of the response variable at unknown test locations while taking into account the spatial heterogeneity of the data. Integrating the LASSO and GWR models enhances prediction accuracy by considering spatial heterogeneity and capturing the local relationships between the predictors and the response variable. The developed hybrid spatial model can be useful for spatial modeling, especially in scenarios involving complex spatial patterns and large datasets with multiple predictor variables.

This package provides a group-specific recommendation system to use dependency information from users and items which share similar characteristics under the singular value decomposition framework. Refer to paper A Group-Specific Recommender System <doi:10.1080/01621459.2016.1219261> for the details.

Statistical testing procedures for detecting GxE (gene-environment) interactions. The main focus lies on GRSxE interaction tests that aim at detecting GxE interactions through GRS (genetic risk scores). Moreover, a novel testing procedure based on bagging and OOB (out-of-bag) predictions is implemented for incorporating all available observations at both GRS construction and GxE testing (Lau et al., 2023, <doi:10.1038/s41598-023-28172-4>).

Causal mediation analysis for a single exposure/treatment and a single mediator, both allowed to be either continuous or binary. The package implements the difference method and provides point and interval estimates as well as testing for the natural direct and indirect effects and the mediation proportion. Nevo, Xiao and Spiegelman (2017) <doi:10.1515/ijb-2017-0006>.

This package provides a dataset about movies. This was previously contained in ggplot2, but has been moved its own package to reduce the download size of ggplot2.

Wrapper ggplot2 functions for publication-quality visualisation. Aligned with ggplot2 and tidyverse'. Covers much of what ggplot2 does.

This package purposes to deal with public survey data of Japanese government via their Application Programming Interface (http://statdb.nstac.go.jp/).

Plot density and distribution functions with automatic selection of suitable regions. Numerically invert (compute quantiles) distribution functions. Simulate real and complex numbers from distributions of their magnitude and arguments. Optionally, the magnitudes and/or arguments may be fixed in almost arbitrary ways. Create polynomials from roots given in Cartesian or polar form. Small programming utilities: check if an object is identical to NA, count positional arguments in a call, set intersection of more than two sets, check if an argument is unnamed, compute the graph of S4 classes in packages.

This package provides functions for the g-and-k and generalised g-and-h distributions.

This package provides a modern visualization grammar that treats interactivity, animation, and composable layouts as first-class concepts rather than afterthoughts. Designed to address key limitations of existing grammars: native hover, click, and zoom events, WebGL'-accelerated rendering for large datasets, built-in multi-plot composition, and a token-based theming system. Renders to interactive HTML widgets via D3.js or static SVG from a single declarative specification.

This package provides a ggplot2 extension that enables visualization of IP (Internet Protocol) addresses and networks. The address space is mapped onto the Cartesian coordinate system using a space-filling curve. Offers full support for both IPv4 and IPv6 (Internet Protocol versions 4 and 6) address spaces.

Divide and conquer approach for estimating low-rank and sparse coefficient matrix in the generalized co-sparse factor regression. Please refer the manuscript Mishra, Aditya, Dipak K. Dey, Yong Chen, and Kun Chen. Generalized co-sparse factor regression. Computational Statistics & Data Analysis 157 (2021): 107127 for more details.

The gRbase package provides graphical modelling features used by e.g. the packages gRain', gRim and gRc'. gRbase implements graph algorithms including (i) maximum cardinality search (for marked and unmarked graphs). (ii) moralization, (iii) triangulation, (iv) creation of junction tree. gRbase facilitates array operations, gRbase implements functions for testing for conditional independence. gRbase illustrates how hierarchical log-linear models may be implemented and describes concept of graphical meta data. The facilities of the package are documented in the book by Højsgaard, Edwards and Lauritzen (2012, <doi:10.1007/978-1-4614-2299-0>) and in the paper by Dethlefsen and Højsgaard, (2005, <doi:10.18637/jss.v014.i17>). Please see citation("gRbase") for citation details.

Plot glycans following the Symbol Nomenclature for Glycans (SNFG) using ggplot2'. SNFG provides a standardized visual representation of glycan structures.

This package provides a tool which allows users the ability to intuitively create flexible, reproducible portable document format reports comprised of aesthetically pleasing tables, images, plots and/or text.

This package implements a generalized goodness-of-fit test based on spacings for general progressive Type-II censored data. The test statistic is based on the work of Qin et al. (2022) <doi:10.1080/02664763.2020.1821613> and extends the methodology of Balakrishnan et al. (2003) <doi:10.1007/978-1-4612-0103-8_8>. Users can test data against any distribution by providing custom pdf, cdf, and survival functions. The package supports both normal approximation and Monte Carlo simulation approaches for computing p-values and critical values.

This package provides a comprehensive framework for visualizing associations and interaction structures in matrix-formatted data using Generalized Association Plots (GAP). The package implements multiple proximity computation methods (e.g., correlation, distance metrics), ordering techniques including hierarchical clustering (HCT) and Rank-2-Ellipse (R2E) seriation, and optional flipping strategies to enhance visual symmetry. It supports a variety of covariate-based color annotations, allows flexible customization of layout and output, and is suitable for analyzing multivariate data across domains such as social sciences, genomics, and medical research. The method is based on Generalized Association Plots introduced by Chen (2002) <https://www3.stat.sinica.edu.tw/statistica/J12N1/J12N11/J12N11.html> and further extended by Wu, Tien, and Chen (2010) <doi:10.1016/j.csda.2008.09.029>.

Datasets analysed in the book Antony Unwin (2024, ISBN:978-0367674007) "Getting (more out of) Graphics".

Quantification, analysis, and visualization of urban greenness within city networks using data from OpenStreetMap <https://www.openstreetmap.org>.

Fits a generalized linear density ratio model (GLDRM). A GLDRM is a semiparametric generalized linear model. In contrast to a GLM, which assumes a particular exponential family distribution, the GLDRM uses a semiparametric likelihood to estimate the reference distribution. The reference distribution may be any discrete, continuous, or mixed exponential family distribution. The model parameters, which include both the regression coefficients and the cdf of the unspecified reference distribution, are estimated by maximizing a semiparametric likelihood. Regression coefficients are estimated with no loss of efficiency, i.e. the asymptotic variance is the same as if the true exponential family distribution were known. Huang (2014) <doi:10.1080/01621459.2013.824892>. Huang and Rathouz (2012) <doi:10.1093/biomet/asr075>. Rathouz and Gao (2008) <doi:10.1093/biostatistics/kxn030>.

Wrappers for functions in the gRain package to emulate some RHugin functionality, allowing the building of Bayesian networks consisting on discrete chance nodes incrementally, through adding nodes, edges and conditional probability tables, the setting of evidence, both hard (boolean) or soft (likelihoods), querying marginal probabilities and normalizing constants, and generating sets of high-probability configurations. Computations will typically not be so fast as they are with RHugin', but this package should assist users without access to Hugin to use code written to use RHugin'.

Fits multiple-group latent class analysis (LCA) for exploring differences between populations in the data with a multilevel structure. There are two approaches to reflect group differences in glca: fixed-effect LCA (Bandeen-Roche et al (1997) <doi:10.1080/01621459.1997.10473658>; Clogg and Goodman (1985) <doi:10.2307/270847>) and nonparametric random-effect LCA (Vermunt (2003) <doi:10.1111/j.0081-1750.2003.t01-1-00131.x>).