This package contains a function called gds() which accepts three input parameters like lower limits, upper limits and the frequencies of the corresponding classes. The gds() function calculate and return the values of mean ('gmean'), median ('gmedian'), mode ('gmode'), variance ('gvar'), standard deviation ('gstdev'), coefficient of variance ('gcv'), quartiles ('gq1', gq2', gq3'), inter-quartile range ('gIQR'), skewness ('g1'), and kurtosis ('g2') which facilitate effective data analysis. For skewness and kurtosis calculations we use moments.

This package provides statistical transformations for plotting empirical ordinary Lorenz curve (Lorenz 1905) <doi:10.2307/2276207> and generalized Lorenz curve (Shorrocks 1983) <doi:10.2307/2554117>.

To calculate the relative risk (RR) for the generalized additive model.

Guided partial least squares (guided-PLS) is the combination of partial least squares by singular value decomposition (PLS-SVD) and guided principal component analysis (guided-PCA). This package provides implementations of PLS-SVD, guided-PLS, and guided-PCA for supervised dimensionality reduction. The guided-PCA function (new in v1.1.0) automatically handles mixed data types (continuous and categorical) in the supervision matrix and provides detailed contribution analysis for interpretability. For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/guidedPLS>.

Create graticule lines and labels for maps. Control the creation of lines or tiles by setting their placement (at particular meridians and parallels) and extent (along parallels and meridians). Labels are created independently of lines.

Integrates game theory and ecological theory to construct social-ecological models that simulate the management of populations and stakeholder actions. These models build off of a previously developed management strategy evaluation (MSE) framework to simulate all aspects of management: population dynamics, manager observation of populations, manager decision making, and stakeholder responses to management decisions. The newly developed generalised management strategy evaluation (GMSE) framework uses genetic algorithms to mimic the decision-making process of managers and stakeholders under conditions of change, uncertainty, and conflict. Simulations can be run using gmse(), gmse_apply(), and gmse_gui() functions.

When you prepare a presentation or a report, you often need to manage a large number of ggplot figures. You need to change the figure size, modify the title, label, themes, etc. It is inconvenient to go back to the original code to make these changes. This package provides a simple way to manage ggplot figures. You can easily add the figure to the database and update them later using CLI (command line interface) or GUI (graphical user interface).

Datos de nombres inscritos en Chile entre 1920 y 2021, de acuerdo al Servicio de Registro Civil. English: Chilean baby names registered from 1920 to 2021 by the Civil Registry Service.

An interface for fitting generalized additive models (GAMs) and generalized additive mixed models (GAMMs) using the lme4 package as the computational engine, as described in Helwig (2024) <doi:10.3390/stats7010003>. Supports default and formula methods for model specification, additive and tensor product splines for capturing nonlinear effects, and automatic determination of spline type based on the class of each predictor. Includes an S3 plot method for visualizing the (nonlinear) model terms, an S3 predict method for forming predictions from a fit model, and an S3 summary method for conducting significance testing using the Bayesian interpretation of a smoothing spline.

This package provides ggplot2 extensions for creating skewed boxplots using several statistical methods (Kimber, 1990 <doi:10.2307/2347808>; Hubert and Vandervieren, 2008 <doi:10.1016/j.csda.2007.11.008>; Adil et al., 2015 <doi:10.18187/pjsor.v11i1.500>; Babura et al., 2017 <doi:10.1063/1.4982872>; Walker et al., 2018 <doi:10.1080/00031305.2018.1448891>). The package implements custom statistical transformations and geometries to visualize data distributions with an emphasis on skewness.

Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling. Full model and computation details are described in Clark et al. (2018) <doi:10.1002/ecm.1241>.

This package implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models. For binary outcome, data can be sampled in prospective cohort studies or case-control studies. Described in Zhang et al. (2020)<doi:10.1093/biomet/asaa014>.

Reads annual financial reports including assets, liabilities, dividends history, stockholder composition and much more from Bovespa's DFP, FRE and FCA systems <http://www.b3.com.br/pt_br/produtos-e-servicos/negociacao/renda-variavel/empresas-listadas.htm>. These are web based interfaces for all financial reports of companies traded at Bovespa. The package is specially designed for large scale data importation, keeping a tabular (long) structure for easier processing.

Interface for extra smooth functions including tensor products, neural networks and decision trees.

This package provides plotting functions for visualizing pedigrees and family trees. The package complements a behavior genetics package BGmisc [Garrison et al. (2024) <doi:10.21105/joss.06203>] by rendering pedigrees using the ggplot2 framework. Features include support for duplicated individuals, complex mating structures, integration with simulated pedigrees, and layout customization. Due to the impending deprecation of kinship2, version 1.0 incorporates the layout helper functions from kinship2. The pedigree alignment algorithms are adapted from kinship2 [Sinnwell et al. (2014) <doi:10.1159/000363105>]. We gratefully acknowledge the original authors: Jason Sinnwell, Terry Therneau, Daniel Schaid, and Elizabeth Atkinson for their foundational work.

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>.

This package provides functions for multiple knockoff inference using summary statistics, e.g. Z-scores. The knockoff inference is a general procedure for controlling the false discovery rate (FDR) when performing variable selection. This package provides a procedure which performs knockoff inference without ever constructing individual knockoffs (GhostKnockoff). It additionally supports multiple knockoff inference for improved stability and reproducibility. Moreover, it supports meta-analysis of multiple overlapping studies.

Using simple input, this package creates plots of gene models. Users can create plots of alternatively spliced gene variants and the positions of mutations and other gene features.

An S3 class groupedHyperframe that inherits from hyper data frame. Batch processes on point-pattern hyper column. Aggregation of function-value-table hyper column(s) and numeric hyper column(s) over a nested grouping structure.

This package implements key features of Gephi for network visualization, including ForceAtlas2 (with LinLog mode), network scaling, and network rotations. It also includes easy network visualization tools such as edge and node color assignment for recreating Gephi'-style graphs in R. The package references layout algorithms developed by Jacomy, M., Venturini T., Heymann S., and Bastian M. (2014) <doi:10.1371/journal.pone.0098679> and Noack, A. (2009) <doi:10.48550/arXiv.0807.4052>.

Ridge regression due to Hoerl and Kennard (1970)<DOI:10.1080/00401706.1970.10488634> and generalized ridge regression due to Yang and Emura (2017)<DOI:10.1080/03610918.2016.1193195> with optimized tuning parameters. These ridge regression estimators (the HK estimator and the YE estimator) are computed by minimizing the cross-validated mean squared errors. Both the ridge and generalized ridge estimators are applicable for high-dimensional regressors (p>n), where p is the number of regressors, and n is the sample size.

Ranked Set Sampling (RSS) is a stratified sampling method known for its efficiency compared to Simple Random Sampling (SRS). When sample allocation is equal across strata, it is referred to as balanced RSS (BRSS) whereas unequal allocation is called unbalanced RSS (URSS), which is particularly effective for asymmetric or skewed distributions. This package offers practical statistical tools and sampling methods for both BRSS and URSS, emphasizing flexible sampling designs and inference for population means, medians, proportions, and Area Under the Curve (AUC). It incorporates parametric and nonparametric tests, including empirical likelihood ratio (LR) methods. The package provides ranked set sampling methods from a given population, including sampling with imperfect ranking using auxiliary variables. Furthermore, it provides tools for efficient sample allocation in URSS, ensuring greater efficiency than SRS and BRSS. For more details, refer e.g. to Chen et al. (2003) <doi:10.1007/978-0-387-21664-5>, Ahn et al. (2022) <doi:10.1007/978-3-031-14525-4_3>, and Ahn et al. (2024) <doi:10.1111/insr.12589>.

This package provides a function to retrieve the system timezone on Unix systems which has been found to find an answer when Sys.timezone() has failed. It is based on an answer by Duane McCully posted on StackOverflow', and adapted to be callable from R. The package also builds on Windows, but just returns NULL.

Find all hierarchical models of specified generalized linear model with information criterion (AIC, BIC, or AICc) within specified cutoff of minimum value. Alternatively, find all such graphical models. Use branch and bound algorithm so we do not have to fit all models.