Perform the Blinder-Oaxaca decomposition for generalized linear model with bootstrapped standard errors. The twofold and threefold decomposition are given, even the generalized linear model output in each group.

Offers various swiss maps as data frames and ggplot2 objects and gives the possibility to add layers of data on the maps. Data are publicly available from the swiss federal statistical office. In addition to the \codemaps2 object (a list of 8 swiss maps, at various levels), there are the data frames with the boundaries used to produce these maps (\codeshp_df, a list with 8 data frames).

Light procedures for learning Global Vector Autoregression model (GVAR) of Pesaran, Schuermann and Weiner (2004) <DOI:10.1198/073500104000000019> and Dees, di Mauro, Pesaran and Smith (2007) <DOI:10.1002/jae.932>.

Create network-style visualizations of pairwise relationships using custom edge glyphs built on top of ggplot2'. The package supports both statistical and non-statistical data and allows users to represent directed relationships. This enables clear, publication-ready graphics for exploring and communicating relational structures in a wide range of domains. The method was first used in Abu-Akel et al. (2021) <doi:10.1371/journal.pone.0245100>. Code is released under the MIT License; included datasets are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0).

Computes probabilities related to group sequential designs for normally distributed test statistics. Enables to derive critical boundaries, power, drift, and confidence intervals of such designs. Supports the alpha spending approach by Lan-DeMets (1994) <doi:10.1002/sim.4780131308>.

This package provides a genetic algorithm framework for regression problems requiring discrete optimization over model spaces with unknown or varying dimension, where gradient-based methods and exhaustive enumeration are impractical. Uses a compact chromosome representation for tasks including spline knot placement and best-subset variable selection, with constraint-preserving crossover and mutation, exact uniform initialization under spacing constraints, steady-state replacement, and optional island-model parallelization from Lu, Lund, and Lee (2010, <doi:10.1214/09-AOAS289>). The computation is built on the GA engine of Scrucca (2017, <doi:10.32614/RJ-2017-008>) and changepointGA engine from Li and Lu (2024, <doi:10.48550/arXiv.2410.15571>). In challenging high-dimensional settings, GAReg enables efficient search and delivers near-optimal solutions when alternative algorithms are not well-justified.

Estimates a counterfactual using Gaussian process projection. It takes a dataframe, creates missingness in the desired outcome variable and estimates counterfactual values based on all information in the dataframe. The package writes Stan code, checks it for convergence and adds artificial noise to prevent overfitting and returns a plot of actual values and estimated counterfactual values using r-base plot.

This package provides functions for performing polygon geometry with grid grobs. This allows complex shapes to be defined by combining simpler shapes.

Some tools for developing general equilibrium models and some general equilibrium models. These models can be used for teaching economic theory and are built by the methods of new structural economics (see LI Wu, 2019, ISBN: 9787521804225, General Equilibrium and Structural Dynamics: Perspectives of New Structural Economics. Beijing: Economic Science Press). The model form and mathematical methods can be traced back to J. von Neumann (1945, A Model of General Economic Equilibrium. The Review of Economic Studies, 13. pp. 1-9), J. G. Kemeny, O. Morgenstern and G. L. Thompson (1956, A Generalization of the von Neumann Model of an Expanding Economy, Econometrica, 24, pp. 115-135) et al. By the way, J. G. Kemeny is a co-inventor of the computer language BASIC.

Some methods for the inference and clustering of univariate and multivariate functional data, using a generalization of Mahalanobis distance, along with some functions useful for the analysis of functional data. For further details, see Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017) <arXiv:1708.00386>.

This package provides a variable selection approach for generalized linear mixed models by L1-penalized estimation is provided, see Groll and Tutz (2014) <doi:10.1007/s11222-012-9359-z>. See also Groll and Tutz (2017) <doi:10.1007/s10985-016-9359-y> for discrete survival models including heterogeneity.

This package provides functions to compute generalized eigenvalues and eigenvectors, the generalized Schur decomposition and the generalized Singular Value Decomposition of a matrix pair, using Lapack routines.

This package provides a novel PRS model is introduced to enhance the prediction accuracy by utilising GxE effects. This package performs Genome Wide Association Studies (GWAS) and Genome Wide Environment Interaction Studies (GWEIS) using a discovery dataset. The package has the ability to obtain polygenic risk scores (PRSs) for a target sample. Finally it predicts the risk values of each individual in the target sample. Users have the choice of using existing models (Li et al., 2015) <doi:10.1093/annonc/mdu565>, (Pandis et al., 2013) <doi:10.1093/ejo/cjt054>, (Peyrot et al., 2018) <doi:10.1016/j.biopsych.2017.09.009> and (Song et al., 2022) <doi:10.1038/s41467-022-32407-9>, as well as newly proposed models for genomic risk prediction (refer to the URL for more details).

Modified versions of the lag() and summary() functions: glag() and gsummary(). The prefix g is a reminder of who to blame if things do not work as they should.

This package provides functions and necessary JavaScript bindings to quickly transfer spatial data from R memory or remote URLs to the browser for use in interactive HTML widgets created with the htmlwidgets R package. Leverages GeoArrow (<https://geoarrow.org/>) data representation for data stored in local R memory which is generally faster than traditional GeoJSON by minimising the amount of copy, serialization and deserialization steps necessary for the data transfer. Furthermore, provides functionality and JavaScript bindings to consume GeoParquet (<https://geoparquet.org/>) files from remote URLs in the browser.

Identify and visualize individuals with unusual association patterns of genetics and geography using the approach of Chang and Schmid (2023) <doi:10.1101/2023.04.06.535838>. It detects potential outliers that violate the isolation-by-distance assumption using the K-nearest neighbor approach. You can obtain a table of outliers with statistics and visualize unusual geo-genetic patterns on a geographical map. This is useful for landscape genomics studies to discover individuals with unusual geography and genetics associations from a large biological sample.

Toolset to create perpendicular profile graphs and swath profiles. Method are based on coordinate rotation algorithm by Schaeben et al. (2024) <doi:10.1002/mma.9823>.

Supports image files and graphic objects to be visualized in ggplot2 graphic system.

Easily explore data by plotting graphs with a few lines of code. Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways, for example, by choosing from colour blind-friendly palettes (12 discreet, 3 continuous and 2 divergent palettes). Use the simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs, and fit non-linear outcomes as a generalised additive model (gam) using mgcv(). Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans()). Also includes small datasets for practising code and teaching basics before users move on to more complex designs. See vignettes for details on usage <https://grafify.shenoylab.com/>. Citation: <doi:10.5281/zenodo.5136508>.

This package provides functions that make it easy to reveal ggplot2 graphs incrementally. The functions take a plot produced with ggplot2 and return a list of plots showing data incrementally by panels, layers, groups, the values in an axis or any arbitrary aesthetic.

This package provides functions to fit geostatistical data. The data can be continuous, binary or count data and the models implemented are flexible. Conjugate priors are assumed on some parameters while inference on the other parameters can be done through a full Bayesian analysis of by empirical Bayes methods.

Discrete scales for the colorblind-friendly Okabe-Ito palette, including color', fill', and edge_colour'. ggokabeito provides ggplot2 and ggraph scales to easily use the Okabe-Ito palette in your data visualizations.

Generalized factor model is implemented for ultra-high dimensional data with mixed-type variables. Two algorithms, variational EM and alternate maximization, are designed to implement the generalized factor model, respectively. The factor matrix and loading matrix together with the number of factors can be well estimated. This model can be employed in social and behavioral sciences, economy and finance, and genomics, to extract interpretable nonlinear factors. More details can be referred to Wei Liu, Huazhen Lin, Shurong Zheng and Jin Liu. (2023) <doi:10.1080/01621459.2021.1999818>.

Collection of tools that facilitates data access and workflow for spatial analysis of Argentina. Includes historical information from censuses, administrative limits at different levels of aggregation, location of human settlements, among others. Since it is expected that the majority of users will be Spanish-speaking, the documentation of the package prioritizes this language, although an effort is made to also offer annotations in English.