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.

Conducts hierarchical partitioning to calculate individual contributions of each predictor (fixed effects) towards marginal R2 for generalized linear mixed-effect model (including lm, glm and glmm) based on output of r.squaredGLMM() in MuMIn', applying the algorithm of Lai J.,Zou Y., Zhang S.,Zhang X.,Mao L.(2022)glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models.Journal of Plant Ecology,15(6)1302-1307<doi:10.1093/jpe/rtac096>.

Density, distribution function, quantile function and random generation for the Generalized Gamma proposed in Stacy, E. W. (1962) <doi:10.1214/aoms/1177704481>.

Streamline the creation of common charts by taking care of a lot of data preprocessing and plot customization for the user. Provides a high-level interface to create plots using ggplot2'.

Simulating, visualizing and comparing tumor clonal data by using simple commands. This aims at providing a tool to help researchers to easily simulate tumor data and analyze the results of their approaches for studying the composition and the evolutionary history of tumors.

This package performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.

Interface for g3-lollipop JavaScript library. Visualize genetic mutation data using an interactive lollipop diagram in RStudio or your web browser.

Convert data to GeoJSON or TopoJSON from various R classes, including vectors, lists, data frames, shape files, and spatial classes. geojsonio does not aim to replace packages like sp', rgdal', rgeos', but rather aims to be a high level client to simplify conversions of data from and to GeoJSON and TopoJSON'.

It provides an interesting solution for handling a high number of segmentation variables in partial least squares structural equation modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez, and Aluja,(2016)<doi:10.1002/asmb.2168>) including the F-coefficient test (Lamberti, Sanchez, and Aluja,(2017)<doi:10.1002/asmb.2270>) to detect the path coefficients responsible for the identified differences). The package also allows running the hybrid multi-group approach (Lamberti (2021) <doi:10.1007/s11135-021-01096-9>).

Extremely efficient procedures for fitting regularization path with l0, l1, and truncated lasso penalty for linear regression and logistic regression models. This version is a completely new version compared with our previous version, which was mainly based on R. New core algorithms are developed and are now written in C++ and highly optimized.

The multiple contrast tests for univariate were proposed by Munko, Ditzhaus, Pauly, Smaga, and Zhang (2023) <doi:10.48550/arXiv.2306.15259>. Recently, they were extended to the multivariate functional data in Munko, Ditzhaus, Pauly, and Smaga (2024) <doi:10.48550/arXiv.2406.01242>. These procedures enable us to evaluate the overall hypothesis regarding equality, as well as specific hypotheses defined by contrasts. In particular, we can perform post hoc tests to examine particular comparisons of interest. Different experimental designs are supported, e.g., one-way and multi-way analysis of variance for functional data.

Process in-situ Gamma-Ray Spectrometry for Luminescence Dating. This package allows to import, inspect and correct the energy shifts of gamma-ray spectra. It provides methods for estimating the gamma dose rate by the use of a calibration curve as described in Mercier and Falguères (2007). The package only supports Canberra CNF and TKA and Kromek SPE files.

Create R functions that interact with OAuth2 Google APIs <https://developers.google.com/apis-explorer/> easily, with auto-refresh and Shiny compatibility.

Genotype plus genotype-by-environment (GGE) biplots rendered using ggplot2'. Provides a command line interface to all of the functionality contained within the archived package GGEBiplotGUI'.

This package provides a collection of I/O tools for handling the most commonly used genomic datafiles, like fasta/-q, bed, gff, gtf, ped/map and vcf.

Simulates from discrete and continuous target distributions using geometric Metropolis-Hastings (MH) algorithms. Users specify the target distribution by an R function that evaluates the log un-normalized pdf or pmf. The package also contains a function implementing a specific geometric MH algorithm for performing high dimensional Bayesian variable selection.

Facilitates the citation of R packages used in analysis projects. Scans project for packages used, gets their citations, and produces a document with citations in the preferred bibliography format, ready to be pasted into reports or manuscripts. Alternatively, grateful can be used directly within an R Markdown or Quarto document.

This package contains the implementation of a binary large margin classifier based on Gabriel Graph. References for this method can be found in L.C.B. Torres et al. (2015) <doi:10.1049/el.2015.1644>.

Extends ggplot2 functionality to the partykit package. ggparty provides the necessary tools to create clearly structured and highly customizable visualizations for tree-objects of the class party'.

This small collection of functions provides what we call elemental graphics for display of analysis of variance results, David C. Hoaglin, Frederick Mosteller and John W. Tukey (1991, ISBN:978-0-471-52735-0), Paul R. Rosenbaum (1989) <doi:10.2307/2684513>, Robert M. Pruzek and James E. Helmreich <https://jse.amstat.org/v17n1/helmreich.html>. The term elemental derives from the fact that each function is aimed at construction of graphical displays that afford direct visualizations of data with respect to the fundamental questions that drive the particular analysis of variance methods. These functions can be particularly helpful for students and non-statistician analysts. But these methods should be quite generally helpful for work-a-day applications of all kinds, as they can help to identify outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data.

This package provides publication-ready volcano plots for visualizing differential expression results, commonly used in RNA-seq and similar analyses. This tool helps create high-quality visual representations of data using the ggplot2 framework Wickham (2016) <doi:10.1007/978-3-319-24277-4>.

Improved version of GRIN software that streamlines its use in practice to analyze genomic lesion data, accelerate its computing, and expand its analysis capabilities to answer additional scientific questions including a rigorous evaluation of the association of genomic lesions with RNA expression. Pounds, Stan, et al. (2013) <DOI:10.1093/bioinformatics/btt372>.

Data from multi environment agronomic trials, which are often carried out by plant breeders, can be analyzed with the tools offered by this package such as the Additive Main effects and Multiplicative Interaction model or AMMI ('Gauch 1992, ISBN:9780444892409) and the Site Regression model or SREG ('Cornelius 1996, <doi:10.1201/9780367802226>). Since these methods present a poor performance under the presence of outliers and missing values, this package includes robust versions of the AMMI model ('Rodrigues 2016, <doi:10.1093/bioinformatics/btv533>), and also imputation techniques specifically developed for this kind of data ('Arciniegas-Alarcón 2014, <doi:10.2478/bile-2014-0006>).

Implementation of functions, which combines binomial calculation and data visualisation, to analyse the differences in publishing authorship by gender described in Day et al. (2020) <doi:10.1039/C9SC04090K>. It should only be used when self-reported gender is unavailable.