Geographical detectors for measuring spatial stratified heterogeneity, as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the optimal discretization of continuous data, four primary functions of geographical detectors, comparison of size effects of spatial unit and the visualizations of results. To use the package and to refer the descriptions of the package, methods and case datasets, please cite Yongze Song (2020) <doi:10.1080/15481603.2020.1760434>. The model has been applied in factor exploration of road performance and multi-scale spatial segmentation for network data, as described in Yongze Song (2018) <doi:10.3390/rs10111696> and Yongze Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.

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.

The aim of od is to provide tools and example datasets for working with origin-destination ('OD') datasets of the type used to describe aggregate urban mobility patterns (Carey et al. 1981) <doi:10.1287/trsc.15.1.32>. The package builds on functions for working with OD data in the package stplanr', (Lovelace and Ellison 2018) <doi:10.32614/RJ-2018-053> with a focus on computational efficiency and support for the sf class system (Pebesma 2018) <doi:10.32614/RJ-2018-009>. With few dependencies and a simple class system based on data frames, the package is intended to facilitate efficient analysis of OD datasets and to provide a place for developing new functions. The package enables the creation and analysis of geographic entities representing large scale mobility patterns, from daily travel between zones in cities to migration between countries.

This package performs (Adaptive) Boosting Trees for Poisson distributed response variables, using log-link function. The code approach is similar to the one used in gbm'/'gbm3'. Moreover, each tree in the expansion is built thanks to the rpart package. This package is based on following books and articles Denuit, M., Hainaut, D., Trufin, J. (2019) <doi:10.1007/978-3-030-25820-7> Denuit, M., Hainaut, D., Trufin, J. (2019) <doi:10.1007/978-3-030-57556-4> Denuit, M., Hainaut, D., Trufin, J. (2019) <doi:10.1007/978-3-030-25827-6> Denuit, M., Hainaut, D., Trufin, J. (2022) <doi:10.1080/03461238.2022.2037016> Denuit, M., Huyghe, J., Trufin, J. (2022) <https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A244325/datastream/PDF_01/view> Denuit, M., Trufin, J., Verdebout, T. (2022) <https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A268577>.

This package provides support software for Statistical Analysis and Data Display (Second Edition, Springer, ISBN 978-1-4939-2121-8, 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) by Richard M. Heiberger and Burt Holland. This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print. All chapters have exercises. All functions introduced in the book are in the package. R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.

In mathematics, rejection sampling is a basic technique used to generate observations from a distribution. It is also commonly called the Acceptance-Rejection method or Accept-Reject algorithm and is a type of Monte Carlo method. Acceptance-Rejection method is based on the observation that to sample a random variable one can perform a uniformly random sampling of the 2D cartesian graph, and keep the samples in the region under the graph of its density function. Package AR is able to generate/simulate random data from a probability density function by Acceptance-Rejection method. Moreover, this package is a useful teaching resource for graphical presentation of Acceptance-Rejection method. From the practical point of view, the user needs to calculate a constant in Acceptance-Rejection method, which package AR is able to compute this constant by optimization tools. Several numerical examples are provided to illustrate the graphical presentation for the Acceptance-Rejection Method.

Reduced-rank regression, diagnostics and graphics.

R implementation of the common parsing tools lex and yacc'.

Play the classic game of tic-tac-toe (naughts and crosses).

Extract the implied risk neutral density from options using various methods.

This package lets you rarefy data, calculate diversity and plot the results.

DBI/RJDBC interface to h2 database. h2 version 2.3.232 is included.

Probabilistic analysis of probe reliability and differential gene expression on short oligonucleotide arrays.

The rpx package implements an interface to proteomics data submitted to the ProteomeXchange consortium.

The ROI is a framework for handling optimization problems in R.

This package provides utilities for Receiver Operating Characteristic (ROC) curves, with a focus on micro arrays.

Deprecated.

This package provides tools for shrunken centroids regularized discriminant analysis for the purpose of classifying high dimensional data.

Finds a robust instrumental variables estimator using a high breakdown point S-estimator of multivariate location and scatter matrix.

Use A Resampling-Based Empirical Bayes Approach to Assess Differential Expression in Two-Color Microarrays and RNA-Seq data sets.

Bayesian Linear Regression.

Automatically apply different strategies to optimize R code. rco functions take R code as input, and returns R code as output.

Flexible statistical modelling using a modular framework for regression, in which groups of transformations are composed together and act on probability distributions.

The evaluation criteria of rangeland health, condition and landscape function analysis based on species diversity and functional diversity of rangeland plant communities.