The solution to some common problems is proposed, as well as a summary of some small functions. In particular, it provides a useful function for some problems in chemistry. For example, monoa(), monob() and mono() function can be used to calculate The pH of weak acid/base. The ggpng() function can save the PNG format with transparent background. The period_table() function will show the periodic table. Also the show_ruler() function will show the ruler. The show_color() function is funny and easier to show colors. I also provide the symb() function to generate multiple symbols at once. The csv2vcf() function provides an easy method to generate a file. The sym2poly() and sym2coef() function can extract coefficients from polynomials.

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.

Deprecated.

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.

Bayesian Linear Regression.

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.

This holds some r markdown and quarto templates and a template to create a research project in "R Studio".

Build experience life tables.

FLR algorithm for classification.

Periodic B Splines Basis.

Utilities for text analysis.