C++ Classes to Embed R in C++ (and C) Applications

C++ classes to embed R in C++ (and C) applications A C++ class providing the R interpreter is offered by this package making it easier to have "R inside" your C++ application. As R itself is embedded into your application, a shared library build of R is required. This works on Linux, OS X and even on Windows provided you use the same tools used to build R itself. Numerous examples are provided in the nine subdirectories of the examples/ directory of the installed package: standard, 'mpi' (for parallel computing), 'qt' (showing how to embed 'RInside' inside a Qt GUI application), 'wt' (showing how to build a "web-application" using the Wt toolkit), 'armadillo' (for 'RInside' use with 'RcppArmadillo'), 'eigen' (for 'RInside' use with 'RcppEigen'), and 'c_interface' for a basic C interface and 'Ruby' illustration. The examples use 'GNUmakefile(s)' with GNU extensions, so a GNU make is required (and will use the 'GNUmakefile' automatically). 'Doxygen'-generated documentation of the C++ classes is available at the 'RInside' website as well.

Provides Progress Bars in 'knitr'

Provides a progress bar similar to 'dplyr' that can write progress out to a variety of locations, including stdout(), stderr(), or from file(). Useful when using 'knitr' or 'rmarkdown', and you still want to see progress of calculations in the terminal.

Biological Risk Assessment

A spatio-temporal exposure-hazard model for assessing biological risk and impact. The model is based on stochastic geometry for describing the landscape and the exposed individuals, a dispersal kernel for the dissemination of contaminants, a set of tools to handle spatio-temporal dataframe and ecotoxicological equations. Walker E, Leclerc M, Rey JF, Beaudouin R, Soubeyrand S, and Messean A, (2017), A Spatio-Temporal Exposure-Hazard Model for Assessing Biological Risk and Impact, Risk Analysis, . Leclerc M, Walker E, Messean A, Soubeyrand S (2018), Spatial exposure-hazard and landscape models for assessing the impact of GM crops on non-target organisms, Science of the Total Environment, 624, 470-479.

Extensions of 'dplyr' and 'fuzzyjoin' Join Functions

We extend 'dplyr' and 'fuzzyjoin' join functions with features to preprocess the data, apply various data checks, and deal with conflicting columns.

Interactive Maps with R and Leaflet

Display spatial data with interactive maps powered by the open- source JavaScript library 'Leaflet' (see < https://leafletjs.com/>). Maps can be rendered in a web browser or displayed in the HTML viewer pane of 'RStudio'. This package is designed to be easy to use and can create complex maps with vector and raster data, web served map tiles and interface elements.

Vectorised Nested if-else Statements Similar to CASE WHEN in 'SQL'

Functions for vectorised conditional recoding of variables. case_when() enables you to vectorise multiple if and else statements (like 'CASE WHEN' in 'SQL'). if_else() is a stricter and more predictable version of ifelse() in 'base' that preserves attributes. These functions are forked from 'dplyr' with all package dependencies removed and behave identically to the originals.

Distributions that are Sometimes Used in Hydrology

Probability distributions that are sometimes useful in hydrology.

Airborne LiDAR Filtering Method Based on Multiscale Curvature

Multiscale Curvature Classification of ground returns in 3-D LiDAR point clouds, designed for forested environments. 'RMCC' is a porting to R of the 'MCC-lidar' method by Evans and Hudak (2007) .

Quantify the Relationship Between Development Rate and Temperature in Ectotherms

A set of functions to quantify the relationship between development rate and temperature and to build phenological models. The package comprises a set of models and estimated parameters borrowed from a literature review in ectotherms. The methods and literature review are described in Rebaudo et al. (2018) , Rebaudo and Rabhi (2018) , and Regnier et al. (2021) . An example can be found in Rebaudo et al. (2017) .

Specify Reserve Demand Curves

Automatic specification and estimation of reserve demand curves for central bank operations. The package can help to choose the best demand curve and identify additional explanatory variables. Various plot and predict options are included. For more details, see Chen et al. (2023) < https://www.imf.org/en/Publications/WP/Issues/2023/09/01/Modeling-the-Reserve-Demand-to-Facilitate-Central-Bank-Operations-538754>.