Incorporate Expert Opinion with Parametric Survival Models

Enables users to incorporate expert opinion with parametric survival analysis using a Bayesian or frequentist approach. Expert Opinion can be provided on the survival probabilities at certain time-point(s) or for the difference in mean survival between two treatment arms. Please reference it's use as Cooney, P., White, A. (2023) .

Multi-Barrier Approach for Water Reuse Risk Assessment

Provides a Quantitative Microbial Risk Assessment (QMRA) framework for the reuse of treated wastewater in agricultural irrigation. Following a multi-barrier approach, the package simulates pathogen inflow and removal along a treatment train, estimates human exposure, and computes health risk indicators such as infection probability, illness probability, and Disability-Adjusted Life Years (DALYs). It also supports an economic analysis of the treatment scenarios considered. For more details see .

Estimate Quantiles Curves

Non-parametric methods as local normal regression, polynomial local regression and penalized cubic B-splines regression are used to estimate quantiles curves. See Fan and Gijbels (1996) and Perperoglou et al.(2019) .

Download Flight and Airport Data from Brazil

Download flight and airport data from Brazil’s Civil Aviation Agency (ANAC) < https://www.gov.br/anac/pt-br>. The data covers detailed information on aircraft, airports, and airport operations registered with ANAC. It also includes data on airfares, all international flights to and from Brazil, and domestic flights within the country.

Applying Landscape Genomic Methods on 'SNP' and 'Silicodart' Data

Provides landscape genomic functions to analyse 'SNP' (single nuclear polymorphism) data, such as least cost path analysis and isolation by distance. Therefore each sample needs to have coordinate data attached (lat/lon) to be able to run most of the functions. 'dartR.spatial' is a package that belongs to the 'dartRverse' suit of packages and depends on 'dartR.base' and 'dartR.data'.

Install and Load the 'dartRverse' Suits of Packages

Provides a single function that supports the installation of all packages belonging to the 'dartRverse'. The 'dartRverse' is a set of packages that work together to analyse SNP (single nuclear polymorphism) data. All packages aim to have a similar 'look and feel' and are based on the same type of data structure ('genlight'), with additional metadata for loci and individuals (samples). For more information visit the 'GitHub' pages < https://github.com/green-striped-gecko/dartRverse>.

Analysing 'SNP' and 'Silicodart' Data Generated by Genome-Wide Restriction Fragment Analysis

Facilitates the analysis of SNP (single nucleotide polymorphism) and silicodart (presence/absence) data. 'dartR.popgen' provides a suit of functions to analyse such data in a population genetics context. It provides several functions to calculate population genetic metrics and to study population structure. Quite a few functions need additional software to be able to run (gl.run.structure(), gl.blast(), gl.LDNe()). You find detailed description in the help pages how to download and link the packages so the function can run the software. 'dartR.popgen' is part of the the 'dartRverse' suit of packages. Gruber et al. (2018) . Mijangos et al. (2022) .

Estimating Aboveground Biomass and Its Uncertainty in Tropical Forests

Contains functions for estimating above-ground biomass/carbon and its uncertainty in tropical forests. These functions allow to (1) retrieve and correct taxonomy, (2) estimate wood density and its uncertainty, (3) build height-diameter models, (4) manage tree and plot coordinates, (5) estimate above-ground biomass/carbon at stand level with associated uncertainty. To cite ‘BIOMASS’, please use citation(‘BIOMASS’). For more information, see Réjou-Méchain et al. (2017) .

Deal with Check Outputs

Deal with packages 'check' outputs and reduce the risk of rejection by 'CRAN' by following policies.

Fits Expectile Regression for Panel Fixed Effect Model

Fits the Expectile Regression for Fixed Effect (ERFE) estimator. The ERFE model extends the within-transformation strategy to solve the incidental parameter problem within the expectile regression framework. The ERFE model estimates the regressor effects on the expectiles of the response distribution. The ERFE estimate corresponds to the classical fixed-effect within-estimator when the asymmetric point is 0.5. The paper by Barry, Oualkacha, and Charpentier (2021, ) gives more details about the ERFE model.