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jeffreyevans

spatialEco:Spatial Analysis and Modelling Utilities

Utilities to support spatial data manipulation, query, sampling and modelling in ecological applications. Functions include models for species population density, spatial smoothing, multivariate separability, point process model for creating pseudo- absences and sub-sampling, Quadrant-based sampling and analysis, auto-logistic modeling, sampling models, cluster optimization, statistical exploratory tools and raster-based metrics.

Maintained by Jeffrey S. Evans. Last updated 27 days ago.

biodiversityconservationecologyr-spatialrasterspatialvector

110 stars 9.55 score 736 scripts 2 dependents

willyanjnr

EstimateBreed:Estimation of Environmental Variables and Genetic Parameters

Performs analyzes and estimates of environmental covariates and genetic parameters related to selection strategies and development of superior genotypes. It has two main functionalities, the first being about prediction models of covariates and environmental processes, while the second deals with the estimation of genetic parameters and selection strategies. Designed for researchers and professionals in genetics and environmental sciences, the package combines statistical methods for modeling and data analysis. This includes the plastochron estimate proposed by Porta et al. (2024) <doi:10.1590/1807-1929/agriambi.v28n10e278299>, Stress indices for genotype selection referenced by Ghazvini et al. (2024) <doi:10.1007/s10343-024-00981-1>, the Environmental Stress Index described by Tazzo et al. (2024) <https://revistas.ufg.br/vet/article/view/77035>, industrial quality indices of wheat genotypes (Szareski et al., 2019), <doi:10.4238/gmr18223>, Ear Indexes estimation (Rigotti et al., 2024), <doi:10.13083/reveng.v32i1.17394>, Selection index for protein and grain yield (de Pelegrin et al., 2017), <doi:10.4236/ajps.2017.813224>, Estimation of the ISGR - Genetic Selection Index for Resilience for environmental resilience (Bandeira et al., 2024) <https://www.cropj.com/Carvalho_18_12_2024_825_830.pdf>, estimation of Leaf Area Index (Meira et al., 2015) <https://www.fag.edu.br/upload/revista/cultivando_o_saber/55d1ef202e494.pdf>, Restriction of control variability (Carvalho et al., 2023) <doi:10.4025/actasciagron.v45i1.56156>, Risk of Disease Occurrence in Soybeans described by Engers et al. (2024) <doi:10.1007/s40858-024-00649-1> and estimation of genetic parameters for selection based on balanced experiments (Yadav et al., 2024) <doi:10.1155/2024/9946332>.

Maintained by Willyan Junior Adorian Bandeira. Last updated 10 days ago.

1 stars 5.00 score

draalmeida

leafR:Calculates the Leaf Area Index (LAD) and Other Related Functions

A set of functions for analyzing the structure of forests based on the leaf area density (LAD) and leaf area index (LAI) measures calculated from Airborne Laser Scanning (ALS), i.e., scanning lidar (Light Detection and Ranging) data. The methodology is discussed and described in Almeida et al. (2019) <doi:10.3390/rs11010092> and Stark et al. (2012) <doi:10.1111/j.1461-0248.2012.01864.x>.

Maintained by Danilo Roberti Alves de Almeida. Last updated 4 years ago.

17 stars 3.93 score 4 scripts

cran

chartreview:Adaptive Multi-Wave Sampling for Efficient Chart Validation

Functionality to perform adaptive multi-wave sampling for efficient chart validation. Code allows one to define strata, adaptively sample using several types of confidence bounds for the quantity of interest (Lai's confidence bands, Bayesian credible intervals, normal confidence intervals), and sampling strategies (random sampling, stratified random sampling, Neyman's sampling, see Neyman (1934) <doi:10.2307/2342192> and Neyman (1938) <doi:10.1080/01621459.1938.10503378>).

Maintained by Georg Hahn. Last updated 2 months ago.

1.00 score

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