Pierre Roudier

Pierre Roudier

10 packages on CRAN

asdreader

cran
99.99th

Percentile

A simple driver that reads binary data created by the ASD Inc. portable spectrometer instruments, such as the FieldSpec (for more information, see <http://www.asdi.com/products/fieldspec-spectroradiometers>). Spectral data can be extracted from the ASD files as raw (DN), white reference, radiance, or reflectance. Additionally, the metadata information contained in the ASD file header can also be accessed.

clhs

cran
99.99th

Percentile

Conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006) <DOI:10.1016/j.cageo.2005.12.009>. This method proposes to stratify sampling in presence of ancillary data. An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012, <DOI:10.1201/b12728>).

dissever

cran
99.99th

Percentile

Spatial downscaling of coarse grid mapping to fine grid mapping using predictive covariates and a model fitted using the 'caret' package. The original dissever algorithm was published by Malone et al. (2012) <doi:10.1016/j.cageo.2011.08.021>, and extended by Roudier et al. (2017) <doi:10.1016/j.compag.2017.08.021>.

spectacles

cran
99.99th

Percentile

Stores and eases the manipulation of spectra and associated data, with dedicated classes for spatial and soil-related data.

spemd

cran
99.99th

Percentile

This implementation of the Empirical Mode Decomposition (EMD) works in 2 dimensions simultaneously, and can be applied on spatial data. It can handle both gridded or un-gridded datasets.

aqp

cran
99.99th

Percentile

The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks <doi:10.1016/j.cageo.2012.10.020>. These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb <https://casoilresource.lawr.ucdavis.edu/soilweb-apps/>. Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice.

cluster

cran
99.99th

Percentile

Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".

mapview

cran
99.99th

Percentile

Quickly and conveniently create interactive visualisations of spatial data with or without background maps. Attributes of displayed features are fully queryable via pop-up windows. Additional functionality includes methods to visualise true- and false-color raster images and bounding boxes.

plotKML

cran
99.99th

Percentile

Writes sp-class, spacetime-class, raster-class and similar spatial and spatio-temporal objects to KML following some basic cartographic rules.

spsann

cran
99.99th

Percentile

Methods to optimize sample configurations using spatial simulated annealing. Multiple objective functions are implemented for various purposes, such as variogram estimation, spatial trend estimation and spatial interpolation. A general purpose spatial simulated annealing function enables the user to define his/her own objective function. Solutions for augmenting existing sample configurations and solving multi-objective optimization problems are available as well.