April 2020 daily data | By service | By one variable

AEMET options | Temporal resolution | Stations | AEMET API Key | Examples | AEMET stations info | AEMET data

AEMET API | tidyverse loop | for loop | MeteoCat API | daily | monthly | yearly

A new way of working with meteoland | Example datasets | The new interpolation process | Topographical information | Meteorological information | Quick interpolation (if everything is ok) | Interpolator object | Writing and reading interpolator objects | Interpolator calibration | Interpolation validation | Interpolation utils | Temporal summary of interpolated data | Calculating rainfall erosivity | Piping all together | Interpolation on raster data | Temporal aggregation in raster | Piping raster interpolation | Appendix | Equivalence table

Introduction | Data required for indicator species analysis | The community data matrix | Defining the classification of sites | Indicator species analysis using multipatt() | Indicator Value analysis with site group combinations | Displaying the results | Examining the indicator value components | Inspecting the indicator species analysis results for all species | Analyzing species ecological preferences with correlation indices | Excluding site group combinations in multipatt() | Indicator species analysis without site groups combinations | Restricting the order of site groups combinations | Specifying the site groups combinations to be considered | Additional functions to estimate and test the association between species and groups of sites | The function strassoc() | The function signassoc() | Determining how well target site groups are covered by indicators | The function coverage() | The function plotcoverage() | Species combinations as indicators of site groups | Generating species combinations | The function indicators() | Determining the coverage for objects of class indicators() | The function pruneindicators() | The function predict.indicators() | Bibliography

Meteorological data format | Converting meteorological data to meteoland format | Converting from data frames | Meteorological data from other R packages | meteospain data | worldmet data | Meteorological data from raster sources

Red de Información Agroclimática de Andalucía (RIA) service | RIA options | Temporal resolution | Stations | Examples | RIA stations info | RIA data

Introduction | Resemblance between diet resources | Resource niche analysis at the population level | Resource use of populations | Niche breadth in populations | Overlap between populations | Resource niche analysis at the individual level | Resource use of individuals | Measuring the degree of individual specialisation | Measuring the degree of overlap between individuals | Bibliography

MeteoCat options | Temporal resolution | Stations | MeteoCat API Key | Examples | MeteoCat stations info | MeteoCat data

Meteoclimatic options | Temporal resolution | Stations | Example | Meteoclimatic stations info | Meteoclimatic data

MeteoGalicia options | Temporal resolution | Stations | Examples | MeteoGalicia stations info | MeteoGalicia data

Introduction | What is this tutorial about? | Example vegetation data | Clustering methods in vegclust | Resemblance space | Prototype-based clustering | Clustering models | Hard (crisp) or fuzzy memberships | Centroids or medoids | Partitive clustering | Noise clustering | Possibilistic clustering | Dissimilarity-based duals | Medoid-based clustering and dissimilarity matrices | Centroid-based clustering and dissimilarity matrices | Managing vegetation classifications | Creating classifications: vegclust and vegclustdist() | The K-means model | The Fuzzy C-means model | The Noise clustering model | Medoid-based clustering | Supervised classification: as.vegclust() and vegclass() | Extending vegetation classifications | Conforming vegetation data sets | Re-calculating the centroids of the initial classification | Calling vegclust with fixed prototypes | Extending or refining classifications? | Using vegclustdist() with fixed prototypes | Cluster characterization | Cluster prototypes: clustcentroid() and clustmedoid() | Cluster internal variability: clustvar() | Distance between clusters: interclustdist() | Constancy classes: clustconst() | Bibliography

Introduction | Post-fire vegetation regeneration data | Cumulative abundance profiles | Cumulative abundance surfaces | Dissimilarities in structure and composition | Classification of vegetation stands | References