From 1926bb10806dc54cb16aafaded73564bc7ac87c7 Mon Sep 17 00:00:00 2001 From: Rekyt Date: Thu, 7 Feb 2019 15:03:32 +0100 Subject: [PATCH] Correct small typos using spelling::spell_check_package() --- NAMESPACE | 2 +- R/RcppExports.R | 6 +- R/nlm_gaussianfield.R | 2 +- R/nlm_mosaicgibbs.R | 2 +- R/nlm_neigh.R | 4 +- R/nlm_randomrectangularcluster.R | 2 +- README.Rmd | 8 +- README.md | 384 ++----------------- man/NLMR-package.Rd | 2 - man/nlm_gaussianfield.Rd | 2 +- man/nlm_mosaicgibbs.Rd | 2 +- man/nlm_neigh.Rd | 4 +- man/nlm_randomrectangularcluster.Rd | 2 +- vignettes/articles/nlm_software_heritage.Rmd | 4 +- vignettes/articles/visualize_nlms.Rmd | 4 +- vignettes/getstarted.Rmd | 2 +- 16 files changed, 64 insertions(+), 368 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 3ccaea2..dcfee56 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -16,4 +16,4 @@ export(nlm_random) export(nlm_randomcluster) export(nlm_randomrectangularcluster) importFrom(Rcpp,sourceCpp) -useDynLib(NLMR) \ No newline at end of file +useDynLib(NLMR, .registration=TRUE) diff --git a/R/RcppExports.R b/R/RcppExports.R index 508c8a7..5e7ec88 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -2,14 +2,14 @@ # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 rcpp_mpd <- function(ncol, nrow, rand_dev, rcpp_roughness, seed) { - .Call('_NLMR_rcpp_mpd', PACKAGE = 'NLMR', ncol, nrow, rand_dev, rcpp_roughness, seed) + .Call(`_NLMR_rcpp_mpd`, ncol, nrow, rand_dev, rcpp_roughness, seed) } rcpp_neigh <- function(nrow, ncol, mat, n_categories, cells_per_cat, neighbourhood, p_neigh, p_empty, seed) { - .Call('_NLMR_rcpp_neigh', PACKAGE = 'NLMR', nrow, ncol, mat, n_categories, cells_per_cat, neighbourhood, p_neigh, p_empty, seed) + .Call(`_NLMR_rcpp_neigh`, nrow, ncol, mat, n_categories, cells_per_cat, neighbourhood, p_neigh, p_empty, seed) } rcpp_randomrectangularcluster <- function(ncol, nrow, minl, maxl, seed) { - .Call('_NLMR_rcpp_randomrectangularcluster', PACKAGE = 'NLMR', ncol, nrow, minl, maxl, seed) + .Call(`_NLMR_rcpp_randomrectangularcluster`, ncol, nrow, minl, maxl, seed) } diff --git a/R/nlm_gaussianfield.R b/R/nlm_gaussianfield.R index 934eae6..5758803 100644 --- a/R/nlm_gaussianfield.R +++ b/R/nlm_gaussianfield.R @@ -30,7 +30,7 @@ #' distinct from one another than they are to closer objects. #' #' @references -#' Kéry & Royle (2016) \emph{Applied Hierarachical Modeling in Ecology} +#' Kéry & Royle (2016) \emph{Applied Hierarchical Modeling in Ecology} #' Chapter 20 #' #' @examples diff --git a/R/nlm_mosaicgibbs.R b/R/nlm_mosaicgibbs.R index caa2b4d..4bf4f91 100644 --- a/R/nlm_mosaicgibbs.R +++ b/R/nlm_mosaicgibbs.R @@ -9,7 +9,7 @@ #' but instead of a random point pattern the algorithm fits a simulated realization of the Strauss #' process. The Strauss process starts with a given number of points and #' uses a minimization approach to fit a point pattern with a given interaction -#' parameter (0 - hardcore process; 1 - Poission process) and interaction radius +#' parameter (0 - hardcore process; 1 - Poisson process) and interaction radius #' (distance of points/germs being apart). #' #' @param ncol [\code{numerical(1)}]\cr diff --git a/R/nlm_neigh.R b/R/nlm_neigh.R index 335c784..9389aad 100644 --- a/R/nlm_neigh.R +++ b/R/nlm_neigh.R @@ -10,8 +10,8 @@ #' Von-Neumann-neighborhood (4 cells), otherwise it is based on \code{p_empty}. To create #' clustered neutral landscape models, \code{p_empty} should be (significantly) smaller than #' \code{p_neigh}. By default, the Von-Neumann-neighborhood is used to check adjacent -#' cells. The algorithm starts with the highest categorial value. If the -#' proportion of cells with this value is reached, the categorial value is +#' cells. The algorithm starts with the highest categorical value. If the +#' proportion of cells with this value is reached, the categorical value is #' reduced by 1. By default, a uniform distribution of the categories is #' applied. #' diff --git a/R/nlm_randomrectangularcluster.R b/R/nlm_randomrectangularcluster.R index f446704..fb018d9 100755 --- a/R/nlm_randomrectangularcluster.R +++ b/R/nlm_randomrectangularcluster.R @@ -14,7 +14,7 @@ #' with rectangles defined by \code{minl} and \code{maxl} until the surface #' of the landscape is completely covered. #' This is one type of realisation of a "falling/dead leaves" algorithm, -#' for more details see Galerne & Goussea (2012). +#' for more details see Galerne & Gousseau (2012). #' #' @return RasterLayer #' diff --git a/README.Rmd b/README.Rmd index 1e68283..e3931bc 100644 --- a/README.Rmd +++ b/README.Rmd @@ -25,7 +25,7 @@ knitr::opts_chunk$set( # NLMR **NLMR** is an ``R`` package for simulating **n**eutral **l**andscape **m**odels (NLM). Designed to be a generic framework like [NLMpy](https://pypi.python.org/pypi/nlmpy), it leverages the ability to simulate the most common NLM that are described in the ecological literature. -**NLMR** builds on the advantages of the **raster** package and returns all simulation as ``RasterLayer`` objects, thus ensuring a direct compability to common GIS tasks and a flexible and simple usage. +**NLMR** builds on the advantages of the **raster** package and returns all simulation as ``RasterLayer`` objects, thus ensuring a direct compatibility to common GIS tasks and a flexible and simple usage. Furthermore, it simulates NLMs within a self-contained, reproducible framework. ## Installation @@ -65,7 +65,7 @@ midpoint_displacememt <- NLMR::nlm_mpd(ncol = 100, ## Overview -**NLMR** supplies 15 NLM algorithms, with several options to simulate derivates of +**NLMR** supplies 15 NLM algorithms, with several options to simulate derivatives of them. The algorithms differ from each other in spatial auto-correlation, from no auto-correlation (random NLM) to a constant gradient (planar gradients): @@ -109,7 +109,7 @@ function_tibble[5,4] <- "Schlather et al. (2015)" # nlm_mosaicfield function_tibble[6,1] <- "nlm_mosaicfield" -function_tibble[6,2] <- "Simulates a mosaic random field neutral landscape model. The algorithm imitates fault lines by repeatedly bisecting the landscape and lowering the values of cells in one half and increasing the values in the other half. If one sets the parameter infinit to TRUE, the algorithm approaches a fractal pattern" +function_tibble[6,2] <- "Simulates a mosaic random field neutral landscape model. The algorithm imitates fault lines by repeatedly bisecting the landscape and lowering the values of cells in one half and increasing the values in the other half. If one sets the parameter infinite to TRUE, the algorithm approaches a fractal pattern" function_tibble[6,3] <- "Figure 1f" function_tibble[6,4] <- "Schlather et al. (2015)" @@ -175,7 +175,7 @@ kable(function_tibble) %>% ## See also **NLMR** was split during its development process - to have a minimal dependency version -for simulating neutral landscape models and an utility toolbox to facilate workflows +for simulating neutral landscape models and an utility toolbox to facilitate workflows with raster data. If you are interested in merging, visualizing or further handling neutral landscape models have a look at the [landscapetools](https://github.com/ropensci/landscapetools/) package. diff --git a/README.md b/README.md index 02433c8..5c0ab8d 100644 --- a/README.md +++ b/README.md @@ -1,25 +1,13 @@ -[![Build Status](https://travis-ci.org/ropensci/NLMR.svg?branch=master)](https://travis-ci.org/ropensci/NLMR)[![Build -status](https://ci.appveyor.com/api/projects/status/djw840fitcvolbxg?svg=true)](https://ci.appveyor.com/project/ropensci/NLMR) -[![codecov](https://codecov.io/gh/ropensci/NLMR/branch/develop/graph/badge.svg?token=MKCm2fVrDa)](https://codecov.io/gh/ropensci/NLMR) -[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/NLMR)](https://cran.r-project.org/package=NLMR) -[![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing) -[![](http://cranlogs.r-pkg.org/badges/grand-total/NLMR)](http://cran.rstudio.com/web/packages/NLMR/index.html) -[![](https://badges.ropensci.org/188_status.svg)](https://github.com/ropensci/onboarding/issues/188) -[![DOI:10.1111/2041-210X.13076](https://zenodo.org/badge/DOI/10.1111/2041-210X.13076.svg)](https://doi.org/10.1111/2041-210X.13076) -# NLMR +[![Build Status](https://travis-ci.org/ropensci/NLMR.svg?branch=master)](https://travis-ci.org/ropensci/NLMR)[![Build status](https://ci.appveyor.com/api/projects/status/djw840fitcvolbxg?svg=true)](https://ci.appveyor.com/project/ropensci/NLMR) [![codecov](https://codecov.io/gh/ropensci/NLMR/branch/develop/graph/badge.svg?token=MKCm2fVrDa)](https://codecov.io/gh/ropensci/NLMR) [![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/NLMR)](https://cran.r-project.org/package=NLMR) [![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing) [![](http://cranlogs.r-pkg.org/badges/grand-total/NLMR)](http://cran.rstudio.com/web/packages/NLMR/index.html) [![](https://badges.ropensci.org/188_status.svg)](https://github.com/ropensci/onboarding/issues/188) [![DOI:10.1111/2041-210X.13076](https://zenodo.org/badge/DOI/10.1111/2041-210X.13076.svg)](https://doi.org/10.1111/2041-210X.13076) -**NLMR** is an `R` package for simulating **n**eutral **l**andscape -**m**odels (NLM). Designed to be a generic framework like -[NLMpy](https://pypi.python.org/pypi/nlmpy), it leverages the ability to -simulate the most common NLM that are described in the ecological -literature. **NLMR** builds on the advantages of the **raster** package -and returns all simulation as `RasterLayer` objects, thus ensuring a -direct compability to common GIS tasks and a flexible and simple usage. -Furthermore, it simulates NLMs within a self-contained, reproducible -framework. +NLMR +================================================================= -## Installation +**NLMR** is an `R` package for simulating **n**eutral **l**andscape **m**odels (NLM). Designed to be a generic framework like [NLMpy](https://pypi.python.org/pypi/nlmpy), it leverages the ability to simulate the most common NLM that are described in the ecological literature. **NLMR** builds on the advantages of the **raster** package and returns all simulation as `RasterLayer` objects, thus ensuring a direct compatibility to common GIS tasks and a flexible and simple usage. Furthermore, it simulates NLMs within a self-contained, reproducible framework. + +Installation +------------ Install the release version from CRAN: @@ -27,18 +15,17 @@ Install the release version from CRAN: install.packages("NLMR") ``` -To install the developmental version of **NLMR**, use the following R -code: +To install the developmental version of **NLMR**, use the following R code: ``` r # install.packages("devtools") devtools::install_github("ropensci/NLMR") ``` -## Example +Example +------- -Each neutral landscape models is simulated with a single function (all -starting with `nlm_`) in `NLMR`, e.g.: +Each neutral landscape models is simulated with a single function (all starting with `nlm_`) in `NLMR`, e.g.: ``` r random_cluster <- NLMR::nlm_randomcluster(nrow = 100, @@ -56,545 +43,256 @@ midpoint_displacememt <- NLMR::nlm_mpd(ncol = 100, roughness = 0.61) ``` -## Overview +Overview +-------- -**NLMR** supplies 15 NLM algorithms, with several options to simulate -derivates of them. The algorithms differ from each other in spatial -auto-correlation, from no auto-correlation (random NLM) to a constant -gradient (planar -gradients): +**NLMR** supplies 15 NLM algorithms, with several options to simulate derivatives of them. The algorithms differ from each other in spatial auto-correlation, from no auto-correlation (random NLM) to a constant gradient (planar gradients): - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- Function - - Description - - Crossreference - - Reference -
- nlm\_curds - - -Simulates a randomly curdled or wheyed neutral landscape model. Random -curdling recursively subdivides the landscape into blocks. At each level -of the recursion, a fraction of these blocks is declared as habitat -while the remaining stays matrix. When option q is set, it simulates a -wheyed curdling model, where previously selected cells that were -declared matrix during recursion, can now contain a proportion of -habitat cells - +Simulates a randomly curdled or wheyed neutral landscape model. Random curdling recursively subdivides the landscape into blocks. At each level of the recursion, a fraction of these blocks is declared as habitat while the remaining stays matrix. When option q is set, it simulates a wheyed curdling model, where previously selected cells that were declared matrix during recursion, can now contain a proportion of habitat cells - Figure 1a,p - - O’Neill, Gardner, and Turner (1992); Keitt (2000) -
- nlm\_distancegradient - - -Simulates a distance gradient neutral landscape model. The gradient is -always measured from a rectangle that one has to specify in the function -(parameter origin) - +Simulates a distance gradient neutral landscape model. The gradient is always measured from a rectangle that one has to specify in the function (parameter origin) - Figure 1b - - Etherington, Holland, and O’Sullivan (2015) -
- nlm\_edgegradient - - -Simulates a linear gradient orientated neutral model. The gradient has a -specified or random direction that has a central peak, which runs -perpendicular to the gradient direction - +Simulates a linear gradient orientated neutral model. The gradient has a specified or random direction that has a central peak, which runs perpendicular to the gradient direction - Figure 1c - - Travis and Dytham (2004); Schlather et al. (2015) -
- nlm\_fbm - - -Simulates neutral landscapes using fractional Brownian motion (fBm). fBm -is an extension of Brownian motion in which the amount of spatial -autocorrelation between steps is controlled by the Hurst coefficient H - +Simulates neutral landscapes using fractional Brownian motion (fBm). fBm is an extension of Brownian motion in which the amount of spatial autocorrelation between steps is controlled by the Hurst coefficient H - Figure 1d - - Schlather et al. (2015) -
- nlm\_gaussianfield - - -Simulates a spatially correlated random fields (Gaussian random fields) -model, where one can control the distance and magnitude of spatial -autocorrelation - +Simulates a spatially correlated random fields (Gaussian random fields) model, where one can control the distance and magnitude of spatial autocorrelation - Figure 1e - - Schlather et al. (2015) -
- nlm\_mosaicfield - - -Simulates a mosaic random field neutral landscape model. The algorithm -imitates fault lines by repeatedly bisecting the landscape and lowering -the values of cells in one half and increasing the values in the other -half. If one sets the parameter infinit to TRUE, the algorithm -approaches a fractal pattern - +Simulates a mosaic random field neutral landscape model. The algorithm imitates fault lines by repeatedly bisecting the landscape and lowering the values of cells in one half and increasing the values in the other half. If one sets the parameter infinite to TRUE, the algorithm approaches a fractal pattern - Figure 1f - - Schlather et al. (2015) -
- nlm\_neigh - - -Simulates a neutral landscape model with land cover classes and -clustering based on neighbourhood characteristics. The cluster are based -on the surrounding cells. If there is a neighbouring cell of the current -value/type, the target cell will more likely turned into a cell of that -type/value - +Simulates a neutral landscape model with land cover classes and clustering based on neighbourhood characteristics. The cluster are based on the surrounding cells. If there is a neighbouring cell of the current value/type, the target cell will more likely turned into a cell of that type/value - Figure 1g - - Scherer et al. (2016) -
- nlm\_percolation - - -Simulates a binary neutral landscape model based on percolation theory. -The probability for a cell to be assigned habitat is drawn from a -uniform distribution - +Simulates a binary neutral landscape model based on percolation theory. The probability for a cell to be assigned habitat is drawn from a uniform distribution - Figure 1h - - Gardner et al. (1989) -
- nlm\_planargradient - - -Simulates a planar gradient neutral landscape model. The gradient is -sloping in a specified or (by default) random direction between 0 and -360 degree - +Simulates a planar gradient neutral landscape model. The gradient is sloping in a specified or (by default) random direction between 0 and 360 degree - Figure 1i - - Palmer (1992) -
- nlm\_mosaictess - - -Simulates a patchy mosaic neutral landscape model based on the -tessellation of a random point process. The algorithm randomly places -points (parameter germs) in the landscape, which are used as the -centroid points for a voronoi tessellation. A higher number of points -therefore leads to a more fragmented landscape - +Simulates a patchy mosaic neutral landscape model based on the tessellation of a random point process. The algorithm randomly places points (parameter germs) in the landscape, which are used as the centroid points for a voronoi tessellation. A higher number of points therefore leads to a more fragmented landscape - Figure 1k - - Gaucherel (2008), Method 1 -
- nlm\_mosaicgibbs - - -Simulates a patchy mosaic neutral landscape model based on the -tessellation of an inhibition point process. This inhibition point -process starts with a given number of points and uses a minimisation -approach to fit a point pattern with a given interaction parameter (0 ‐ -hardcore process; 1 ‐ Poisson process) and interaction radius (distance -of points/germs being apart) - +Simulates a patchy mosaic neutral landscape model based on the tessellation of an inhibition point process. This inhibition point process starts with a given number of points and uses a minimisation approach to fit a point pattern with a given interaction parameter (0 - hardcore process; 1 - Poisson process) and interaction radius (distance of points/germs being apart) - Figure 1l - - Gaucherel (2008), Method 2 -
- nlm\_random - - -Simulates a spatially random neutral landscape model with values drawn a -uniform distribution - +Simulates a spatially random neutral landscape model with values drawn a uniform distribution - Figure 1m - - With and Crist (1995) -
- nlm\_randomcluster - - -Simulates a random cluster nearest‐neighbour neutral landscape. The -parameter ai controls for the number and abundance of land cover classes -and p controls for proportion of elements randomly selected to form -clusters - +Simulates a random cluster nearest-neighbour neutral landscape. The parameter ai controls for the number and abundance of land cover classes and p controls for proportion of elements randomly selected to form clusters - Figure 1n - - Saura and Martínez-Millán (2000) -
- nlm\_mpd - - -Simulates a midpoint displacement neutral landscape model where the -parameter roughness controls the level of spatial autocorrelation - +Simulates a midpoint displacement neutral landscape model where the parameter roughness controls the level of spatial autocorrelation - Figure 1n - - Peitgen and Saupe (1988) -
- nlm\_randomrectangularcluster - - -Simulates a random rectangular cluster neutral landscape model. The -algorithm randomly distributes overlapping rectangles until the -landscape is filled - +Simulates a random rectangular cluster neutral landscape model. The algorithm randomly distributes overlapping rectangles until the landscape is filled - Figure 1o - - -Gustafson and Parker -(1992) - +Gustafson and Parker (1992)
- -## See also +See also +-------- -**NLMR** was split during its development process - to have a minimal -dependency version for simulating neutral landscape models and an -utility toolbox to facilate workflows with raster data. If you are -interested in merging, visualizing or further handling neutral landscape -models have a look at the -[landscapetools](https://github.com/ropensci/landscapetools/) package. +**NLMR** was split during its development process - to have a minimal dependency version for simulating neutral landscape models and an utility toolbox to facilitate workflows with raster data. If you are interested in merging, visualizing or further handling neutral landscape models have a look at the [landscapetools](https://github.com/ropensci/landscapetools/) package. -## Meta +Meta +---- - - Please [report any issues or - bugs](https://github.com/ropensci/NLMR/issues/new/). - - License: GPL3 - - Get citation information for `NLMR` in R doing `citation(package = - 'NLMR')` - - Additionally, we keep a [record of - publications](https://ropensci.github.io/NLMR/articles/publication_record.html/) - that use **NLMR**. Hence, if you used **NLMR** please [file an - issue on GitHub](https://github.com/ropensci/NLMR/issues/new/) - so we can add it to the list. - - We are very open to contributions - if you are interested check out - our [Contributor Guidelines](CONTRIBUTING.md). - - Please note that this project is released with a [Contributor - Code of Conduct](CONDUCT.md). By participating in this project - you agree to abide by its -terms. +- Please [report any issues or bugs](https://github.com/ropensci/NLMR/issues/new/). +- License: GPL3 +- Get citation information for `NLMR` in R doing `citation(package = 'NLMR')` + - Additionally, we keep a [record of publications](https://ropensci.github.io/NLMR/articles/publication_record.html/) that use **NLMR**. Hence, if you used **NLMR** please [file an issue on GitHub](https://github.com/ropensci/NLMR/issues/new/) so we can add it to the list. +- We are very open to contributions - if you are interested check out our [Contributor Guidelines](CONTRIBUTING.md). + - Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms. [![ropensci\_footer](https://ropensci.org/public_images/github_footer.png)](http://ropensci.org) diff --git a/man/NLMR-package.Rd b/man/NLMR-package.Rd index 7b02f90..f73daf7 100644 --- a/man/NLMR-package.Rd +++ b/man/NLMR-package.Rd @@ -17,8 +17,6 @@ of the \emph{NLMR} package. The vignettes in this package are listed below. Quickstart Guide}}{Short walk-through of the \emph{NLMR} package and how to handle the simulations.} } - -#' @useDynLib NLMR, .registration=TRUE } \seealso{ Useful links: diff --git a/man/nlm_gaussianfield.Rd b/man/nlm_gaussianfield.Rd index ed98d25..d09e4bc 100644 --- a/man/nlm_gaussianfield.Rd +++ b/man/nlm_gaussianfield.Rd @@ -61,6 +61,6 @@ landscapetools::show_landscape(gaussian_field) } \references{ -Kéry & Royle (2016) \emph{Applied Hierarachical Modeling in Ecology} +Kéry & Royle (2016) \emph{Applied Hierarchical Modeling in Ecology} Chapter 20 } diff --git a/man/nlm_mosaicgibbs.Rd b/man/nlm_mosaicgibbs.Rd index 2ef567d..657194d 100644 --- a/man/nlm_mosaicgibbs.Rd +++ b/man/nlm_mosaicgibbs.Rd @@ -43,7 +43,7 @@ The method works in principal like the tessellation method (\code{nlm_mosaictess but instead of a random point pattern the algorithm fits a simulated realization of the Strauss process. The Strauss process starts with a given number of points and uses a minimization approach to fit a point pattern with a given interaction -parameter (0 - hardcore process; 1 - Poission process) and interaction radius +parameter (0 - hardcore process; 1 - Poisson process) and interaction radius (distance of points/germs being apart). } \examples{ diff --git a/man/nlm_neigh.Rd b/man/nlm_neigh.Rd index 11dfa2a..2ee3605 100644 --- a/man/nlm_neigh.Rd +++ b/man/nlm_neigh.Rd @@ -55,8 +55,8 @@ The algorithm draws a random cell and turns it into a given category based on Von-Neumann-neighborhood (4 cells), otherwise it is based on \code{p_empty}. To create clustered neutral landscape models, \code{p_empty} should be (significantly) smaller than \code{p_neigh}. By default, the Von-Neumann-neighborhood is used to check adjacent - cells. The algorithm starts with the highest categorial value. If the - proportion of cells with this value is reached, the categorial value is + cells. The algorithm starts with the highest categorical value. If the + proportion of cells with this value is reached, the categorical value is reduced by 1. By default, a uniform distribution of the categories is applied. } diff --git a/man/nlm_randomrectangularcluster.Rd b/man/nlm_randomrectangularcluster.Rd index 9df8c54..7d46f20 100644 --- a/man/nlm_randomrectangularcluster.Rd +++ b/man/nlm_randomrectangularcluster.Rd @@ -31,7 +31,7 @@ The random rectangular cluster algorithm starts to fill a raster randomly with rectangles defined by \code{minl} and \code{maxl} until the surface of the landscape is completely covered. This is one type of realisation of a "falling/dead leaves" algorithm, -for more details see Galerne & Goussea (2012). +for more details see Galerne & Gousseau (2012). } \examples{ # simulate random rectangular cluster diff --git a/vignettes/articles/nlm_software_heritage.Rmd b/vignettes/articles/nlm_software_heritage.Rmd index ca3f921..5bec3b0 100644 --- a/vignettes/articles/nlm_software_heritage.Rmd +++ b/vignettes/articles/nlm_software_heritage.Rmd @@ -28,7 +28,7 @@ probably sense to revisit here in the future. One of the reasons to actually use neutral landscape models is the statistical comparison and testing and development of landscape metrics. Therefore, one needs -replicats +replicates ### Built in parameters @@ -67,7 +67,7 @@ If you are interested in landscapes that share a metric which is not a built-in parameter, the most clever way I can came up with is to simulate models as long as it takes to have the desired number of landscapes with a certain metric. -An examplary workflow for this could look like this: +An exemplary workflow for this could look like this: ```{r warning=FALSE} library(NLMR) diff --git a/vignettes/articles/visualize_nlms.Rmd b/vignettes/articles/visualize_nlms.Rmd index 859785d..a1bbfe8 100644 --- a/vignettes/articles/visualize_nlms.Rmd +++ b/vignettes/articles/visualize_nlms.Rmd @@ -16,7 +16,7 @@ As the ever growing R package environment can be a rough terrain to navigate and ### landscapetools **landscapetools** function `show_landscape` was developed to help users to adhere to -some standards concerning color scales and typographie. This means for example +some standards concerning color scales and typography. This means for example that by default the [viridis color scale](https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html) is applied which makes your plots easier to read by those with colorblindness. @@ -45,7 +45,7 @@ show_landscape(landscape) + ### rasterVis -**rasterVis** also offers some convience functions to plot raster, for example: +**rasterVis** also offers some convenience functions to plot raster, for example: ```{r , fig.height=7, fig.width=7, message=FALSE, warning=FALSE, fig.align='center'} library("NLMR") diff --git a/vignettes/getstarted.Rmd b/vignettes/getstarted.Rmd index 7c4a01c..ec9db7c 100644 --- a/vignettes/getstarted.Rmd +++ b/vignettes/getstarted.Rmd @@ -115,4 +115,4 @@ nr_classified <- landscapetools::util_classify(nr, weighting = c(0.3, 0.3, 0.3)) plot(nr_classified) ``` -##References +## References