fixed spelling errors

Author: bmaitner

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: S4DM
-Title: Small Sample Size Species Distribution Modelling
+Title: Small Sample Size Species Distribution Modeling
 Version: 0.0.1
 Authors@R:
     c(
@@ -25,7 +25,7 @@ Authors@R:
            role = c("aut"),
            email = "cory.merow@gmail.com")
            )
-Description: This package implements multiple distribution modelling methods that are suited to species with small sample sizes (e.g., poorly sampled species or rare species).
+Description: This package implements multiple distribution modeling methods that are suited to species with small sample sizes (e.g., poorly sampled species or rare species).
 Depends: 
         R (>= 3.5.0)
 License: MIT + file LICENSE

R/dr_maxnet.R

@@ -1,12 +1,12 @@
-#'Density-ratio SDM estimation with MAXNET
+#'Density-ratio SDM estimation with Maxnet
 #'
-#'dr_maxnet is an internal function for density-ratio estimation with MAXNET
+#'dr_maxnet is an internal function for density-ratio estimation with Maxnet
 #' @param presence_data dataframe of covariates
 #' @param background_data dataframe of covariates
 #' @param projection_data dataframe of covariates
-#' @param f MAXNET formula to use. Default (NULL) will use the MAXNET default.
-#' @param regmult MAXNET regularization multiplier. Default is 1.
-#' @param regfun MAXNET regularization function. Default is the MAXNET default.
+#' @param f Maxnet formula to use. Default (NULL) will use the Maxnet default.
+#' @param regmult Maxnet regularization multiplier. Default is 1.
+#' @param regfun Maxnet regularization function. Default is the Maxnet default.
 #' @param addsamplestobackground If TRUE (the default), any presences that aren't in the background will be added.
 #' @param clamp If TRUE (the default), predictions will be limited to ranges seen in the training dataset.
 #' @param type Type of response required.  Defaults to link, exponential, cloglog, and logistic.

R/ensemble_range_map.R

@@ -18,7 +18,7 @@
 #' @note Either `method` or both `presence_method` and `background_method` must be supplied.
 #' @details Current plug-and-play methods include: "gaussian", "kde","vine","rangebagging", "lobagoc", and "none".
 #' Current density ratio methods include: "ulsif", "rulsif".
-#' @return List object containing elements (1) spatRaster ensemble layer showing the proportion of maps that are inclued in the range across the ensemble,
+#' @return List object containing elements (1) spatRaster ensemble layer showing the proportion of maps that are included in the range across the ensemble,
 #'  (2) spatRasters for individual models, and (3) model quality information.
 #' @importFrom pROC roc auc
 #' @importFrom terra app nlyr ncell

R/evaluate_range_map.R

@@ -1,5 +1,5 @@
 #' @name evaluate_range_map
-#' @title Evalute PBSDM range map quality
+#' @title Evaluate PBSDM range map quality
 #' @description This function uses 5-fold, spatially stratified, cross-validation to evaluate distribution model quality.
 #' @param occurrences Presence coordinates in long,lat format.
 #' @param env Environmental rasters

R/make_range_map.R

@@ -10,7 +10,7 @@
 #' "numbag" (presence function is bootstrapped),
 #' or "doublebag" (presence and background functions are bootstrapped).
 #' @param bootstrap_reps Integer.  Number of bootstrap replicates to use (default is 100)
-#' @param quantile Quantile to use for thresholding.  Default is 0.05 (5 pct training presence). Set to 0 for minimum trainin presence (MTP), set to NULL to return continuous raster.
+#' @param quantile Quantile to use for thresholding.  Default is 0.05 (5 pct training presence). Set to 0 for minimum training presence (MTP), set to NULL to return continuous raster.
 #' @param background_buffer_width The width (in m for unprojected rasters and map units for projected rasters) of the buffer to use for background data.
 #' Defaults to NULL, which will take the maximum distance between occurrence records.
 #' @param constraint_regions See get_env_bg documentation

R/pnp_lobagoc.R

@@ -1,6 +1,6 @@
 #' @name pnp_lobagoc
 #' @title Internal function for fitting lobagoc distributions in plug-and-play SDMs.
-#' @description This function both fits lobgaoc distributions \insertCite{Drake2014-qz}{S4DM} and projects those distributions to new covariates.
+#' @description This function both fits lobagoc distributions \insertCite{Drake2014-qz}{S4DM} and projects those distributions to new covariates.
 #' @param data dataframe of covariates
 #' @param method one of either "fit" or "predict"
 #' @param object fitted object returned by a pnp_... function. Only needed when method = "predict"

README.md

@@ -1,7 +1,7 @@
-# Small Sample Size Species Distribution Modelling
+# Small Sample Size Species Distribution Modeling
 
 ## The S4DM R package
-This repository contains an R package that implements Species Distribution Modelling methods which work even when there are relatively few occurrence records (as is the case for poorly-sample or range-restricted species). These methods were primarily developed by the Drake lab, and include three types of methods: 1) Plug-and-play models, 2) environmental-range models, and 3) density-ratio models. Most of the important functions in this package are wrappers around existing functions that handle density estimation or density-ratio estimation.  Much of this code was created by modifying existing code at https://github.com/DrakeLab/PlugNPlay in order to make functions more modular and extensible.
+This repository contains an R package that implements Species Distribution Modeling methods which work even when there are relatively few occurrence records (as is the case for poorly-sample or range-restricted species). These methods were primarily developed by the Drake lab, and include three types of methods: 1) Plug-and-play models, 2) environmental-range models, and 3) density-ratio models. Most of the important functions in this package are wrappers around existing functions that handle density estimation or density-ratio estimation.  Much of this code was created by modifying existing code at https://github.com/DrakeLab/PlugNPlay in order to make functions more modular and extensible.
 
 ## How it works
 The package is build on a hierarchy of modular functions, each of which calls on lower-level functions:

man/dr_maxnet.Rd

@@ -24,7 +24,7 @@ This R package implements multiple species distribution modeling (SDM) methods t
 
 ### Plug-and-play models
 
-The plug-and-play SDM framework was developed by Drake and Richards (2018).  Plug-and-play models fit SDMs as the ratio of a presence distribution and a background distribution. Plug-and-play refers to the ability of the methodology to accommodate any method for estimating density functions (e.g., Gaussian, kernal-density estimation). Users are thus able to choose from multiple methods for estimating SDMs using an evolving set of methods within one unified framework.
+The plug-and-play SDM framework was developed by Drake and Richards (2018).  Plug-and-play models fit SDMs as the ratio of a presence distribution and a background distribution. Plug-and-play refers to the ability of the methodology to accommodate any method for estimating density functions (e.g., Gaussian, kernel-density estimation). Users are thus able to choose from multiple methods for estimating SDMs using an evolving set of methods within one unified framework.
 
 ### Density-ratio models
 
@@ -339,7 +339,7 @@ sample_points_gaussian_kde_predictions <- project_plug_and_play(pnp_model = samp
 
 ## Density-ratio models
 
-In addition to methods that estimate the presence and background distributions separately, the package accommodates methods that directly estimate the density ratio, including uLSIF and MaxNet.
+In addition to methods that estimate the presence and background distributions separately, the package accommodates methods that directly estimate the density ratio, including uLSIF and Maxnet.
 
 ```{r maxnet}