typos

R/movegroup.R

@@ -69,7 +69,7 @@
 #' @param timeDiffUnits Character. Unit for timeDiffLong. Default "hours".
 #' @param center US English alternate to centre. Do you want to center the move object within 
 #' extent? See spTransform. Default TRUE.
-#' @param centre British English alternate to center. Do you want to center the move object within 
+#' @param centre British English alternate to center. Do you want to centre the move object within 
 #' extent? See spTransform. Default NULL.
 #' @param buffpct Buffer extent for raster creation, proportion of 1. Default 0.3, can try e.g. 3 
 #' for a large buffer to avoid clipping, at the cost of file size, but later cropping in 
@@ -92,7 +92,7 @@
 # #' MoveLocErrorCalc function for satellite data with state space modelled locations with 95%
 # #' confidence intervals for latlon i.e. lat and lon025 and 975.
 #' @param movemargin Margin size for variance calc in move::brownian.motion.variance.dyn and 
-#' behavioral change point analysis in move::brownian.bridge.dyn. Must be an odd number. Default 11.
+#' behavioural change point analysis in move::brownian.bridge.dyn. Must be an odd number. Default 11.
 #' @param dbbext Ext param in the 'brownian.bridge.dyn' function in the 'move' package. Extends 
 #' bounding box around track. Numeric single (all edges), double (x & y), or 4 (xmin xmax ymin ymax)
 #' . Default 3. Excessive buffering will get cropped automatically.

README.Rmd

@@ -63,9 +63,9 @@ A minimal time difference among relocations is then suggestive of a straight-lin
 
 ![Variances](./figures/BBvariancesvignette.png)
 
-Kranstauber et al. (2012) later introduced the dynamic BBMM (dBBMM), which improves upon the traditiional BBMM in its calculation of utilization distribution (UD) statistics. While the Brownian motion variance in the BBMM is static i.e., constant throughout the movement track, the dBBMM allows this variance to vary to allow for changes in in behavior along the movement trajectory (e.g., foraging, traveling, resting, etc.). The result is improved performance in predicting animal locations. The dBBMM is also better equipped in dealing with irregular sampling of tracks, and would therefore be applied more appropriately to telemetry data for which you can anticipate gaps in detection (e.g., due to tags requiring to breach the ocean surface to transmit their location to a satellite ('Smart Position Only Tag' or SPOT), which depends on the animal's behavior, or tag-equipped aquatic animals leaving a fixed acoustic receiver array, or a tag's line of sight with a satellite being obstructed by physical structures, etc.).
+Kranstauber et al. (2012) later introduced the dynamic BBMM (dBBMM), which improves upon the traditional BBMM in its calculation of utilization distribution (UD) statistics. While the Brownian motion variance in the BBMM is static i.e., constant throughout the movement track, the dBBMM allows this variance to vary to allow for changes in in behaviour along the movement trajectory (e.g., foraging, travelling, resting, etc.). The result is improved performance in predicting animal locations. The dBBMM is also better equipped in dealing with irregular sampling of tracks, and would therefore be applied more appropriately to telemetry data for which you can anticipate gaps in detection (e.g., due to tags requiring to breach the ocean surface to transmit their location to a satellite ('Smart Position Only Tag' or SPOT), which depends on the animal's behaviour, or tag-equipped aquatic animals leaving a fixed acoustic receiver array, or a tag's line of sight with a satellite being obstructed by physical structures, etc.).
 
-The dBBMM is calculated using the 'move' package. While this package is great for calculating UDs, the package can only calculate a model and output a UD for a single individual. Grouping individuals together to create a group-level/aggregated UD is not realistic, because the brownian.bridge.dyn() function requires a chronological movement path as input; grouping multiple individuals together would imply that individuals can teleport. This package that builds on the move package by being able to handle multiple individuals simultaneously, and aggregates individual UDs in a single group-level UD, offering significant advancements in the investigation of group-/population level space use estimation of telemetered animals. Of additional benefit is the ability to incorporate heterogenous survey design e.g. unbalanced numbers of receivers across multiple arrays. All functions are designed to maximally automate the typical methodological pipeline, offloading the workload and technical skill required to (e.g.) scale and reproject multiple movement tracks to an optimal shared projection and extent, and plot output maps containing various disparate elements.
+The dBBMM is calculated using the 'move' package. While this package is great for calculating UDs, the package can only calculate a model and output a UD for a single individual. Grouping individuals together to create a group-level/aggregated UD is not realistic, because the brownian.bridge.dyn() function requires a chronological movement path as input; grouping multiple individuals together would imply that individuals can teleport. This package that builds on the move package by being able to handle multiple individuals simultaneously, and aggregates individual UDs in a single group-level UD, offering significant advancements in the investigation of group-/population level space use estimation of telemetered animals. Of additional benefit is the ability to incorporate heterogeneous survey design e.g. unbalanced numbers of receivers across multiple arrays. All functions are designed to maximally automate the typical methodological pipeline, offloading the workload and technical skill required to (e.g.) scale and reproject multiple movement tracks to an optimal shared projection and extent, and plot output maps containing various disparate elements.
 
 We strongly recommend that you download papers:
 
@@ -244,7 +244,7 @@ moveLocError calculator for ARGOS or state space models resulting in 95percent l
 
 Builds a dataframe of original locations plus rowmeans of mean distance of location extremities lon975, lat; lon025, lat; lon, lat975; lon, lat025 from the centre point lon, lat.
 
-`movegroup`'s `moveLocError` parameter allows a vector of error distances corresponding to the same-length vector of positions supplied in your dataset. For acoustic data, the error is likely static, in which case one can use a single value which is repeated for all positions. For satellite data, each position can have a different error based on changing strength of satellite uplink connection, number of satelllites for triangulation, etc. If you have positions possibly filtered by `argosfilter::sdafilter` and with state space models applied using the `aniMotum` package (a process you can follow thanks to scripts [1](https://github.com/SimonDedman/SavingTheBlue/blob/main/R/06A_Filter_SPOT_data.R) & [2](https://github.com/SimonDedman/SavingTheBlue/blob/main/R/06B_CTCRW_SPOT_data_usin_animotum.R) by Vital Heim), this function converts those 95% confidence interval latitude and longitude locations into a mean error distance per position.
+`movegroup`'s `moveLocError` parameter allows a vector of error distances corresponding to the same-length vector of positions supplied in your dataset. For acoustic data, the error is likely static, in which case one can use a single value which is repeated for all positions. For satellite data, each position can have a different error based on changing strength of satellite uplink connection, number of satellites for triangulation, etc. If you have positions possibly filtered by `argosfilter::sdafilter` and with state space models applied using the `aniMotum` package (a process you can follow thanks to scripts [1](https://github.com/SimonDedman/SavingTheBlue/blob/main/R/06A_Filter_SPOT_data.R) & [2](https://github.com/SimonDedman/SavingTheBlue/blob/main/R/06B_CTCRW_SPOT_data_usin_animotum.R) by Vital Heim), this function converts those 95% confidence interval latitude and longitude locations into a mean error distance per position.
 
 ***
 

README.md

@@ -104,18 +104,18 @@ which would result in a larger Brownian motion variance of the bridge.
 </figure>
 
 Kranstauber et al. (2012) later introduced the dynamic BBMM (dBBMM),
-which improves upon the traditiional BBMM in its calculation of
+which improves upon the traditional BBMM in its calculation of
 utilization distribution (UD) statistics. While the Brownian motion
 variance in the BBMM is static i.e., constant throughout the movement
 track, the dBBMM allows this variance to vary to allow for changes in in
-behavior along the movement trajectory (e.g., foraging, traveling,
+behaviour along the movement trajectory (e.g., foraging, travelling,
 resting, etc.). The result is improved performance in predicting animal
 locations. The dBBMM is also better equipped in dealing with irregular
 sampling of tracks, and would therefore be applied more appropriately to
 telemetry data for which you can anticipate gaps in detection (e.g., due
 to tags requiring to breach the ocean surface to transmit their location
 to a satellite (‘Smart Position Only Tag’ or SPOT), which depends on the
-animal’s behavior, or tag-equipped aquatic animals leaving a fixed
+animal’s behaviour, or tag-equipped aquatic animals leaving a fixed
 acoustic receiver array, or a tag’s line of sight with a satellite being
 obstructed by physical structures, etc.).
 
@@ -130,7 +130,7 @@ by being able to handle multiple individuals simultaneously, and
 aggregates individual UDs in a single group-level UD, offering
 significant advancements in the investigation of group-/population level
 space use estimation of telemetered animals. Of additional benefit is
-the ability to incorporate heterogenous survey design e.g. unbalanced
+the ability to incorporate heterogeneous survey design e.g. unbalanced
 numbers of receivers across multiple arrays. All functions are designed
 to maximally automate the typical methodological pipeline, offloading
 the workload and technical skill required to (e.g.) scale and reproject
@@ -424,7 +424,7 @@ distances corresponding to the same-length vector of positions supplied
 in your dataset. For acoustic data, the error is likely static, in which
 case one can use a single value which is repeated for all positions. For
 satellite data, each position can have a different error based on
-changing strength of satellite uplink connection, number of satelllites
+changing strength of satellite uplink connection, number of satellites
 for triangulation, etc. If you have positions possibly filtered by
 `argosfilter::sdafilter` and with state space models applied using the
 `aniMotum` package (a process you can follow thanks to scripts