Fixed mispellings

NEWS.md

@@ -8,7 +8,7 @@
 ## Improvements
 
 * Updated `register()` to perform 3 sequential registrations when using Nelder-Mead, this improves the results of optimal stretch and shift parameters.
-* Updated `calc_loglik()` to use `sigma_squared` in every timepoint in the sum.
+* Updated `calc_loglik()` to use `sigma_squared` in every time point in the sum.
 * Updated `scaled_data()` and `preprocess_data()` to return `all_data` object only, instead of a `list()` containing `all_data`.
 * Updated `compare_H1_and_H2()` to return `BIC_diff` column (`BIC_combined - BIC_separate`), instead of `BIC_combined` and `BIC_separate` on their own.
 * Updated `explore_manual_search_space()` to use `BIC_diff` instead of `BIC_combined` to calculate `best_params` from `model_comparison` table.
@@ -44,7 +44,7 @@
 * Switched to manual calculation of log likelihood via `calc_loglik()` instead of `stats::logLik()`.
 * Reduced computation time up to 1000 times, (x30 speed-up from package rewrite, and x35 speed-up from switching default optimisation method).
 * Removed {dplyr}, {magrittr}, {purrr}, {rlang}, and {stringr} as package dependencies.
-* Added {neldermead} as a package depedency.
+* Added {neldermead} as a package dependency.
 * Updated list of exported functions:
   * `register()`
   * `summarise_registration()`
@@ -57,7 +57,7 @@
 
 * Simplified parameters of main `register()` function, and added `scaling_method`.
 * Simplified structure of output object of `register()`.
-* Simplfied parameters of `summarise_registration()`, `plot_registration_results()`, `plot_heatmap()`, `calculate_distance()` to simply require `results` object from `register()`, vastly simplifing usage.
+* Simplified parameters of `summarise_registration()`, `plot_registration_results()`, `plot_heatmap()`, `calculate_distance()` to simply require `results` object from `register()`, vastly simplifying usage.
 * Improved messages, errors, and progress indicators with {cli}.
 * Added correct pluralisation in {cli} messages.
 * Rewrote unit tests to use {data.table} exclusively for data manipulation.

vignettes/process-results.Rmd

@@ -63,7 +63,7 @@ reg_summary$non_registered_genes
 
 ### Plot distribution of registration parameters
 
-The function `plot()` allows users to plot the bivariate distribution of the registration parameters. Non-registered genes can be ignored by selecting `type = "registered"` insted of the default `type = "all"`. Similarly, the marginal distribution type can be changed from `type_dist = "histogram"` (defult) to `type_dist = "density"`.
+The function `plot()` allows users to plot the bivariate distribution of the registration parameters. Non-registered genes can be ignored by selecting `type = "registered"` instead of the default `type = "all"`. Similarly, the marginal distribution type can be changed from `type_dist = "histogram"` (default) to `type_dist = "density"`.
 
 ```{r plot-summary-results, fig.align='center', fig.height=4, fig.width=4.5, warning=FALSE}
 plot(
@@ -86,7 +86,7 @@ plot(
 
 Notice that the plot includes a label indicating if the particular genes are registered or non-registered, as well as the registration parameters in case the registration was successful.
 
-For more details on the other function paramaters, go to `plot()`.
+For more details on the other function parameters, go to `plot()`.
 
 ## Analysing similarity of expression profiles over time before and after registering
 

vignettes/register-data.Rmd

@@ -52,7 +52,7 @@ b_rapa_data[, .SD[1:6], by = accession][, .(gene_id, accession, timepoint, expre
 
 ## Registering the data
 
-To align gene expression time-course between *Arabidopsis* Col-0 and *B. rapa* Ro18, we can use function `register()`. By default, the best registration parameters are optimised via Nelder-Mead (`optimisation_method = "nm"`). When using the default `use_optimisation = TRUE`, the stretch and shift search space is automatically estimated. For more details on the other function paramaters, go to `register()`.
+To align gene expression time-course between *Arabidopsis* Col-0 and *B. rapa* Ro18, we can use function `register()`. By default, the best registration parameters are optimised via Nelder-Mead (`optimisation_method = "nm"`). When using the default `use_optimisation = TRUE`, the stretch and shift search space is automatically estimated. For more details on the other function parameters, go to `register()`.
 
 ```{r register-data-raw, message=FALSE, warning=FALSE, eval=FALSE}
 registration_results <- register(
@@ -90,7 +90,7 @@ registration_results <- register(
 )
 ```
 
-As noted in the [input data requirements](https://ruthkr.github.io/greatR/articles/data-requirement.html) vignette, `register()` also accepts a list of data frames or a list of refernce and query vectors as `input`:
+As noted in the [input data requirements](https://ruthkr.github.io/greatR/articles/data-requirement.html) vignette, `register()` also accepts a list of data frames or a list of reference and query vectors as `input`:
 
 ```{r register-data-list-vectors, message=FALSE, warning=FALSE}
 registration_results_vectors <- register(