update minor typos

R/define_tdm_init_model.R

@@ -4,7 +4,7 @@
 #'
 #' @param def model definition, named recursive list with at least the
 #' objects `misc$model_type`, `parameters` and `variables`
-#' @return model defintion with `state_init` object added describing how to initializing the compartments.
+#' @return model definition with `state_init` object added describing how to initializing the compartments.
 #' @keywords internal
 define_tdm_init_model <- function(def){
   if(!is.null(def$state_init)) {

R/get_fixed_parameters.R

@@ -1,4 +1,4 @@
-#' Get fixed parameters from model defintion.
+#' Get fixed parameters from model definition.
 #'
 #' Get fixed parameters listed in model definition. This function is used when
 #' parsing model specifications before the model has been compiled. Please see

R/new_ode_model.R

@@ -36,7 +36,7 @@
 #' @param lib_location install into folder (`--library` argument)
 #' @param verbose show more output
 #' @param as_is use C-code as-is, don't substitute line-endings or shift indices
-#' @param nonmem add nonmem code as attribute to model object
+#' @param nonmem add NONMEM code as attribute to model object
 #' @param comments comments for model
 #' @param version number of library
 #' @param quiet passed on to `system2` as setting for stderr and stdout; how to

R/sim_core.R

@@ -4,7 +4,7 @@
 #' @param sim_object list with design and simulation parameters
 #' @param ode ode
 #' @param duplicate_t_obs allow duplicate t_obs in output? E.g. for optimal design calculations when t_obs = c(0,1,2,2,3). Default is FALSE.
-#' @param t_init time of initization of the ODE system. Usually 0.
+#' @param t_init time of initialization of the ODE system. Usually 0.
 #' @export
 #' @return Data frame with simulation results
 sim_core <- function(
@@ -16,12 +16,12 @@ sim_core <- function(
              design = sim_object$design,
              par = sim_object$p,
              iov_bins = sim_object$iov_bins,
-             step_size = sim_object$int_step_size)       
+             step_size = sim_object$int_step_size)
   out <- data.frame(t = tmp$time, y = tmp$obs, obs_type = tmp$obs_type)
   if(duplicate_t_obs) {
     # use match to ensure that duplicates in t_obs is possible
     # make sure rounding level is same! We use 6 digits in sim.R
-    out <- out[match(paste(sim_object$obs_type, round(sim_object$t_obs, 6), sep="_"), 
+    out <- out[match(paste(sim_object$obs_type, round(sim_object$t_obs, 6), sep="_"),
                      paste(out$obs_type, round(out$t, 6), sep = "_")),]
   } else {
     out <- out[!duplicated(out$t) & out$t %in% sim_object$t_obs,]

vignettes/covariates.Rmd

@@ -27,7 +27,7 @@ The names in the covariate list-object should correspond **exactly** with the na
 
 ## Time-varying covariates
 
-Time-varing covariates, such as creatinine values can be implemented easily as well. They just require the additional `times` argument:
+Time-varying covariates, such as creatinine values can be implemented easily as well. They just require the additional `times` argument:
 
 ```{r timevarying-cov}
 covariates <- list(

vignettes/model.Rmd

@@ -50,7 +50,7 @@ pk1 <- new_ode_model(code = "
 
 The input code should adhere to the follow rules:
 
-- the derivaties in the ODE system are defined using `dAdt`
+- the derivatives in the ODE system are defined using `dAdt`omputate
 - array indices for the derivatives and compartments are indicated with `[ ]`. 
 Compartments indices can start at either 0 or 1. If the latter, all indices will
 be reduced by 1 in the translation to C++ code.

vignettes/regimen.Rmd

@@ -39,7 +39,7 @@ reg1 <- new_regimen(
 )
 ```
 
-Be careful: if the length of the `amt` vector is not the same as the lenght in `times`, the function will repeat the first `amt` specified. The same goes for the arguments `type`, `cmt`, and `t_inf`.
+Be careful: if the length of the `amt` vector is not the same as the length in `times`, the function will repeat the first `amt` specified. The same goes for the arguments `type`, `cmt`, and `t_inf`.
 
 By default, the `new_regimen()` function assumes the dose is given as bolus. If you want to specify that these should be given as infusion, specify the `type` and `t_inf` arguments: