spell check for CRAN

DESCRIPTION

@@ -29,7 +29,7 @@ Description: The weibulltools package focuses on statistical methods and
              beta-binomial confidence bounds. 
              If desired, mixture model analysis can be done with segmented regression
              and the EM algorithm.
-             Besides the well-known weibull analysis, the package also contains 
+             Besides the well-known Weibull analysis, the package also contains 
              Monte Carlo methods for the correction and completion of imprecisely 
              recorded or unknown lifetime characteristics.
              (Verband der Automobilindustrie e.V. (VDA), 2016, <ISSN:0943-9412>). 

NEWS.md

@@ -20,7 +20,7 @@
 * `predict_quantile()`: Renamed `loc_sc_params` with `dist_params`.
 * `plot_mod.default()` (former `plot_mod()`): Renamed `event` with `status`. Renamed `loc_sc_params` with `dist_params`. Removed `y`.
 * `plot_mod_mix()`: Deprecated, use `plot_mod()` instead. Renamed `event` with `status`.
-* `plot_pop()`: Added argument `tol` to restrict the range of failure probabilities. Removed argument `color`. Renamed argument `params` to `dist_params_tbl`, which only supports location and scale parameters (also for `distribution = "weibull"`). Changed behaviour of `dist_params_tbl`: A `tibble` is now recommended instead of a vector.
+* `plot_pop()`: Added argument `tol` to restrict the range of failure probabilities. Removed argument `color`. Renamed argument `params` to `dist_params_tbl`, which only supports location and scale parameters (also for `distribution = "weibull"`). Changed behavior of `dist_params_tbl`: A `tibble` is now recommended instead of a vector.
 
 ### Confidence Intervals
 * `confint_betabinom.default()` (former `confint_betabinom()`): Renamed `event` with `status`. Renamed `loc_sc_params` with `dist_params`. Added argument `b_lives` which allows the user to specify probabilities `p` for `B_p-lives` to be considered.
@@ -102,10 +102,10 @@
 * Function `plot_mod_mix()` was revised and updated in the way that the obtained results of the function `mixmod_em()` can be visualized. 
 * Function `plot_prob_mix()` was revised and updated in the way that the obtained results of the function `mixmod_em()` can be visualized. 
 * Implementation of EM-Algorithm using Newton-Raphson. The algorithm is written in c++ (`mixture_em_cpp()`) and is called in `mixmod_em()`. 
-* New method for the computation of Fisher's Confidence Bounds regarding probabilities is used. These method is called "z-Procedure" and is more appropriate to manage the bend-back behaviour. Therefore an adjustment of functions `delta_method()` and `confint_fisher()` was made. 
-* Implementation of log-location-scale models with threshold parameter like three-parametric weibull ("weibull3"), three-parametric lognormal ("lognormal3") and three-parametric loglogistic ("loglogistic3"). 
+* New method for the computation of Fisher's Confidence Bounds regarding probabilities is used. These method is called "z-Procedure" and is more appropriate to manage the bend-back behavior. Therefore an adjustment of functions `delta_method()` and `confint_fisher()` was made. 
+* Implementation of log-location-scale models with threshold parameter like three-parametric Weibull ("weibull3"), three-parametric lognormal ("lognormal3") and three-parametric loglogistic ("loglogistic3"). 
 * Implementation of location-scale models like smallest extreme value ("sev"), normal ("normal") and logistic ("logistic"). 
 * Implementation of _Log-Likelihood Profiling_ for three-parametric models in function `loglik_profiling()`. In general this function is used inside `ml_estimation()` for the purpose of estimating threshold parameter of three-parametric models. 
 * Implementation of _R-Squared Profiling_ for three-parametric models in function `r_squared_profiling()`. In general this function is used inside `rank_regression()` for the purpose of estimating threshold parameter of three-parametric models.
 * Implementation of _Log-Likelihood Function_ for all implemented models in function `loglik_function()`. In general this function is used inside `ml_estimation()` for the purpose of estimating the variance-covariance matrix of location-scale models "sev", "normal" and "logistic". The function is also used to estimate the variance-covariance matrix of log-location-scale models with a threshold parameter, i.e. "weibull3", "lognormal3" and "loglogistic3".
-* new argument in function `ml_estimation()`: _wts_ for case weights. 
+* new argument in function `ml_estimation()`: `wts` for case weights. 

R/RcppExports.R

@@ -52,7 +52,7 @@ normalize <- function(M) {
 #'  cycles.
 #' @param status a vector of binary data (0 or 1) indicating whether unit \emph{i}
 #'   is a right censored observation (= 0) or a failure (= 1).
-#' @param post a numeric matrix specifiying initial a-posteriori probabilities.
+#' @param post a numeric matrix specifying initial a-posteriori probabilities.
 #'   The number of rows have to be in line with observations \code{x} and the
 #'   number of columns must equal the mixture components \code{k}.
 #' @param distribution supposed distribution of mixture model components.

R/data.R

@@ -25,7 +25,7 @@
 #'   \item{distance}{Observed distance.}
 #'   \item{failure_mode}{
 #'     One of two failure modes (\code{mode_1} and \code{mode_2})
-#'     or \code{censored} if no failure occured.
+#'     or \code{censored} if no failure occurred.
 #'   }
 #'   \item{status}{
 #'     If \code{failure_mode} is either \code{mode_1} or \code{mode_2}
@@ -42,14 +42,14 @@
 #' High Voltage Stress Test for the Dielectric Insulation of Generator armature bars
 #'
 #' A sample of 58 segments of bars were subjected to a high voltage stress test.
-#' Two failure modes occured, Mode D (degradation failure) and Mode E (early failure).
+#' Two failure modes occurred, Mode D (degradation failure) and Mode E (early failure).
 #'
 #' @format A tibble with 58 rows and 3 variables:
 #' \describe{
 #'   \item{hours}{Observed hours.}
 #'   \item{failure_mode}{
 #'     One of two failure modes (\code{D} and \code{E})
-#'     or \code{censored} if no failure occured.
+#'     or \code{censored} if no failure occurred.
 #'   }
 #'   \item{status}{
 #'     If \code{failure_mode} is either \code{D} or \code{E}

R/delay_distributions.R

@@ -802,7 +802,7 @@ dist_delay_report <- function(date_repair,
 #' to \code{\link{mcs_delay}} is recommended.
 #'
 #' @details
-#' The delay in report describes the time between the occurence of a damage and
+#' The delay in report describes the time between the occurrence of a damage and
 #' the registration in the warranty database. For a given date where the analysis
 #' is made there could be units which had a failure but are not registered in the
 #' database and therefore treated as censored units. To overcome this problem

R/mixture_identification.R

@@ -93,7 +93,7 @@
 #'   methods = c("johnson", "kaplan", "nelson")
 #' )
 #'
-#' # Example 1 - Mixture identification using k = 2 two-parametric weibull models:
+#' # Example 1 - Mixture identification using k = 2 two-parametric Weibull models:
 #' mix_mod_weibull <- mixmod_regression(
 #'   x = prob_mix,
 #'   distribution = "weibull",
@@ -239,7 +239,7 @@ mixmod_regression.wt_cdf_estimation <- function(
 #'   method = "johnson"
 #' )
 #'
-#'  # Example 1 - Mixture identification using k = 2 two-parametric weibull models:
+#'  # Example 1 - Mixture identification using k = 2 two-parametric Weibull models:
 #' mix_mod_weibull <- mixmod_regression(
 #'    x = prob_mix$x,
 #'    y = prob_mix$prob,
@@ -446,7 +446,7 @@ print.wt_mixmod_regression_list <- function(x,
 #'
 #' @description
 #' This method applies the expectation-maximization (EM) algorithm to estimate the
-#' parameters of a univariate weibull mixture model. See 'Details'.
+#' parameters of a univariate Weibull mixture model. See 'Details'.
 #'
 #' @details
 #' The EM algorithm is an iterative algorithm for which starting values must be
@@ -467,7 +467,7 @@ print.wt_mixmod_regression_list <- function(x,
 #' @param x An object of class \code{wt_reliability_data} returned from
 #'   \code{\link{reliability_data}}.
 #' @param distribution \code{"weibull"} until further distributions are implemented.
-#' @param conf_level Confidence level for the intervals of the weibull parameters
+#' @param conf_level Confidence level for the intervals of the Weibull parameters
 #' of every component \code{k}.
 #' @param k Number of mixture components.
 #' @param method \code{"EM"} until other methods are implemented.

R/plot_functions.R

@@ -359,7 +359,7 @@ plot_prob.wt_mixmod_regression_list <- function(x,
 #'   x axis.
 #' @param title_y A character string which is assigned to the title of the
 #'   y axis.
-#' @param title_trace A character string whis is assigned to the trace shown in
+#' @param title_trace A character string which is assigned to the trace shown in
 #'   the legend.
 #' @param plot_method Package, which is used for generating the plot output.
 #' @template dots
@@ -494,7 +494,7 @@ plot_prob_ <- function(
 #'
 #' The name of the legend entry is a combination of the \code{title_trace} and the
 #' number of determined subgroups. If \code{title_trace = "Group"} and the data
-#' could be splitted in two groups, the legend entries would be "Group 1" and "Group 2".
+#' could be split in two groups, the legend entries would be "Group 1" and "Group 2".
 #'
 #' @encoding UTF-8
 #' @references Doganaksoy, N.; Hahn, G.; Meeker, W. Q., Reliability Analysis by
@@ -592,7 +592,7 @@ plot_prob_mix <- function(
 #' The name of the legend entry is a combination of the \code{title_trace} and
 #' the number of determined subgroups from \code{\link{mixmod_regression}} or
 #' \code{\link{mixmod_em}}. If \code{title_trace = "Line"} and the
-#' data could be splitted in two groups, the legend entries would be "Line: 1"
+#' data could be split in two groups, the legend entries would be "Line: 1"
 #' and "Line: 2".
 #'
 #' @param p_obj A plot object returned from \code{\link{plot_prob}}.
@@ -1037,7 +1037,7 @@ plot_mod_mix <- function(p_obj,
 #' regression line, the latter is provided as well.
 #'
 #' @param p_obj A plot object returned from \code{\link{plot_prob}}.
-#' @param x Confindence interval as returned by \code{\link{confint_betabinom}}
+#' @param x Confidence interval as returned by \code{\link{confint_betabinom}}
 #'   or \code{\link{confint_fisher}}.
 #' @param title_trace_mod A character string which is assigned to the mod trace
 #'   in the legend.

R/probability_estimators.R

@@ -499,7 +499,7 @@ johnson_method_ <- function(data) {
 #' Whereas the non-parametric Kaplan-Meier estimator is used to estimate the
 #' survival function \emph{S(t)} in terms of (multiple) right censored data, the
 #' complement is an estimate of the cumulative distribution function \emph{F(t)}.
-#' One modification is made in contrast to the orginial Kaplan-Meier estimator
+#' One modification is made in contrast to the original Kaplan-Meier estimator
 #' (see 'References').
 #'
 #' \strong{Note} : The \emph{Kaplan-Meier} estimator does not assign ranks to

R/weibulltools-package.R

@@ -9,7 +9,7 @@
 #' make the examination and adjustment as well as the analysis and
 #' interpretation of field data (and bench tests) as simple as possible.
 #'
-#' Besides the well-known weibull analysis, the package supports multiple
+#' Besides the well-known Weibull analysis, the package supports multiple
 #' lifetime distributions and also contains Monte Carlo methods for the
 #' correction and completion of imprecisely recorded or unknown lifetime
 #' characteristics.

README.Rmd

@@ -26,7 +26,7 @@ options(tibble.print_min = 5, tibble.print_max = 5)
 
 The *weibulltools* package focuses on statistical methods and visualizations that are often used in reliability engineering. It provides a compact and easily accessible set of methods and visualization tools that make the examination and adjustment as well as the analysis and interpretation of field data (and bench tests) as simple as possible.  
 
-Besides the well-known weibull analysis, the package supports multiple lifetime distributions and also contains Monte Carlo methods for the correction and completion of imprecisely recorded or unknown lifetime characteristics.  
+Besides the well-known Weibull analysis, the package supports multiple lifetime distributions and also contains Monte Carlo methods for the correction and completion of imprecisely recorded or unknown lifetime characteristics.  
 
 Plots are created statically ([ggplot2](https://ggplot2.tidyverse.org/)) or interactively ([plotly](https://plotly.com/r/)) and can be customized with functions of the respective visualization package.
 

README.md

@@ -21,7 +21,7 @@ visualization tools that make the examination and adjustment as well as
 the analysis and interpretation of field data (and bench tests) as
 simple as possible.
 
-Besides the well-known weibull analysis, the package supports multiple
+Besides the well-known Weibull analysis, the package supports multiple
 lifetime distributions and also contains Monte Carlo methods for the
 correction and completion of imprecisely recorded or unknown lifetime
 characteristics.