| Title: | Comprehensive Tools for Drug Formulation Analysis and Visualization |
| Version: | 1.0.0 |
| Description: | This presents a comprehensive set of tools for the analysis and visualization of drug formulation data. It includes functions for statistical analysis, regression modeling, hypothesis testing, and comparative analysis to assess the impact of formulation parameters on drug release and other critical attributes. Additionally, the package offers a variety of data visualization functions, such as scatterplots, histograms, and boxplots, to facilitate the interpretation of formulation data. With its focus on usability and efficiency, this package aims to streamline the drug formulation process and aid researchers in making informed decisions during formulation design and optimization. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| Depends: | dplyr, ggplot2 |
| Imports: | utils, knitr |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2024-03-19 16:32:45 UTC; USER |
| Author: | Oche Ambrose George
 [aut, cre] |
| Maintainer: | Oche Ambrose George <ocheab1@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-03-20 09:50:05 UTC |
| Suggests: | rmarkdown |
Perform ANOVA analysis
Description
This function conducts analysis of variance (ANOVA) to assess the impact of formulation parameters on key response variables.
Usage
anova_analysis(formulation_data)
Arguments
formulation_data |
A data frame containing the formulation data. |
Value
A summary of the ANOVA analysis results.
Examples
formulation_data <- data.frame(
Excipient_Concentration = runif(100, min = 0, max = 1),
Drug_Release = rnorm(100, mean = 50, sd = 10),
Particle_Size = rnorm(100, mean = 100, sd = 20)
)
anova_analysis(formulation_data)
Assess batch-to-batch variability
Description
This function calculates the batch-to-batch variability of a specified parameter.
This function calculates the batch-to-batch variability of a specified parameter.
Usage
batch_variability(formulation_data, parameter)
batch_variability(formulation_data, parameter)
Arguments
formulation_data |
A data frame containing formulation data. |
parameter |
The parameter for which batch-to-batch variability is calculated. |
Value
The batch-to-batch variability of the specified parameter.
The batch-to-batch variability of the specified parameter.
Generate boxplot
Description
This function generates a boxplot to compare the distribution of a variable across different groups.
Usage
boxplot(formulation_data, x, y)
Arguments
formulation_data |
A data frame containing the formulation data. |
x |
The name of the grouping variable. |
y |
The name of the variable. |
Value
A boxplot.
Examples
formulation_data <- data.frame(
Formulation_Type = sample(c("Type A", "Type B"), 100, replace = TRUE),
Drug_Release = rnorm(100, mean = 50, sd = 10)
)
boxplot(formulation_data, "Formulation_Type", "Drug_Release")
Compare distributions across groups
Description
This function compares the distributions of a response variable across groups specified by group_var.
This function compares the distributions of a response variable across groups specified by group_var.
Usage
compare_distributions(formulation_data, group_var, response_var)
compare_distributions(formulation_data, group_var, response_var)
Arguments
formulation_data |
A data frame containing formulation data. |
group_var |
The variable defining the groups for comparison. |
response_var |
The response variable to compare across groups. |
Value
A boxplot comparing the distributions across groups.
A boxplot comparing the distributions across groups.
Compare means across groups
Description
This function compares the means of a response variable across groups specified by group_var.
This function compares the means of a response variable across groups specified by group_var.
Usage
compare_means(formulation_data, group_var, response_var)
compare_means(formulation_data, group_var, response_var)
Arguments
formulation_data |
A data frame containing formulation data. |
group_var |
The variable defining the groups for comparison. |
response_var |
The response variable to compare across groups. |
Value
Results of the t-test comparing means across groups.
Results of the t-test comparing means across groups.
Confidence intervals of drug release
Description
This function computes confidence intervals for drug release based on the provided formulation data.
This function computes confidence intervals for drug release based on the provided formulation data.
Usage
confidence_intervals(formulation_data)
confidence_intervals(formulation_data)
Arguments
formulation_data |
A data frame containing formulation data. |
Value
Confidence intervals for drug release.
Confidence intervals for drug release.
Control chart for quality control
Description
This function generates a control chart for monitoring the quality control parameter over time.
This function generates a control chart for monitoring the quality control parameter over time.
Usage
control_chart(formulation_data, parameter)
control_chart(formulation_data, parameter)
Arguments
formulation_data |
A data frame containing formulation data. |
parameter |
The quality control parameter to monitor. |
Value
A control chart for the specified quality control parameter.
A control chart for the specified quality control parameter.
Generate histogram
Description
This function generates a histogram to visualize the distribution of a variable.
Usage
histogram(formulation_data, x, bins = 20)
Arguments
formulation_data |
A data frame containing the formulation data. |
x |
The name of the variable. |
bins |
The number of bins for the histogram. |
Value
A histogram.
Examples
formulation_data <- data.frame(
Drug_Release = rnorm(100, mean = 50, sd = 10)
)
histogram(formulation_data, "Drug_Release")
Perform hypothesis testing
Description
This function conducts hypothesis testing to compare means between different formulation groups.
Usage
hypothesis_testing(formulation_data)
Arguments
formulation_data |
A data frame containing the formulation data. |
Value
The results of the hypothesis testing.
Examples
formulation_data <- data.frame(
Formulation_Type = sample(c("Type A", "Type B"), 100, replace = TRUE),
Drug_Release = rnorm(100, mean = 50, sd = 10)
)
hypothesis_testing(formulation_data)
Perform regression analysis
Description
This function conducts regression analysis to model relationships between formulation parameters and response variables.
Usage
regression_analysis(formulation_data)
Arguments
formulation_data |
A data frame containing the formulation data. |
Value
A summary of the regression analysis results.
Examples
formulation_data <- data.frame(
Excipient_Concentration = runif(100, min = 0, max = 1),
Drug_Release = rnorm(100, mean = 50, sd = 10),
Particle_Size = rnorm(100, mean = 100, sd = 20)
)
regression_analysis(formulation_data)
Generate scatterplot
Description
This function generates a scatterplot to visualize the relationship between two variables.
Usage
scatterplot(formulation_data, x, y)
Arguments
formulation_data |
A data frame containing the formulation data. |
x |
The name of the x-variable. |
y |
The name of the y-variable. |
Value
A scatterplot.
Examples
formulation_data <- data.frame(
Excipient_Concentration = runif(100, min = 0, max = 1),
Drug_Release = rnorm(100, mean = 50, sd = 10)
)
scatterplot(formulation_data, "Excipient_Concentration", "Drug_Release")
Summary statistics of formulation data
Description
This function calculates summary statistics of the provided formulation data.
This function calculates summary statistics of the provided formulation data.
Usage
summary_statistics(formulation_data)
summary_statistics(formulation_data)
Arguments
formulation_data |
A data frame containing formulation data. |
Value
Summary statistics of the formulation data.
Summary statistics of the formulation data.