| Title: | Comprehensive Tools for Drug Formulation Analysis and Visualization |
|---|---|
| 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. |
| Authors: | Oche Ambrose George [aut, cre]
|
| Maintainer: | Oche Ambrose George <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.0.0 |
| Built: | 2024-11-16 03:49:34 UTC |
| Source: | https://github.com/cran/FormulR |
This function conducts analysis of variance (ANOVA) to assess the impact of formulation parameters on key response variables.
anova_analysis(formulation_data)anova_analysis(formulation_data)
formulation_data |
A data frame containing the formulation data. |
A summary of the ANOVA analysis results.
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)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)
This function calculates the batch-to-batch variability of a specified parameter.
This function calculates the batch-to-batch variability of a specified parameter.
batch_variability(formulation_data, parameter) batch_variability(formulation_data, parameter)batch_variability(formulation_data, parameter) batch_variability(formulation_data, parameter)
formulation_data |
A data frame containing formulation data. |
parameter |
The parameter for which batch-to-batch variability is calculated. |
The batch-to-batch variability of the specified parameter.
The batch-to-batch variability of the specified parameter.
This function generates a boxplot to compare the distribution of a variable across different groups.
boxplot(formulation_data, x, y)boxplot(formulation_data, x, y)
formulation_data |
A data frame containing the formulation data. |
x |
The name of the grouping variable. |
y |
The name of the variable. |
A boxplot.
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")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")
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.
compare_distributions(formulation_data, group_var, response_var) compare_distributions(formulation_data, group_var, response_var)compare_distributions(formulation_data, group_var, response_var) compare_distributions(formulation_data, group_var, response_var)
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. |
A boxplot comparing the distributions across groups.
A boxplot comparing the distributions across groups.
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.
compare_means(formulation_data, group_var, response_var) compare_means(formulation_data, group_var, response_var)compare_means(formulation_data, group_var, response_var) compare_means(formulation_data, group_var, response_var)
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. |
Results of the t-test comparing means across groups.
Results of the t-test comparing means across groups.
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.
confidence_intervals(formulation_data) confidence_intervals(formulation_data)confidence_intervals(formulation_data) confidence_intervals(formulation_data)
formulation_data |
A data frame containing formulation data. |
Confidence intervals for drug release.
Confidence intervals for drug release.
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.
control_chart(formulation_data, parameter) control_chart(formulation_data, parameter)control_chart(formulation_data, parameter) control_chart(formulation_data, parameter)
formulation_data |
A data frame containing formulation data. |
parameter |
The quality control parameter to monitor. |
A control chart for the specified quality control parameter.
A control chart for the specified quality control parameter.
This function generates a histogram to visualize the distribution of a variable.
histogram(formulation_data, x, bins = 20)histogram(formulation_data, x, bins = 20)
formulation_data |
A data frame containing the formulation data. |
x |
The name of the variable. |
bins |
The number of bins for the histogram. |
A histogram.
formulation_data <- data.frame( Drug_Release = rnorm(100, mean = 50, sd = 10) ) histogram(formulation_data, "Drug_Release")formulation_data <- data.frame( Drug_Release = rnorm(100, mean = 50, sd = 10) ) histogram(formulation_data, "Drug_Release")
This function conducts hypothesis testing to compare means between different formulation groups.
hypothesis_testing(formulation_data)hypothesis_testing(formulation_data)
formulation_data |
A data frame containing the formulation data. |
The results of the hypothesis testing.
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)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)
This function conducts regression analysis to model relationships between formulation parameters and response variables.
regression_analysis(formulation_data)regression_analysis(formulation_data)
formulation_data |
A data frame containing the formulation data. |
A summary of the regression analysis results.
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)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)
This function generates a scatterplot to visualize the relationship between two variables.
scatterplot(formulation_data, x, y)scatterplot(formulation_data, x, y)
formulation_data |
A data frame containing the formulation data. |
x |
The name of the x-variable. |
y |
The name of the y-variable. |
A scatterplot.
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")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")
This function calculates summary statistics of the provided formulation data.
This function calculates summary statistics of the provided formulation data.
summary_statistics(formulation_data) summary_statistics(formulation_data)summary_statistics(formulation_data) summary_statistics(formulation_data)
formulation_data |
A data frame containing formulation data. |
Summary statistics of the formulation data.
Summary statistics of the formulation data.