This R package was designed to help beginners in biostatistics get started with ease. The package offers a set of user-friendly functions that fill the gaps in existing tools, making it easier for newcomers to perform essential biostatistical analyses without needing advanced programming skills.

Construct confidence intervals for the mean of a given variable. You can specify the confidence level and the alternative hypothesis.`mean_CI()`

Calculate the power or sample size for paired proportions. You need to specify proportions \(p_1\) and \(p_2\), and either the power or the sample size. You can also specify the confidence level and the alternative hypothesis.`power.paired.prop()`

Calculate the power or sample size(s) for independent proportions, for both balanced and unbalanced designs. You need to specify proportions \(p_1\) and \(p_2\). Additionally, you can specify:`power.2p.2n()`

- The confidence level and alternative hypothesis.
- The sample sizes \(n_1\) and \(n_2\) if you want to calculate the power.
- The sample size \(n_1\) to calculate \(n_2\) (or vice versa) for a desired power.
- The sample size ratio \(n_2\) / \(n_1\) to calculate both \(n_1\) and \(n_2\) for a desired power.

Create a plot for a linear regression model that includes the line of best fit, confidence intervals, and prediction intervals.`lm_plot()`

You can install the released version of **biostats101**
from CRAN:

`install.packages("biostats101")`

This package has minimal dependencies:

,`mean_CI()`

, and`power.paired.prop()`

do not require any additional R packages.`power.2p.2n()`

requires the following R packages:`lm_plot()`

`dplyr`

`tidyr`

`ggplot2`

By default, ** lm_plot()** will check if
these packages are installed and automatically install them if needed.
You can also choose to skip the automatic installation by setting

`install_packages = FALSE`

.Here’s are examples of how to use the functions in
**biostats101**:

`mean_CI`

```
library(biostats101)
# Example data
= c(5.2, 4.8, 6.3, 6.1, 7.2, 3.5, 4.9, 2.2, 3.7, 3.5, 8.9)
values
# Construct a 95% confidence interval for the mean
mean_CI(values, conf.level = 0.95, alternative = 'two.sided')
```

`power.paired.prop`

```
library(biostats101)
# Calculate the power given the sample size for paired proportions
power.paired.prop(p1 = 0.1, p2 = 0.15, n = 900)
# Calculate the sample size given the power for paired proportions
power.paired.prop(p1 = 0.15, p2 = 0.1, power = 0.8)
```

`power.2p.2n`

```
library(biostats101)
# Calculate the power for independent proportions given the sample sizes
power.2p.2n(p1 = 0.45, p2 = 0.6, n1 = 260, n2 = 130)
# Calculate the sample size for independent proportions (default power = 0.8)
power.2p.2n(p1 = 0.45, p2 = 0.6)
# Calculate sample sizes for independent proportions given the nratio (n2/n1)
power.2p.2n(p1 = 0.44, p2 = 0.6, nratio = 2)
# Calculate the sample size n2 given sample size n1 for independent proportions
power.2p.2n(p1 = 0.44, p2 = 0.6, n1 = 108)
```

`lm_plot`

```
library(biostats101)
# Example dataset
<- data.frame(
mydata x = rnorm(100, mean = 50, sd = 10),
y = 3 + 0.5 * rnorm(100, mean = 50, sd = 10) + rnorm(100)
)
# Run a regression model
<- lm(y ~ x, mydata)
my_model
# Create a plot with the line of best fit, confidence limits, and prediction limits
lm_plot(my_model)
# Customize plot labels
lm_plot(my_model) + xlab("Your x-axis label") + ylab("Your y-axis label")
```

The methods implemented in this package are based on the following
works: - Connor, R. J. (1987). Sample size for testing differences in
proportions for the paired-sample design. *Biometrics*, 207-211.
https://doi.org/10.2307/2531961. - Fleiss, J. L., Levin, B., & Paik,
M. C. (2013). *Statistical methods for rates and proportions*.
John Wiley & Sons. - Levin, B., & Chen, X. (1999). Is the
one-half continuity correction used once or twice to derive a well-known
approximate sample size formula to compare two independent binomial
distributions?. *The American Statistician*, 53(1), 62-66.
https://doi.org/10.1080/00031305.1999.10474431. - McNemar, Q. (1947).
Note on the sampling error of the difference between correlated
proportions or percentages. *Psychometrika*, 12(2), 153-157.
https://doi.org/10.1007/BF02295996.