### Install flexsurvcure Package

Source: https://github.com/jrdnmdhl/flexsurvcure/blob/master/readme.md

Installs the flexsurv and devtools packages, then installs the development version of flexsurvcure from GitHub.

```r
install.packages("flexsurv")
install.packages("devtools")
devtools::install_github('jrdnmdhl/flexsurvcure')
```

--------------------------------

### Fit Complex Cure Model with Covariates

Source: https://github.com/jrdnmdhl/flexsurvcure/blob/master/vignettes/flexsurvcure.html

Fit a more complex cure model by adding covariates to the parametric distribution of uncured individuals. This example uses the `anc` argument to include `group` as a covariate for the `scale` parameter in a WeibullPH distribution.

```R
cure_model_complex <- flexsurvcure(Surv(rectime, censrec)~group, data=bc, link="logistic", dist="weibullPH", mixture=T, anc=list(scale=~group))
```

```R
print(cure_model_complex)
```

--------------------------------

### Complex Cure Model Summary

Source: https://github.com/jrdnmdhl/flexsurvcure/blob/master/vignettes/flexsurvcure.html

Displays a summary of a complex cure model fit, including model coefficients, event counts, and likelihood information. This output is typically generated after fitting a model using the flexsurvcure package.

```r
## scale                      NA         NA         NA
## groupMedium          1.69e-01         NA         NA
## groupPoor            7.96e+00         NA         NA
## scale(groupMedium)   2.29e+00         NA         NA
## scale(groupPoor)     8.22e+00         NA         NA
## 
## N = 686,  Events: 299,  Censored: 387
## Total time at risk: 771400
## Log-likelihood = -2571.857, df = 7
## AIC = 5157.713
```

--------------------------------

### Generate Survival Probabilities with Flexsurvcure

Source: https://github.com/jrdnmdhl/flexsurvcure/blob/master/vignettes/flexsurvcure.html

Use the `summary` S3 method to generate predicted survival probabilities at specified time points for different groups. The `tidy=T` argument ensures the output is in a tidy data frame format.

```R
summary(cure_model, t=seq(from=0,to=3000,by=1000), type="survival", tidy=T)
```

--------------------------------

### Fit a Mixture Cure Model

Source: https://github.com/jrdnmdhl/flexsurvcure/blob/master/readme.md

Fits a Weibull proportional hazards mixture cure model with a logistic link function. Requires the 'bc' dataset and 'flexsurvcure' package.

```r
mixture = flexsurvcure(Surv(rectime,censrec)~group, data=bc, dist="weibullPH", link="logistic", mixture = T)
plot(mixture)
```

--------------------------------

### Fit Non-Mixture Cure Model with Covariates on Scale Parameter

Source: https://github.com/jrdnmdhl/flexsurvcure/blob/master/readme.md

Fits a Weibull proportional hazards non-mixture cure model with covariates on the scale parameter, using a log-log link function. Requires the 'bc' dataset and 'flexsurvcure' package.

```r
non_mixture_covarite_scale = flexsurvcure(Surv(rectime,censrec)~group, data=bc, anc=list(scale=~group), dist="weibullPH", link="loglog", mixture = F)
plot(non_mixture_covarite_scale)
```

--------------------------------

### Plot Mixture Cure Model Results in R

Source: https://github.com/jrdnmdhl/flexsurvcure/blob/master/vignettes/flexsurvcure.html

Displays a graphical representation of the fitted mixture cure model results. This function is an S3 method for flexsurvcure objects.

```R
plot(cure_model)
```

--------------------------------

### Fit a Non-Mixture Cure Model

Source: https://github.com/jrdnmdhl/flexsurvcure/blob/master/readme.md

Fits a Weibull proportional hazards non-mixture cure model with a log-log link function. Requires the 'bc' dataset and 'flexsurvcure' package.

```r
non_mixture = flexsurvcure(Surv(rectime,censrec)~group, data=bc, dist="weibullPH", link="loglog", mixture = F)
plot(non_mixture)
```

--------------------------------

### Plotting a Cure Model

Source: https://github.com/jrdnmdhl/flexsurvcure/blob/master/vignettes/flexsurvcure.html

Generates a plot from a fitted cure model object. This is useful for visualizing survival curves, predicted probabilities, or other model outputs.

```r
plot(cure_model_complex)
```

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