Function to fit the input data.

## Usage

```
fit(
cov.df,
vaf.df = NULL,
infer_phylogenies = TRUE,
infer_growth = TRUE,
k_interval = c(5, 15),
n_runs = 1,
steps = 500,
min_steps = 20,
lr = 0.005,
p = 1,
min_frac = 0,
max_IS = NULL,
check_conv = TRUE,
covariance = "full",
hyperparams = list(),
default_lm = TRUE,
timepoints_to_int = list(),
show_progr = FALSE,
store_grads = TRUE,
store_losses = TRUE,
store_params = FALSE,
seed_optim = TRUE,
seed = 6,
seed_init = reticulate::py_none(),
sample_id = ""
)
```

## Arguments

- cov.df
Input coverage dataset. It must have at least the columns

`coverage`

, and`IS`

, and additional columns`timepoints`

and`lineage`

, will be added if missing, assuming single timepoint and lineage.- vaf.df
Input VAF dataset. If not

`NULL`

, the mutations clustering will be performed. It must have at least the columns`mutation`

,`IS`

,`alt`

,`dp`

, and additional`vaf`

,`timepoints`

,`lineage`

,`IS`

,`mutation`

, with the number of reads for the mutated allele, overall depth, vaf values, timepoint, lineage, IS and mutation, respectively.- infer_phylogenies
A Boolean. If set to

`TRUE`

, the function will also compute and attach to the returned object the phylogenetic trees for each cluster.- k_interval
Interval of K values to test.

- n_runs
Number of runs to perform for each K.

- steps
Maximum number of steps for the inference.

- lr
Learning rate used in the inference.

- p
Numeric value used to check the convergence of the parameters.

- min_frac
add

- max_IS
add

- check_conv
A Boolean. If set to

`TRUE`

, the function will check for early convergence, otherwise it will perform`steps`

iterations.- covariance
Covariance type for the Multivariate Gaussian.

- hyperparams
add

- default_lm
add

- timepoints_to_int
add

- show_progr
A Boolean. If

`TRUE`

, the progression bar will be shown during inference.- store_grads
A Booolean. If

`TRUE`

, the gradient norms for the parameters at each iteration will be stored.- store_losses
A Boolean. If

`TRUE`

, the computed losses for the parameters at each iteration will be stored.- store_params
A Boolean. If

`TRUE`

, the estimated parameters at each iteration will be stored.- seed_optim
add

- seed
Value of the seed.

- sample_id
add