Bifurcating autoregressive (BAR) models are commonly used to model
binary tree-structured data that appear in many applications, most
famously cell-lineage applications. The BAR model is an extension of the
autoregressive (AR) model where each line of descent considers an AR
process with the modification that the observations on the two sibling
cells who share the same parent are correlated. In practice, the BAR
model is used to explain the progression of single-cell proliferation.
The goal of the `bifurcatingr`

package is to provide a
collection of functions that can be used for analyzing bifurcating
autoregressive data. The package implements the least squares estimation
of bifurcating autoregressive models of any order, p, BAR(p), and allows
for executing several types of bias correction on the least-squares
estimators of the autoregressive parameters including different types of
confidence intervals. Currently, the bias correction methods supported
include bootstrap (single, double, and fast-double) bias correction and
linear-bias-function -based bias correction. The library also contains
functions for generating and plotting bifurcating autoregressive data
from any BAR(p) model.

You can install the development version of bifurcatingr like so:

```
`bifurcatingr` from
You can install the released version of ://CRAN.R-project.org) with:
[CRAN](https
install.packages("bifurcatingr")
```

This is a basic example which shows you how to use
`bifurcatingr`

to fit a bifurcating autoregressive model to
the `ecoli`

dataset which contains the lifetimes of lineage
E. coli cells.

Loading the `bifurcatingr`

library and the
`ecoli`

dataset:

```
library(bifurcatingr)
#> Loading required package: fMultivar
data("ecoli")
## Fitting a BAR(p=1) model to the `ecoli` dataset:
bfa.ls(ecoli$lifetime, p = 1, conf = TRUE, conf.level = 0.95,
p.value = TRUE, cov.matrix = TRUE)
#> $coef
#> Intercept X_[t/2]
#> [1,] 17.61658 0.355198
#>
#> $p.value
#> Intercept X_[t/2]
#> [1,] 0.01079535 0.181183
#>
#> $error.cor
#> [1] 0.5836975
#>
#> $cov.matrix
#> Intercept X_[t/2]
#> Intercept 1480.39874 -56.147524
#> X_[t/2] -56.14752 2.187566
#>
#> $asymptotic.ci
#> 2.5% 97.5%
#> Intercept 4.0722831 31.1608852
#> X_[t/2] -0.1654543 0.8758503
#>
#> $bootstarp.ci
#> 2.5% 97.5%
#> Intercept 2.787469 32.445699
#> X_[t/2] -0.162691 0.873087
#>
#> $percentile.ci
#> 2.5% 97.5%
#> Intercept 3.91627094 29.0253668
#> X_[t/2] -0.05318311 0.8051654
```