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. 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 released version of `bifurcatingr`

from CRAN with:

```
install.packages("bifurcatingr")
#> Installing package into '/private/var/folders/7g/qtr08lf57lg62btzppl4cf4w0000gn/T/Rtmp1aFm9S/temp_libpath475cf58b299'
#> (as 'lib' is unspecified)
#> Warning: package 'bifurcatingr' is not available for this version of R
#>
#> A version of this package for your version of R might be available elsewhere,
#> see the ideas at
#> https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages
```

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)
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
#>
#> $conf
#> 2.5% 97.5%
#> Intercept 4.0722831 31.1608852
#> X_[t/2] -0.1654543 0.8758503
#>
#> $p.value
#> Intercept X_[t/2]
#> [1,] 0.01079535 0.181183
#>
#> $error.cor
#> [1] 0.5836975
#>
#> $cov.matrix
#> [,1] [,2]
#> [1,] 1480.39874 -56.147524
#> [2,] -56.14752 2.187566
```