Suppose we observe N subjects, each subject at multiple timepoints and we want to estimate a trajectory of progression of measurements in individual subjects. For example, suppose you observe BMI of N children at different ages, as presented below

Suppose we observe N subjects, each subject at multiple timepoints and we want to estimate a trajectory of progression of measurements in individual subjects. For example, suppose you observe BMI of N children at different ages, as presented below

Here, the connected dots come from individual subjects and the black thick line corresponds to the population mean.

**I’ve build the package to fit trajectories using matrix completion and I described the methodology in my recent paper Kidziński, Hastie (2018).** To this end, we discretize the time grid some continous basis and find a low-rank decomposition of the dense matrix.

In the classical matrix completion, we look for matrices `W`

and `A`

that fit the observed points in `Y`

(green points in the image above). In our method, in order to impose smoothness, we additionaly assume the basis `B`

and again we look for the reprezentation minimizing the errror.

The interface of the package is based on the mixed-effect models in `R`

. In particular, if we are given temporal observations `Y`

in the long format with columns `id`

, `age`

and `bmi`

, while additional covariates `X`

, constant over time are given as a data frame with columns `id`

and, say, `gender`

, we can fit the model by writing

```
model = fregression(bmi ~ age + gender | id, data = Y, covariates = X)
print(model)
```

For more information, please refer to the manual and to vignettes.