# funcharts 1.2.0

- improved backward compatibility,
`funcharts`

now depends
on an older version of R, i.e., >3.6.0 instead of >4.0.0
`fof_pc()`

now is much faster especially when the number
of basis functions of the functional coefficient is large since the
tensor product has been vectorized.
- the argument
`seed`

has been deprecated in all functions,
so that reproducibility is achieved by setting externally a seed with
`set.seed()`

, as it is commonly done in R.
`sim_funcharts()`

simulates data sets automatically using
the function `simulate_mfd()`

. The only input required is the
sample size for the Phase I, tuning and Phase II data sets.
`control_charts_pca()`

allows automatic selection of
components.
`get_mfd_list()`

and `get_mfd_array()`

, with
the corresponding real time versions, are now much faster.
- cross-validation in scalar-on-function regression is now much
faster, since the for loop is avoided
- inner products are more precise and much faster, because they rely
on the pre-computed inner products of the B-spline basis functions,
calculated via
`inprod.bspline()`

.
- argument
`seed`

is deprecated in all functions. Instead,
a seed must be set before calling the functions by using
`set.seed()`

.

# funcharts 1.1.0

## Major changes

`simulate_mfd()`

simulates example data for
`funcharts`

. It creates a data set with three functional
covariates, a functional response generated as a function of the three
functional covariates through a function-on-function linear model, and a
scalar response generated as a function of the three functional
covariates through a scalar-on-function linear model. This function
covers the simulation study in Centofanti et al. (2020) for the
function-on-function case and also simulates data in a similar way for
the scalar response case.

## Minor changes

- Added a
`NEWS.md`

file to track changes to the
package.
`inprod_mfd_diag()`

calculates the inner product between
two multivariate functional data objects observation by observation,
avoiding calculating it between all possible couples of observations.
Therefore, there are \(n\) calculations
instead of \(n^2\), saving much
computational time when calculating the squared prediction error
statistic when \(n\) is large.
- Code has been improved so that
`scale_mfd()`

is
pre-computed and therefore is not called many times unnecessarily along
the different functions.

# funcharts 1.0.0