# 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

• Initial release