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

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.

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.