AMFEWMA_PhaseI() performs Phase I of the adaptive
multivariate functional EWMA control chart of Capezza et
al. (2024).AMFEWMA_PhaseII() performs Phase II of the adaptive
multivariate functional EWMA control chart of Capezza et
al. (2024).References:
RoMFCC_PhaseI() to be
consistent with the choices proposed in in Capezza et al. (2024).References:
rpca_mfd() performs multivariate functional principal
component analysis as described in Capezza et al. (2024).functional_filter() performs the functional filtering
step of the robust multivariate functional control chart framework of
Capezza et al. (2024).RoMFDI() performs the robust multivariate functional
data imputation step as described in Capezza et al. (2024).RoMFCC_PhaseI() performs Phase I of the robust
multivariate functional control chart framework of Capezza et
al. (2024).RoMFCC_PhaseII() performs Phase II of the robust
multivariate functional control chart framework of Capezza et
al. (2024).References:
parametric_limits argument in
regr_cc_sof() is now set to FALSE.fda
package now can be used also with funcharts, which
previously it could be used only with B-spline basis. In particular,
Fourier, exponential, monomial, polygonal, power and constant basis
function systems are available.get_outliers_mfd() allows to find outliers among
multivariate functional data using the functional boxplot through the
fbplot() function of the roahd package.control_charts_sof() and
control_charts_sof_real_time() have been deprecated.
Instead, use regr_cc_sof() and
regr_cc_sof_real_time(), respectively, with argument
include_covariates = TRUE. This has been done to make more
consistent the regression control chart functions for the scalar
(regr_cc_sof() and regr_cc_sof_real_time())
and functional (regr_cc_fof() and
regr_cc_fof_real_time()) response cases.alpha parameter in all control charting functions,
which previously could only be a list with manually specified values of
the type-I error probability in each control chart, now can also be a
single number between 0 and 1. In this case, Bonferroni correction is
automatically applied to take into account the multiplicity problem when
more than one control chart is applied.plot_bifd() now allows to choose to produce also
contour or perspective plots of bifd objects.simulate_mfd() is much more general, now it allows to
simulate as many covariates as one wants (before the number was fixed to
three), it is possible to provide manually the mean and variance
function for each variable, it is possible to select the type of
correlation function for each variable.plot_mfd() now relies on patchwork, while the new
function lines_mfd() allows to add new curve to an existing
plot.funcharts now depends
on an older version of R, i.e., >3.6.0 instead of >4.0.0fof_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.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.inprod.bspline().seed is deprecated in all functions. Instead,
a seed must be set before calling the functions by using
set.seed().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.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 squared n, saving much
computational time when calculating the squared prediction error
statistic when n is large.scale_mfd() is
pre-computed and therefore is not called many times unnecessarily along
the different functions.