Package index • rjd3filters

Create Specific Moving Averages

dfa_filter(): Direct Filter Approach
fst_filter(): Estimation of a filter using the Fidelity-Smoothness-Timeliness criteria
localpolynomials(): Apply Local Polynomials Filters
lp_filter(): Local Polynomials Filters
simple_ma(): Simple Moving Average
rkhs_filter(): Reproducing Kernel Hilbert Space (RKHS) Filters
rkhs_kernel(): Get RKHS kernel function
rkhs_optimal_bw(): Optimal Bandwidth of Reproducing Kernel Hilbert Space (RKHS) Filters
rkhs_optimization_fun(): Optimization Function of Reproducing Kernel Hilbert Space (RKHS) Filters
get_kernel(): Get the coefficients of a kernel
mmsre_filter(): Mean Square Revision Error (mmsre) filter

Stastics on Moving Averages and estimates

cve() cv() loocve() rt() cp(): Diagnostics and goodness of fit of filtered series
diagnostic_matrix(): Compute quality criteria for asymmetric filters
fst(): FST criteria
implicit_forecasts(): Retrieve implicit forecasts corresponding to the asymmetric filters
underlying_forecasts(): Retrieve underlying forecasts corresponding to the asymmetric filters
get_properties_function(): Get properties of filters
mse(): Accuracy/smoothness/timeliness criteria through spectral decomposition
var_estimator(): Variance Estimator
df_var(): Compute the degrees of freedom for the variance estimator
confint_filter(): Confidence intervals
polynomial_matrix(): Create polynomial matrix

Manipulation of Moving Averages and Finite Filters

filter(): Linear Filtering on a Time Series
sum(<moving_average>) `[`(<moving_average>,<numeric>) `[`(<moving_average>,<logical>) `[<-`(<moving_average>,<ANY>,<missing>,<numeric>) cbind(<moving_average>) rbind(<moving_average>) `+`(<moving_average>,<moving_average>) `+`(<moving_average>,<numeric>) `+`(<numeric>,<moving_average>) `+`(<moving_average>,<missing>) `-`(<moving_average>,<missing>) `-`(<moving_average>,<moving_average>) `-`(<moving_average>,<numeric>) `-`(<numeric>,<moving_average>) `*`(<moving_average>,<moving_average>) `*`(<moving_average>,<numeric>) `*`(<numeric>,<moving_average>) `*`(<ANY>,<moving_average>) `*`(<moving_average>,<ANY>) `/`(<moving_average>,<numeric>) `^`(<moving_average>,<numeric>) `*`(<finite_filters>,<moving_average>) `*`(<moving_average>,<finite_filters>) `*`(<finite_filters>,<numeric>) `*`(<ANY>,<finite_filters>) `*`(<finite_filters>,<ANY>) `+`(<numeric>,<finite_filters>) `+`(<finite_filters>,<moving_average>) `+`(<moving_average>,<finite_filters>) `+`(<finite_filters>,<missing>) `-`(<finite_filters>,<missing>) `-`(<finite_filters>,<moving_average>) `-`(<moving_average>,<finite_filters>) `-`(<finite_filters>,<numeric>) `-`(<numeric>,<finite_filters>) `/`(<finite_filters>,<numeric>) `^`(<finite_filters>,<numeric>) `*`(<finite_filters>,<finite_filters>) `+`(<finite_filters>,<finite_filters>) `-`(<finite_filters>,<finite_filters>) `[`(<finite_filters>,<missing>) `[`(<finite_filters>,<ANY>): Operations on Filters
finite_filters() is.finite_filters() to_seasonal(<finite_filters>) show(<finite_filters>): Manipulating Finite Filters
get_moving_average(): Get Moving Averages from ARIMA model
impute_last_obs(): Impute Incomplete Finite Filters
moving_average() is.moving_average() coef(<moving_average>) is_symmetric() upper_bound() lower_bound() mirror() rev(<moving_average>) length(<moving_average>) to_seasonal() show(<moving_average>): Manipulation of moving averages
plot_coef() plot_gain() plot_phase(): Plots filters properties

Data

retailsa: Seasonally Adjusted Retail Sales

Wrangling Java objects

Functions to easily interact between R and Java objects

.jd2r_finitefilters(): Java Utility Function