diff --git a/pkgdown.yml b/pkgdown.yml index 12163f54..8284d5ae 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -2,7 +2,7 @@ pandoc: 3.1.11 pkgdown: 2.1.1 pkgdown_sha: ~ articles: {} -last_built: 2024-10-23T09:26Z +last_built: 2024-10-28T07:45Z urls: reference: https://rjdverse.github.io/rjd3toolkit/reference article: https://rjdverse.github.io/rjd3toolkit/articles diff --git a/reference/add_usrdefvar.html b/reference/add_usrdefvar.html index 558bb834..8bc34e15 100644 --- a/reference/add_usrdefvar.html +++ b/reference/add_usrdefvar.html @@ -108,7 +108,7 @@

Details

"Seasonal": after the decomposition the effect is allocated to the seasonal component, like a Seasonal-outlier

  • "Series": after the decomposition the effect is allocated to the raw series: \(yc_t=y_t+ effect\)

    • -

  • "Seasonally Adjusted": after the decomposition the effect is allocated to +

  • "SeasonallyAdjusted": after the decomposition the effect is allocated to the seasonally adjusted series: \(sa_t=T+I+effect\)

    • @@ -125,19 +125,21 @@

    See alsoExamples

    # creating one or several external regressors (TS objects),
 # which will be gathered in one or several groups
-iv1<-intervention_variable(12, c(2000, 1), 60,
-starts = "2001-01-01", ends = "2001-12-01")
-iv2<- intervention_variable(12, c(2000, 1), 60,
-starts = "2001-01-01", ends = "2001-12-01", delta = 1)
+iv1 <- intervention_variable(12, c(2000, 1), 60,
+    starts = "2001-01-01", ends = "2001-12-01"
+)
+iv2 <- intervention_variable(12, c(2000, 1), 60,
+    starts = "2001-01-01", ends = "2001-12-01", delta = 1
+)
 # configuration 1: regressors in the same default group (named "r")
-variables<-list("iv1"=iv1, "iv2"=iv2)
+variables <- list("iv1" = iv1, "iv2" = iv2)
 # to use those regressors, input : name=r.iv1 and r.iv2 in add_usrdefvar function
 # configuration 2: group names are user-defined
 # here: regressors as a list of two groups (lists) reg1 and reg2
-vars<-list(reg1=list(iv1 = iv1),reg2=list(iv2 = iv2) )
+vars <- list(reg1 = list(iv1 = iv1), reg2 = list(iv2 = iv2))
 # to use those regressors, input : name=reg1.iv1 and name=reg2.iv2 in add_usrdefvar function
 # creating the modelling context
-my_context<-modelling_context(variables=vars)
+my_context <- modelling_context(variables = vars)
 # customize a default specification
 # init_spec <- rjd3x13::x13_spec("RSA5c")
 # regressors have to be added one by one
diff --git a/reference/aggregate.html b/reference/aggregate.html
index 3e5f75f9..a264a600 100644
--- a/reference/aggregate.html
+++ b/reference/aggregate.html
@@ -76,7 +76,7 @@ 
    Value
    
     
    Examples
    
-    
    s = ABS$X0.2.09.10.M
+    
    s <- ABS$X0.2.09.10.M
 # Annual sum
 aggregate(s, nfreq = 1, conversion = "Sum") # first and last years removed
 #> Time Series:
diff --git a/reference/arima_difference.html b/reference/arima_difference.html
index f3dc4bde..77680419 100644
--- a/reference/arima_difference.html
+++ b/reference/arima_difference.html
@@ -64,8 +64,8 @@ 
    Value
    
     
    Examples
    
-    
    mod1 = arima_model(delta = c(1,-2,1))
-mod2 = arima_model(variance=.01)
+    
    mod1 <- arima_model(delta = c(1, -2, 1))
+mod2 <- arima_model(variance = .01)
 diff <- arima_difference(mod1, mod2)
 sum <- arima_sum(diff, mod2)
 # sum should be equal to mod1
diff --git a/reference/arima_model.html b/reference/arima_model.html
index 34232dfb..a96123eb 100644
--- a/reference/arima_model.html
+++ b/reference/arima_model.html
@@ -72,7 +72,7 @@ 
    Value
    
     
    Examples
    
-    
    model <- arima_model("trend", ar=c(1,-.8), delta = c(1,-1), ma=c(1,-.5), var=100)
+    
    model <- arima_model("trend", ar = c(1, -.8), delta = c(1, -1), ma = c(1, -.5), var = 100)