diff --git a/DESCRIPTION b/DESCRIPTION
index 38e55d1..79f226e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -31,7 +31,8 @@ Imports:
     stats,
     utils
 Suggests:
-    spelling
+    spelling, 
+    knitr
 VignetteBuilder: 
     knitr
 Encoding: UTF-8
diff --git a/R/display.R b/R/display.R
index 15518e5..287a04e 100644
--- a/R/display.R
+++ b/R/display.R
@@ -190,7 +190,8 @@ print.summary.JD3_SARIMA_ESTIMATION <- function(x, digits = max(3L, getOption("d
         stde <- sqrt(diag(x$parameters$cov))
         t <- estimate / stde
         pval <- 2 * pt(abs(t), ndf, lower.tail = FALSE)
-        table <- data.frame(estimate, "ESTIMATED", stde, t, pval)
+        table <- data.frame(estimate, "ESTIMATED", stde, t, pval,
+                            stringsAsFactors = FALSE)
         colnames(table) <- c(
             "Estimate", "Type", "Std. Error",
             "T-stat", "Pr(>|t|)"
@@ -398,7 +399,8 @@ print.JD3_SARIMA_ESTIMATE <- function(x, digits = max(3L, getOption("digits") -
         stde <- sqrt(diag(x$bvar))
         t <- estimate / stde
         pval <- 2 * pt(abs(t), ndf, lower.tail = FALSE)
-        table <- data.frame(estimate, "ESTIMATED", stde, t, pval)
+        table <- data.frame(estimate, "ESTIMATED", stde, t, pval,
+                            stringsAsFactors = FALSE)
         colnames(table) <- c(
             "Estimate", "Type", "Std. Error",
             "T-stat", "Pr(>|t|)"
diff --git a/R/spec_regarima.R b/R/spec_regarima.R
index fbda555..875e170 100644
--- a/R/spec_regarima.R
+++ b/R/spec_regarima.R
@@ -1497,7 +1497,7 @@ set_transform.default <- function(x,
 #' - "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: \eqn{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: \eqn{sa_t=T+I+effect}
 #' @examples
 #' # creating one or several external regressors (TS objects),
diff --git a/man/add_usrdefvar.Rd b/man/add_usrdefvar.Rd
index 311c330..0500b30 100644
--- a/man/add_usrdefvar.Rd
+++ b/man/add_usrdefvar.Rd
@@ -54,26 +54,28 @@ for the decomposition.
 \item "Seasonal": after the decomposition the effect is allocated to the seasonal component, like a Seasonal-outlier
 \item "Series": after the decomposition the effect is allocated to
 the raw series: \eqn{yc_t=y_t+ effect}
-\item "Seasonally Adjusted": after the decomposition the effect is allocated to
+\item "SeasonallyAdjusted": after the decomposition the effect is allocated to
 the seasonally adjusted series: \eqn{sa_t=T+I+effect}
 }
 }
 \examples{
 # 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/man/aggregate.Rd b/man/aggregate.Rd
index 4d340db..d9ef5fb 100644
--- a/man/aggregate.Rd
+++ b/man/aggregate.Rd
@@ -30,7 +30,7 @@ A new time series of frequency \code{nfreq}.
 Makes a frequency change of this series.
 }
 \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
 aggregate(s, nfreq = 1, conversion = "Sum", complete = FALSE)
diff --git a/man/arima_difference.Rd b/man/arima_difference.Rd
index adf6ad3..2bfcc56 100644
--- a/man/arima_difference.Rd
+++ b/man/arima_difference.Rd
@@ -20,8 +20,8 @@ a \code{"JD3_ARIMA"} model.
 Remove an arima model from an existing one. More exactly, m_diff = m_left - m_right iff m_left = m_right + m_diff.
 }
 \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/man/arima_model.Rd b/man/arima_model.Rd
index 5b07c94..874f7e8 100644
--- a/man/arima_model.Rd
+++ b/man/arima_model.Rd
@@ -24,5 +24,5 @@ a \code{"JD3_ARIMA"} model.
 ARIMA Model
 }
 \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)
 }
diff --git a/man/arima_properties.Rd b/man/arima_properties.Rd
index 9682975..f6576cb 100644
--- a/man/arima_properties.Rd
+++ b/man/arima_properties.Rd
@@ -20,6 +20,6 @@ A list with tha auto-covariances and with the (pseudo-)spectrum
 Properties of an ARIMA model; the (pseudo-)spectrum and the auto-covariances of the model are returned
 }
 \examples{
-mod1 <- arima_model(ar = c(0.1, 0.2), delta = c(1,-1), ma = 0)
+mod1 <- arima_model(ar = c(0.1, 0.2), delta = c(1, -1), ma = 0)
 arima_properties(mod1)
 }
diff --git a/man/arima_sum.Rd b/man/arima_sum.Rd
index 4922187..31b4ac3 100644
--- a/man/arima_sum.Rd
+++ b/man/arima_sum.Rd
@@ -25,7 +25,7 @@ polynomials is then computed and factorized, to get the moving average
 polynomial and innovation variance of the sum.
 }
 \examples{
-mod1 = arima_model(ar = c(0.1, 0.2), delta = 0, ma = 0)
-mod2 = arima_model(ar = 0, delta = 0, ma = c(0.4))
+mod1 <- arima_model(ar = c(0.1, 0.2), delta = 0, ma = 0)
+mod2 <- arima_model(ar = 0, delta = 0, ma = c(0.4))
 arima_sum(mod1, mod2)
 }
diff --git a/man/autocorrelations.Rd b/man/autocorrelations.Rd
index 345f955..7d50de2 100644
--- a/man/autocorrelations.Rd
+++ b/man/autocorrelations.Rd
@@ -26,7 +26,7 @@ If \code{FALSE}, we consider that the (known) mean is 0 and that the series has
 Autocorrelation Functions
 }
 \examples{
-x = ABS$X0.2.09.10.M
+x <- ABS$X0.2.09.10.M
 autocorrelations(x)
 autocorrelations_partial(x)
 autocorrelations_inverse(x)
diff --git a/man/calendar_td.Rd b/man/calendar_td.Rd
index 9837dc8..596762d 100644
--- a/man/calendar_td.Rd
+++ b/man/calendar_td.Rd
@@ -55,7 +55,7 @@ Regressors are corrected for long-term mean if \code{contrasts = TRUE}.
 }
 \examples{
 BE <- national_calendar(list(
-    fixed_day(7,21),
+    fixed_day(7, 21),
     special_day("NEWYEAR"),
     special_day("CHRISTMAS"),
     special_day("MAYDAY"),
@@ -64,9 +64,12 @@ BE <- national_calendar(list(
     special_day("WHITMONDAY"),
     special_day("ASSUMPTION"),
     special_day("ALLSAINTSDAY"),
-    special_day("ARMISTICE")))
-calendar_td(BE, 12, c(1980,1), 240, holiday=7, groups=c(1,1,1,2,2,3,0),
-contrasts = FALSE)
+    special_day("ARMISTICE")
+))
+calendar_td(BE, 12, c(1980, 1), 240,
+    holiday = 7, groups = c(1, 1, 1, 2, 2, 3, 0),
+    contrasts = FALSE
+)
 }
 \references{
 More information on calendar correction in JDemetra+ online documentation:
diff --git a/man/chained_calendar.Rd b/man/chained_calendar.Rd
index 455c5d0..ff0b026 100644
--- a/man/chained_calendar.Rd
+++ b/man/chained_calendar.Rd
@@ -23,9 +23,9 @@ In such a case two calendars describing the situation before and after the chang
 and bound together, one before the break and one after the break.
 }
 \examples{
-Belgium <- national_calendar(list(special_day("NEWYEAR"),fixed_day(7,21)))
-France <- national_calendar(list(special_day("NEWYEAR"),fixed_day(7,14)))
-chained_cal<-chained_calendar(France, Belgium, "2000-01-01")
+Belgium <- national_calendar(list(special_day("NEWYEAR"), fixed_day(7, 21)))
+France <- national_calendar(list(special_day("NEWYEAR"), fixed_day(7, 14)))
+chained_cal <- chained_calendar(France, Belgium, "2000-01-01")
 
 }
 \references{
diff --git a/man/data_to_ts.Rd b/man/data_to_ts.Rd
index 1fb6a60..25c52fe 100644
--- a/man/data_to_ts.Rd
+++ b/man/data_to_ts.Rd
@@ -15,6 +15,6 @@ data_to_ts(s, name)
 Promote a R time series to a "full" \code{ts} of JDemetra+
 }
 \examples{
-s<-ABS$X0.2.09.10.M
-t<-data_to_ts(s,"test")
+s <- ABS$X0.2.09.10.M
+t <- data_to_ts(s, "test")
 }
diff --git a/man/differences.Rd b/man/differences.Rd
index 70d5f4c..7da8221 100644
--- a/man/differences.Rd
+++ b/man/differences.Rd
@@ -20,6 +20,6 @@ The differenced series.
 Differencing of a series
 }
 \examples{
-differences(retail$BookStores, c(1,1,12), FALSE)
+differences(retail$BookStores, c(1, 1, 12), FALSE)
 
 }
diff --git a/man/differencing_fast.Rd b/man/differencing_fast.Rd
index d977c5b..195013e 100644
--- a/man/differencing_fast.Rd
+++ b/man/differencing_fast.Rd
@@ -33,6 +33,6 @@ Stationary transformation
 The series is differenced till its variance is decreasing.
 }
 \examples{
-differencing_fast(log(ABS$X0.2.09.10.M),12)
+differencing_fast(log(ABS$X0.2.09.10.M), 12)
 
 }
diff --git a/man/do_stationary.Rd b/man/do_stationary.Rd
index 7a0170d..b4ae123 100644
--- a/man/do_stationary.Rd
+++ b/man/do_stationary.Rd
@@ -29,5 +29,5 @@ The series should not be seasonal.
 Source: Tramo
 }
 \examples{
-do_stationary(log(ABS$X0.2.09.10.M),12)
+do_stationary(log(ABS$X0.2.09.10.M), 12)
 }
diff --git a/man/easter_dates.Rd b/man/easter_dates.Rd
index d07629c..fd400d0 100644
--- a/man/easter_dates.Rd
+++ b/man/easter_dates.Rd
@@ -20,7 +20,7 @@ Allows to display the date of Easter Sunday for each year, in the defined period
 displayed in "YYYY-MM-DD" format and as a number of days since January 1st 1970.
 }
 \examples{
-#Dates from 2018(included) to 2023 (included)
+# Dates from 2018(included) to 2023 (included)
 easter_dates(2018, 2023)
 }
 \references{
diff --git a/man/easter_day.Rd b/man/easter_day.Rd
index a14c799..f5f1d04 100644
--- a/man/easter_day.Rd
+++ b/man/easter_day.Rd
@@ -19,12 +19,14 @@ easter_day(offset, julian = FALSE, weight = 1, validity = NULL)
 Allows to define a holiday which date is related to Easter Sunday.
 }
 \examples{
-easter_day(1) #Easter Monday
+easter_day(1) # Easter Monday
 easter_day(-2) # Easter Good Friday
 # Corpus Christi 60 days after Easter
 # Sunday in Julian calendar with weight 0.5, from January 2000 to December 2020
-easter_day(offset=60,julian=TRUE,weight=0.5,
-validity = list(start="2000-01-01", end = "2020-12-01"))
+easter_day(
+    offset = 60, julian = TRUE, weight = 0.5,
+    validity = list(start = "2000-01-01", end = "2020-12-01")
+)
 }
 \references{
 More information on calendar correction in JDemetra+ online documentation:
diff --git a/man/easter_variable.Rd b/man/easter_variable.Rd
index e4456d2..ac9154f 100644
--- a/man/easter_variable.Rd
+++ b/man/easter_variable.Rd
@@ -40,8 +40,8 @@ A time series (object of class \code{"ts"})
 Allows to generate a regressor taking into account the (Julian) Easter effect in monthly or quarterly time series.
 }
 \examples{
-#Monthly regressor, five-year long, duration 8 days, effect finishing on Easter Monday
-ee<-easter_variable(12, c(2020,1),length=5*12,duration=8, endpos=1)
+# Monthly regressor, five-year long, duration 8 days, effect finishing on Easter Monday
+ee <- easter_variable(12, c(2020, 1), length = 5 * 12, duration = 8, endpos = 1)
 }
 \references{
 More information on calendar correction in JDemetra+ online documentation:
diff --git a/man/fixed_day.Rd b/man/fixed_day.Rd
index 86d5a66..c6f02f7 100644
--- a/man/fixed_day.Rd
+++ b/man/fixed_day.Rd
@@ -22,10 +22,10 @@ like Christmas which is always celebrated on December 25th.
 }
 \examples{
 day <- fixed_day(7, 21, .9)
-day  # July 21st, with weight=0.9, on the whole sample
+day # July 21st, with weight=0.9, on the whole sample
 day <- fixed_day(12, 25, .5, validity = list(start = "2010-01-01"))
 day # December 25th, with weight=0.5, from January 2010
-day <- fixed_day(12, 25, .5, validity = list(start="1968-02-01", end = "2010-01-01"))
+day <- fixed_day(12, 25, .5, validity = list(start = "1968-02-01", end = "2010-01-01"))
 day # December 25th, with weight=0.9, from February 1968 until January 2010
 }
 \references{
diff --git a/man/holidays.Rd b/man/holidays.Rd
index f6abede..4c55014 100644
--- a/man/holidays.Rd
+++ b/man/holidays.Rd
@@ -44,18 +44,19 @@ This kind of non-aggregated regressors are used for calendar correction in daily
 }
 \examples{
 BE <- national_calendar(list(
-  fixed_day(7,21),
-  special_day("NEWYEAR"),
-  special_day("CHRISTMAS"),
-  special_day("MAYDAY"),
-  special_day("EASTERMONDAY"),
-  special_day("ASCENSION"),
-  special_day("WHITMONDAY"),
-  special_day("ASSUMPTION"),
-  special_day("ALLSAINTSDAY"),
-  special_day("ARMISTICE")))
-q<-holidays(BE, "2021-01-01", 366*10, type="All")
-plot(apply(q,1, max))
+    fixed_day(7, 21),
+    special_day("NEWYEAR"),
+    special_day("CHRISTMAS"),
+    special_day("MAYDAY"),
+    special_day("EASTERMONDAY"),
+    special_day("ASCENSION"),
+    special_day("WHITMONDAY"),
+    special_day("ASSUMPTION"),
+    special_day("ALLSAINTSDAY"),
+    special_day("ARMISTICE")
+))
+q <- holidays(BE, "2021-01-01", 366 * 10, type = "All")
+plot(apply(q, 1, max))
 }
 \references{
 More information on calendar correction in JDemetra+ online documentation:
diff --git a/man/intervention_variable.Rd b/man/intervention_variable.Rd
index 78ef25a..7356182 100644
--- a/man/intervention_variable.Rd
+++ b/man/intervention_variable.Rd
@@ -48,17 +48,19 @@ by the parameters \code{starts} and \code{ends}. With \code{delta = 1} and \code
 the cumulative sum of temporary level shifts, once differenced the regressor will become a classical level shift.
 }
 \examples{
-iv1<-intervention_variable(12, c(2000, 1), 60,
-    starts = "2001-01-01", ends = "2001-12-01")
+iv1 <- intervention_variable(12, c(2000, 1), 60,
+    starts = "2001-01-01", ends = "2001-12-01"
+)
 plot(iv1)
-iv2<- intervention_variable(12, c(2000, 1), 60,
-    starts = "2001-01-01", ends = "2001-12-01", delta = 1)
-plot (iv2)
+iv2 <- intervention_variable(12, c(2000, 1), 60,
+    starts = "2001-01-01", ends = "2001-12-01", delta = 1
+)
+plot(iv2)
 # using one variable in a a seasonal adjustment process
 # regressors as a list of two groups reg1 and reg2
-vars<-list(reg1=list(x = iv1),reg2=list(x = iv2) )
+vars <- list(reg1 = list(x = iv1), reg2 = list(x = iv2))
 # 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")
 # new_spec<- add_usrdefvar(init_spec,id = "reg1.iv1", regeffect="Trend")
diff --git a/man/ljungbox.Rd b/man/ljungbox.Rd
index 8ceda81..4bae32c 100644
--- a/man/ljungbox.Rd
+++ b/man/ljungbox.Rd
@@ -29,6 +29,6 @@ A \code{c("JD3_TEST", "JD3")} object (see \code{\link[=statisticaltest]{statisti
 Compute Ljung-Box test to check the independence of a data.
 }
 \examples{
-ljungbox(random_t(2, 100), lag = 24, k =1)
-ljungbox(ABS$X0.2.09.10.M, lag = 24, k =1)
+ljungbox(random_t(2, 100), lag = 24, k = 1)
+ljungbox(ABS$X0.2.09.10.M, lag = 24, k = 1)
 }
diff --git a/man/long_term_mean.Rd b/man/long_term_mean.Rd
index 04f5034..c263b27 100644
--- a/man/long_term_mean.Rd
+++ b/man/long_term_mean.Rd
@@ -39,17 +39,19 @@ A long-term mean is a probability based computation of the average value for eve
 }
 \examples{
 BE <- national_calendar(list(
-fixed_day(7,21),
-special_day("NEWYEAR"),
-special_day("CHRISTMAS"),
-special_day("MAYDAY"),
-special_day("EASTERMONDAY"),
-special_day("ASCENSION"),
-special_day("WHITMONDAY"),
-special_day("ASSUMPTION"),
-special_day("ALLSAINTSDAY"),
-special_day("ARMISTICE")))
-lt<-long_term_mean(BE,12,
-                  groups = c(1,1,1,1,1,0,0),
-                  holiday = 7)
+    fixed_day(7, 21),
+    special_day("NEWYEAR"),
+    special_day("CHRISTMAS"),
+    special_day("MAYDAY"),
+    special_day("EASTERMONDAY"),
+    special_day("ASCENSION"),
+    special_day("WHITMONDAY"),
+    special_day("ASSUMPTION"),
+    special_day("ALLSAINTSDAY"),
+    special_day("ARMISTICE")
+))
+lt <- long_term_mean(BE, 12,
+    groups = c(1, 1, 1, 1, 1, 0, 0),
+    holiday = 7
+)
 }
diff --git a/man/lp_variable.Rd b/man/lp_variable.Rd
index 7fccf57..7646b59 100644
--- a/man/lp_variable.Rd
+++ b/man/lp_variable.Rd
@@ -32,8 +32,8 @@ Allows to generate a regressor correcting for the leap year or length-of-period
 }
 \examples{
 # Leap years occur in year 2000, 2004, 2008 and 2012
-lp_variable(4, start = c(2000, 1), length = 4*13)
-lper<-lp_variable(12,c(2000,1),length=10*12,type ="LengthOfPeriod")
+lp_variable(4, start = c(2000, 1), length = 4 * 13)
+lper <- lp_variable(12, c(2000, 1), length = 10 * 12, type = "LengthOfPeriod")
 }
 \references{
 More information on calendar correction in JDemetra+ online documentation:
diff --git a/man/mad.Rd b/man/mad.Rd
index 7fef9fe..ec1d66e 100644
--- a/man/mad.Rd
+++ b/man/mad.Rd
@@ -20,6 +20,6 @@ The median absolute deviation
 Compute a robust median absolute deviation (MAD)
 }
 \examples{
-y<-rnorm(1000)
-m<-rjd3toolkit::mad(y, centile=70)
+y <- rnorm(1000)
+m <- rjd3toolkit::mad(y, centile = 70)
 }
diff --git a/man/modelling_context.Rd b/man/modelling_context.Rd
index b00e486..48ac647 100644
--- a/man/modelling_context.Rd
+++ b/man/modelling_context.Rd
@@ -22,14 +22,16 @@ or come from any other source, provided they are \code{ts} class objects.
 \examples{
 # 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
+)
 # regressors as a list of two groups reg1 and reg2
-vars<-list(reg1=list(x = iv1),reg2=list(x = iv2) )
+vars <- list(reg1 = list(x = iv1), reg2 = list(x = iv2))
 # 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")
 # new_spec<- add_usrdefvar(init_spec,name = "reg1.iv1", regeffect="Trend")
diff --git a/man/national_calendar.Rd b/man/national_calendar.Rd
index a26faa0..445e676 100644
--- a/man/national_calendar.Rd
+++ b/man/national_calendar.Rd
@@ -21,23 +21,26 @@ The holidays have to be generated by one of these functions: \code{fixed_day()},
 \code{fixed_week_day()}, \code{easter_day()}, \code{special_day()} or \code{single_day()}.
 }
 \examples{
-#Fictional calendar using all possibilities to set the required holidays
+# Fictional calendar using all possibilities to set the required holidays
 MyCalendar <- national_calendar(list(
-  fixed_day(7,21),
-  special_day("NEWYEAR"),
-  special_day("CHRISTMAS"),
-  fixed_week_day(7, 2, 3), # second Wednesday of July
-  special_day("MAYDAY"),
-  easter_day(1), # Easter Monday
-  easter_day(-2), # Good Friday
-  single_day("2001-09-11"), # appearing once
-  special_day("ASCENSION"),
-  easter_day(offset=60, julian=FALSE, weight=0.5,
-  validity = list(start="2000-01-01", end = "2020-12-01")),  # Corpus Christi
-  special_day("WHITMONDAY"),
-  special_day("ASSUMPTION"),
-  special_day("ALLSAINTSDAY"),
-  special_day("ARMISTICE")))
+    fixed_day(7, 21),
+    special_day("NEWYEAR"),
+    special_day("CHRISTMAS"),
+    fixed_week_day(7, 2, 3), # second Wednesday of July
+    special_day("MAYDAY"),
+    easter_day(1), # Easter Monday
+    easter_day(-2), # Good Friday
+    single_day("2001-09-11"), # appearing once
+    special_day("ASCENSION"),
+    easter_day(
+        offset = 60, julian = FALSE, weight = 0.5,
+        validity = list(start = "2000-01-01", end = "2020-12-01")
+    ), # Corpus Christi
+    special_day("WHITMONDAY"),
+    special_day("ASSUMPTION"),
+    special_day("ALLSAINTSDAY"),
+    special_day("ARMISTICE")
+))
 }
 \references{
 More information on calendar correction in JDemetra+ online documentation:
diff --git a/man/normality_tests.Rd b/man/normality_tests.Rd
index d97ce53..13b1f5d 100644
--- a/man/normality_tests.Rd
+++ b/man/normality_tests.Rd
@@ -46,7 +46,7 @@ Set of functions to test the normality of a time series.
 
 }}
 \examples{
-x <- rnorm(100)  # null
+x <- rnorm(100) # null
 bowmanshenton(x)
 doornikhansen(x)
 jarquebera(x)
diff --git a/man/outliers_variables.Rd b/man/outliers_variables.Rd
index 5b984ea..6f14f97 100644
--- a/man/outliers_variables.Rd
+++ b/man/outliers_variables.Rd
@@ -52,11 +52,13 @@ A seasonal outlier (SO, \code{so_variable}) is defined as (if \code{zeroended =
  -\frac{1}{s-1} & \text{otherwise }\end{cases}}
 }
 \examples{
-#Outliers in February 2002
-ao <- ao_variable(12, c(2000,1), length = 12*4, date = "2002-02-01")
-ls <- ls_variable(12, c(2000,1), length = 12*4, date = "2002-02-01")
-tc <- tc_variable(12, c(2000,1), length = 12*4, date = "2002-02-01")
-so <- so_variable(12, c(2000,1), length = 12*4, date = "2002-02-01")
-plot.ts(ts.union(ao, ls, tc, so), plot.type = "single",
-        col = c("black", "orange", "green", "gray"))
+# Outliers in February 2002
+ao <- ao_variable(12, c(2000, 1), length = 12 * 4, date = "2002-02-01")
+ls <- ls_variable(12, c(2000, 1), length = 12 * 4, date = "2002-02-01")
+tc <- tc_variable(12, c(2000, 1), length = 12 * 4, date = "2002-02-01")
+so <- so_variable(12, c(2000, 1), length = 12 * 4, date = "2002-02-01")
+plot.ts(ts.union(ao, ls, tc, so),
+    plot.type = "single",
+    col = c("black", "orange", "green", "gray")
+)
 }
diff --git a/man/periodic.dummies.Rd b/man/periodic.dummies.Rd
index c5e5573..f91e5a7 100644
--- a/man/periodic.dummies.Rd
+++ b/man/periodic.dummies.Rd
@@ -28,8 +28,8 @@ The periodic.contrasts function is based on periodic.dummies but adds -1 to the
 }
 \examples{
 # periodic dummies for a quarterly series
-p<-periodic.dummies(4, c(2000,1), 60)
-#periodic contrasts for a quarterly series
-q<-periodic.contrasts(4, c(2000,1), 60)
-q[1:9,]
+p <- periodic.dummies(4, c(2000, 1), 60)
+# periodic contrasts for a quarterly series
+q <- periodic.contrasts(4, c(2000, 1), 60)
+q[1:9, ]
 }
diff --git a/man/r2jd_calendarts.Rd b/man/r2jd_calendarts.Rd
index 86c203c..255b717 100644
--- a/man/r2jd_calendarts.Rd
+++ b/man/r2jd_calendarts.Rd
@@ -13,9 +13,9 @@ r2jd_calendarts(calendarobs)
 Create Java CalendarTimeSeries
 }
 \examples{
-obs<-list(
-list(start=as.Date("1980-01-01"), end=as.Date("1999-12-31"), value=2000),
-list(start=as.Date("2000-01-01"), end=as.Date("2010-01-01"), value=1000)
+obs <- list(
+    list(start = as.Date("1980-01-01"), end = as.Date("1999-12-31"), value = 2000),
+    list(start = as.Date("2000-01-01"), end = as.Date("2010-01-01"), value = 1000)
 )
-jobj<-r2jd_calendarts(obs)
+jobj <- r2jd_calendarts(obs)
 }
diff --git a/man/ramp_variable.Rd b/man/ramp_variable.Rd
index df7545b..e1062a4 100644
--- a/man/ramp_variable.Rd
+++ b/man/ramp_variable.Rd
@@ -33,8 +33,8 @@ A ramp between two dates \eqn{t_0} and \eqn{t_1} is defined as:
 }
 \examples{
 # Ramp variable from January 2001 to September 2001
-rp <- ramp_variable(12, c(2000,1), length = 12*4, range = c(13, 21))
+rp <- ramp_variable(12, c(2000, 1), length = 12 * 4, range = c(13, 21))
 # Or equivalently
-rp<-ramp_variable(12, c(2000,1), length = 12*4, range = c("2001-01-01", "2001-09-02"))
+rp <- ramp_variable(12, c(2000, 1), length = 12 * 4, range = c("2001-01-01", "2001-09-02"))
 plot.ts(rp)
 }
diff --git a/man/rangemean_tstat.Rd b/man/rangemean_tstat.Rd
index 3dc8df0..7abc9dc 100644
--- a/man/rangemean_tstat.Rd
+++ b/man/rangemean_tstat.Rd
@@ -49,19 +49,19 @@ The function \code{rangemean_tstat} returns the T-statistic associated to \eqn{\
 If it is significantly higher than 0, log transformation is recommended.
 }
 \examples{
-y = ABS$X0.2.09.10.M
+y <- ABS$X0.2.09.10.M
 # Multiplicative pattern
 plot(y)
-period = 12
-rm_t = rangemean_tstat(y, period = period, groupsize = period)
+period <- 12
+rm_t <- rangemean_tstat(y, period = period, groupsize = period)
 rm_t # higher than 0
 # Can be tested:
 pt(rm_t, period - 2, lower.tail = FALSE)
 # Or :
-1-cdf_t(period-2, rm_t)
+1 - cdf_t(period - 2, rm_t)
 
 # Close to 0
-rm_t_log = rangemean_tstat(log(y), period = period, groupsize = period)
+rm_t_log <- rangemean_tstat(log(y), period = period, groupsize = period)
 rm_t_log
 pt(rm_t_log, period - 2, lower.tail = FALSE)
 }
diff --git a/man/sarima_decompose.Rd b/man/sarima_decompose.Rd
index 7491a3a..a669768 100644
--- a/man/sarima_decompose.Rd
+++ b/man/sarima_decompose.Rd
@@ -20,7 +20,7 @@ An UCARIMA model
 Decompose SARIMA Model into three components trend, seasonal, irregular
 }
 \examples{
-model <- sarima_model(period = 12, d =1, bd = 1, theta = -0.6, btheta = -0.5)
+model <- sarima_model(period = 12, d = 1, bd = 1, theta = -0.6, btheta = -0.5)
 ucm <- sarima_decompose(model)
 
 }
diff --git a/man/sarima_estimate.Rd b/man/sarima_estimate.Rd
index 818726d..4703ca9 100644
--- a/man/sarima_estimate.Rd
+++ b/man/sarima_estimate.Rd
@@ -32,5 +32,5 @@ Estimate SARIMA Model
 }
 \examples{
 y <- ABS$X0.2.09.10.M
-sarima_estimate(y, order = c(0,1,1), seasonal = c(0,1,1))
+sarima_estimate(y, order = c(0, 1, 1), seasonal = c(0, 1, 1))
 }
diff --git a/man/sarima_hannan_rissanen.Rd b/man/sarima_hannan_rissanen.Rd
index d0faa56..2c247b7 100644
--- a/man/sarima_hannan_rissanen.Rd
+++ b/man/sarima_hannan_rissanen.Rd
@@ -32,5 +32,5 @@ Title
 }
 \examples{
 y <- ABS$X0.2.09.10.M
-sarima_hannan_rissanen(y, order = c(0,1,1), seasonal = c(0,1,1))
+sarima_hannan_rissanen(y, order = c(0, 1, 1), seasonal = c(0, 1, 1))
 }
diff --git a/man/sarima_random.Rd b/man/sarima_random.Rd
index 2111169..59b9e31 100644
--- a/man/sarima_random.Rd
+++ b/man/sarima_random.Rd
@@ -24,7 +24,7 @@ Simulate Seasonal ARIMA
 }
 \examples{
 # Airline model
-s_model <- sarima_model(period = 12, d =1, bd = 1, theta = 0.2, btheta = 0.2)
+s_model <- sarima_model(period = 12, d = 1, bd = 1, theta = 0.2, btheta = 0.2)
 x <- sarima_random(s_model, length = 64, seed = 0)
 plot(x, type = "l")
 }
diff --git a/man/seasonality_canovahansen.Rd b/man/seasonality_canovahansen.Rd
index ccb43b0..1d82344 100644
--- a/man/seasonality_canovahansen.Rd
+++ b/man/seasonality_canovahansen.Rd
@@ -36,7 +36,7 @@ list with the FTest on seasonal variables, the joint test and the details for th
 Canova-Hansen seasonality test
 }
 \examples{
-s<-log(ABS$X0.2.20.10.M)
-seasonality_canovahansen(s, 12, type="Contrast")
-seasonality_canovahansen(s, 12, type="Trigonometric")
+s <- log(ABS$X0.2.20.10.M)
+seasonality_canovahansen(s, 12, type = "Contrast")
+seasonality_canovahansen(s, 12, type = "Trigonometric")
 }
diff --git a/man/seasonality_canovahansen_trigs.Rd b/man/seasonality_canovahansen_trigs.Rd
index 5e82404..f6644c8 100644
--- a/man/seasonality_canovahansen_trigs.Rd
+++ b/man/seasonality_canovahansen_trigs.Rd
@@ -30,7 +30,7 @@ seasonality_canovahansen_trigs(
 Canova-Hansen test using trigonometric variables
 }
 \examples{
-s<-log(ABS$X0.2.20.10.M)
-freqs<-seq(0.01, 0.5, 0.001)
-plot(seasonality_canovahansen_trigs(s, 1/freqs, original = FALSE), type='l')
+s <- log(ABS$X0.2.20.10.M)
+freqs <- seq(0.01, 0.5, 0.001)
+plot(seasonality_canovahansen_trigs(s, 1 / freqs, original = FALSE), type = "l")
 }
diff --git a/man/seasonality_combined.Rd b/man/seasonality_combined.Rd
index 6d63b53..33e6142 100644
--- a/man/seasonality_combined.Rd
+++ b/man/seasonality_combined.Rd
@@ -29,7 +29,7 @@ If \code{data} is not a \code{"ts"} object, \code{firstperiod = 1} by default.}
 Combined test on the presence of identifiable seasonality (see Ladiray and Quenneville, 1999).
 }
 \examples{
-s<-do_stationary(log(ABS$X0.2.09.10.M))$ddata
+s <- do_stationary(log(ABS$X0.2.09.10.M))$ddata
 seasonality_combined(s)
 seasonality_combined(random_t(2, 1000), 7)
 }
diff --git a/man/seasonality_f.Rd b/man/seasonality_f.Rd
index eb5dec3..ac25531 100644
--- a/man/seasonality_f.Rd
+++ b/man/seasonality_f.Rd
@@ -27,6 +27,6 @@ F-test on seasonal dummies
 Estimation of a model with seasonal dummies. Joint F-test on the coefficients of the dummies.
 }
 \examples{
-seasonality_f(ABS$X0.2.09.10.M, model="D1")
+seasonality_f(ABS$X0.2.09.10.M, model = "D1")
 seasonality_f(random_t(2, 1000), 7)
 }
diff --git a/man/seasonality_friedman.Rd b/man/seasonality_friedman.Rd
index 107e051..f5cb399 100644
--- a/man/seasonality_friedman.Rd
+++ b/man/seasonality_friedman.Rd
@@ -25,7 +25,7 @@ Friedman Seasonality Test
 Non parametric test ("ANOVA"-type).
 }
 \examples{
-s<-do_stationary(log(ABS$X0.2.09.10.M))$ddata
+s <- do_stationary(log(ABS$X0.2.09.10.M))$ddata
 seasonality_friedman(s)
 seasonality_friedman(random_t(2, 1000), 12)
 }
diff --git a/man/seasonality_kruskalwallis.Rd b/man/seasonality_kruskalwallis.Rd
index fce4dbd..c8090e9 100644
--- a/man/seasonality_kruskalwallis.Rd
+++ b/man/seasonality_kruskalwallis.Rd
@@ -25,7 +25,7 @@ Kruskall-Wallis Seasonality Test
 Non parametric test on the ranks.
 }
 \examples{
-s<-do_stationary(log(ABS$X0.2.09.10.M))$ddata
+s <- do_stationary(log(ABS$X0.2.09.10.M))$ddata
 seasonality_kruskalwallis(s)
 seasonality_kruskalwallis(random_t(2, 1000), 7)
 }
diff --git a/man/seasonality_modified_qs.Rd b/man/seasonality_modified_qs.Rd
index 8a3ef0e..1bd7b35 100644
--- a/man/seasonality_modified_qs.Rd
+++ b/man/seasonality_modified_qs.Rd
@@ -28,6 +28,6 @@ Remark: the length of the series has some impact on the p-values, mainly on
 short series. Not critical.
 }
 \examples{
-s<-do_stationary(log(ABS$X0.2.09.10.M))$ddata
+s <- do_stationary(log(ABS$X0.2.09.10.M))$ddata
 seasonality_modified_qs(s)
 }
diff --git a/man/seasonality_periodogram.Rd b/man/seasonality_periodogram.Rd
index 87462ca..1b6038b 100644
--- a/man/seasonality_periodogram.Rd
+++ b/man/seasonality_periodogram.Rd
@@ -25,7 +25,7 @@ Periodogram Seasonality Test
 Tests on the sum of a periodogram at seasonal frequencies.
 }
 \examples{
-s<-do_stationary(log(ABS$X0.2.09.10.M))$ddata
+s <- do_stationary(log(ABS$X0.2.09.10.M))$ddata
 seasonality_periodogram(s)
 seasonality_periodogram(random_t(2, 1000), 7)
 }
diff --git a/man/seasonality_qs.Rd b/man/seasonality_qs.Rd
index b915e10..40a17e8 100644
--- a/man/seasonality_qs.Rd
+++ b/man/seasonality_qs.Rd
@@ -25,7 +25,7 @@ A \code{c("JD3_TEST", "JD3")} object (see \code{\link[=statisticaltest]{statisti
 QS (seasonal Ljung-Box) test.
 }
 \examples{
-s<-do_stationary(log(ABS$X0.2.09.10.M))$ddata
+s <- do_stationary(log(ABS$X0.2.09.10.M))$ddata
 seasonality_qs(s)
 seasonality_qs(random_t(2, 1000), 7)
 }
diff --git a/man/set_tradingdays.Rd b/man/set_tradingdays.Rd
index 02ab1eb..f6cd233 100644
--- a/man/set_tradingdays.Rd
+++ b/man/set_tradingdays.Rd
@@ -117,20 +117,21 @@ generated with \code{rjd3tramoseats::spec_tramoseats()} or "JD3_TRAMO_SPEC" gene
 # Pre-defined regressors based on user-defined calendar
 ### create a calendar
 BE <- national_calendar(list(
-fixed_day(7,21),
- special_day("NEWYEAR"),
- special_day("CHRISTMAS"),
- special_day("MAYDAY"),
- special_day("EASTERMONDAY"),
- special_day("ASCENSION"),
- special_day("WHITMONDAY"),
- special_day("ASSUMPTION"),
- special_day("ALLSAINTSDAY"),
- special_day("ARMISTICE")))
+    fixed_day(7, 21),
+    special_day("NEWYEAR"),
+    special_day("CHRISTMAS"),
+    special_day("MAYDAY"),
+    special_day("EASTERMONDAY"),
+    special_day("ASCENSION"),
+    special_day("WHITMONDAY"),
+    special_day("ASSUMPTION"),
+    special_day("ALLSAINTSDAY"),
+    special_day("ARMISTICE")
+))
 ## put into a context
-my_context<-modelling_context(calendars = list(cal=BE))
+my_context <- modelling_context(calendars = list(cal = BE))
 ## create a specification
-#init_spec <- rjd3x13::x13_spec("RSA5c")
+# init_spec <- rjd3x13::x13_spec("RSA5c")
 ## modify the specification
 # new_spec<-set_tradingdays(init_spec,
 #                          option = "TradingDays", calendar.name="cal")
diff --git a/man/special_day.Rd b/man/special_day.Rd
index f2601b2..73ae50c 100644
--- a/man/special_day.Rd
+++ b/man/special_day.Rd
@@ -49,8 +49,10 @@ CHRISTMAS      \tab Fixed holiday, falls on December, 25th.
 # To add Easter Monday
 special_day("EASTERMONDAY")
 # To define a holiday for the day after Christmas, with validity and weight
-special_day("CHRISTMAS", offset = 1, weight = 0.8,
-validity = list(start="2000-01-01", end = "2020-12-01"))
+special_day("CHRISTMAS",
+    offset = 1, weight = 0.8,
+    validity = list(start = "2000-01-01", end = "2020-12-01")
+)
 }
 \references{
 More information on calendar correction in JDemetra+ online documentation:
diff --git a/man/statisticaltest.Rd b/man/statisticaltest.Rd
index f94f642..0eedf9e 100644
--- a/man/statisticaltest.Rd
+++ b/man/statisticaltest.Rd
@@ -30,7 +30,7 @@ statisticaltest(val, pval, dist = NULL)
 Generic function to format the results of 'JDemetra+' tests.
 }
 \examples{
-udr_test = testofupdownruns(random_t(5, 1000))
+udr_test <- testofupdownruns(random_t(5, 1000))
 udr_test # default print
 print(udr_test, details = TRUE) # with the distribution
 
diff --git a/man/td.Rd b/man/td.Rd
index a93e9d4..c6cf2f5 100644
--- a/man/td.Rd
+++ b/man/td.Rd
@@ -46,10 +46,10 @@ the reference group (0).
 \examples{
 # Monthly regressors for Trading Days: each type of day is different
 # contrasts to Sundays (6 series)
-regs_td<- td(12,c(2020,1),60, groups = c(1, 2, 3, 4, 5, 6, 0), contrasts = TRUE)
+regs_td <- td(12, c(2020, 1), 60, groups = c(1, 2, 3, 4, 5, 6, 0), contrasts = TRUE)
 # Quarterly regressors for Working Days: week days are similar
 # contrasts to week-end days (1 series)
-regs_wd<- td(4,c(2020,1),60, groups = c(1, 1, 1, 1, 1, 0, 0), contrasts = TRUE)
+regs_wd <- td(4, c(2020, 1), 60, groups = c(1, 1, 1, 1, 1, 0, 0), contrasts = TRUE)
 }
 \references{
 More information on calendar correction in JDemetra+ online documentation:
diff --git a/man/td_canovahansen.Rd b/man/td_canovahansen.Rd
index b210cec..7dff89b 100644
--- a/man/td_canovahansen.Rd
+++ b/man/td_canovahansen.Rd
@@ -27,6 +27,6 @@ list with the ftest on td, the joint test and the details for the stability of t
 Canova-Hansen test for stable trading days
 }
 \examples{
-s<-log(ABS$X0.2.20.10.M)
-td_canovahansen(s, c(1,12))
+s <- log(ABS$X0.2.20.10.M)
+td_canovahansen(s, c(1, 12))
 }
diff --git a/man/td_timevarying.Rd b/man/td_timevarying.Rd
index 509e83b..99451fd 100644
--- a/man/td_timevarying.Rd
+++ b/man/td_timevarying.Rd
@@ -22,6 +22,6 @@ A Chi2 test
 Likelihood ratio test on time varying trading days
 }
 \examples{
-s<-log(ABS$X0.2.20.10.M)
+s <- log(ABS$X0.2.20.10.M)
 td_timevarying(s)
 }
diff --git a/man/tsdata_of.Rd b/man/tsdata_of.Rd
index 6d1a462..e83425f 100644
--- a/man/tsdata_of.Rd
+++ b/man/tsdata_of.Rd
@@ -21,7 +21,7 @@ Title
 }
 \examples{
 # Annual series
-s<-tsdata_of(c(1,2,3,4), c("1990-01-01", "1995-01-01", "1996-01-01", "2000-11-01"))
+s <- tsdata_of(c(1, 2, 3, 4), c("1990-01-01", "1995-01-01", "1996-01-01", "2000-11-01"))
 # Quarterly series
-t<-tsdata_of(c(1,2,3,NA,4), c("1990-01-01", "1995-01-01", "1996-01-01", "2000-08-01", "2000-11-01"))
+t <- tsdata_of(c(1, 2, 3, NA, 4), c("1990-01-01", "1995-01-01", "1996-01-01", "2000-08-01", "2000-11-01"))
 }
diff --git a/man/ucarima_canonical.Rd b/man/ucarima_canonical.Rd
index ddc6b74..7b2cbd4 100644
--- a/man/ucarima_canonical.Rd
+++ b/man/ucarima_canonical.Rd
@@ -20,8 +20,8 @@ A new UCARIMA model
 Makes a UCARIMA model canonical; more specifically, put all the noise of the components in one dedicated component
 }
 \examples{
-mod1 <- arima_model("trend", delta = c(1,-2,1))
+mod1 <- arima_model("trend", delta = c(1, -2, 1))
 mod2 <- arima_model("noise", var = 1600)
-hp <- ucarima_model(components=list(mod1, mod2))
-hpc <- ucarima_canonical(hp, cmp=2)
+hp <- ucarima_model(components = list(mod1, mod2))
+hpc <- ucarima_canonical(hp, cmp = 2)
 }
diff --git a/man/ucarima_estimate.Rd b/man/ucarima_estimate.Rd
index 7e998c9..3bd3680 100644
--- a/man/ucarima_estimate.Rd
+++ b/man/ucarima_estimate.Rd
@@ -20,12 +20,12 @@ A matrix containing the different components and their standard deviations if st
 Estimate UCARIMA Model
 }
 \examples{
-mod1 <- arima_model("trend", delta = c(1,-2,1))
+mod1 <- arima_model("trend", delta = c(1, -2, 1))
 mod2 <- arima_model("noise", var = 16)
-hp <- ucarima_model(components=list(mod1, mod2))
+hp <- ucarima_model(components = list(mod1, mod2))
 s <- log(aggregate(retail$AutomobileDealers))
-all <- ucarima_estimate(s, hp, stdev=TRUE)
-plot(s, type = 'l')
-t <- ts(all[,1], frequency = frequency(s), start = start(s))
-lines(t, col='blue')
+all <- ucarima_estimate(s, hp, stdev = TRUE)
+plot(s, type = "l")
+t <- ts(all[, 1], frequency = frequency(s), start = start(s))
+lines(t, col = "blue")
 }
diff --git a/man/ucarima_model.Rd b/man/ucarima_model.Rd
index c7f7ae2..3bc363b 100644
--- a/man/ucarima_model.Rd
+++ b/man/ucarima_model.Rd
@@ -26,8 +26,8 @@ Creates an UCARIMA model, which is composed of ARIMA models with independent
 innovations.
 }
 \examples{
-mod1 <- arima_model("trend", delta = c(1,-2,1))
+mod1 <- arima_model("trend", delta = c(1, -2, 1))
 mod2 <- arima_model("noise", var = 1600)
-hp<-ucarima_model(components=list(mod1, mod2))
+hp <- ucarima_model(components = list(mod1, mod2))
 print(hp$model)
 }
diff --git a/man/ucarima_wk.Rd b/man/ucarima_wk.Rd
index 54616b1..0559c8b 100644
--- a/man/ucarima_wk.Rd
+++ b/man/ucarima_wk.Rd
@@ -24,10 +24,10 @@ A list with the (pseudo-)spectrum, the weights of the filter and the squared-gai
 Wiener Kolmogorov Estimators
 }
 \examples{
-mod1 <- arima_model("trend", delta = c(1,-2,1))
+mod1 <- arima_model("trend", delta = c(1, -2, 1))
 mod2 <- arima_model("noise", var = 1600)
-hp<-ucarima_model(components=list(mod1, mod2))
-wk1<-ucarima_wk(hp, 1, nwk=50)
-wk2<-ucarima_wk(hp, 2)
-plot(wk1$filter, type='h')
+hp <- ucarima_model(components = list(mod1, mod2))
+wk1 <- ucarima_wk(hp, 1, nwk = 50)
+wk2 <- ucarima_wk(hp, 2)
+plot(wk1$filter, type = "h")
 }
diff --git a/man/weighted_calendar.Rd b/man/weighted_calendar.Rd
index 0962693..79b45af 100644
--- a/man/weighted_calendar.Rd
+++ b/man/weighted_calendar.Rd
@@ -25,9 +25,9 @@ For example, in Germany public holidays are determined by the federal states.
 Therefore, Epiphany is celebrated only in Baden-Wurttemberg, Bavaria and in Saxony-Anhalt, while from 1994 Day of Repentance and Prayer is celebrated only in Saxony.
 }
 \examples{
-Belgium <- national_calendar(list(special_day("NEWYEAR"),fixed_day(7,21)))
-France <- national_calendar(list(special_day("NEWYEAR"),fixed_day(7,14)))
-composite_calendar<- weighted_calendar(list(France,Belgium), weights = c(1,2))
+Belgium <- national_calendar(list(special_day("NEWYEAR"), fixed_day(7, 21)))
+France <- national_calendar(list(special_day("NEWYEAR"), fixed_day(7, 14)))
+composite_calendar <- weighted_calendar(list(France, Belgium), weights = c(1, 2))
 }
 \references{
 More information on calendar correction in JDemetra+ online documentation: