diff --git a/.lintr b/.lintr
index d640649..4596275 100644
--- a/.lintr
+++ b/.lintr
@@ -1,28 +1,9 @@
 linters: linters_with_defaults(
         indentation_linter = lintr::indentation_linter(indent = 4L),
         # line_length_linter = lintr::line_length_linter(80L),
-        line_length_linter = lintr::line_length_linter(500L),
-        indentation_linter = NULL,
-        assignment_linter = NULL,
-        trailing_blank_lines_linter = NULL,
-        trailing_whitespace_linter = NULL,
-        whitespace_linter = NULL,
-        brace_linter = NULL,
-        infix_spaces_linter = NULL,
-        paren_body_linter = NULL,
-        indentation_linter = NULL,
-        function_left_parentheses_linter = NULL,
-        spaces_left_parentheses_linter = NULL,
-        commas_linter = NULL,
-        quotes_linter = NULL,
-        spaces_inside_linter = NULL,
-        vector_logic_linter = NULL,
-        seq_linter = NULL,
-        object_length_linter = NULL,
-        semicolon_linter = NULL,
-        cyclocomp_linter = NULL,
-        object_usage_linter = NULL,
-        object_name_linter = NULL,
-        commented_code_linter = NULL
+        line_length_linter = lintr::line_length_linter(150L),
+        # cyclocomp_linter = lintr::cyclocomp_linter(complexity_limit = 15L),
+        cyclocomp_linter = lintr::cyclocomp_linter(complexity_limit = 50L),
+        object_name_linter = NULL
     )
 encoding: "UTF-8"
diff --git a/R/deprecated.R b/R/deprecated.R
index 8410a1c..595cd25 100644
--- a/R/deprecated.R
+++ b/R/deprecated.R
@@ -22,13 +22,19 @@ spec_x11 <- function() {
 }
 #' @name deprecated-rjd3x13
 #' @export
-fast_x13 <- function(ts, spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"), context = NULL, userdefined = NULL) {
+fast_x13 <- function(ts,
+                     spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"),
+                     context = NULL,
+                     userdefined = NULL) {
     .Deprecated("x13_fast")
     x13_fast(ts, spec, context, userdefined)
 }
 #' @name deprecated-rjd3x13
 #' @export
-fast_regarima <- function(ts, spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"), context = NULL, userdefined = NULL) {
+fast_regarima <- function(ts,
+                          spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"),
+                          context = NULL,
+                          userdefined = NULL) {
     .Deprecated("regarima_fast")
     regarima_fast(ts, spec, context, userdefined)
 }
diff --git a/R/print.R b/R/print.R
index 70d1d3e..ca886e2 100644
--- a/R/print.R
+++ b/R/print.R
@@ -92,7 +92,10 @@ summary.JD3_X13_OUTPUT <- function(object, ...) {
 }
 
 #' @export
-print.summary.JD3_X13_RSLTS <- function(x, digits = max(3L, getOption("digits") - 3L), signif.stars = getOption("show.signif.stars"), ...) {
+print.summary.JD3_X13_RSLTS <- function(x,
+                                        digits = max(3L, getOption("digits") - 3L),
+                                        signif.stars = getOption("show.signif.stars"),
+                                        ...) {
     cat("Model: X-13\n")
     print(x$preprocessing, digits = digits, signif.stars = signif.stars, ...)
     cat("\n", "Decomposition", "\n", sep = "")
@@ -105,7 +108,9 @@ print.summary.JD3_X13_RSLTS <- function(x, digits = max(3L, getOption("digits")
 }
 
 #' @export
-print.JD3_X13_OUTPUT <- function(x, digits = max(3L, getOption("digits") - 3L), summary_info = getOption("summary_info"),
+print.JD3_X13_OUTPUT <- function(x,
+                                 digits = max(3L, getOption("digits") - 3L),
+                                 summary_info = getOption("summary_info"),
                                  ...) {
     print(x$result, digits = digits, summary_info = summary_info, ...)
     return(invisible(x))
@@ -365,11 +370,25 @@ print.JD3_X13_SPEC <- function(x, ...) {
         cat("Is enabled: No\n")
     } else {
         cat("Enabled: Yes\n", sep = "")
-        cat("Target: ", x$benchmarking$target, ifelse(x$benchmarking$target == "TARGET_CALENDARADJUSTED", " (Auto)", ""), "\n", sep = "")
-        cat("Lambda: ", x$benchmarking$lambda, ifelse(x$benchmarking$lambda == 1, " (Auto)", ""), "\n", sep = "")
-        cat("Rho: ", x$benchmarking$rho, ifelse(x$benchmarking$rho == 1, " (Auto)", ""), "\n", sep = "")
-        cat("Bias: ", x$benchmarking$bias, ifelse(x$benchmarking$bias == "BIAS_NONE", " (Auto)", ""), "\n", sep = "")
-        cat("Use forecast: ", ifelse(x$benchmarking$forecast, "Yes", "No (Auto)"), "\n", sep = "")
+        cat("Target: ", x$benchmarking$target,
+            ifelse(
+                test = x$benchmarking$target == "TARGET_CALENDARADJUSTED",
+                yes = " (Auto)",
+                no = ""
+            ),
+            "\n", sep = "")
+        cat("Lambda: ", x$benchmarking$lambda,
+            ifelse(test = x$benchmarking$lambda == 1, yes = " (Auto)", no = ""),
+            "\n", sep = "")
+        cat("Rho: ", x$benchmarking$rho,
+            ifelse(test = x$benchmarking$rho == 1, yes = " (Auto)", no = ""),
+            "\n", sep = "")
+        cat("Bias: ", x$benchmarking$bias,
+            ifelse(test = x$benchmarking$bias == "BIAS_NONE", yes = " (Auto)", no = ""),
+            "\n", sep = "")
+        cat("Use forecast: ",
+            ifelse(test = x$benchmarking$forecast, yes = "Yes", no = "No (Auto)"),
+            "\n", sep = "")
     }
 
     return(invisible(x))
diff --git a/R/regarima_outliers.R b/R/regarima_outliers.R
index 62fe660..4c6cea2 100644
--- a/R/regarima_outliers.R
+++ b/R/regarima_outliers.R
@@ -8,11 +8,13 @@ NULL
 #' @param mean Boolean to include or not the mean.
 #' @param X user defined regressors (other than calendar).
 #' @param X.td calendar regressors.
-#' @param ao,ls,so,tc Boolean to indicate which type of outliers should be detected.
-#' @param cv  `numeric`. The entered critical value for the outlier detection procedure.
-#' If equal to 0 the critical value for the outlier detection procedure is automatically determined
-#' by the number of observations.
-#' @param clean Clean missing values at the beginning/end of the series. Regression variables are automatically resized, if need be.
+#' @param ao,ls,so,tc Boolean to indicate which type of outliers should be
+#' detected.
+#' @param cv  `numeric`. The entered critical value for the outlier detection
+#' procedure. If equal to 0 the critical value for the outlier detection
+#' procedure is automatically determined by the number of observations.
+#' @param clean Clean missing values at the beginning/end of the series.
+#' Regression variables are automatically resized, if need be.
 #'
 #' @return a `"JD3_REGARIMA_OUTLIERS"` object, containing input variables and results
 #'
@@ -20,21 +22,32 @@ NULL
 #' regarima_outliers(rjd3toolkit::ABS$X0.2.09.10.M)
 #'
 #' @export
-regarima_outliers <- function(y, order = c(0L, 1L, 1L), seasonal = c(0L, 1L, 1L), mean = FALSE,
-                              X = NULL, X.td = NULL, ao = TRUE, ls = TRUE, tc = FALSE, so = FALSE, cv = 0, clean = FALSE) {
+regarima_outliers <- function(y,
+                              order = c(0L, 1L, 1L),
+                              seasonal = c(0L, 1L, 1L),
+                              mean = FALSE,
+                              X = NULL,
+                              X.td = NULL,
+                              ao = TRUE,
+                              ls = TRUE,
+                              tc = FALSE,
+                              so = FALSE,
+                              cv = 0,
+                              clean = FALSE) {
     if (!is.ts(y)) {
         stop("y must be a time series")
     }
     if (!is.null(X.td)) {
-        sy <- start(y)
         td <- rjd3toolkit::td(s = y, groups = X.td)
         X <- cbind(X, td)
     }
 
 
     jregarima <- .jcall(
-        "jdplus/x13/base/r/RegArimaOutliersDetection", "Ljdplus/x13/base/r/RegArimaOutliersDetection$Results;", "process",
-        rjd3toolkit::.r2jd_tsdata(y), as.integer(order), as.integer(seasonal), mean, rjd3toolkit::.r2jd_matrix(X),
+        "jdplus/x13/base/r/RegArimaOutliersDetection",
+        "Ljdplus/x13/base/r/RegArimaOutliersDetection$Results;", "process",
+        rjd3toolkit::.r2jd_tsdata(y), as.integer(order), as.integer(seasonal),
+        mean, rjd3toolkit::.r2jd_matrix(X),
         ao, ls, tc, so, cv, clean
     )
     model <- list(
diff --git a/R/revisions.R b/R/revisions.R
index d70ea5f..3d36058 100644
--- a/R/revisions.R
+++ b/R/revisions.R
@@ -79,7 +79,8 @@ x13_revisions <- function(ts, spec, data_ids = NULL, ts_ids = NULL, cmp_ids = NU
     lts <- NULL
     if (!is.null(ts_ids)) {
         lts <- lapply(ts_ids, function(ts_id) {
-            w <- .jcall(jr, "Ljdplus/toolkit/base/api/timeseries/TsData;", "tsHistory", ts_id$id, ts_id$period, ts_id$start)
+            w <- .jcall(jr, "Ljdplus/toolkit/base/api/timeseries/TsData;",
+                        "tsHistory", ts_id$id, ts_id$period, ts_id$start)
             return(rjd3toolkit::.jd2r_tsdata(w))
         })
         names(lts) <- sapply(ts_ids, `[[`, "id")
@@ -87,7 +88,8 @@ x13_revisions <- function(ts, spec, data_ids = NULL, ts_ids = NULL, cmp_ids = NU
     lcmp <- NULL
     if (!is.null(cmp_ids)) {
         lcmp <- lapply(cmp_ids, function(cmp_id) {
-            w <- .jcall(jr, "Ljdplus/toolkit/base/api/timeseries/TsDataTable;", "tsSelect", cmp_id$id, cmp_id$start, cmp_id$end)
+            w <- .jcall(jr, "Ljdplus/toolkit/base/api/timeseries/TsDataTable;",
+                        "tsSelect", cmp_id$id, cmp_id$start, cmp_id$end)
             return(rjd3toolkit::.jd2r_mts(w))
         })
         names(lcmp) <- sapply(cmp_ids, `[[`, "id")
diff --git a/R/set_x11_spec.R b/R/set_x11_spec.R
index 8b83c8e..bb9c26f 100644
--- a/R/set_x11_spec.R
+++ b/R/set_x11_spec.R
@@ -1,35 +1,57 @@
 #' Set X-11 Specification
 #'
-#' @param x the specification to be modified, object of class "JD3_X11_SPEC", default X11 spec can be obtained as 'x=x11_spec()'
-#' @param mode character: the decomposition mode. Determines the mode of the seasonal adjustment decomposition to be performed:
-#' `"Undefined"` - no assumption concerning the relationship between the time series components is made;
+#' @param x the specification to be modified, object of class "JD3_X11_SPEC",
+#' default X11 spec can be obtained as 'x=x11_spec()'
+#' @param mode character: the decomposition mode. Determines the mode of the
+#' seasonal adjustment decomposition to be performed:
+#' `"Undefined"` - no assumption concerning the relationship between the time
+#' series components is made;
 #' `"Additive"` - assumes an additive relationship;
 #' `"Multiplicative"` - assumes a multiplicative relationship;
-#' `"LogAdditive"` - performs an additive decomposition of the logarithms of the series being adjusted;
-#' `"PseudoAdditive"` - assumes an pseudo-additive relationship. Could be changed by the program, if needed.
-#' @param seasonal.comp logical: if `TRUE`, the program computes a seasonal component. Otherwise, the seasonal component
-#' is not estimated and its values are all set to 0 (additive decomposition) or 1 (multiplicative decomposition).
-#' @param lsigma numeric: the lower sigma boundary for the detection of extreme values, > 0.5, default=1.5.
-#' @param usigma numeric: the upper sigma boundary for the detection of extreme values, > lsigma, default=2.5.
-#' @param henderson.filter numeric: the length of the Henderson filter (odd number between 3 and 101). If `henderson.filter = 0` an automatic selection of the Henderson filter's length
-#' for the trend estimation is enabled.
-#' @param seasonal.filter a vector of character(s) specifying which seasonal moving average (i.e. seasonal filter)
-#' will be used to estimate the seasonal factors for the entire series. The vector can be of length:
-#' 1 - the same seasonal filter is used for all periods (e.g.: `seasonal.filter = "Msr"` or `seasonal.filter = "S3X3"` );
-#' or have a different value for each quarter (length 4) or each month (length 12) - (e.g. for quarterly series: `seasonal.filter = c("S3X3", "Msr", "S3X3", "Msr")`).
-#' Possible filters are: `"Msr"`, `"Stable"`, `"X11Default"`, `"S3X1"`, `"S3X3"`, `"S3X5"`, `"S3X9"`, `"S3X15"`.
-#' `"Msr"` - the program chooses the final seasonal filter automatically.
-#' @param bcasts,fcasts numeric: the number of backasts (`bcasts`) or forecasts (`fcasts`) generated by the RegARIMA model in periods (positive values) or years (negative values).Default values: fcasts=-1 and bcasts=0.
-#' @param calendar.sigma character to specify if the standard errors used for extreme values detection and adjustment are computed:
-#' from 5 year spans of irregulars (`"None"`, default value);
-#' separately for each calendar period (`"All"`);
-#' separately for each period only if Cochran's hypothesis test determines that the irregular component is heteroskedastic
-#' by calendar month/quarter (`"Signif"`);
-#' separately for two complementary sets of calendar months/quarters specified by the x11.sigmaVector parameter (`"Select"`,
-#' see parameter `sigma.vector`).
-#' @param sigma.vector a vector to specify one of the two groups of periods for which standard errors used for extreme values
-#' detection and adjustment will be computed separately. Only used if `calendar.sigma = "Select"`. Possible values are: `1` or `2`.
-#' @param exclude.forecast Boolean to exclude forecasts and backcasts. If `TRUE`, the RegARIMA model forecasts and backcasts are not used during the detection of extreme values in the seasonal adjustment routines.Default= FALSE.
+#' `"LogAdditive"` - performs an additive decomposition of the logarithms of the
+#' series being adjusted;
+#' `"PseudoAdditive"` - assumes an pseudo-additive relationship. Could be
+#' changed by the program, if needed.
+#' @param seasonal.comp logical: if `TRUE`, the program computes a seasonal
+#' component. Otherwise, the seasonal component is not estimated and its values
+#' are all set to 0 (additive decomposition) or 1 (multiplicative
+#' decomposition).
+#' @param lsigma numeric: the lower sigma boundary for the detection of extreme
+#' values, > 0.5, default=1.5.
+#' @param usigma numeric: the upper sigma boundary for the detection of extreme
+#' values, > lsigma, default=2.5.
+#' @param henderson.filter numeric: the length of the Henderson filter (odd
+#' number between 3 and 101). If `henderson.filter = 0` an automatic selection
+#' of the Henderson filter's length for the trend estimation is enabled.
+#' @param seasonal.filter a vector of character(s) specifying which seasonal
+#' moving average (i.e. seasonal filter) will be used to estimate the seasonal
+#' factors for the entire series. The vector can be of length: 1 - the same
+#' seasonal filter is used for all periods (e.g.: `seasonal.filter = "Msr"` or
+#' `seasonal.filter = "S3X3"` ); or have a different value for each quarter
+#' (length 4) or each month (length 12) - (e.g. for quarterly series:
+#' `seasonal.filter = c("S3X3", "Msr", "S3X3", "Msr")`). Possible filters are:
+#' `"Msr"`, `"Stable"`, `"X11Default"`, `"S3X1"`, `"S3X3"`, `"S3X5"`, `"S3X9"`,
+#' `"S3X15"`. `"Msr"` - the program chooses the final seasonal filter
+#' automatically.
+#' @param bcasts,fcasts numeric: the number of backasts (`bcasts`) or forecasts
+#' (`fcasts`) generated by the RegARIMA model in periods (positive values) or
+#' years (negative values).Default values: fcasts=-1 and bcasts=0.
+#' @param calendar.sigma character to specify if the standard errors used for
+#' extreme values detection and adjustment are computed: from 5 year spans of
+#' irregulars (`"None"`, default value); separately for each calendar period
+#' (`"All"`); separately for each period only if Cochran's hypothesis test
+#' determines that the irregular component is heteroskedastic by calendar
+#' month/quarter (`"Signif"`); separately for two complementary sets of calendar
+#' months/quarters specified by the x11.sigmaVector parameter (`"Select"`, see
+#' parameter `sigma.vector`).
+#' @param sigma.vector a vector to specify one of the two groups of periods for
+#' which standard errors used for extreme values detection and adjustment will
+#' be computed separately. Only used if `calendar.sigma = "Select"`. Possible
+#' values are: `1` or `2`.
+#' @param exclude.forecast Boolean to exclude forecasts and backcasts. If
+#' `TRUE`, the RegARIMA model forecasts and backcasts are not used during the
+#' detection of extreme values in the seasonal adjustment routines.
+#' Default = FALSE.
 #' @param bias TODO.
 #' @return  a "JD3_X11_SPEC" object, containing all the parameters.
 #' @seealso [x13_spec()] and [x11_spec()].
@@ -67,19 +89,20 @@ set_x11 <- function(x,
     UseMethod("set_x11", x)
 }
 #' @export
-set_x11.JD3_X11_SPEC <- function(x,
-                                 mode = c(NA, "Undefined", "Additive", "Multiplicative", "LogAdditive", "PseudoAdditive"),
-                                 seasonal.comp = NA,
-                                 seasonal.filter = NA,
-                                 henderson.filter = NA,
-                                 lsigma = NA,
-                                 usigma = NA,
-                                 fcasts = NA,
-                                 bcasts = NA,
-                                 calendar.sigma = c(NA, "None", "Signif", "All", "Select"),
-                                 sigma.vector = NA,
-                                 exclude.forecast = NA,
-                                 bias = c(NA, "LEGACY")) {
+set_x11.JD3_X11_SPEC <- function(
+        x,
+        mode = c(NA, "Undefined", "Additive", "Multiplicative", "LogAdditive", "PseudoAdditive"),
+        seasonal.comp = NA,
+        seasonal.filter = NA,
+        henderson.filter = NA,
+        lsigma = NA,
+        usigma = NA,
+        fcasts = NA,
+        bcasts = NA,
+        calendar.sigma = c(NA, "None", "Signif", "All", "Select"),
+        sigma.vector = NA,
+        exclude.forecast = NA,
+        bias = c(NA, "LEGACY")) {
     mode <- match.arg(
         toupper(mode[1]),
         c(
diff --git a/R/udvar.R b/R/udvar.R
index 5f50d9d..f00ecc3 100644
--- a/R/udvar.R
+++ b/R/udvar.R
@@ -20,35 +20,34 @@
 #' Display a list of all the available output objects
 #'
 #' @description
-#' Function generating a comprehensive list of available output variables (series, parameters, diagnostics) from the estimation process
-#' by the  `x13()`, `regarima()` and `x11()` functions.
-#' Some items are available in the default estimation output but the remainder can be added
-#' using the `userdefined` parameter.
-#' User-defined objects can the be retrieved from the list of lists generated by the estimation process
+#' Function generating a comprehensive list of available output variables
+#' (series, parameters, diagnostics) from the estimation process by the
+#' `x13()`, `regarima()` and `x11()` functions. Some items are available in the
+#' default estimation output but the remainder can be added using the
+#' `userdefined` parameter. User-defined objects can the be retrieved from the
+#' list of lists generated by the estimation process
 #'
-#' @param x a character to indicate the estimation function for which the output items list will be displayed.
+#' @param x a character to indicate the estimation function for which the output
+#' items list will be displayed.
 #'
 #' @examples
 #' userdefined_variables_x13("x13")
 #' userdefined_variables_x13("regarima")
 #' userdefined_variables_x13("x11")
-#' @return  a vector containing the names of all the available output objects (series, diagnostics, parameters)
+#'
+#' @return  a vector containing the names of all the available output objects
+#' (series, diagnostics, parameters)
+#'
 #' @references
 #' More information and examples related to 'JDemetra+' features in the online documentation:
 #' \url{https://jdemetra-new-documentation.netlify.app/}
+#'
 #' @export
+#'
 userdefined_variables_x13 <- function(x = c("X-13", "RegArima", "X-11")) {
     x <- match.arg(gsub("-", "", tolower(x)),
         choices = c("x13", "regarima", "x11")
     )
-
-    # library(rjd3x13)
-    # jts<-rjd3toolkit::.r2jd_tsdata(rjd3toolkit::ABS$X0.2.09.10.M)
-    # jrslt<- rJava::.jcall("jdplus/x13/base/r/X13", "Ljdplus/x13/base/core/x13/X13Output;", "fullProcess", jts, "RSA3")
-    # rjd3toolkit::dictionary(rjd3toolkit::.jd3_object(jrslt$getResult(), result = TRUE)) |>
-    #   sort() |>
-    #   dput()
-
     sa_x13 <- c(
         "adjust", "arima.bd", "arima.bp", "arima.bphi(*)", "arima.bq",
         "arima.btheta(*)", "arima.d", "arima.p", "arima.phi(*)", "arima.q",
@@ -143,12 +142,6 @@ userdefined_variables_x13 <- function(x = c("X-13", "RegArima", "X-11")) {
         "ycal", "ycal_f(?)"
     )
 
-    # jts<-rjd3toolkit::.r2jd_tsdata(rjd3toolkit::ABS$X0.2.09.10.M)
-    # jrslt<- rJava::.jcall("jdplus/x13/base/r/RegArima", "Ljdplus/x13/base/core/x13/regarima/RegArimaOutput;", "fullProcess", jts, "RG3")
-    # rjd3toolkit::dictionary(rjd3toolkit::.jd3_object(jrslt$getResult(), result = TRUE)) |>
-    #   sort() |>
-    #   dput()
-
     sa_regarima <- c(
         "adjust", "arima.bd", "arima.bp", "arima.bphi(*)", "arima.bq",
         "arima.btheta(*)", "arima.d", "arima.p", "arima.phi(*)", "arima.q",
@@ -185,13 +178,6 @@ userdefined_variables_x13 <- function(x = c("X-13", "RegArima", "X-11")) {
         "y_eb(?)", "y_ef(?)", "y_f(?)", "yc", "ycal", "ycal_f(?)"
     )
 
-    # jts<-rjd3toolkit::.r2jd_tsdata(rjd3toolkit::ABS$X0.2.09.10.M)
-    # jrslt<- rJava::.jcall("jdplus/x13/base/r/X11", "Ljdplus/x13/base/core/x11/X11Results;", "process", jts,
-    #                       rjd3x13::.r2jd_spec_x11(rjd3x13::spec_x11()))
-    # rjd3toolkit::dictionary(rjd3toolkit::.jd3_object(jrslt, result = TRUE)) |>
-    #   sort() |>
-    #   dput()
-
     sa_x11 <- c(
         "b1", "b10", "b11", "b13", "b17", "b2", "b20", "b3", "b4",
         "b5", "b6", "b7", "b8", "b9", "c1", "c10", "c11", "c13", "c17",
diff --git a/R/x13.R b/R/x13.R
index 4796150..9fa15c0 100644
--- a/R/x13.R
+++ b/R/x13.R
@@ -4,11 +4,17 @@ NULL
 #' RegARIMA model, pre-adjustment in X13
 #'
 #' @param ts an univariate time series.
-#' @param spec the model specification. Can be either the name of a predefined specification or a user-defined specification.
-#' @param context list of external regressors (calendar or other) to be used for estimation
-#' @param userdefined a vector containing additional output variables (see [x13_dictionary()]).
+#' @param spec the model specification. Can be either the name of a predefined
+#' specification or a user-defined specification.
+#' @param context list of external regressors (calendar or other) to be used for
+#' estimation
+#' @param userdefined a vector containing additional output variables
+#' (see [x13_dictionary()]).
 #'
-#' @return the `regarima()` function returns a list with the results (`"JD3_REGARIMA_RSLTS"` object), the estimation specification and the result specification, while `regarima_fast()` is a faster function that only returns the results.
+#' @return the `regarima()` function returns a list with the results
+#' (`"JD3_REGARIMA_RSLTS"` object), the estimation specification and the result
+#' specification, while `regarima_fast()` is a faster function that only returns
+#' the results.
 #'
 #' @examples
 #' y <- rjd3toolkit::ABS$X0.2.09.10.M
@@ -28,14 +34,17 @@ NULL
 #' sp <- rjd3toolkit::set_outlier(sp, outliers.type = c("AO"))
 #' regarima_fast(y, spec = sp)
 #' @export
-regarima <- function(ts, spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"), context = NULL, userdefined = NULL) {
+regarima <- function(ts, spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"),
+                     context = NULL, userdefined = NULL) {
     jts <- rjd3toolkit::.r2jd_tsdata(ts)
     if (is.character(spec)) {
         spec <- gsub("sa", "g", tolower(spec), fixed = TRUE)
         spec <- match.arg(spec[1],
             choices = c("rg0", "rg1", "rg2c", "rg3", "rg4", "rg5c")
         )
-        jrslt <- .jcall("jdplus/x13/base/r/RegArima", "Ljdplus/x13/base/core/x13/regarima/RegArimaOutput;", "fullProcess", jts, spec)
+        jrslt <- .jcall("jdplus/x13/base/r/RegArima",
+                        "Ljdplus/x13/base/core/x13/regarima/RegArimaOutput;",
+                        "fullProcess", jts, spec)
     } else {
         jspec <- .r2jd_spec_regarima(spec)
         if (is.null(context)) {
@@ -43,7 +52,9 @@ regarima <- function(ts, spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"), c
         } else {
             jcontext <- rjd3toolkit::.r2jd_modellingcontext(context)
         }
-        jrslt <- .jcall("jdplus/x13/base/r/RegArima", "Ljdplus/x13/base/core/x13/regarima/RegArimaOutput;", "fullProcess", jts, jspec, jcontext)
+        jrslt <- .jcall("jdplus/x13/base/r/RegArima",
+                        "Ljdplus/x13/base/core/x13/regarima/RegArimaOutput;",
+                        "fullProcess", jts, jspec, jcontext)
     }
     if (is.jnull(jrslt)) {
         return(NULL)
@@ -54,14 +65,19 @@ regarima <- function(ts, spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"), c
 }
 #' @export
 #' @rdname regarima
-regarima_fast <- function(ts, spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"), context = NULL, userdefined = NULL) {
+regarima_fast <- function(ts,
+                          spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"),
+                          context = NULL,
+                          userdefined = NULL) {
     jts <- rjd3toolkit::.r2jd_tsdata(ts)
     if (is.character(spec)) {
         spec <- gsub("sa", "g", tolower(spec), fixed = TRUE)
         spec <- match.arg(spec[1],
             choices = c("rg0", "rg1", "rg2c", "rg3", "rg4", "rg5c")
         )
-        jrslt <- .jcall("jdplus/x13/base/r/RegArima", "Ljdplus/toolkit/base/core/regsarima/regular/RegSarimaModel;", "process", jts, spec)
+        jrslt <- .jcall("jdplus/x13/base/r/RegArima",
+                        "Ljdplus/toolkit/base/core/regsarima/regular/RegSarimaModel;",
+                        "process", jts, spec)
     } else {
         jspec <- .r2jd_spec_regarima(spec)
         if (is.null(context)) {
@@ -69,7 +85,9 @@ regarima_fast <- function(ts, spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c
         } else {
             jcontext <- rjd3toolkit::.r2jd_modellingcontext(context)
         }
-        jrslt <- .jcall("jdplus/x13/base/r/RegArima", "Ljdplus/toolkit/base/core/regsarima/regular/RegSarimaModel;", "process", jts, jspec, jcontext)
+        jrslt <- .jcall("jdplus/x13/base/r/RegArima",
+                        "Ljdplus/toolkit/base/core/regsarima/regular/RegSarimaModel;",
+                        "process", jts, jspec, jcontext)
     }
     if (is.jnull(jrslt)) {
         return(NULL)
@@ -125,20 +143,31 @@ regarima_fast <- function(ts, spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c
 #' )
 #' x13_fast(y, spec = sp)
 #'
-#' @return the `x13()` function returns a list with the results, the estimation specification and the result specification, while `x13_fast()` is a faster function that only returns the results.
-#' The `.jx13()` functions only returns results in a java object which will allow to customize outputs in other packages (use [rjd3toolkit::dictionary()] to
-#' get the list of variables and [rjd3toolkit::result()] to get a specific variable).
-#' In the estimation functions `x13()` and `x13_fast()` you can directly use a specification name (string).
-#' If you want to customize a specification you have to create a specification object first.
+#' @return the `x13()` function returns a list with the results, the estimation
+#' specification and the result specification, while `x13_fast()` is a faster
+#' function that only returns the results. The `.jx13()` functions only returns
+#' results in a java object which will allow to customize outputs in other
+#' packages (use [rjd3toolkit::dictionary()] to get the list of variables and
+#' [rjd3toolkit::result()] to get a specific variable). In the estimation
+#' functions `x13()` and `x13_fast()` you can directly use a specification name
+#' (string). If you want to customize a specification you have to create a
+#' specification object first.
+#'
 #' @export
-x13 <- function(ts, spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"), context = NULL, userdefined = NULL) {
+#'
+x13 <- function(ts,
+                spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"),
+                context = NULL,
+                userdefined = NULL) {
     jts <- rjd3toolkit::.r2jd_tsdata(ts)
     if (is.character(spec)) {
         spec <- gsub("g", "sa", tolower(spec), fixed = TRUE)
         spec <- match.arg(spec[1],
             choices = c("rsa0", "rsa1", "rsa2c", "rsa3", "rsa4", "rsa5c")
         )
-        jrslt <- .jcall("jdplus/x13/base/r/X13", "Ljdplus/x13/base/core/x13/X13Output;", "fullProcess", jts, spec)
+        jrslt <- .jcall("jdplus/x13/base/r/X13",
+                        "Ljdplus/x13/base/core/x13/X13Output;",
+                        "fullProcess", jts, spec)
     } else {
         jspec <- .r2jd_spec_x13(spec)
         if (is.null(context)) {
@@ -146,7 +175,9 @@ x13 <- function(ts, spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"),
         } else {
             jcontext <- rjd3toolkit::.r2jd_modellingcontext(context)
         }
-        jrslt <- .jcall("jdplus/x13/base/r/X13", "Ljdplus/x13/base/core/x13/X13Output;", "fullProcess", jts, jspec, jcontext)
+        jrslt <- .jcall("jdplus/x13/base/r/X13",
+                        "Ljdplus/x13/base/core/x13/X13Output;",
+                        "fullProcess", jts, jspec, jcontext)
     }
     if (is.jnull(jrslt)) {
         return(NULL)
@@ -159,7 +190,10 @@ x13 <- function(ts, spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"),
 
 #' @export
 #' @rdname x13
-x13_fast <- function(ts, spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"), context = NULL, userdefined = NULL) {
+x13_fast <- function(ts,
+                     spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"),
+                     context = NULL,
+                     userdefined = NULL) {
     jts <- rjd3toolkit::.r2jd_tsdata(ts)
     if (is.character(spec)) {
         spec <- gsub("g", "sa", tolower(spec), fixed = TRUE)
@@ -174,7 +208,9 @@ x13_fast <- function(ts, spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5
         } else {
             jcontext <- rjd3toolkit::.r2jd_modellingcontext(context)
         }
-        jrslt <- .jcall("jdplus/x13/base/r/X13", "Ljdplus/x13/base/core/x13/X13Results;", "process", jts, jspec, jcontext)
+        jrslt <- .jcall("jdplus/x13/base/r/X13",
+                        "Ljdplus/x13/base/core/x13/X13Results;",
+                        "process", jts, jspec, jcontext)
     }
     if (is.jnull(jrslt)) {
         return(NULL)
@@ -201,7 +237,9 @@ x13_fast <- function(ts, spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5
         } else {
             jcontext <- rjd3toolkit::.r2jd_modellingcontext(context)
         }
-        jrslt <- .jcall("jdplus/x13/base/r/X13", "Ljdplus/x13/base/core/x13/X13Output;", "fullProcess", jts, jspec, jcontext)
+        jrslt <- .jcall("jdplus/x13/base/r/X13",
+                        "Ljdplus/x13/base/core/x13/X13Output;",
+                        "fullProcess", jts, jspec, jcontext)
     }
     if (is.jnull(jrslt)) {
         return(NULL)
@@ -255,44 +293,66 @@ x11 <- function(ts, spec = x11_spec(), userdefined = NULL) {
 #' Refresh a specification with constraints
 #'
 #' @description
-#' Function allowing to create a new specification by updating a specification used for a previous estimation.
-#' Some selected parameters will be kept fixed (previous estimation results) while others will be freed for re-estimation
-#' in a domain of constraints. See details and examples.
+#' Function allowing to create a new specification by updating a specification
+#' used for a previous estimation. Some selected parameters will be kept fixed
+#' (previous estimation results) while others will be freed for re-estimation in
+#' a domain of constraints. See details and examples.
 #'
 #' @details
-#' The selection of constraints to be kept fixed or re-estimated is called a revision policy.
-#' User-defined parameters are always copied to the new refreshed specifications.
-#' In X-13 only the reg-arima part can be refreshed. X-11 decomposition will be completely re-run,
-#' keeping all the user-defined parameters from the original specification.
+#' The selection of constraints to be kept fixed or re-estimated is called a
+#' revision policy. User-defined parameters are always copied to the new
+#' refreshed specifications. In X-13 only the reg-arima part can be refreshed.
+#' X-11 decomposition will be completely re-run, keeping all the user-defined
+#' parameters from the original specification.
 #'
 #' Available refresh policies are:
 #'
-#' \strong{Current}: applying the current pre-adjustment reg-arima model and handling the new raw data points, or any sub-span of the series as Additive Outliers (defined as new intervention variables)
+#' \strong{Current}: applying the current pre-adjustment reg-arima model and
+#' handling the new raw data points, or any sub-span of the series as Additive
+#' Outliers (defined as new intervention variables)
 #'
-#' \strong{Fixed}: applying the current pre-adjustment reg-arima model and replacing forecasts by new raw data points.
+#' \strong{Fixed}: applying the current pre-adjustment reg-arima model and
+#' replacing forecasts by new raw data points.
 #'
-#' \strong{FixedParameters}: pre-adjustment reg-arima model is partially modified: regression coefficients will be re-estimated but regression variables, Arima orders
-#' and coefficients are unchanged.
+#' \strong{FixedParameters}: pre-adjustment reg-arima model is partially
+#' modified: regression coefficients will be re-estimated but regression
+#' variables, Arima orders and coefficients are unchanged.
 #'
-#' \strong{FixedAutoRegressiveParameters}: same as FixedParameters but Arima Moving Average coefficients (MA) are also re-estimated, Auto-regressive (AR) coefficients are kept fixed.
+#' \strong{FixedAutoRegressiveParameters}: same as FixedParameters but Arima
+#' Moving Average coefficients (MA) are also re-estimated, Auto-regressive (AR)
+#' coefficients are kept fixed.
 #'
-#' \strong{FreeParameters}: all regression and Arima model coefficients are re-estimated, regression variables and Arima orders are kept fixed.
+#' \strong{FreeParameters}: all regression and Arima model coefficients are
+#' re-estimated, regression variables and Arima orders are kept fixed.
 #'
-#' \strong{Outliers}: regression variables and Arima orders are kept fixed, but outliers will be re-detected on the defined span, thus all regression and Arima model coefficients are re-estimated
+#' \strong{Outliers}: regression variables and Arima orders are kept fixed, but
+#' outliers will be re-detected on the defined span, thus all regression and
+#' Arima model coefficients are re-estimated
 #'
-#' \strong{Outliers_StochasticComponent}: same as "Outliers" but Arima model orders (p,d,q)(P,D,Q) can also be re-identified.
+#' \strong{Outliers_StochasticComponent}: same as "Outliers" but Arima model
+#' orders (p,d,q)(P,D,Q) can also be re-identified.
 #'
 #' @param spec the current specification to be refreshed (`"result_spec"`).
-#' @param refspec the reference specification used to define the domain considered for re-estimation (`"domain_spec"`).
+#' @param refspec the reference specification used to define the domain
+#' considered for re-estimation (`"domain_spec"`).
 #' By default this is the `"RG5c"` or `"RSA5"` specification.
 #' @param policy the refresh policy to apply (see details).
-#' @param period,start,end  additional parameters used to specify the span on which additive outliers (AO) are introduced when `policy = "Current"`
-#' or to specify the span on which outliers will be re-detected when `policy = "Outliers"` or `policy = "Outliers_StochasticComponent"`,
-#' is this case \code{end} is unused.
-#' If \code{start} is not specified, outliers will be re-identified on the whole series.
-#' Span definition: \code{period}: numeric, number of observations in a year (12, 4...).
-#' \code{start} and \code{end}: defined as arrays of two elements: year and first period (for example, `period = 12` and `c(1980, 1)` stands for January 1980)
-#' The dates corresponding \code{start} and \code{end} are included in the span definition.
+#'
+#' @param period,start,end  additional parameters used to specify the span on
+#' which additive outliers (AO) are introduced when `policy = "Current"` or to
+#' specify the span on which outliers will be re-detected when
+#' `policy = "Outliers"` or `policy = "Outliers_StochasticComponent"`, is this
+#' case \code{end} is unused.
+#' If \code{start} is not specified, outliers will be re-identified on the whole
+#' series.
+#' Span definition: \code{period}: numeric, number of observations in a year
+#' (12, 4...).
+#' \code{start} and \code{end}: defined as arrays of two elements: year and
+#' first period (for example, `period = 12` and `c(1980, 1)` stands for January
+#' 1980)
+#' The dates corresponding \code{start} and \code{end} are included in the span
+#' definition.
+#'
 #' @return a new specification, an object of class `"JD3_X13_SPEC"` or
 #' `"JD3_REGARIMA_SPEC"`.
 #'
@@ -346,14 +406,24 @@ x11 <- function(ts, spec = x11_spec(), userdefined = NULL) {
 #' @name refresh
 #' @rdname refresh
 #' @export
-regarima_refresh <- function(spec, refspec = NULL, policy = c("FreeParameters", "Complete", "Outliers_StochasticComponent", "Outliers", "FixedParameters", "FixedAutoRegressiveParameters", "Fixed", "Current"), period = 0, start = NULL, end = NULL) {
+regarima_refresh <- function(spec,
+                             refspec = NULL,
+                             policy = c("FreeParameters", "Complete",
+                                        "Outliers_StochasticComponent",
+                                        "Outliers", "FixedParameters",
+                                        "FixedAutoRegressiveParameters",
+                                        "Fixed", "Current"),
+                             period = 0,
+                             start = NULL,
+                             end = NULL) {
     policy <- match.arg(policy)
     if (!inherits(spec, "JD3_REGARIMA_SPEC")) {
         stop("Invalid specification type")
     }
     jspec <- .r2jd_spec_regarima(spec)
     if (is.null(refspec)) {
-        jrefspec <- .jcall("jdplus/x13/base/api/regarima/RegArimaSpec", "Ljdplus/x13/base/api/regarima/RegArimaSpec;", "fromString", "rg4")
+        jrefspec <- .jcall("jdplus/x13/base/api/regarima/RegArimaSpec",
+                           "Ljdplus/x13/base/api/regarima/RegArimaSpec;", "fromString", "rg4")
     } else {
         if (!inherits(refspec, "JD3_REGARIMA_SPEC")) {
             stop("Invalid specification type")
@@ -368,19 +438,24 @@ regarima_refresh <- function(spec, refspec = NULL, policy = c("FreeParameters",
     } else {
         jdom <- jdom <- rjd3toolkit::.jdomain(0, NULL, NULL)
     }
-    jnspec <- .jcall("jdplus/x13/base/r/RegArima", "Ljdplus/x13/base/api/regarima/RegArimaSpec;", "refreshSpec", jspec, jrefspec, jdom, policy)
+    jnspec <- .jcall("jdplus/x13/base/r/RegArima",
+                     "Ljdplus/x13/base/api/regarima/RegArimaSpec;",
+                     "refreshSpec", jspec, jrefspec, jdom, policy)
     return(.jd2r_spec_regarima(jnspec))
 }
 
 #' @rdname refresh
 #' @export
-x13_refresh <- function(spec, refspec = NULL, policy = c(
-                            "FreeParameters", "Complete",
-                            "Outliers_StochasticComponent", "Outliers",
-                            "FixedParameters",
-                            "FixedAutoRegressiveParameters", "Fixed",
-                            "Current"
-                        ), period = 0, start = NULL, end = NULL) {
+x13_refresh <- function(spec,
+                        refspec = NULL,
+                        policy = c("FreeParameters", "Complete",
+                                   "Outliers_StochasticComponent", "Outliers",
+                                   "FixedParameters",
+                                   "FixedAutoRegressiveParameters", "Fixed",
+                                   "Current"),
+                        period = 0,
+                        start = NULL,
+                        end = NULL) {
     policy <- match.arg(policy)
     if (!inherits(spec, "JD3_X13_SPEC")) {
         stop("Invalid specification type")
diff --git a/R/x13_spec.R b/R/x13_spec.R
index 01fe88e..c631e09 100644
--- a/R/x13_spec.R
+++ b/R/x13_spec.R
@@ -2,17 +2,22 @@
 NULL
 
 
-#' RegARIMA/X-13 Default Specifications
+#' @title RegARIMA/X-13 Default Specifications
 #'
-#' Set of functions to create default specification objects associated with the X-13ARIMA seasonal adjustment method.
+#' @description
+#' Set of functions to create default specification objects associated with the
+#' X-13ARIMA seasonal adjustment method.
 #'
-#' Specification setting of sheer X-11 decomposition method (without reg-arima pre-adjustment) is supported by the `x11_spec()` function only
-#' and doesn't appear among the possible X13-Arima default specifications
+#' Specification setting of sheer X-11 decomposition method (without reg-arima
+#' pre-adjustment) is supported by the `x11_spec()` function only and doesn't
+#' appear among the possible X13-Arima default specifications.
 #'
-#' Specification setting can be restricted to the reg-arima part with the `regarima_spec()` function,
-#' without argument `regarima_spec()` yields a RG5c specification
+#' Specification setting can be restricted to the reg-arima part with the
+#' `regarima_spec()` function, without argument `regarima_spec()` yields a RG5c
+#' specification.
 #'
-#' When setting a complete X13-Arima spec, `x13_spec()` without argument  yields a RSA5c specification
+#' When setting a complete X13-Arima spec, `x13_spec()` without argument yields
+#' a RSA5c specification.
 #'
 #'
 #' @param name the name of a predefined specification.
@@ -39,7 +44,14 @@ NULL
 #' RSA5c/RG5c |\tab automatic |\tab AO/LS/TC |\tab 7 td vars + Easter |\tab automatic
 #' }
 #' @seealso
-#' - To set the pre-processing parameters: [rjd3toolkit::set_arima()], [rjd3toolkit::set_automodel()], [rjd3toolkit::set_basic()], [rjd3toolkit::set_easter()], [rjd3toolkit::set_estimate()], [rjd3toolkit::set_outlier()], [rjd3toolkit::set_tradingdays()], [rjd3toolkit::set_transform()], [rjd3toolkit::add_outlier()], [rjd3toolkit::remove_outlier()], [rjd3toolkit::add_ramp()], [rjd3toolkit::remove_ramp()], [rjd3toolkit::add_usrdefvar()].
+#' - To set the pre-processing parameters:
+#' [rjd3toolkit::set_arima()], [rjd3toolkit::set_automodel()],
+#' [rjd3toolkit::set_basic()], [rjd3toolkit::set_easter()],
+#' [rjd3toolkit::set_estimate()], [rjd3toolkit::set_outlier()],
+#' [rjd3toolkit::set_tradingdays()], [rjd3toolkit::set_transform()],
+#' [rjd3toolkit::add_outlier()], [rjd3toolkit::remove_outlier()],
+#' [rjd3toolkit::add_ramp()], [rjd3toolkit::remove_ramp()],
+#' [rjd3toolkit::add_usrdefvar()].
 #' - To set the decomposition parameters: [set_x11()].
 #' - To set the benchmarking parameters: [rjd3toolkit::set_benchmarking()].
 #' @name x13_spec
@@ -50,7 +62,9 @@ regarima_spec <- function(name = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"))
     name <- match.arg(name[1],
         choices = c("rg0", "rg1", "rg2c", "rg3", "rg4", "rg5c")
     )
-    return(.jd2r_spec_regarima(.jcall("jdplus/x13/base/api/regarima/RegArimaSpec", "Ljdplus/x13/base/api/regarima/RegArimaSpec;", "fromString", name)))
+    return(.jd2r_spec_regarima(.jcall("jdplus/x13/base/api/regarima/RegArimaSpec",
+                                      "Ljdplus/x13/base/api/regarima/RegArimaSpec;",
+                                      "fromString", name)))
 }
 
 
@@ -61,7 +75,9 @@ x13_spec <- function(name = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"))
     name <- match.arg(name[1],
         choices = c("rsa0", "rsa1", "rsa2c", "rsa3", "rsa4", "rsa5c")
     )
-    return(.jd2r_spec_x13(.jcall("jdplus/x13/base/api/x13/X13Spec", "Ljdplus/x13/base/api/x13/X13Spec;", "fromString", name)))
+    return(.jd2r_spec_x13(.jcall("jdplus/x13/base/api/x13/X13Spec",
+                                 "Ljdplus/x13/base/api/x13/X13Spec;",
+                                 "fromString", name)))
 }
 
 
diff --git a/man/refresh.Rd b/man/refresh.Rd
index 4345a44..45603e2 100644
--- a/man/refresh.Rd
+++ b/man/refresh.Rd
@@ -29,88 +29,111 @@ x13_refresh(
 \arguments{
 \item{spec}{the current specification to be refreshed (\code{"result_spec"}).}
 
-\item{refspec}{the reference specification used to define the domain considered for re-estimation (\code{"domain_spec"}).
+\item{refspec}{the reference specification used to define the domain
+considered for re-estimation (\code{"domain_spec"}).
 By default this is the \code{"RG5c"} or \code{"RSA5"} specification.}
 
 \item{policy}{the refresh policy to apply (see details).}
 
-\item{period, start, end}{additional parameters used to specify the span on which additive outliers (AO) are introduced when \code{policy = "Current"}
-or to specify the span on which outliers will be re-detected when \code{policy = "Outliers"} or \code{policy = "Outliers_StochasticComponent"},
-is this case \code{end} is unused.
-If \code{start} is not specified, outliers will be re-identified on the whole series.
-Span definition: \code{period}: numeric, number of observations in a year (12, 4...).
-\code{start} and \code{end}: defined as arrays of two elements: year and first period (for example, \code{period = 12} and \code{c(1980, 1)} stands for January 1980)
-The dates corresponding \code{start} and \code{end} are included in the span definition.}
+\item{period, start, end}{additional parameters used to specify the span on
+which additive outliers (AO) are introduced when \code{policy = "Current"} or to
+specify the span on which outliers will be re-detected when
+\code{policy = "Outliers"} or \code{policy = "Outliers_StochasticComponent"}, is this
+case \code{end} is unused.
+If \code{start} is not specified, outliers will be re-identified on the whole
+series.
+Span definition: \code{period}: numeric, number of observations in a year
+(12, 4...).
+\code{start} and \code{end}: defined as arrays of two elements: year and
+first period (for example, \code{period = 12} and \code{c(1980, 1)} stands for January
+1980)
+The dates corresponding \code{start} and \code{end} are included in the span
+definition.}
 }
 \value{
 a new specification, an object of class \code{"JD3_X13_SPEC"} or
 \code{"JD3_REGARIMA_SPEC"}.
 }
 \description{
-Function allowing to create a new specification by updating a specification used for a previous estimation.
-Some selected parameters will be kept fixed (previous estimation results) while others will be freed for re-estimation
-in a domain of constraints. See details and examples.
+Function allowing to create a new specification by updating a specification
+used for a previous estimation. Some selected parameters will be kept fixed
+(previous estimation results) while others will be freed for re-estimation in
+a domain of constraints. See details and examples.
 }
 \details{
-The selection of constraints to be kept fixed or re-estimated is called a revision policy.
-User-defined parameters are always copied to the new refreshed specifications.
-In X-13 only the reg-arima part can be refreshed. X-11 decomposition will be completely re-run,
-keeping all the user-defined parameters from the original specification.
+The selection of constraints to be kept fixed or re-estimated is called a
+revision policy. User-defined parameters are always copied to the new
+refreshed specifications. In X-13 only the reg-arima part can be refreshed.
+X-11 decomposition will be completely re-run, keeping all the user-defined
+parameters from the original specification.
 
 Available refresh policies are:
 
-\strong{Current}: applying the current pre-adjustment reg-arima model and handling the new raw data points, or any sub-span of the series as Additive Outliers (defined as new intervention variables)
+\strong{Current}: applying the current pre-adjustment reg-arima model and
+handling the new raw data points, or any sub-span of the series as Additive
+Outliers (defined as new intervention variables)
 
-\strong{Fixed}: applying the current pre-adjustment reg-arima model and replacing forecasts by new raw data points.
+\strong{Fixed}: applying the current pre-adjustment reg-arima model and
+replacing forecasts by new raw data points.
 
-\strong{FixedParameters}: pre-adjustment reg-arima model is partially modified: regression coefficients will be re-estimated but regression variables, Arima orders
-and coefficients are unchanged.
+\strong{FixedParameters}: pre-adjustment reg-arima model is partially
+modified: regression coefficients will be re-estimated but regression
+variables, Arima orders and coefficients are unchanged.
 
-\strong{FixedAutoRegressiveParameters}: same as FixedParameters but Arima Moving Average coefficients (MA) are also re-estimated, Auto-regressive (AR) coefficients are kept fixed.
+\strong{FixedAutoRegressiveParameters}: same as FixedParameters but Arima
+Moving Average coefficients (MA) are also re-estimated, Auto-regressive (AR)
+coefficients are kept fixed.
 
-\strong{FreeParameters}: all regression and Arima model coefficients are re-estimated, regression variables and Arima orders are kept fixed.
+\strong{FreeParameters}: all regression and Arima model coefficients are
+re-estimated, regression variables and Arima orders are kept fixed.
 
-\strong{Outliers}: regression variables and Arima orders are kept fixed, but outliers will be re-detected on the defined span, thus all regression and Arima model coefficients are re-estimated
+\strong{Outliers}: regression variables and Arima orders are kept fixed, but
+outliers will be re-detected on the defined span, thus all regression and
+Arima model coefficients are re-estimated
 
-\strong{Outliers_StochasticComponent}: same as "Outliers" but Arima model orders (p,d,q)(P,D,Q) can also be re-identified.
+\strong{Outliers_StochasticComponent}: same as "Outliers" but Arima model
+orders (p,d,q)(P,D,Q) can also be re-identified.
 }
 \examples{
-y<- rjd3toolkit::ABS$X0.2.08.10.M
+y <- rjd3toolkit::ABS$X0.2.08.10.M
 # raw series for first estimation
-y_raw <-window(y,end = c(2016,12))
+y_raw <- window(y, end = c(2016, 12))
 # raw series for second (refreshed) estimation
-y_new <-window(y,end = c(2017,6))
+y_new <- window(y, end = c(2017, 6))
 # specification for first estimation
-spec_x13_1<-x13_spec("rsa5c")
+spec_x13_1 <- x13_spec("rsa5c")
 # first estimation
-sa_x13<- x13(y_raw, spec_x13_1)
+sa_x13 <- x13(y_raw, spec_x13_1)
 # refreshing the specification
 current_result_spec <- sa_x13$result_spec
 current_domain_spec <- sa_x13$estimation_spec
 # policy = "Fixed"
 spec_x13_ref <- x13_refresh(current_result_spec, # point spec to be refreshed
-  current_domain_spec, #domain spec (set of constraints)
-  policy = "Fixed")
+    current_domain_spec, # domain spec (set of constraints)
+    policy = "Fixed"
+)
 # 2nd estimation with refreshed specification
 sa_x13_ref <- x13(y_new, spec_x13_ref)
 # policy = "Outliers"
 spec_x13_ref <- x13_refresh(current_result_spec,
-  current_domain_spec,
-  policy = "Outliers",
-  period=12,
-  start=c(2017,1)) # outliers will be re-detected from January 2017 included
+    current_domain_spec,
+    policy = "Outliers",
+    period = 12,
+    start = c(2017, 1)
+) # outliers will be re-detected from January 2017 included
 # 2nd estimation with refreshed specification
 sa_x13_ref <- x13(y_new, spec_x13_ref)
 
 # policy = "Current"
 spec_x13_ref <- x13_refresh(current_result_spec,
-  current_domain_spec,
-  policy = "Current",
-  period=12,
-  start=c(2017,1),
-  end=end(y_new))
-  # points from January 2017 (included) until the end of the series will be treated
-  # as Additive Outliers, the previous reg-Arima model being otherwise kept fixed
+    current_domain_spec,
+    policy = "Current",
+    period = 12,
+    start = c(2017, 1),
+    end = end(y_new)
+)
+# points from January 2017 (included) until the end of the series will be treated
+# as Additive Outliers, the previous reg-Arima model being otherwise kept fixed
 # 2nd estimation with refreshed specification
 sa_x13_ref <- x13(y_new, spec_x13_ref)
 
diff --git a/man/regarima.Rd b/man/regarima.Rd
index 94f346c..f1f1fdb 100644
--- a/man/regarima.Rd
+++ b/man/regarima.Rd
@@ -22,32 +22,39 @@ regarima_fast(
 \arguments{
 \item{ts}{an univariate time series.}
 
-\item{spec}{the model specification. Can be either the name of a predefined specification or a user-defined specification.}
+\item{spec}{the model specification. Can be either the name of a predefined
+specification or a user-defined specification.}
 
-\item{context}{list of external regressors (calendar or other) to be used for estimation}
+\item{context}{list of external regressors (calendar or other) to be used for
+estimation}
 
-\item{userdefined}{a vector containing additional output variables (see \code{\link[=x13_dictionary]{x13_dictionary()}}).}
+\item{userdefined}{a vector containing additional output variables
+(see \code{\link[=x13_dictionary]{x13_dictionary()}}).}
 }
 \value{
-the \code{regarima()} function returns a list with the results (\code{"JD3_REGARIMA_RSLTS"} object), the estimation specification and the result specification, while \code{regarima_fast()} is a faster function that only returns the results.
+the \code{regarima()} function returns a list with the results
+(\code{"JD3_REGARIMA_RSLTS"} object), the estimation specification and the result
+specification, while \code{regarima_fast()} is a faster function that only returns
+the results.
 }
 \description{
 RegARIMA model, pre-adjustment in X13
 }
 \examples{
-y = rjd3toolkit::ABS$X0.2.09.10.M
-sp = regarima_spec("rg5c")
-sp = rjd3toolkit::add_outlier(sp,
-                 type = c("AO"), c("2015-01-01", "2010-01-01"))
+y <- rjd3toolkit::ABS$X0.2.09.10.M
+sp <- regarima_spec("rg5c")
+sp <- rjd3toolkit::add_outlier(sp,
+    type = c("AO"), c("2015-01-01", "2010-01-01")
+)
 regarima_fast(y, spec = sp)
-sp = rjd3toolkit::set_transform(
-   rjd3toolkit::set_tradingdays(
-     rjd3toolkit::set_easter(sp, enabled = FALSE),
-    option = "workingdays"
-  ),
-  fun = "None"
+sp <- rjd3toolkit::set_transform(
+    rjd3toolkit::set_tradingdays(
+        rjd3toolkit::set_easter(sp, enabled = FALSE),
+        option = "workingdays"
+    ),
+    fun = "None"
 )
 regarima_fast(y, spec = sp)
-sp =  rjd3toolkit::set_outlier(sp, outliers.type = c("AO"))
+sp <- rjd3toolkit::set_outlier(sp, outliers.type = c("AO"))
 regarima_fast(y, spec = sp)
 }
diff --git a/man/regarima_outliers.Rd b/man/regarima_outliers.Rd
index d0df004..ed972bd 100644
--- a/man/regarima_outliers.Rd
+++ b/man/regarima_outliers.Rd
@@ -30,13 +30,15 @@ regarima_outliers(
 
 \item{X.td}{calendar regressors.}
 
-\item{ao, ls, so, tc}{Boolean to indicate which type of outliers should be detected.}
+\item{ao, ls, so, tc}{Boolean to indicate which type of outliers should be
+detected.}
 
-\item{cv}{\code{numeric}. The entered critical value for the outlier detection procedure.
-If equal to 0 the critical value for the outlier detection procedure is automatically determined
-by the number of observations.}
+\item{cv}{\code{numeric}. The entered critical value for the outlier detection
+procedure. If equal to 0 the critical value for the outlier detection
+procedure is automatically determined by the number of observations.}
 
-\item{clean}{Clean missing values at the beginning/end of the series. Regression variables are automatically resized, if need be.}
+\item{clean}{Clean missing values at the beginning/end of the series.
+Regression variables are automatically resized, if need be.}
 }
 \value{
 a \code{"JD3_REGARIMA_OUTLIERS"} object, containing input variables and results
diff --git a/man/userdefined_variables_x13.Rd b/man/userdefined_variables_x13.Rd
index 69c669e..f8b3c41 100644
--- a/man/userdefined_variables_x13.Rd
+++ b/man/userdefined_variables_x13.Rd
@@ -7,22 +7,26 @@
 userdefined_variables_x13(x = c("X-13", "RegArima", "X-11"))
 }
 \arguments{
-\item{x}{a character to indicate the estimation function for which the output items list will be displayed.}
+\item{x}{a character to indicate the estimation function for which the output
+items list will be displayed.}
 }
 \value{
-a vector containing the names of all the available output objects (series, diagnostics, parameters)
+a vector containing the names of all the available output objects
+(series, diagnostics, parameters)
 }
 \description{
-Function generating a comprehensive list of available output variables (series, parameters, diagnostics) from the estimation process
-by the  \code{x13()}, \code{regarima()} and \code{x11()} functions.
-Some items are available in the default estimation output but the remainder can be added
-using the \code{userdefined} parameter.
-User-defined objects can the be retrieved from the list of lists generated by the estimation process
+Function generating a comprehensive list of available output variables
+(series, parameters, diagnostics) from the estimation process by the
+\code{x13()}, \code{regarima()} and \code{x11()} functions. Some items are available in the
+default estimation output but the remainder can be added using the
+\code{userdefined} parameter. User-defined objects can the be retrieved from the
+list of lists generated by the estimation process
 }
 \examples{
 userdefined_variables_x13("x13")
 userdefined_variables_x13("regarima")
 userdefined_variables_x13("x11")
+
 }
 \references{
 More information and examples related to 'JDemetra+' features in the online documentation:
diff --git a/man/x11.Rd b/man/x11.Rd
index 294e7d5..8dc20fe 100644
--- a/man/x11.Rd
+++ b/man/x11.Rd
@@ -11,7 +11,8 @@ x11(ts, spec = x11_spec(), userdefined = NULL)
 
 \item{spec}{the specification.}
 
-\item{userdefined}{a vector containing additional output variables (see \code{\link[=x13_dictionary]{x13_dictionary()}}).}
+\item{userdefined}{a vector containing additional output variables
+(see \code{\link[=x13_dictionary]{x13_dictionary()}}).}
 }
 \description{
 X-11 Decomposition Algorithm
diff --git a/man/x11_spec.Rd b/man/x11_spec.Rd
index 94f170d..b5a5929 100644
--- a/man/x11_spec.Rd
+++ b/man/x11_spec.Rd
@@ -22,46 +22,68 @@ set_x11(
 )
 }
 \arguments{
-\item{x}{the specification to be modified, object of class "JD3_X11_SPEC", default X11 spec can be obtained as 'x=x11_spec()'}
+\item{x}{the specification to be modified, object of class "JD3_X11_SPEC",
+default X11 spec can be obtained as 'x=x11_spec()'}
 
-\item{mode}{character: the decomposition mode. Determines the mode of the seasonal adjustment decomposition to be performed:
-\code{"Undefined"} - no assumption concerning the relationship between the time series components is made;
+\item{mode}{character: the decomposition mode. Determines the mode of the
+seasonal adjustment decomposition to be performed:
+\code{"Undefined"} - no assumption concerning the relationship between the time
+series components is made;
 \code{"Additive"} - assumes an additive relationship;
 \code{"Multiplicative"} - assumes a multiplicative relationship;
-\code{"LogAdditive"} - performs an additive decomposition of the logarithms of the series being adjusted;
-\code{"PseudoAdditive"} - assumes an pseudo-additive relationship. Could be changed by the program, if needed.}
+\code{"LogAdditive"} - performs an additive decomposition of the logarithms of the
+series being adjusted;
+\code{"PseudoAdditive"} - assumes an pseudo-additive relationship. Could be
+changed by the program, if needed.}
 
-\item{seasonal.comp}{logical: if \code{TRUE}, the program computes a seasonal component. Otherwise, the seasonal component
-is not estimated and its values are all set to 0 (additive decomposition) or 1 (multiplicative decomposition).}
+\item{seasonal.comp}{logical: if \code{TRUE}, the program computes a seasonal
+component. Otherwise, the seasonal component is not estimated and its values
+are all set to 0 (additive decomposition) or 1 (multiplicative
+decomposition).}
 
-\item{seasonal.filter}{a vector of character(s) specifying which seasonal moving average (i.e. seasonal filter)
-will be used to estimate the seasonal factors for the entire series. The vector can be of length:
-1 - the same seasonal filter is used for all periods (e.g.: \code{seasonal.filter = "Msr"} or \code{seasonal.filter = "S3X3"} );
-or have a different value for each quarter (length 4) or each month (length 12) - (e.g. for quarterly series: \code{seasonal.filter = c("S3X3", "Msr", "S3X3", "Msr")}).
-Possible filters are: \code{"Msr"}, \code{"Stable"}, \code{"X11Default"}, \code{"S3X1"}, \code{"S3X3"}, \code{"S3X5"}, \code{"S3X9"}, \code{"S3X15"}.
-\code{"Msr"} - the program chooses the final seasonal filter automatically.}
+\item{seasonal.filter}{a vector of character(s) specifying which seasonal
+moving average (i.e. seasonal filter) will be used to estimate the seasonal
+factors for the entire series. The vector can be of length: 1 - the same
+seasonal filter is used for all periods (e.g.: \code{seasonal.filter = "Msr"} or
+\code{seasonal.filter = "S3X3"} ); or have a different value for each quarter
+(length 4) or each month (length 12) - (e.g. for quarterly series:
+\code{seasonal.filter = c("S3X3", "Msr", "S3X3", "Msr")}). Possible filters are:
+\code{"Msr"}, \code{"Stable"}, \code{"X11Default"}, \code{"S3X1"}, \code{"S3X3"}, \code{"S3X5"}, \code{"S3X9"},
+\code{"S3X15"}. \code{"Msr"} - the program chooses the final seasonal filter
+automatically.}
 
-\item{henderson.filter}{numeric: the length of the Henderson filter (odd number between 3 and 101). If \code{henderson.filter = 0} an automatic selection of the Henderson filter's length
-for the trend estimation is enabled.}
+\item{henderson.filter}{numeric: the length of the Henderson filter (odd
+number between 3 and 101). If \code{henderson.filter = 0} an automatic selection
+of the Henderson filter's length for the trend estimation is enabled.}
 
-\item{lsigma}{numeric: the lower sigma boundary for the detection of extreme values, > 0.5, default=1.5.}
+\item{lsigma}{numeric: the lower sigma boundary for the detection of extreme
+values, > 0.5, default=1.5.}
 
-\item{usigma}{numeric: the upper sigma boundary for the detection of extreme values, > lsigma, default=2.5.}
+\item{usigma}{numeric: the upper sigma boundary for the detection of extreme
+values, > lsigma, default=2.5.}
 
-\item{bcasts, fcasts}{numeric: the number of backasts (\code{bcasts}) or forecasts (\code{fcasts}) generated by the RegARIMA model in periods (positive values) or years (negative values).Default values: fcasts=-1 and bcasts=0.}
+\item{bcasts, fcasts}{numeric: the number of backasts (\code{bcasts}) or forecasts
+(\code{fcasts}) generated by the RegARIMA model in periods (positive values) or
+years (negative values).Default values: fcasts=-1 and bcasts=0.}
 
-\item{calendar.sigma}{character to specify if the standard errors used for extreme values detection and adjustment are computed:
-from 5 year spans of irregulars (\code{"None"}, default value);
-separately for each calendar period (\code{"All"});
-separately for each period only if Cochran's hypothesis test determines that the irregular component is heteroskedastic
-by calendar month/quarter (\code{"Signif"});
-separately for two complementary sets of calendar months/quarters specified by the x11.sigmaVector parameter (\code{"Select"},
-see parameter \code{sigma.vector}).}
+\item{calendar.sigma}{character to specify if the standard errors used for
+extreme values detection and adjustment are computed: from 5 year spans of
+irregulars (\code{"None"}, default value); separately for each calendar period
+(\code{"All"}); separately for each period only if Cochran's hypothesis test
+determines that the irregular component is heteroskedastic by calendar
+month/quarter (\code{"Signif"}); separately for two complementary sets of calendar
+months/quarters specified by the x11.sigmaVector parameter (\code{"Select"}, see
+parameter \code{sigma.vector}).}
 
-\item{sigma.vector}{a vector to specify one of the two groups of periods for which standard errors used for extreme values
-detection and adjustment will be computed separately. Only used if \code{calendar.sigma = "Select"}. Possible values are: \code{1} or \code{2}.}
+\item{sigma.vector}{a vector to specify one of the two groups of periods for
+which standard errors used for extreme values detection and adjustment will
+be computed separately. Only used if \code{calendar.sigma = "Select"}. Possible
+values are: \code{1} or \code{2}.}
 
-\item{exclude.forecast}{Boolean to exclude forecasts and backcasts. If \code{TRUE}, the RegARIMA model forecasts and backcasts are not used during the detection of extreme values in the seasonal adjustment routines.Default= FALSE.}
+\item{exclude.forecast}{Boolean to exclude forecasts and backcasts. If
+\code{TRUE}, the RegARIMA model forecasts and backcasts are not used during the
+detection of extreme values in the seasonal adjustment routines.
+Default = FALSE.}
 
 \item{bias}{TODO.}
 }
@@ -74,18 +96,19 @@ Set X-11 Specification
 \examples{
 init_spec <- x11_spec()
 new_spec <- set_x11(init_spec,
-                   mode = "LogAdditive",
-                   seasonal.comp = 1,
-                   seasonal.filter = "S3X9",
-                   henderson.filter = 7,
-                   lsigma = 1.7,
-                   usigma = 2.7,
-                   fcasts = -1,
-                   bcasts = -1,
-                   calendar.sigma ="All",
-                   sigma.vector = NA,
-                   exclude.forecast = FALSE,
-                   bias = "LEGACY")
+    mode = "LogAdditive",
+    seasonal.comp = 1,
+    seasonal.filter = "S3X9",
+    henderson.filter = 7,
+    lsigma = 1.7,
+    usigma = 2.7,
+    fcasts = -1,
+    bcasts = -1,
+    calendar.sigma = "All",
+    sigma.vector = NA,
+    exclude.forecast = FALSE,
+    bias = "LEGACY"
+)
 }
 \seealso{
 \code{\link[=x13_spec]{x13_spec()}} and \code{\link[=x11_spec]{x11_spec()}}.
diff --git a/man/x13.Rd b/man/x13.Rd
index 9379c0a..7d45eb6 100644
--- a/man/x13.Rd
+++ b/man/x13.Rd
@@ -30,42 +30,51 @@ x13_fast(
 \arguments{
 \item{ts}{an univariate time series.}
 
-\item{spec}{the model specification. Can be either the name of a predefined specification or a user-defined specification.}
+\item{spec}{the model specification. Can be either the name of a predefined
+specification or a user-defined specification.}
 
-\item{context}{list of external regressors (calendar or other) to be used for estimation}
+\item{context}{list of external regressors (calendar or other) to be used for
+estimation}
 
-\item{userdefined}{a vector containing additional output variables (see \code{\link[=x13_dictionary]{x13_dictionary()}}).}
+\item{userdefined}{a vector containing additional output variables
+(see \code{\link[=x13_dictionary]{x13_dictionary()}}).}
 }
 \value{
-the \code{x13()} function returns a list with the results, the estimation specification and the result specification, while \code{x13_fast()} is a faster function that only returns the results.
-The \code{.jx13()} functions only returns results in a java object which will allow to customize outputs in other packages (use \code{\link[rjd3toolkit:dictionary]{rjd3toolkit::dictionary()}} to
-get the list of variables and \code{\link[rjd3toolkit:dictionary]{rjd3toolkit::result()}} to get a specific variable).
-In the estimation functions \code{x13()} and \code{x13_fast()} you can directly use a specification name (string).
-If you want to customize a specification you have to create a specification object first.
+the \code{x13()} function returns a list with the results, the estimation
+specification and the result specification, while \code{x13_fast()} is a faster
+function that only returns the results. The \code{.jx13()} functions only returns
+results in a java object which will allow to customize outputs in other
+packages (use \code{\link[rjd3toolkit:dictionary]{rjd3toolkit::dictionary()}} to get the list of variables and
+\code{\link[rjd3toolkit:dictionary]{rjd3toolkit::result()}} to get a specific variable). In the estimation
+functions \code{x13()} and \code{x13_fast()} you can directly use a specification name
+(string). If you want to customize a specification you have to create a
+specification object first.
 }
 \description{
 Seasonal Adjustment with  X13-ARIMA
 }
 \examples{
-y = rjd3toolkit::ABS$X0.2.09.10.M
-x13_fast(y,"rsa3")
-x13(y,"rsa5c")
-regarima_fast(y,"rg0")
-regarima(y,"rg3")
+y <- rjd3toolkit::ABS$X0.2.09.10.M
+x13_fast(y, "rsa3")
+x13(y, "rsa5c")
+regarima_fast(y, "rg0")
+regarima(y, "rg3")
 
-sp = x13_spec("rsa5c")
-sp = rjd3toolkit::add_outlier(sp,
-                 type = c("AO"), c("2015-01-01", "2010-01-01"))
-sp =  rjd3toolkit::set_transform(
-   rjd3toolkit::set_tradingdays(
-     rjd3toolkit::set_easter(sp, enabled = FALSE),
-    option = "workingdays"
-  ),
-  fun = "None"
+sp <- x13_spec("rsa5c")
+sp <- rjd3toolkit::add_outlier(sp,
+    type = c("AO"), c("2015-01-01", "2010-01-01")
+)
+sp <- rjd3toolkit::set_transform(
+    rjd3toolkit::set_tradingdays(
+        rjd3toolkit::set_easter(sp, enabled = FALSE),
+        option = "workingdays"
+    ),
+    fun = "None"
+)
+x13(y, spec = sp)
+sp <- set_x11(sp,
+    henderson.filter = 13
 )
-x13(y,spec=sp)
-sp = set_x11(sp,
-             henderson.filter = 13)
 x13_fast(y, spec = sp)
 
 }
diff --git a/man/x13_revisions.Rd b/man/x13_revisions.Rd
index 63ae810..9319031 100644
--- a/man/x13_revisions.Rd
+++ b/man/x13_revisions.Rd
@@ -48,19 +48,22 @@ Compute revisions history
 s <- rjd3toolkit::ABS$X0.2.09.10.M
 sa_mod <- x13(s)
 data_ids <- list(
-  # Get the coefficient of the trading-day coefficient from 2005-jan
-  list(start = "2005-01-01", id = "regression.td(1)"),
-  # Get the ljung-box statistics on residuals from 2010-jan
-  list(start = "2010-01-01", id = "residuals.lb"))
+    # Get the coefficient of the trading-day coefficient from 2005-jan
+    list(start = "2005-01-01", id = "regression.td(1)"),
+    # Get the ljung-box statistics on residuals from 2010-jan
+    list(start = "2010-01-01", id = "residuals.lb")
+)
 ts_ids <- list(
-  # Get the SA component estimates of 2010-jan from 2010-jan
-  list(period = "2010-01-01", start = "2010-01-01", id = "sa"),
-  # Get the irregular component estimates of 2010-jan from 2015-jan
-  list(period = "2010-01-01", start = "2015-01-01", id = "i"))
+    # Get the SA component estimates of 2010-jan from 2010-jan
+    list(period = "2010-01-01", start = "2010-01-01", id = "sa"),
+    # Get the irregular component estimates of 2010-jan from 2015-jan
+    list(period = "2010-01-01", start = "2015-01-01", id = "i")
+)
 cmp_ids <- list(
-  # Get the SA component estimates (full time series) 2010-jan to 2020-jan
-  list(start = "2010-01-01", end = "2020-01-01", id = "sa"),
-  # Get the trend component estimates (full time series)  2010-jan to 2020-jan
-  list(start = "2010-01-01", end = "2020-01-01", id = "t"))
+    # Get the SA component estimates (full time series) 2010-jan to 2020-jan
+    list(start = "2010-01-01", end = "2020-01-01", id = "sa"),
+    # Get the trend component estimates (full time series)  2010-jan to 2020-jan
+    list(start = "2010-01-01", end = "2020-01-01", id = "t")
+)
 rh <- x13_revisions(s, sa_mod$result_spec, data_ids, ts_ids, cmp_ids)
 }
diff --git a/man/x13_spec.Rd b/man/x13_spec.Rd
index b4fa5ee..a53de71 100644
--- a/man/x13_spec.Rd
+++ b/man/x13_spec.Rd
@@ -21,17 +21,21 @@ an object of class \code{"JD3_X13_SPEC"} (\code{x13_spec()}),
 \code{"JD3_X11_SPEC"} (\code{x11_spec()}).
 }
 \description{
-Set of functions to create default specification objects associated with the X-13ARIMA seasonal adjustment method.
-}
-\details{
-Specification setting of sheer X-11 decomposition method (without reg-arima pre-adjustment) is supported by the \code{x11_spec()} function only
-and doesn't appear among the possible X13-Arima default specifications
+Set of functions to create default specification objects associated with the
+X-13ARIMA seasonal adjustment method.
 
-Specification setting can be restricted to the reg-arima part with the \code{regarima_spec()} function,
-without argument \code{regarima_spec()} yields a RG5c specification
+Specification setting of sheer X-11 decomposition method (without reg-arima
+pre-adjustment) is supported by the \code{x11_spec()} function only and doesn't
+appear among the possible X13-Arima default specifications.
 
-When setting a complete X13-Arima spec, \code{x13_spec()} without argument  yields a RSA5c specification
+Specification setting can be restricted to the reg-arima part with the
+\code{regarima_spec()} function, without argument \code{regarima_spec()} yields a RG5c
+specification.
 
+When setting a complete X13-Arima spec, \code{x13_spec()} without argument yields
+a RSA5c specification.
+}
+\details{
 The available predefined 'JDemetra+' model specifications are described in the table below:
 
 \tabular{rrrrrrr}{
@@ -52,7 +56,14 @@ init_spec <- x13_spec("rsa5c")
 }
 \seealso{
 \itemize{
-\item To set the pre-processing parameters: \code{\link[rjd3toolkit:set_arima]{rjd3toolkit::set_arima()}}, \code{\link[rjd3toolkit:set_automodel]{rjd3toolkit::set_automodel()}}, \code{\link[rjd3toolkit:set_basic]{rjd3toolkit::set_basic()}}, \code{\link[rjd3toolkit:set_easter]{rjd3toolkit::set_easter()}}, \code{\link[rjd3toolkit:set_estimate]{rjd3toolkit::set_estimate()}}, \code{\link[rjd3toolkit:set_outlier]{rjd3toolkit::set_outlier()}}, \code{\link[rjd3toolkit:set_tradingdays]{rjd3toolkit::set_tradingdays()}}, \code{\link[rjd3toolkit:set_transform]{rjd3toolkit::set_transform()}}, \code{\link[rjd3toolkit:add_outlier]{rjd3toolkit::add_outlier()}}, \code{\link[rjd3toolkit:add_outlier]{rjd3toolkit::remove_outlier()}}, \code{\link[rjd3toolkit:add_outlier]{rjd3toolkit::add_ramp()}}, \code{\link[rjd3toolkit:add_outlier]{rjd3toolkit::remove_ramp()}}, \code{\link[rjd3toolkit:add_usrdefvar]{rjd3toolkit::add_usrdefvar()}}.
+\item To set the pre-processing parameters:
+\code{\link[rjd3toolkit:set_arima]{rjd3toolkit::set_arima()}}, \code{\link[rjd3toolkit:set_automodel]{rjd3toolkit::set_automodel()}},
+\code{\link[rjd3toolkit:set_basic]{rjd3toolkit::set_basic()}}, \code{\link[rjd3toolkit:set_easter]{rjd3toolkit::set_easter()}},
+\code{\link[rjd3toolkit:set_estimate]{rjd3toolkit::set_estimate()}}, \code{\link[rjd3toolkit:set_outlier]{rjd3toolkit::set_outlier()}},
+\code{\link[rjd3toolkit:set_tradingdays]{rjd3toolkit::set_tradingdays()}}, \code{\link[rjd3toolkit:set_transform]{rjd3toolkit::set_transform()}},
+\code{\link[rjd3toolkit:add_outlier]{rjd3toolkit::add_outlier()}}, \code{\link[rjd3toolkit:add_outlier]{rjd3toolkit::remove_outlier()}},
+\code{\link[rjd3toolkit:add_outlier]{rjd3toolkit::add_ramp()}}, \code{\link[rjd3toolkit:add_outlier]{rjd3toolkit::remove_ramp()}},
+\code{\link[rjd3toolkit:add_usrdefvar]{rjd3toolkit::add_usrdefvar()}}.
 \item To set the decomposition parameters: \code{\link[=set_x11]{set_x11()}}.
 \item To set the benchmarking parameters: \code{\link[rjd3toolkit:set_benchmarking]{rjd3toolkit::set_benchmarking()}}.
 }