lint indentation

.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"

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)
 }

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))

R/regarima_outliers.R

@@ -8,33 +8,46 @@ 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
 #'
 #' @examples
 #' 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(

R/revisions.R

@@ -79,15 +79,17 @@ 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")
     }
     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")

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(