fixed export statements for functions

NAMESPACE

@@ -1,7 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(solve,a)
-S3method(solve,sigma)
 export(adaptive.bin)
 export(adjust.time)
 export(aggregate_by_rt)
@@ -16,6 +14,9 @@ export(compute_breaks_3)
 export(compute_chromatographic_profile)
 export(compute_clusters)
 export(compute_clusters_simple)
+export(compute_comb)
+export(compute_corrected_features)
+export(compute_curr_rec_with_enough_peaks)
 export(compute_delta_rt)
 export(compute_densities)
 export(compute_dx)
@@ -27,33 +28,43 @@ export(compute_mass_density)
 export(compute_mass_values)
 export(compute_mu_sc_std)
 export(compute_mz_sd)
+export(compute_peaks_and_valleys)
+export(compute_pks_vlys_rt)
+export(compute_rectangle)
 export(compute_rt_intervals_indices)
 export(compute_scale)
+export(compute_sel)
 export(compute_start_bound)
 export(compute_target_times)
 export(compute_template)
+export(compute_template_adjusted_rt)
 export(compute_uniq_grp)
 export(correct_time)
+export(count_peaks)
 export(create_aligned_feature_table)
 export(create_output)
 export(create_rows)
 export(draw_rt_correction_plot)
 export(draw_rt_normal_peaks)
 export(duplicate.row.remove)
+export(fill_missing_values)
 export(filter_based_on_density)
 export(find.turn.point)
 export(find_local_maxima)
 export(find_mz_match)
 export(find_mz_tolerance)
 export(find_optima)
 export(get_custom_rt_tol)
+export(get_features_in_rt_range)
 export(get_mzrange_bound_indices)
 export(get_num_workers)
 export(get_rt_region_indices)
+export(get_single_occurrence_mask)
 export(get_times_to_use)
 export(hybrid)
 export(increment_counter)
 export(interpol.area)
+export(l2normalize)
 export(label_val_to_keep)
 export(load.lcms)
 export(load_aligned_features)
@@ -69,18 +80,21 @@ export(plot_raw_profile_histogram)
 export(plot_rt_profile)
 export(predict_mz_break_indices)
 export(predict_smoothed_rt)
-export(prep.uv)
+export(prep_uv)
 export(preprocess_bandwidth)
 export(preprocess_profile)
 export(prof.to.features)
 export(recover.weaker)
+export(refine_selection)
 export(remove_noise)
 export(rev_cum_sum)
 export(rm.ridge)
 export(run_filter)
 export(select_mz)
 export(select_rt)
 export(semi.sup)
+export(solve_a)
+export(solve_sigma)
 export(sort_data)
 export(span)
 export(two.step.hybrid)

R/adjust.time.R

@@ -2,6 +2,7 @@
 NULL
 #> NULL
 
+#' @export
 compute_comb <- function(template_features, features) {
   combined <- dplyr::bind_rows(
     template_features,
@@ -11,6 +12,7 @@ compute_comb <- function(template_features, features) {
   return(combined)
 }
 
+#' @export
 compute_sel <- function(combined, mz_tol_relative, rt_tol_relative) {
   l <- nrow(combined)
   sel <- which(combined$mz[2:l] - combined$mz[1:(l - 1)] <
@@ -20,6 +22,7 @@ compute_sel <- function(combined, mz_tol_relative, rt_tol_relative) {
   return(sel)
 }
 
+#' @export
 compute_template_adjusted_rt <- function(combined, sel, j) {
   all_features <- cbind(combined$rt[sel], combined$rt[sel + 1])
   flip_indices <- which(combined$sample_id[sel] == j)
@@ -34,6 +37,7 @@ compute_template_adjusted_rt <- function(combined, sel, j) {
   return(all_features)
 }
 
+#' @export
 compute_corrected_features <- function(features, delta_rt, avg_time) {
   features <- features[order(features$rt, features$mz), ]
   corrected <- features$rt
@@ -58,6 +62,7 @@ compute_corrected_features <- function(features, delta_rt, avg_time) {
   return(features)
 }
 
+#' @export
 fill_missing_values <- function(orig.feature, this.feature) {
   missing_values <- which(is.na(this.feature$rt))
   for (i in missing_values) {

R/prof.to.features.R

@@ -102,7 +102,7 @@ compute_gaussian_peak_shape <- function(rt_profile, bw, component_eliminate, BIC
 #' @param sigma.1 left standard deviation of the gaussian curve
 #' @param sigma.2 right standard deviation of the gaussian curve
 #' @export
-solve.a <- function(x, t, a, sigma.1, sigma.2) {
+solve_a <- function(x, t, a, sigma.1, sigma.2) {
   # This function is a part of bigauss.esti.EM and is not covered by any of test-cases
   w <- x * (as.numeric(t < a) / sigma.1 + as.numeric(t >= a) / sigma.2)
   return(sum(t * w) / sum(w))
@@ -115,7 +115,7 @@ solve.a <- function(x, t, a, sigma.1, sigma.2) {
 #' @param t A vector of numerical values (rt).
 #' @param a A vector of peak summits.
 #' @export
-prep.uv <- function(x, t, a) {
+prep_uv <- function(x, t, a) {
   # This function is a part of bigauss.esti.EM and is not covered by any of test-cases
   temp <- (t - a)^2 * x
   u <- sum(temp * as.numeric(t < a))
@@ -138,9 +138,9 @@ prep.uv <- function(x, t, a) {
 #'   \item standard deviation at the right side of the gaussian curve
 #' }
 #' @export
-solve.sigma <- function(x, t, a) {
+solve_sigma <- function(x, t, a) {
   # This function is a part of bigauss.esti.EM and is not covered by any of test-cases
-  tt <- prep.uv(x, t, a)
+  tt <- prep_uv(x, t, a)
   sigma.1 <- tt$u / tt$x.sum * ((tt$v / tt$u)^(1 / 3) + 1)
   sigma.2 <- tt$v / tt$x.sum * ((tt$u / tt$v)^(1 / 3) + 1)
   return(list(
@@ -192,11 +192,11 @@ bigauss.esti.EM <- function(t, x, max.iter = 50, epsilon = 0.005, do.plot = FALS
   while ((change > epsilon) & (n.iter < max.iter)) {
     a.old <- a.new
     n.iter <- n.iter + 1
-    sigma <- solve.sigma(x, t, a.old)
+    sigma <- solve_sigma(x, t, a.old)
     if (n.iter == 1) {
         sigma[is.na(sigma)] <- as.numeric(sigma[which(!is.na(sigma))])[1] / 10
     }
-    a.new <- solve.a(x, t, a.old, sigma$sigma.1, sigma$sigma.2)
+    a.new <- solve_a(x, t, a.old, sigma$sigma.1, sigma$sigma.2)
     change <- abs(a.old - a.new)
   }
   d <- x

R/recover.weaker.R

@@ -58,6 +58,7 @@ compute_delta_rt <- function(times) {
 #' @description x / sum(x)
 #' @param x Data to normalize.
 #' @return Normalized data.
+#' @export
 l2normalize <- function(x) {
   x / sum(x)
 }
@@ -157,6 +158,7 @@ compute_target_times <- function(aligned_rts,
 #' checks which values only occur a single time.
 #' @param values vector Values for which to compute the mask.
 #' @return vector Boolean vector which is the mask of values occuring only once.
+#' @export
 get_single_occurrence_mask <- function(values) {
   ttt <- table(values)
   mask <- values %in% as.numeric(names(ttt)[ttt == 1])
@@ -275,6 +277,7 @@ get_rt_region_indices <- function(target_time, features, rt_tol) {
 #'   \item pks - vector - The data points at which the density peaks.
 #'   \item vlys - vector - The points in the data where the density is low
 #'                         (forming a valley in the function).
+#' @export
 get_features_in_rt_range <- function(features, times, bw) {
   time_curve <- times[between(times, min(features$rt), max(features$rt))]
 
@@ -297,6 +300,7 @@ get_features_in_rt_range <- function(features, times, bw) {
 #' @param roi list Named list with vectors `pks` and `vlys`.
 #' @param times vector Retention time values
 #' @return vector Numbers of peaks within each region defined by a peak and the two valley points.
+#' @export
 count_peaks <- function(roi, times) {
   num_peaks <- rep(0, length(roi$pks))
 
@@ -319,6 +323,7 @@ count_peaks <- function(roi, times) {
 #'   \item pks - vector - The data points at which the density peaks with at least `recover_min_count` peaks between the valley points.
 #'   \item vlys - vector - The points in the data where the density is low
 #'                         (forming a valley in the function).
+#' @export
 compute_pks_vlys_rt <- function(features, times, bandwidth, target_rt, recover_min_count) {
   roi <- get_features_in_rt_range(
     features,
@@ -383,6 +388,7 @@ compute_mu_sc_std <- function(features, aver_diff) {
 #' @param delta_rt vector Differences between consecutive retention time values (diff(times)).
 #' @importFrom dplyr between
 #' @return list Triplet of mz, label and intensity for the feature.
+#' @export
 compute_curr_rec_with_enough_peaks <- function(mz,
                                                peak,
                                                valleys,
@@ -448,6 +454,7 @@ compute_boundaries <- function(valley_points, peak) {
 #'   \item vlys - vector - The points in the data where the density is low
 #'                         (forming a valley in the function).
 #' }
+#' @export
 compute_peaks_and_valleys <- function(dens) {
   turns <- find.turn.point(dens$y)
   pks <- dens$x[turns$pks] # mz values with highest density
@@ -474,6 +481,7 @@ compute_peaks_and_valleys <- function(dens) {
 #' @param min_bandwidth float Minimum bandwidth to use.
 #' @param max_bandwidth float Maximum bandwidth to use.
 #' @return tibble Tibble with `mz`, `rt` and `intensities` columns.
+#' @export 
 compute_rectangle <- function(data_table,
                               aligned_feature_mz,
                               breaks,
@@ -602,6 +610,7 @@ compute_rectangle <- function(data_table,
 #' @param rt_tol float Retention time tolerance.
 #' @param mz_tol float Mz tolerance to use.
 #' @return int Index of value in rectable closest to `target_rt` and `aligned_mz`.
+#' @export
 refine_selection <- function(target_rt, rectangle, aligned_mz, rt_tol, mz_tol) {
   if (!is.na(target_rt)) {
     rt_term <- (rectangle$rt - target_rt)^2 / rt_tol^2

R/utils.R

@@ -40,7 +40,6 @@ register_functions_to_cluster <- function(cluster) {
         'compute_e_step',
         'compute_start_bound',
         'compute_end_bound',
-        'compute_bounds',
         'compute_scale',
         'span',
         'compute_uniq_grp',
@@ -55,17 +54,16 @@ register_functions_to_cluster <- function(cluster) {
         "create_output",
         "comb",
         'bigauss.esti.EM',
-        'solve.sigma',
-        'prep.uv',
-        'solve.a',
+        'solve_sigma',
+        'prep_uv',
+        'solve_a',
         'correct_time',
         'compute_comb',
         'compute_sel',
         'compute_template_adjusted_rt',
         'compute_corrected_features',
         'fill_missing_values',
         'recover.weaker',
-        'load_file',
         'get_custom_rt_tol',
         'compute_target_times',
         'predict_mz_break_indices',

tests/testthat/test-find.tol.time.R

@@ -17,5 +17,5 @@ test_that("compute_rt_tol_relative computes something", {
     max.bins
   )
 
-  expect_equal(actual, 1.0416)
+  expect_equal(actual, 1.04167, tolerance=0.001)
 })

tests/testthat/test-find_mz_tolerance.R

@@ -11,5 +11,5 @@ test_that("mz tolerance is found", {
     do.plot = FALSE
   )
 
-  expect_equal(mz_tol_relative, 0.0166409666685641)
+  expect_equal(mz_tol_relative, 0.01664097, tolerance=0.001)
 })