dendRoAnalyst: end-to-end workflow and function tour

The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Package vignette

Overview

This vignette presents a complete workflow for dendRoAnalyst and also acts as a compact function tour for the user-facing functions covered by the uploaded source files. The sequence follows a typical dendrometer analysis pipeline:

read dendrometer and climate data,
check time resolution, jumps, and gaps,
resample and truncate,
smooth the signal,
compute daily summaries,
classify stem-cycle phases,
classify zero-growth phases,
perform superposed epoch analysis,
fit growth curves and compare methods,
detrend and standardize,
compute moving climate correlations,
analyze harsh-climate episodes,
interpolate missing values from a network, and
inspect periodicity with wavelet tools.

The vignette is written in a CRAN-friendly style. Lightweight sections are executed; heavier, interactive, or computationally expensive sections are shown as code examples with eval = FALSE. All the summary and plot functions can also be executed by simply applying summary() and plot() functions.

Example datasets

data(gf_nepa17)
data(nepa17)
data(ktm_clim_hourly)
data(ktm_rain17)

head(gf_nepa17[, 1:3])
#>                  Time       T2       T3
#> 1 2017-01-01 00:00:00 62.26459 48.16801
#> 2 2017-01-01 01:00:00 62.27330 48.16559
#> 3 2017-01-01 02:00:00 62.30913 48.16269
#> 4 2017-01-01 03:00:00 62.34835 48.16414
#> 5 2017-01-01 04:00:00 62.37740 48.16269
#> 6 2017-01-01 05:00:00 62.42001 48.16705
head(ktm_clim_hourly)
#> # A tibble: 6 × 5
#>   TIME                 temp  prec   VPD    RH
#>   <dttm>              <dbl> <dbl> <dbl> <dbl>
#> 1 2017-01-01 00:00:00  7.02  6.28 0.162  83.8
#> 2 2017-01-01 01:00:00  6.86  1.22 0.179  82.0
#> 3 2017-01-01 02:00:00  6.80  2.41 0.154  84.4
#> 4 2017-01-01 03:00:00  8.89  2.99 0.194  83.0
#> 5 2017-01-01 04:00:00  9.96  3.61 0.282  77.0
#> 6 2017-01-01 05:00:00 10.8   4.44 0.296  77.2

Throughout this vignette we use:

gf_nepa17 for subdaily dendrometer workflows,
nepa17[1:1000, ] for compact daily examples,
ktm_clim_hourly for climate-feature examples, and
ktm_rain17 for harsh-climate examples.

dm_hourly <- gf_nepa17
dm_daily_demo <- nepa17[1:1000, ]
dm_phase_demo <- gf_nepa17[1:800, ]
clim_hourly <- ktm_clim_hourly
clim_rain <- ktm_rain17

1. Reading dendrometer and climate data

`read.dendrometer()`

When users start from raw files, read.dendrometer() is the recommended entry point.

raw_dm <- read.dendrometer(
  file = "inst/extdata/my_dendrometer_file.csv",
  detect_resolution = TRUE,
  return_report = TRUE,
  quiet = FALSE
)

This call returns an imported dendrometer table and, optionally, an import report describing parsing and time-resolution diagnostics.

`read.climate()`

clim_std <- read.climate(
  x = clim_hourly,
  time_col = "TIME",
  vars = c("temp", "prec", "VPD", "RH"),
  verbose = FALSE
)
head(clim_std)
#> # A tibble: 6 × 5
#>   TIME                 temp  prec   VPD    RH
#>   <dttm>              <dbl> <dbl> <dbl> <dbl>
#> 1 2017-01-01 00:00:00  7.02  6.28 0.162  83.8
#> 2 2017-01-01 01:00:00  6.86  1.22 0.179  82.0
#> 3 2017-01-01 02:00:00  6.80  2.41 0.154  84.4
#> 4 2017-01-01 03:00:00  8.89  2.99 0.194  83.0
#> 5 2017-01-01 04:00:00  9.96  3.61 0.282  77.0
#> 6 2017-01-01 05:00:00 10.8   4.44 0.296  77.2

The standardized climate object can later be aggregated to daily scale, converted to subdaily climate features, or joined directly to dendrometer outputs.

2. Looking for jumps or gaps in dendrometer data

`reso_dm()`

reso_dm(dm_hourly[[1]])
#> [1] 60

This returns the dominant sampling interval in minutes and helps diagnose irregular logging intervals before subsequent processing.

`jump.locator()` and `i.jump.locator()`

jump_free_manual <- jump.locator(
  df = dm_daily_demo[, 1:3],
  detection_method = "manual",
  manual_threshold = 1,
  adjustment_method = "window_median"
)
head(jump_free_manual)
#>                  TIME       T2       T3
#> 1 2017-01-01 00:00:00 62.26459 48.16801
#> 2 2017-01-01 01:00:00 62.27330 48.16559
#> 3 2017-01-01 02:00:00 62.30913 48.16269
#> 4 2017-01-01 03:00:00 62.34835 48.16414
#> 5 2017-01-01 04:00:00 62.37740 48.16269
#> 6 2017-01-01 05:00:00 62.42001 48.16705

jump_free_auto <- jump.locator(
  df = dm_daily_demo[, 1:3],
  detection_method = "auto",
  auto_method_penalty = 10,
  adjustment_method = "window_median"
)

# Interactive graphical workflow
if (RUN_INTERACTIVE) {
  i.jump.locator(df = dm_daily_demo[, 1:3])
}

`dm.na.interpolation()` and its plot methods

gap_demo <- dm_daily_demo[, 1:3]
gap_demo[150:170, 2] <- NA

gap_info <- dm.na.interpolation(
  df = gap_demo,
  resolution = 60,
  fill = FALSE,
  assess = FALSE
)
#> # A tibble: 1 × 9
#>   type        series prev_time next_time start_missing      
#>   <chr>       <chr>  <dttm>    <dttm>    <dttm>             
#> 1 internal_na T2     NA        NA        2017-01-07 05:00:00
#> # ℹ 4 more variables: end_missing <dttm>, n_consecutive <int>,
#> #   duration_min <int>, duration_hr <dbl>
head(gap_info$gap_info)
#> # A tibble: 1 × 9
#>   type        series prev_time next_time start_missing      
#>   <chr>       <chr>  <dttm>    <dttm>    <dttm>             
#> 1 internal_na T2     NA        NA        2017-01-07 05:00:00
#> # ℹ 4 more variables: end_missing <dttm>, n_consecutive <int>,
#> #   duration_min <int>, duration_hr <dbl>

gap_fill <- dm.na.interpolation(
  df = gap_demo,
  resolution = 60,
  fill = TRUE,
  method = "spline",
  assess = TRUE,
  assess_lengths_hours = c(6, 12)
)
#> # A tibble: 1 × 9
#>   type        series prev_time next_time start_missing      
#>   <chr>       <chr>  <dttm>    <dttm>    <dttm>             
#> 1 internal_na T2     NA        NA        2017-01-07 05:00:00
#> # ℹ 4 more variables: end_missing <dttm>, n_consecutive <int>,
#> #   duration_min <int>, duration_hr <dbl>
head(gap_fill$data)
#> # A tibble: 6 × 3
#>   TIME                   T2    T3
#>   <dttm>              <dbl> <dbl>
#> 1 2017-01-01 00:00:00  62.3  48.2
#> 2 2017-01-01 01:00:00  62.3  48.2
#> 3 2017-01-01 02:00:00  62.3  48.2
#> 4 2017-01-01 03:00:00  62.3  48.2
#> 5 2017-01-01 04:00:00  62.4  48.2
#> 6 2017-01-01 05:00:00  62.4  48.2

plot(gap_fill)

plot(gap_fill)

plot(gap_fill, original = gap_demo)

plot(gap_fill)

These functions provide both the filled series and visual diagnostics of where and how the gaps were handled.

3. Resample and truncate the dataset

`dendro.resample()`

resampled_hourly <- dendro.resample(
  df = dm_hourly,
  by = "H",
  value = "mean",
  method = "auto"
)
head(resampled_hourly)
#> # A tibble: 6 × 3
#>   TIME                   T2    T3
#>   <dttm>              <dbl> <dbl>
#> 1 2017-01-01 00:00:00  62.3  48.2
#> 2 2017-01-01 01:00:00  62.3  48.2
#> 3 2017-01-01 02:00:00  62.3  48.2
#> 4 2017-01-01 03:00:00  62.3  48.2
#> 5 2017-01-01 04:00:00  62.4  48.2
#> 6 2017-01-01 05:00:00  62.4  48.2

`dendro.truncate()`

truncated_demo <- dendro.truncate(dm_hourly, CalYear = 2017, DOY = c(1, 120))
head(truncated_demo)
#> # A tibble: 6 × 3
#>   TIME                   T2    T3
#>   <dttm>              <dbl> <dbl>
#> 1 2017-01-01 00:00:00  62.3  48.2
#> 2 2017-01-01 01:00:00  62.3  48.2
#> 3 2017-01-01 02:00:00  62.3  48.2
#> 4 2017-01-01 03:00:00  62.3  48.2
#> 5 2017-01-01 04:00:00  62.4  48.2
#> 6 2017-01-01 05:00:00  62.4  48.2

Resampling harmonizes time steps; truncation helps focus on one year or one season.

4. Smoothing

`smooth_dm()`

res_minutes <- reso_dm(dm_phase_demo[[1]])
smoothed_medmean <- smooth_dm(
  time = dm_phase_demo[[1]],
  dm = dm_phase_demo[[2]],
  resolution_min = res_minutes,
  method = "median_mean",
  window_hours = 3
)
head(smoothed_medmean)
#> [1] 62.26894 62.28234 62.31026 62.34496 62.38192 62.41565

smoothed_pspline <- smooth_dm(
  time = dm_phase_demo[[1]],
  dm = dm_phase_demo[[2]],
  resolution_min = res_minutes,
  method = "pspline",
  window_hours = 6
)

Smoothing is often applied before phase classification to suppress short spikes.

5. Daily approach and daily climate functions

`daily.data()` and `plot.daily_output()`

daily_out <- daily.data(df = dm_daily_demo, TreeNum = 1)
head(daily_out)
#> # A tibble: 6 × 14
#>   DATE         Min Time_min   Max Time_max  mean median amplitude Time_min_h
#>   <date>     <dbl> <chr>    <dbl> <chr>    <dbl>  <dbl>     <dbl>      <dbl>
#> 1 2017-01-01  62.3 00:00:00  62.5 08:00:00  62.4   62.5    0.205           0
#> 2 2017-01-02  62.5 14:00:00  62.5 08:00:00  62.5   62.5    0.0760         14
#> 3 2017-01-03  62.5 00:00:00  62.8 15:00:00  62.6   62.6    0.243           0
#> 4 2017-01-04  62.6 17:00:00  62.8 08:00:00  62.7   62.8    0.238          17
#> 5 2017-01-05  62.6 15:00:00  62.8 09:00:00  62.7   62.6    0.149          15
#> 6 2017-01-06  62.5 17:00:00  62.7 09:00:00  62.7   62.7    0.199          17
#> # ℹ 5 more variables: Time_max_h <dbl>, lag_h <dbl>, Remarks <chr>,
#> #   Max_diff <dbl>, Day_status <chr>

plot(daily_out)

plot(daily_out, type = "timing")

plot(daily_out, type = "change")

plot(daily_out, type = "timing_violin", Year = 2017, DOY = c(1, 6))
plot(daily_out, type = "boxplot", stat = "amplitude", by = "month_of_year")
plot(daily_out, type = "heatmap")

`dm_daily_clim()` and `dm_join_daily_clim()`

clim_day <- dm_daily_clim(
  clim_std,
  mean_vars = c("temp", "VPD", "RH"),
  max_vars  = c("VPD"),
  sum_vars  = c("prec"),
  lag_vars = c("VPD_max", "temp_mean"),
  lagmean_vars = c("temp_mean", "VPD_mean"),
  lagsum_vars = c("prec_sum"),
  lag_days = c(1, 3, 7)
)
head(clim_day)
#> # A tibble: 6 × 21
#>   DATE       temp_mean VPD_mean RH_mean VPD_max prec_sum VPD_max_lag_1d
#>   <date>         <dbl>    <dbl>   <dbl>   <dbl>    <dbl>          <dbl>
#> 1 2017-01-01      8.64    0.262    77.8   0.602  195.            NA    
#> 2 2017-01-02      8.70    0.358    69.9   0.711   45.2            0.602
#> 3 2017-01-03      8.85    0.375    68.9   0.713    5.90           0.711
#> 4 2017-01-04      9.55    0.474    63.4   0.879    0.236          0.713
#> 5 2017-01-05      8.10    0.271    78.1   0.741   15.2            0.879
#> 6 2017-01-06      8.12    0.190    84.3   0.478    3.39           0.741
#> # ℹ 14 more variables: VPD_max_lag_3d <dbl>, VPD_max_lag_7d <dbl>,
#> #   temp_mean_lag_1d <dbl>, temp_mean_lag_3d <dbl>, temp_mean_lag_7d <dbl>,
#> #   temp_mean_lagmean_1d <dbl>, temp_mean_lagmean_3d <dbl>,
#> #   temp_mean_lagmean_7d <dbl>, VPD_mean_lagmean_1d <dbl>,
#> #   VPD_mean_lagmean_3d <dbl>, VPD_mean_lagmean_7d <dbl>,
#> #   prec_sum_lagsum_1d <dbl>, prec_sum_lagsum_3d <dbl>,
#> #   prec_sum_lagsum_7d <dbl>

daily_clim_joined <- dm_join_daily_clim(daily_out, clim_day)
head(daily_clim_joined)
#> # A tibble: 6 × 34
#>   DATE         Min Time_min   Max Time_max  mean median amplitude Time_min_h
#>   <date>     <dbl> <chr>    <dbl> <chr>    <dbl>  <dbl>     <dbl>      <dbl>
#> 1 2017-01-01  62.3 00:00:00  62.5 08:00:00  62.4   62.5    0.205           0
#> 2 2017-01-02  62.5 14:00:00  62.5 08:00:00  62.5   62.5    0.0760         14
#> 3 2017-01-03  62.5 00:00:00  62.8 15:00:00  62.6   62.6    0.243           0
#> 4 2017-01-04  62.6 17:00:00  62.8 08:00:00  62.7   62.8    0.238          17
#> 5 2017-01-05  62.6 15:00:00  62.8 09:00:00  62.7   62.6    0.149          15
#> 6 2017-01-06  62.5 17:00:00  62.7 09:00:00  62.7   62.7    0.199          17
#> # ℹ 25 more variables: Time_max_h <dbl>, lag_h <dbl>, Remarks <chr>,
#> #   Max_diff <dbl>, Day_status <chr>, temp_mean <dbl>, VPD_mean <dbl>,
#> #   RH_mean <dbl>, VPD_max <dbl>, prec_sum <dbl>, VPD_max_lag_1d <dbl>,
#> #   VPD_max_lag_3d <dbl>, VPD_max_lag_7d <dbl>, temp_mean_lag_1d <dbl>,
#> #   temp_mean_lag_3d <dbl>, temp_mean_lag_7d <dbl>, temp_mean_lagmean_1d <dbl>,
#> #   temp_mean_lagmean_3d <dbl>, temp_mean_lagmean_7d <dbl>,
#> #   VPD_mean_lagmean_1d <dbl>, VPD_mean_lagmean_3d <dbl>, …

`dm_add_climate()` for daily outputs

daily_clim_added <- dm_add_climate(
  daily_out,
  clim_hourly,
  scale = "daily",
  mean_vars = c("temp", "VPD", "RH"),
  max_vars  = c("temp", "VPD"),
  sum_vars  = c("prec")
)
head(daily_clim_added)
#> # A tibble: 6 × 20
#>   DATE         Min Time_min   Max Time_max  mean median amplitude Time_min_h
#>   <date>     <dbl> <chr>    <dbl> <chr>    <dbl>  <dbl>     <dbl>      <dbl>
#> 1 2017-01-01  62.3 00:00:00  62.5 08:00:00  62.4   62.5    0.205           0
#> 2 2017-01-02  62.5 14:00:00  62.5 08:00:00  62.5   62.5    0.0760         14
#> 3 2017-01-03  62.5 00:00:00  62.8 15:00:00  62.6   62.6    0.243           0
#> 4 2017-01-04  62.6 17:00:00  62.8 08:00:00  62.7   62.8    0.238          17
#> 5 2017-01-05  62.6 15:00:00  62.8 09:00:00  62.7   62.6    0.149          15
#> 6 2017-01-06  62.5 17:00:00  62.7 09:00:00  62.7   62.7    0.199          17
#> # ℹ 11 more variables: Time_max_h <dbl>, lag_h <dbl>, Remarks <chr>,
#> #   Max_diff <dbl>, Day_status <chr>, temp_mean <dbl>, VPD_mean <dbl>,
#> #   RH_mean <dbl>, temp_max <dbl>, VPD_max <dbl>, prec_sum <dbl>

6. Stem-cycle approach and associated functions

`phase.sc()` and `plot.SC_output()`

sc_out <- phase.sc(df = dm_phase_demo, TreeNum = 1)
sc_out_sm <- phase.sc(df = dm_phase_demo, TreeNum = 1, smoothing = 12)
head(sc_out$SC_phase)
#> # A tibble: 6 × 3
#>   TIME                   dm Phases
#>   <dttm>              <dbl>  <dbl>
#> 1 2017-01-01 00:00:00  62.3     NA
#> 2 2017-01-01 01:00:00  62.3      3
#> 3 2017-01-01 02:00:00  62.3      3
#> 4 2017-01-01 03:00:00  62.3      3
#> 5 2017-01-01 04:00:00  62.4      3
#> 6 2017-01-01 05:00:00  62.4      3
head(sc_out$SC_cycle)
#> # A tibble: 6 × 8
#>   Phases Start               End                 Duration_h Duration_m Magnitude
#>    <dbl> <dttm>              <dttm>                   <dbl>      <dbl>     <dbl>
#> 1      3 2017-01-01 00:00:00 2017-01-01 08:00:00          8        480   0.205  
#> 2      1 2017-01-01 08:00:00 2017-01-01 12:00:00          4        240  -0.0223 
#> 3      2 2017-01-01 12:00:00 2017-01-01 15:00:00          3        180   0.00871
#> 4      1 2017-01-01 15:00:00 2017-01-01 16:00:00          1         60  -0.00290
#> 5      2 2017-01-01 16:00:00 2017-01-01 20:00:00          4        240   0.0121 
#> 6      1 2017-01-01 20:00:00 2017-01-01 21:00:00          1         60  -0.00145
#> # ℹ 2 more variables: rate <dbl>, DOY <dbl>

plot(sc_out)

plot(sc_out, type = "ribbon")

plot(sc_out, type = "increment")

plot(sc_out, type = "transition", x_axis = "doy", Year = 2017, DOY = c(50, 100))
plot(sc_out, type = "balance", temporal = "daily")
plot(sc_out, type = "frequency", temporal = "monthly")
plot(sc_out, type = "heatmap", temporal = "daily")
plot(sc_out, type = "boxplot", stat = "Magnitude", temporal = "monthly")

`dm_subdaily_clim()`, `dm_join_subdaily_clim()`, `dm_join_phase_clim()`, and `dm_add_climate()`

clim_sub <- dm_subdaily_clim(
  clim_hourly,
  mean_vars = c("temp", "VPD", "RH"),
  sum_vars  = c("prec"),
  lag_vars  = c("temp", "VPD", "RH"),
  roll_hours = c(1, 3, 6, 24),
  lag_hours  = c(1, 3, 6, 24)
)
head(clim_sub)
#> # A tibble: 6 × 33
#>   TIME                 temp  prec   VPD    RH temp_rollmean_1h temp_rollmean_3h
#>   <dttm>              <dbl> <dbl> <dbl> <dbl>            <dbl>            <dbl>
#> 1 2017-01-01 00:00:00  7.02  6.28 0.162  83.8             7.02            NA   
#> 2 2017-01-01 01:00:00  6.86  1.22 0.179  82.0             6.86            NA   
#> 3 2017-01-01 02:00:00  6.80  2.41 0.154  84.4             6.80             6.89
#> 4 2017-01-01 03:00:00  8.89  2.99 0.194  83.0             8.89             7.52
#> 5 2017-01-01 04:00:00  9.96  3.61 0.282  77.0             9.96             8.55
#> 6 2017-01-01 05:00:00 10.8   4.44 0.296  77.2            10.8              9.88
#> # ℹ 26 more variables: temp_rollmean_6h <dbl>, temp_rollmean_24h <dbl>,
#> #   VPD_rollmean_1h <dbl>, VPD_rollmean_3h <dbl>, VPD_rollmean_6h <dbl>,
#> #   VPD_rollmean_24h <dbl>, RH_rollmean_1h <dbl>, RH_rollmean_3h <dbl>,
#> #   RH_rollmean_6h <dbl>, RH_rollmean_24h <dbl>, prec_rollsum_1h <dbl>,
#> #   prec_rollsum_3h <dbl>, prec_rollsum_6h <dbl>, prec_rollsum_24h <dbl>,
#> #   temp_lag_1h <dbl>, temp_lag_3h <dbl>, temp_lag_6h <dbl>,
#> #   temp_lag_24h <dbl>, VPD_lag_1h <dbl>, VPD_lag_3h <dbl>, VPD_lag_6h <dbl>, …

sc_phase_clim <- dm_join_phase_clim(
  sc_out,
  clim_hourly,
  mean_vars = c("temp", "VPD", "RH"),
  max_vars  = c("temp", "VPD"),
  sum_vars  = c("prec")
)
head(sc_phase_clim$SC_cycle)
#> # A tibble: 6 × 15
#>   Phases Start               End                 Duration_h Duration_m Magnitude
#>    <dbl> <dttm>              <dttm>                   <dbl>      <dbl>     <dbl>
#> 1      3 2017-01-01 00:00:00 2017-01-01 08:00:00          8        480   0.205  
#> 2      1 2017-01-01 08:00:00 2017-01-01 12:00:00          4        240  -0.0223 
#> 3      2 2017-01-01 12:00:00 2017-01-01 15:00:00          3        180   0.00871
#> 4      1 2017-01-01 15:00:00 2017-01-01 16:00:00          1         60  -0.00290
#> 5      2 2017-01-01 16:00:00 2017-01-01 20:00:00          4        240   0.0121 
#> 6      1 2017-01-01 20:00:00 2017-01-01 21:00:00          1         60  -0.00145
#> # ℹ 9 more variables: rate <dbl>, DOY <dbl>, n_clim <int>,
#> #   temp_mean_phase <dbl>, VPD_mean_phase <dbl>, RH_mean_phase <dbl>,
#> #   temp_max_phase <dbl>, VPD_max_phase <dbl>, prec_sum_phase <dbl>

sc_point_clim <- dm_join_subdaily_clim(sc_out_sm, clim_sub)
head(sc_point_clim$SC_phase)
#> # A tibble: 6 × 35
#>   TIME                   dm Phases  temp  prec   VPD    RH temp_rollmean_1h
#>   <dttm>              <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>            <dbl>
#> 1 2017-01-01 00:00:00  62.3     NA  7.02  6.28 0.162  83.8             7.02
#> 2 2017-01-01 01:00:00  62.3      3  6.86  1.22 0.179  82.0             6.86
#> 3 2017-01-01 02:00:00  62.3      3  6.80  2.41 0.154  84.4             6.80
#> 4 2017-01-01 03:00:00  62.3      3  8.89  2.99 0.194  83.0             8.89
#> 5 2017-01-01 04:00:00  62.4      3  9.96  3.61 0.282  77.0             9.96
#> 6 2017-01-01 05:00:00  62.4      3 10.8   4.44 0.296  77.2            10.8 
#> # ℹ 27 more variables: temp_rollmean_3h <dbl>, temp_rollmean_6h <dbl>,
#> #   temp_rollmean_24h <dbl>, VPD_rollmean_1h <dbl>, VPD_rollmean_3h <dbl>,
#> #   VPD_rollmean_6h <dbl>, VPD_rollmean_24h <dbl>, RH_rollmean_1h <dbl>,
#> #   RH_rollmean_3h <dbl>, RH_rollmean_6h <dbl>, RH_rollmean_24h <dbl>,
#> #   prec_rollsum_1h <dbl>, prec_rollsum_3h <dbl>, prec_rollsum_6h <dbl>,
#> #   prec_rollsum_24h <dbl>, temp_lag_1h <dbl>, temp_lag_3h <dbl>,
#> #   temp_lag_6h <dbl>, temp_lag_24h <dbl>, VPD_lag_1h <dbl>, …

sc_added_clim <- dm_add_climate(
  sc_out_sm,
  clim_hourly,
  scale = "subdaily",
  sub_mean_vars = c("temp", "VPD", "RH"),
  sub_sum_vars  = c("prec"),
  sub_lag_vars  = c("temp", "VPD", "RH"),
  roll_hours = c(1, 3, 6, 24),
  lag_hours  = c(1, 3, 6, 24)
)
head(sc_added_clim$SC_phase)
#> # A tibble: 6 × 35
#>   TIME                   dm Phases  temp  prec   VPD    RH temp_rollmean_1h
#>   <dttm>              <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>            <dbl>
#> 1 2017-01-01 00:00:00  62.3     NA  7.02  6.28 0.162  83.8             7.02
#> 2 2017-01-01 01:00:00  62.3      3  6.86  1.22 0.179  82.0             6.86
#> 3 2017-01-01 02:00:00  62.3      3  6.80  2.41 0.154  84.4             6.80
#> 4 2017-01-01 03:00:00  62.3      3  8.89  2.99 0.194  83.0             8.89
#> 5 2017-01-01 04:00:00  62.4      3  9.96  3.61 0.282  77.0             9.96
#> 6 2017-01-01 05:00:00  62.4      3 10.8   4.44 0.296  77.2            10.8 
#> # ℹ 27 more variables: temp_rollmean_3h <dbl>, temp_rollmean_6h <dbl>,
#> #   temp_rollmean_24h <dbl>, VPD_rollmean_1h <dbl>, VPD_rollmean_3h <dbl>,
#> #   VPD_rollmean_6h <dbl>, VPD_rollmean_24h <dbl>, RH_rollmean_1h <dbl>,
#> #   RH_rollmean_3h <dbl>, RH_rollmean_6h <dbl>, RH_rollmean_24h <dbl>,
#> #   prec_rollsum_1h <dbl>, prec_rollsum_3h <dbl>, prec_rollsum_6h <dbl>,
#> #   prec_rollsum_24h <dbl>, temp_lag_1h <dbl>, temp_lag_3h <dbl>,
#> #   temp_lag_6h <dbl>, temp_lag_24h <dbl>, VPD_lag_1h <dbl>, …

7. Zero-growth approach and associated functions

`phase.zg()`, `twd.maxima()`, and `plot.ZG_output()`

zg_out <- phase.zg(df = dm_phase_demo, TreeNum = 1)
zg_out_sm <- phase.zg(df = dm_phase_demo, TreeNum = 1, smoothing = 6)
head(zg_out$ZG_phase)
#> # A tibble: 6 × 5
#>   TIME                   dm Phases   TWD   GRO
#>   <dttm>              <dbl>  <int> <dbl> <dbl>
#> 1 2017-01-01 00:00:00  62.3      2     0  62.3
#> 2 2017-01-01 01:00:00  62.3      2     0  62.3
#> 3 2017-01-01 02:00:00  62.3      2     0  62.3
#> 4 2017-01-01 03:00:00  62.3      2     0  62.3
#> 5 2017-01-01 04:00:00  62.4      2     0  62.4
#> 6 2017-01-01 05:00:00  62.4      2     0  62.4
head(zg_out$ZG_cycle)
#> # A tibble: 6 × 14
#>   Phases Start               End                 Duration_h Magnitude  rate
#>    <int> <dttm>              <dttm>                   <dbl>     <dbl> <dbl>
#> 1      2 2017-01-01 00:00:00 2017-01-01 09:00:00          9    0.205  22.8 
#> 2      1 2017-01-01 09:00:00 2017-01-02 00:00:00         15   NA      NA   
#> 3      2 2017-01-02 00:00:00 2017-01-02 09:00:00          9    0.0731  8.12
#> 4      1 2017-01-02 09:00:00 2017-01-03 02:00:00         17   NA      NA   
#> 5      2 2017-01-03 02:00:00 2017-01-03 10:00:00          8    0.0692  8.65
#> 6      1 2017-01-03 10:00:00 2017-01-03 14:00:00          4   NA      NA   
#> # ℹ 8 more variables: max.twd <dbl>, Max.twd.time <dttm>, AUC.load <dbl>,
#> #   AUC.total <dbl>, ABr.value <dbl>, Avg.twd <dbl>, STD.twd <dbl>, DOY <dbl>

max_twd_tbl <- twd.maxima(dm_phase_demo, TreeNum = 1, smoothing = 5)
head(max_twd_tbl)
#> # A tibble: 6 × 7
#>   Start.time          End.time            TIME                Phases    TWD
#>   <dttm>              <dttm>              <dttm>               <int>  <dbl>
#> 1 2017-01-01 09:00:00 2017-01-02 00:00:00 2017-01-01 12:00:00      1 0.0223
#> 2 2017-01-02 09:00:00 2017-01-03 02:00:00 2017-01-02 14:00:00      1 0.0760
#> 3 2017-01-03 10:00:00 2017-01-03 14:00:00 2017-01-03 13:00:00      1 0.0692
#> 4 2017-01-04 09:00:00 2017-01-19 07:00:00 2017-01-04 16:00:00      1 0.236 
#> 5 2017-01-04 09:00:00 2017-01-19 07:00:00 2017-01-04 17:00:00      1 0.238 
#> 6 2017-01-04 09:00:00 2017-01-19 07:00:00 2017-01-05 15:00:00      1 0.216 
#> # ℹ 2 more variables: Duration_from_start <drtn>, twd.number <int>

plot(zg_out)

plot(zg_out, temporal = "daily")

plot(zg_out, temporal = "monthly", type = "twd")

plot(zg_out, temporal = "monthly", type = "abr")
plot(zg_out, type = "transition", x_axis = "doy", Year = 2017, DOY = c(50, 100))
plot(zg_out, type = "boxplot", stat = "max.twd", temporal = "daily")

Climate joins and climate-phase plots

zg_phase_clim <- dm_join_phase_clim(
  zg_out,
  clim_hourly,
  mean_vars = c("temp", "VPD", "RH"),
  max_vars  = c("temp", "VPD"),
  sum_vars  = c("prec")
)
head(zg_phase_clim$ZG_cycle)
#> # A tibble: 6 × 21
#>   Phases Start               End                 Duration_h Magnitude  rate
#>    <int> <dttm>              <dttm>                   <dbl>     <dbl> <dbl>
#> 1      2 2017-01-01 00:00:00 2017-01-01 09:00:00          9    0.205  22.8 
#> 2      1 2017-01-01 09:00:00 2017-01-02 00:00:00         15   NA      NA   
#> 3      2 2017-01-02 00:00:00 2017-01-02 09:00:00          9    0.0731  8.12
#> 4      1 2017-01-02 09:00:00 2017-01-03 02:00:00         17   NA      NA   
#> 5      2 2017-01-03 02:00:00 2017-01-03 10:00:00          8    0.0692  8.65
#> 6      1 2017-01-03 10:00:00 2017-01-03 14:00:00          4   NA      NA   
#> # ℹ 15 more variables: max.twd <dbl>, Max.twd.time <dttm>, AUC.load <dbl>,
#> #   AUC.total <dbl>, ABr.value <dbl>, Avg.twd <dbl>, STD.twd <dbl>, DOY <dbl>,
#> #   n_clim <int>, temp_mean_phase <dbl>, VPD_mean_phase <dbl>,
#> #   RH_mean_phase <dbl>, temp_max_phase <dbl>, VPD_max_phase <dbl>,
#> #   prec_sum_phase <dbl>

zg_point_clim <- dm_join_subdaily_clim(zg_out_sm, clim_sub)
head(zg_point_clim$ZG_phase)
#> # A tibble: 6 × 37
#>   TIME                   dm Phases   TWD   GRO  temp  prec   VPD    RH
#>   <dttm>              <dbl>  <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2017-01-01 00:00:00  62.3      2     0  62.3  7.02  6.28 0.162  83.8
#> 2 2017-01-01 01:00:00  62.3      2     0  62.3  6.86  1.22 0.179  82.0
#> 3 2017-01-01 02:00:00  62.3      2     0  62.3  6.80  2.41 0.154  84.4
#> 4 2017-01-01 03:00:00  62.3      2     0  62.3  8.89  2.99 0.194  83.0
#> 5 2017-01-01 04:00:00  62.4      2     0  62.4  9.96  3.61 0.282  77.0
#> 6 2017-01-01 05:00:00  62.4      2     0  62.4 10.8   4.44 0.296  77.2
#> # ℹ 28 more variables: temp_rollmean_1h <dbl>, temp_rollmean_3h <dbl>,
#> #   temp_rollmean_6h <dbl>, temp_rollmean_24h <dbl>, VPD_rollmean_1h <dbl>,
#> #   VPD_rollmean_3h <dbl>, VPD_rollmean_6h <dbl>, VPD_rollmean_24h <dbl>,
#> #   RH_rollmean_1h <dbl>, RH_rollmean_3h <dbl>, RH_rollmean_6h <dbl>,
#> #   RH_rollmean_24h <dbl>, prec_rollsum_1h <dbl>, prec_rollsum_3h <dbl>,
#> #   prec_rollsum_6h <dbl>, prec_rollsum_24h <dbl>, temp_lag_1h <dbl>,
#> #   temp_lag_3h <dbl>, temp_lag_6h <dbl>, temp_lag_24h <dbl>, …

zg_phase_added <- dm_add_climate(
  zg_out,
  clim_hourly,
  scale = "phase",
  mean_vars = c("temp", "VPD", "RH"),
  max_vars  = c("temp", "VPD"),
  sum_vars  = c("prec")
)
head(zg_phase_added$ZG_cycle)
#> # A tibble: 6 × 21
#>   Phases Start               End                 Duration_h Magnitude  rate
#>    <int> <dttm>              <dttm>                   <dbl>     <dbl> <dbl>
#> 1      2 2017-01-01 00:00:00 2017-01-01 09:00:00          9    0.205  22.8 
#> 2      1 2017-01-01 09:00:00 2017-01-02 00:00:00         15   NA      NA   
#> 3      2 2017-01-02 00:00:00 2017-01-02 09:00:00          9    0.0731  8.12
#> 4      1 2017-01-02 09:00:00 2017-01-03 02:00:00         17   NA      NA   
#> 5      2 2017-01-03 02:00:00 2017-01-03 10:00:00          8    0.0692  8.65
#> 6      1 2017-01-03 10:00:00 2017-01-03 14:00:00          4   NA      NA   
#> # ℹ 15 more variables: max.twd <dbl>, Max.twd.time <dttm>, AUC.load <dbl>,
#> #   AUC.total <dbl>, ABr.value <dbl>, Avg.twd <dbl>, STD.twd <dbl>, DOY <dbl>,
#> #   n_clim <int>, temp_mean_phase <dbl>, VPD_mean_phase <dbl>,
#> #   RH_mean_phase <dbl>, temp_max_phase <dbl>, VPD_max_phase <dbl>,
#> #   prec_sum_phase <dbl>

plot(zg_phase_added, climate_var = "VPD_mean_phase", scale = "cycle", type = "boxplot", temporal = 'daily')

plot(zg_phase_added, climate_var = "temp_mean_phase", scale = "cycle", type = "timeseries")

plot(
  zg_phase_added,
  climate_vars = c("temp_mean_phase", "VPD_mean_phase", "RH_mean_phase"),
  scale = "cycle",
  type = "boxplot"
)

plot(
  zg_phase_added,
  scale = "cycle",
  type = "cor_heatmap",
  numeric_vars = c("Duration_h", "max.twd", "ABr.value",
                   "temp_mean_phase", "VPD_mean_phase", "RH_mean_phase")
)

8. Superposed epoch and associated functions

`dm_event_times()`, `dm_epoch_extract()`, `dm_epoch_test()`, and method dispatch

event_tbl <- dm_event_times(zg_out, event = "all")
head(event_tbl)
#> # A tibble: 6 × 19
#>   event_id event_time          event_type phase Phases Start              
#>      <int> <dttm>              <chr>      <chr>  <int> <dttm>             
#> 1        1 2017-01-01 00:00:00 GRO_start  GRO        2 2017-01-01 00:00:00
#> 2        2 2017-01-01 09:00:00 TWD_start  TWD        1 2017-01-01 09:00:00
#> 3        3 2017-01-01 09:00:00 GRO_end    GRO        2 2017-01-01 00:00:00
#> 4        4 2017-01-01 12:00:00 MaxTWD     TWD        1 2017-01-01 09:00:00
#> 5        5 2017-01-02 00:00:00 TWD_end    TWD        1 2017-01-01 09:00:00
#> 6        6 2017-01-02 00:00:00 GRO_start  GRO        2 2017-01-02 00:00:00
#> # ℹ 13 more variables: End <dttm>, Duration_h <dbl>, Magnitude <dbl>,
#> #   rate <dbl>, max.twd <dbl>, Max.twd.time <dttm>, AUC.load <dbl>,
#> #   AUC.total <dbl>, ABr.value <dbl>, Avg.twd <dbl>, STD.twd <dbl>, DOY <dbl>,
#> #   phase_label <chr>

epoch_extract <- dm_epoch_extract(
  x = zg_out,
  clim = clim_hourly,
  event = "phase_start",
  phase = "all",
  response_var = c("temp", "VPD"),
  before = 24,
  after = 24,
  unit = "hours"
)
head(epoch_extract)

epoch_test <- dm_epoch_test(
  x = zg_out,
  clim = clim_hourly,
  event = "phase_start",
  phase = "all",
  response_var = c("temp", "VPD"),
  before = 24,
  after = 24,
  unit = "hours",
  n_boot = 99
)

epoch_summary <- summary(epoch_test)   # summary.dm_epoch()
print(epoch_summary)                   # print.summary.dm_epoch()
plot(epoch_test)

9. Event-based climate functions

evt_zg <- dm_event_climate(
  zg_out,
  clim_hourly,
  event = "phase_start",
  phase = "all",
  windows = c(0, 3, 6, 12, 24),
  mean_vars = c("temp", "VPD", "RH"),
  max_vars  = c("VPD"),
  sum_vars  = c("prec")
)
head(evt_zg)

evt_summary <- dm_event_climate_summary(
  evt_zg,
  climate_var = "VPD_mean_prev_6h",
  group_var = "Phase",
  by = "month_of_year"
)
head(evt_summary)

evt_test <- dm_event_climate_test(
  evt_zg,
  climate_var = "VPD_mean_prev_6h",
  group_var = "Phase",
  by = "month_of_year",
  method = "auto"
)
head(evt_test$summary)
head(evt_test$overall_tests)
head(evt_test$pairwise_tests)

plot(
  evt_zg,
  climate_var = "VPD_mean_prev_6h",
  group_var = "Phase",
  facet_by = "month_of_year",
  geom = "both",
  add_test = TRUE
)

evt_maxtwd <- dm_event_climate(
  zg_out,
  clim_hourly,
  event = "max_twd",
  windows = c(0, 3, 6, 12, 24),
  mean_vars = c("temp", "VPD", "RH"),
  max_vars  = c("VPD"),
  sum_vars  = c("prec")
)

plot(
  evt_maxtwd,
  climate_var = "VPD_mean_prev_6h",
  response_var = "max.twd",
  group_var = "Phase"
)

10. Growth fit and evaluation

`dm.fit.gompertz()`

gomp_legacy <- dm.fit.gompertz(df = dm_hourly, TreeNum = 1, CalYear = 2017, verbose = FALSE)
head(gomp_legacy)

`dm.growth.fit()`, `plot.dm_growth_fit()`, and print/summary methods

fit_single <- dm.growth.fit(
  df = dm_hourly,
  TreeNum = 1:2,
  method = "gompertz",
  year_mode = "yearly",
  verbose = FALSE
)

print(fit_single)                 # print.dm_growth_fit()
fit_single_summary <- summary(fit_single)   # summary.dm_growth_fit()
print(fit_single_summary)         # print.summary.dm_growth_fit()

plot(fit_single, type = "fit")
plot(fit_single, type = "season")
plot(fit_single, type = "residuals")
plot(fit_single, type = "timing")
plot(fit_single, type = "overlay")
plot(fit_single, type = "parameters")

`dm.growth.fit.double()` and `dm.growth.evaluate()`

fit_double <- dm.growth.fit.double(
  df = dm_hourly,
  TreeNum = 1:2,
  method = "gompertz",
  year_mode = "yearly",
  verbose = FALSE
)
print(fit_double)

growth_eval <- dm.growth.evaluate(
  df = dm_hourly,
  TreeNum = 1:2,
  methods = c("gompertz", "loess", "spline", "double_gompertz"),
  year_mode = "yearly",
  verbose = FALSE
)
head(growth_eval)

plot(growth_eval, metric = "rmse", type = "boxplot")
plot(growth_eval, metric = "r2", type = "heatmap")

11. Detrending and associated functions

detrended_fit <- dm.detrend.fit(fit_single)
plot(detrended_fit, type = "compare")
plot(detrended_fit, type = "fit")
plot(detrended_fit, type = "residual")
plot(detrended_fit, type = "detrended")
plot(detrended_fit, type = "boxplot")

mean_det <- mean_detrended.dm(detrended_fit)
head(mean_det)
plot(mean_det)

`dm_standardize()` and `plot.dm_standardized()`

std_nh <- dm_standardize(
  df = dm_hourly,
  season_type = "NH",
  method = "robust_amplitude"
)
head(std_nh$data)
#> # A tibble: 6 × 5
#>   TIME                season_year in_season       T2         T3
#>   <dttm>                    <dbl> <lgl>        <dbl>      <dbl>
#> 1 2017-01-01 00:00:00        2017 TRUE      0         0        
#> 2 2017-01-01 01:00:00        2017 TRUE      0.000566 -0.000200 
#> 3 2017-01-01 02:00:00        2017 TRUE      0.00289  -0.000439 
#> 4 2017-01-01 03:00:00        2017 TRUE      0.00544  -0.000319 
#> 5 2017-01-01 04:00:00        2017 TRUE      0.00733  -0.000439 
#> 6 2017-01-01 05:00:00        2017 TRUE      0.0101   -0.0000798
head(std_nh$parameters)
#> # A tibble: 2 × 17
#>   tree  season_year season_type method        ref_type ref_n_days ref_doys_start
#>   <chr>       <dbl> <chr>       <chr>         <chr>         <int>          <int>
#> 1 T2           2017 NH          robust_ampli… first_v…         NA             NA
#> 2 T3           2017 NH          robust_ampli… first_v…         NA             NA
#> # ℹ 10 more variables: ref_doys_end <int>, q_low <dbl>, q_high <dbl>,
#> #   n_total <int>, n_nonmiss <int>, season_start <dttm>, season_end <dttm>,
#> #   reference_value <dbl>, center_value <dbl>, denominator <dbl>

plot(std_nh)

12. Moving correlations and associated functions

mov_out <- mov.cor.dm(
  df = dm_hourly,
  Clim = clim_hourly,
  TreeNum = 1,
  win_size = 15,
  cor_method = "pearson",
  boot = FALSE
)

print(mov_out)                # print.mov_cor_dm()
mov_summary <- summary(mov_out)   # summary.mov_cor_dm()
print(mov_summary)            # print.summary_mov_cor_dm()

plot(mov_out, type = "heatmap")
plot(mov_out, type = "line", annotate_peak = TRUE)
plot(mov_out, type = "facet")
plot(mov_out, type = "peak")
plot(mov_out, type = "line")
plot(mov_summary, type = "peak_corr")

mov_boot <- mov.cor.dm(
  df = dm_hourly,
  Clim = clim_hourly,
  TreeNum = 1,
  win_size = 15,
  cor_method = "pearson",
  boot = TRUE,
  R = 99,
  set_seed = 1
)
plot(mov_boot, type = "line", show_ci = TRUE)

13. Harsh climate using `clim.twd()` and associated functions

rel_out <- clim.twd(
  df = dm_hourly,
  Clim = clim_rain,
  thresholdClim = "<10",
  thresholdDays = ">5",
  showPlot = FALSE
)

clim_stats <- clim.twd.stats(
  rel_out,
  response = "full_period_change",
  group_by = "tree"
)

clim_stats_summary <- summary(clim_stats)   # summary.clim_twd_stats()
print(clim_stats_summary)                   # print.summary.clim_twd_stats()
plot(clim_stats, type = "trajectory")

tree_info <- data.frame(
  tree = c("T2", "T3"),
  species = c("Pinus", "Pinus"),
  site = c("Ktm", "Ktm")
)

clim_test <- clim.twd.test(
  rel_out,
  tree_info = tree_info,
  parameter = "lag_to_return_zero",
  compare_by = "species",
  within = "year"
)

clim_test_summary <- summary(clim_test)   # summary.clim_twd_test()
print(clim_test_summary)                  # print.summary.clim_twd_test()
plot(clim_test)

14. Network interpolation

`network.interpolation()` and `plot.network_interpolation()`

df1 <- dm_hourly
df1[40:50, "T2"] <- NA

ref_net <- cbind(dm_hourly, dm_hourly[, 2:3], dm_hourly[, 2:3])
colnames(ref_net) <- c("Time", "T1", "T2", "T3", "T4", "T5", "T6")

net_interp <- network.interpolation(
  df1,
  ref_net,
  niMethod = "proportional",
  n_boot = 100,
  return_flags = TRUE,
  return_pi = TRUE,
  assess = TRUE,
  assess_lengths_steps = c(1, 2, 4),
  correct_gap_jumps = TRUE,
  jump_threshold = 0.05
)
head(net_interp)
# to get the assessment stats we can use attr() function
attr(out, "network_validation_summary")
attr(out, "network_validation_points")
attr(out, "network_validation_seasonal")

plot(net_interp)
plot(net_interp, type = "uncertainty")
plot(net_interp, type = "availability")
plot(net_interp, type = "summary", summary_metric = "n_gap_jumps_removed")
plot(net_interp, type = "validation", validation_metric = "MAPE")
plot(net_interp, type = "coverage")
plot(net_interp, type = "compare")
plot(net_interp, type = "seasonal_error", seasonal_metric = "mean_abs_error")

15. Wavelet analysis and associated functions

`dm_wavelet()`, `summary.dm_wavelet()`, and `plot.dm_wavelet()`

wv <- dm_wavelet(
  x = dm_hourly,
  TreeNum = 1:2,
  source = "raw",
  make_pval = TRUE,
  verbose = FALSE
)

wv_summary <- summary(wv)   # summary.dm_wavelet()
print(wv_summary)           # print.summary.dm_wavelet()

plot(wv, type = "series")
plot(wv, type = "average")
plot(wv, type = "power")
plot(wv, series = names(wv$results)[1], type = "power")

`dm_wavelet_reconstruct()` and its methods

rec_extract <- dm_wavelet_reconstruct(
  wv,
  mode = "extract",
  lower_hours = 20,
  upper_hours = 28,
  only_sig = TRUE
)

rec_remove <- dm_wavelet_reconstruct(
  wv,
  mode = "remove",
  lower_hours = 20,
  upper_hours = 28,
  only_sig = TRUE
)

rec_extract_summary <- summary(rec_extract)   # summary.dm_wavelet_reconstruct()
print(rec_extract_summary)                    # print.summary.dm_wavelet_reconstruct()

plot(rec_extract)
plot(rec_remove)

`dm_wavelet_coherence()` and `plot.dm_wavelet_coherence()`

wc <- dm_wavelet_coherence(
  dm = dm_hourly,
  clim = clim_hourly,
  TreeNum = 1,
  clim_vars = c("temp", "VPD"),
  source = "raw",
  dm_fun = "mean",
  clim_funs = c(temp = "mean", VPD = "mean"),
  loess_span = 0,
  make.pval = TRUE,
  n.sim = 10,
  verbose = FALSE
)

plot(wc)
plot(wc, type = "cross_power")
plot(wc, type = "average_coherence")
plot(wc, type = "phase")
plot(wc, type = "series")

Closing note

This vignette is intended to be both a workflow guide and a compact reference for the main user-facing functions in the current dendRoAnalyst analysis pipeline. For classroom teaching, the companion demonstration script can be run section by section; for package documentation, this vignette keeps only the lighter sections evaluated and leaves heavy or interactive examples in place for users to run locally.

For suggestions, comments and questions please contact : sugam.aryal@fau.de

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.

dendRoAnalyst: end-to-end workflow and function tour

Package vignette

Overview

Example datasets

1. Reading dendrometer and climate data

read.dendrometer()

read.climate()

2. Looking for jumps or gaps in dendrometer data

reso_dm()

jump.locator() and i.jump.locator()

dm.na.interpolation() and its plot methods

3. Resample and truncate the dataset

dendro.resample()

dendro.truncate()

4. Smoothing

smooth_dm()

5. Daily approach and daily climate functions

daily.data() and plot.daily_output()

dm_daily_clim() and dm_join_daily_clim()

dm_add_climate() for daily outputs

6. Stem-cycle approach and associated functions

phase.sc() and plot.SC_output()

dm_subdaily_clim(), dm_join_subdaily_clim(), dm_join_phase_clim(), and dm_add_climate()

7. Zero-growth approach and associated functions

phase.zg(), twd.maxima(), and plot.ZG_output()

Climate joins and climate-phase plots

8. Superposed epoch and associated functions

dm_event_times(), dm_epoch_extract(), dm_epoch_test(), and method dispatch

9. Event-based climate functions

10. Growth fit and evaluation

dm.fit.gompertz()

dm.growth.fit(), plot.dm_growth_fit(), and print/summary methods

dm.growth.fit.double() and dm.growth.evaluate()

11. Detrending and associated functions

dm_standardize() and plot.dm_standardized()

12. Moving correlations and associated functions

13. Harsh climate using clim.twd() and associated functions

14. Network interpolation

network.interpolation() and plot.network_interpolation()

15. Wavelet analysis and associated functions

dm_wavelet(), summary.dm_wavelet(), and plot.dm_wavelet()

dm_wavelet_reconstruct() and its methods

dm_wavelet_coherence() and plot.dm_wavelet_coherence()

Closing note

For suggestions, comments and questions please contact : sugam.aryal@fau.de

`read.dendrometer()`

`read.climate()`

`reso_dm()`

`jump.locator()` and `i.jump.locator()`

`dm.na.interpolation()` and its plot methods

`dendro.resample()`

`dendro.truncate()`

`smooth_dm()`

`daily.data()` and `plot.daily_output()`

`dm_daily_clim()` and `dm_join_daily_clim()`

`dm_add_climate()` for daily outputs

`phase.sc()` and `plot.SC_output()`

`dm_subdaily_clim()`, `dm_join_subdaily_clim()`, `dm_join_phase_clim()`, and `dm_add_climate()`

`phase.zg()`, `twd.maxima()`, and `plot.ZG_output()`

`dm_event_times()`, `dm_epoch_extract()`, `dm_epoch_test()`, and method dispatch

`dm.fit.gompertz()`

`dm.growth.fit()`, `plot.dm_growth_fit()`, and print/summary methods

`dm.growth.fit.double()` and `dm.growth.evaluate()`

`dm_standardize()` and `plot.dm_standardized()`

13. Harsh climate using `clim.twd()` and associated functions

`network.interpolation()` and `plot.network_interpolation()`

`dm_wavelet()`, `summary.dm_wavelet()`, and `plot.dm_wavelet()`

`dm_wavelet_reconstruct()` and its methods

`dm_wavelet_coherence()` and `plot.dm_wavelet_coherence()`