aniMotum

https://ianjonsen.github.io/aniMotum/
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aniMotum is an R package that fits continuous-time state-space models to animal tracking data for...
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### Example: fit_ssm, sim_post, and plot

Source: https://ianjonsen.github.io/aniMotum/reference/plot.sim_post.html

Example workflow: fits a correlated random walk SSM to the 'ellie' dataset, simulates posterior tracks with sim_post, and plots them as lines. The output from fit_ssm is shown as comments.

```r
fit <- fit_ssm(ellie, model = "crw", time.step = 24)
#> fitting crw SSM to 1 tracks...
#> 
 pars:   1 1 0 -2.91153      
 pars:   0.38512 0.37191 -0.01692 -3.38813      
 pars:   -1.4595 -1.51236 -0.06769 -4.81792      
 pars:   -2.20058 -2.24383 -0.11527 -4.95732      
 pars:   -0.38346 -0.4056 -0.04615 -3.85456      
 pars:   -1.23664 -1.20844 -0.10327 -3.65984      
 pars:   -2.25711 -2.16871 -0.17159 -3.42693      
 pars:   -3.07868 -3.34599 -0.26131 -1.24778      
 pars:   -4.46619 -1.62751 -0.49092 0.12579      
 pars:   -3.18442 -2.86245 -0.30197 -0.80151      
 pars:   -3.40955 -3.30581 -0.49919 -0.40195      
 pars:   -3.06893 -3.16418 -1.05454 -0.36585      
 pars:   -3.5513 -2.81435 -1.34642 -0.44033      
 pars:   -3.30092 -3.01944 -1.10395 -0.39263      
 pars:   -3.27833 -3.03821 -1.38152 -0.39631      
 pars:   -3.23173 -2.97174 -2.21731 -0.40477      
 pars:   -3.28771 -3.08271 -5.28966 -0.34232      
 pars:   -3.30222 -3.07091 -5.31779 -0.38305      
 pars:   -3.30222 -3.07091 -5.31779 -0.38305      
psim <- sim_post(fit, what = "p", reps = 10)
plot(psim, type = "lines")
```

--------------------------------

### Example: fit_ssm and sim_fit usage

Source: https://ianjonsen.github.io/aniMotum/reference/sim_fit.html

Example showing how to fit an SSM to the ellie dataset and then simulate predicted tracks with sim_fit. The output includes the fitting progress messages and the final plot call.

```r
fit <- fit_ssm(ellie, model = "crw", time.step = 24)
#> fitting crw SSM to 1 tracks...
#> 
 pars:   1 1 0 -2.91153      
 pars:   0.38512 0.37191 -0.01692 -3.38813      
 pars:   -1.4595 -1.51236 -0.06769 -4.81792      
 pars:   -2.20058 -2.24383 -0.11527 -4.95732      
 pars:   -0.38346 -0.4056 -0.04615 -3.85456      
 pars:   -1.23664 -1.20844 -0.10327 -3.65984      
 pars:   -2.25711 -2.16871 -0.17159 -3.42693      
 pars:   -3.07868 -3.34599 -0.26131 -1.24778      
 pars:   -4.46619 -1.62751 -0.49092 0.12579      
 pars:   -3.18442 -2.86245 -0.30197 -0.80151      
 pars:   -3.40955 -3.30581 -0.49919 -0.40195      
 pars:   -3.06893 -3.16418 -1.05454 -0.36585      
 pars:   -3.5513 -2.81435 -1.34642 -0.44033      
 pars:   -3.30092 -3.01944 -1.10395 -0.39263      
 pars:   -3.27833 -3.03821 -1.38152 -0.39631      
 pars:   -3.23173 -2.97174 -2.21731 -0.40477      
 pars:   -3.28771 -3.08271 -5.28966 -0.34232      
 pars:   -3.30222 -3.07091 -5.31779 -0.38305      
 pars:   -3.30222 -3.07091 -5.31779 -0.38305      
trs <- sim_fit(fit, what = "predicted", reps = 3)
plot(trs)
```

--------------------------------

### Create ssm fit object for print example

Source: https://ianjonsen.github.io/aniMotum/reference/print.ssm.html

Generate an ssm fit object for the example. Uses the ellie dataset with a random walk model and 24-hour time step.

```R
xs <- fit_ssm(ellie, spdf=FALSE, model = "rw", time.step=24, 
control = ssm_control(se = FALSE, verbose = 0))
```

--------------------------------

### Example: fit_ssm and osar usage

Source: https://ianjonsen.github.io/aniMotum/reference/osar.html

Example showing how to generate a ssm fit object and then calculate one-step-ahead residuals with osar. The fit_ssm call is for speed only.

```r
# generate a ssm fit object (call is for speed only)
xs <- fit_ssm(ellie, spdf=FALSE, model = "rw", time.step=24, control = ssm_control(verbose = 0))
#> 

res <- osar(xs)
```

--------------------------------

### Example: fit, simulate, and plot tracks

Source: https://ianjonsen.github.io/aniMotum/reference/plot.sim_fit.html

Example workflow: fits a correlated random walk SSM to the 'ellie' dataset with a 24-hour time step, simulates tracks from the fitted model using 'sim_fit' with what='p' and 2 replicates, then plots the simulated tracks. The fitting output shows progress lines from the optimizer.

```r
fit <- fit_ssm(ellie, model = "crw", time.step = 24)
#> fitting crw SSM to 1 tracks...
#> 
 pars:   1 1 0 -2.91153      
 pars:   0.38512 0.37191 -0.01692 -3.38813      
 pars:   -1.4595 -1.51236 -0.06769 -4.81792      
 pars:   -2.20058 -2.24383 -0.11527 -4.95732      
 pars:   -0.38346 -0.4056 -0.04615 -3.85456      
 pars:   -1.23664 -1.20844 -0.10327 -3.65984      
 pars:   -2.25711 -2.16871 -0.17159 -3.42693      
 pars:   -3.07868 -3.34599 -0.26131 -1.24778      
 pars:   -4.46619 -1.62751 -0.49092 0.12579      
 pars:   -3.18442 -2.86245 -0.30197 -0.80151      
 pars:   -3.40955 -3.30581 -0.49919 -0.40195      
 pars:   -3.06893 -3.16418 -1.05454 -0.36585      
 pars:   -3.5513 -2.81435 -1.34642 -0.44033      
 pars:   -3.30092 -3.01944 -1.10395 -0.39263      
 pars:   -3.27833 -3.03821 -1.38152 -0.39631      
 pars:   -3.23173 -2.97174 -2.21731 -0.40477      
 pars:   -3.28771 -3.08271 -5.28966 -0.34232      
 pars:   -3.30222 -3.07091 -5.31779 -0.38305      
 pars:   -3.30222 -3.07091 -5.31779 -0.38305      
trs <- sim_fit(fit, what = "p", reps = 2)
plot(trs)
```

--------------------------------

### Install aniMotum binary from local file

Source: https://ianjonsen.github.io/aniMotum/index.html

Installs a manually downloaded binary version of aniMotum on Windows or Mac. Replace path_to_file with the actual location of the downloaded file. On Mac, the file must have a .tgz extension, not .tar.

```r
# for Windows
install.packages("path_to_file\aniMotum_1.2-06.zip", 
                 repos=NULL, type="win.binary", dependencies = TRUE)

# for Mac
install.packages("path_to_file/aniMotum_1.2-06.tgz", 
                 repos=NULL, type="mac.binary", dependencies = TRUE)
```

--------------------------------

### smru_haulout usage examples with fit_ssm

Source: https://ianjonsen.github.io/aniMotum/reference/smru_haulout.html

Example usage of smru_haulout and fit_ssm. It shows three ways to add the haulout indicator: calling smru_haulout directly before fitting, passing the haulout file path to fit_ssm's haulout argument, and pre-reading/filtering the haulout file before passing it. The final example demonstrates using id_fun to transform IDs when naming conventions differ between files. The code is wrapped in \dontrun and is not executed.

```r
if (FALSE) { # \dontrun{
## called directly - add ho before fitting
d <- read.csv("my_tracks.csv")
d <- smru_haulout(d, "haulout_ct189.csv")
fit <- fit_ssm(d, model = "mp", time.step = 24,
               control = ssm_control(ho_scale = 0.01, verbose = 0))

## called via fit_ssm haulout argument - equivalent to above
fit <- fit_ssm(d, model = "mp", time.step = 24,
               haulout = "haulout_ct189.csv",
               control = ssm_control(ho_scale = 0.01, verbose = 0))

## pre-read and filter the haulout file before passing to fit_ssm
ho_raw <- read.csv("haulout_ct189.csv")
ho_raw <- subset(ho_raw, cid == "ct189")
fit <- fit_ssm(d, model = "mp", time.step = 24,
               haulout = ho_raw,
               control = ssm_control(ho_scale = 0.01, verbose = 0))

## IDs differ between files: haulout uses "ct189-576-25",
## tracking data uses "ct189_576_25"
d <- smru_haulout(d, "haulout_ct189.csv",
                  id_fun = function(x) gsub("-", "_", x))
} # }
```

--------------------------------

### Example: fit SSM and MPM, then plot

Source: https://ianjonsen.github.io/aniMotum/reference/plot.mpm_df.html

Workflow example: fit an SSM (for speed only), fit an MPM, then plot the 1-D MPM time series on a single page. The fit_ssm call uses spdf=FALSE, model='rw', time.step=72, and ssm_control(verbose=0). The fit_mpm call uses model='jmpm' and prints fitting progress. The plot call uses pages=1.

```r
# generate a ssm fit object (call is for speed only)
xs <- fit_ssm(sese2, spdf=FALSE, model = "rw", time.step=72, control = ssm_control(verbose = 0))
#> 
#> 

# fit mpm to ssm fits
xm <- fit_mpm(xs, model = "jmpm")
#> fitting jmpm...
#> 
 pars:   0 0 0      
 pars:   -0.63038 -0.77416 0.05745      
 pars:   -0.21961 -0.90381 0.95993      
 pars:   0.1024 -0.34798 0.19353      
 pars:   -0.12912 -0.74761 0.74455      
 pars:   -0.17853 -0.8329 0.86216      
 pars:   -0.23363 -0.801 0.86597      
 pars:   -0.27635 -0.61987 0.76146      
 pars:   -0.40458 -0.79384 0.62587      
 pars:   -0.36412 -0.6719 0.40545      
 pars:   -0.38583 -0.70377 0.60569      
 pars:   -0.43697 -0.67572 0.577      
 pars:   -0.38583 -0.70377 0.60569      

# plot 1-D mp timeseries on 1 page
plot(xm, pages = 1)
```

--------------------------------

### Example: fit_ssm with ssm_control using nlminb

Source: https://ianjonsen.github.io/aniMotum/reference/ssm_control.html

Shows how to pass ssm_control to fit_ssm, specifying the nlminb optimizer and setting eval.max to 2000. The output shows the fitting progress with parameter traces.

```R
fit <- fit_ssm(ellie,
vmax = 4,
model = "crw",
time.step = 72,
control = ssm_control(
    optim = "nlminb",
    eval.max = 2000)
    )
#> fitting crw SSM to 1 tracks...
#> 
 pars:   1 1 0 -3.35398      
 pars:   0.39011 0.36995 -0.01715 -3.83437      
 pars:   -1.43958 -1.5202 -0.06862 -5.27553      
 pars:   -2.32555 -2.54729 -0.19371 -4.12372      
 pars:   -0.8182 -0.89535 -0.06643 -4.50381      
 pars:   -1.32518 -1.50336 -0.13293 -2.80663      
 pars:   -2.70765 -2.68894 -0.23564 -2.37746      
 pars:   -3.27563 -3.39761 -0.40156 -0.74674      
 pars:   -4.54561 -2.25375 -0.86806 -0.13627      
 pars:   -3.3297 -3.27793 -0.43777 -0.61077      
 pars:   -3.36938 -3.29518 -0.51676 -0.44068      
 pars:   -3.37129 -3.28379 -0.68713 -0.35188      
 pars:   -3.37129 -3.28379 -0.68713 -0.35188      

```

--------------------------------

### Example: fit SSM model and filter simulations

Source: https://ianjonsen.github.io/aniMotum/reference/sim_filter.html

Complete example workflow: fit a crw SSM to Argos LS data, generate 5 simulated paths, filter to top 40% of flag values, and compare unfiltered and filtered paths with a plot. The plot comparison is wrapped in \donttest{}.

```r
## fit crw model to Argos LS data
fit <- fit_ssm(ellie, model = "crw", time.step = 72)
#> fitting crw SSM to 1 tracks...
#> 
 pars:   1 1 0 -3.35398      
 pars:   0.39011 0.36995 -0.01715 -3.83437      
 pars:   -1.43958 -1.5202 -0.06862 -5.27553      
 pars:   -2.32555 -2.54729 -0.19371 -4.12372      
 pars:   -0.8182 -0.89535 -0.06643 -4.50381      
 pars:   -1.32518 -1.50336 -0.13293 -2.80663      
 pars:   -2.70765 -2.68894 -0.23564 -2.37746      
 pars:   -3.27563 -3.39761 -0.40156 -0.74674      
 pars:   -4.54561 -2.25375 -0.86806 -0.13627      
 pars:   -3.3297 -3.27793 -0.43777 -0.61077      
 pars:   -3.36938 -3.29518 -0.51676 -0.44068      
 pars:   -3.37129 -3.28379 -0.68713 -0.35188      
 pars:   -3.37129 -3.28379 -0.68713 -0.35188      

set.seed(pi)
## generate 5 simulated paths from ssm fit
trs <- sim_fit(fit, what = "predicted", reps = 5)

## filter simulations and keep paths in top 40% of flag values
trs_f <- sim_filter(trs, keep = 0.4, flag = 2)

## compare unfiltered and filtered simulated paths
# \donttest{
plot(trs) | plot(trs_f)

# }
```

--------------------------------

### Install aniMotum from R-Universe

Source: https://ianjonsen.github.io/aniMotum/index.html

Install aniMotum from the R-Universe repository. This installs all Imported and Suggested R packages from CRAN and R-universe. Answer 'Yes' if prompted to install the source version. First-time installation may take a while if dependent packages need to be compiled.

```R
# install from my R-universe repository
install.packages("aniMotum", 
                 repos = c("https://cloud.r-project.org",
                 "https://ianjonsen.r-universe.dev"),
                 dependencies = TRUE)
```

--------------------------------

### Example: fit_ssm and grab predicted values

Source: https://ianjonsen.github.io/aniMotum/reference/grab.html

Example from the documentation: first generate an ssm fit object using fit_ssm on the ellie dataset, then grab predicted values as an un-projected tibble. The output of fit_ssm is not shown in the source.

```r
## generate an ssm fit object
xs <- fit_ssm(ellie, spdf=FALSE, model = "rw", time.step=24, control = ssm_control(verbose = 0))
#> 

## grab predicted values as an un-projected tibble
preds <- grab(xs, what = "predicted")
```

--------------------------------

### Install aniMotum from GitHub with remotes

Source: https://ianjonsen.github.io/aniMotum/index.html

Installs aniMotum from GitHub source using remotes. Requires proper compiler tools and libraries, such as Rtools on Windows and Xcode Command Line Tools plus a Gnu Fortran compiler on Mac.

```r
remotes::install_github("ianjonsen/aniMotum")
```

--------------------------------

### Example: fit ssm and mpm models, then join predicted values

Source: https://ianjonsen.github.io/aniMotum/reference/join.html

Complete example from the documentation: loads example aniMotum fit objects, fits an ssm model, fits an mpm model, then joins the predicted values into an un-projected tibble. The output shows the resulting tibble structure with columns including id, date, lon, lat, x, y, x.se, y.se, gap_flag, logit_g, logit_g.se, and g.

```r
## load example aniMotum fit objects (to save time)
## generate a ssm fit object
xs <- fit_ssm(ellie, spdf=FALSE, model = "rw", time.step=24, control = ssm_control(verbose = 0))
#> 
xm <- fit_mpm(xs, what = "p", model = "mpm")
#> fitting mpm...
#> 
 pars:   0 0 0      
 pars:   -0.65944 -0.75114 -0.03049      
 pars:   -2.63775 -3.00457 -0.12194      
 pars:   -2.73937 -4.15687 -0.30416      
 pars:   -0.86743 -1.09171 -0.05785      
 pars:   -1.18976 -1.82073 -0.12602      
 pars:   -0.8491 -2.52724 -0.28347      
 pars:   -1.13954 -1.92487 -0.14923      
 pars:   -1.13979 -1.98651 -0.24977      
 pars:   -1.12989 -1.92935 -0.35244      
 pars:   -1.12789 -1.95509 -0.46751      
 pars:   -1.12669 -1.93037 -0.58281      
 pars:   -1.12848 -1.92558 -0.81238      
 pars:   -1.12944 -1.87726 -1.377      
 pars:   -0.809 -1.84174 -2.25192      
 pars:   -1.0756 -1.9101 -1.45917      
 pars:   -1.1263 -1.96239 -1.53282      
 pars:   -1.1094 -1.91349 -1.46293      
 pars:   -1.1047 -1.90968 -1.49657      
 pars:   -1.10821 -1.90297 -1.55182      
 pars:   -1.1032 -1.91244 -1.6407      
 pars:   -1.10231 -1.91103 -1.70906      
 pars:   -1.10231 -1.91103 -1.70906      

## join predicted values as an un-projected tibble
xsm <- join(xs, xm)
xsm
#> # A tibble: 113 × 12
#>    id    date                  lon   lat      x      y  x.se   y.se gap_flag
#>    <chr> <dttm>              <dbl> <dbl>  <dbl>  <dbl> <dbl>  <dbl>    <int>
#>  1 54591 2012-03-05 05:00:00  111. -66.4 12309. -9956.  1.38  1.44         0
#>  2 54591 2012-03-06 05:00:00  110. -66.4 12287. -9947. 25.6   0.419        0
#>  3 54591 2012-03-07 05:00:00  110. -66.5 12298. -9963.  4.48  2.47         0
#>  4 54591 2012-03-08 05:00:00  110. -66.4 12288. -9949. 32.7  28.8          0
#>  5 54591 2012-03-09 05:00:00  110. -66.5 12299. -9972.  9.07  2.55         0
#>  6 54591 2012-03-10 05:00:00  111. -66.4 12302. -9958. 21.3  18.6          0
#>  7 54591 2012-03-11 05:00:00  111. -66.5 12323. -9967. 22.3  19.6          0
#>  8 54591 2012-03-12 05:00:00  110. -66.5 12287. -9962. 28.2   9.78         0
#>  9 54591 2012-03-13 05:00:00  110. -66.4 12288. -9950. 35.4  31.1          0
#> 10 54591 2012-03-14 05:00:00  110. -66.4 12291. -9938. 32.8  28.9          0
#> # ℹ 103 more rows
#> # ℹ 3 more variables: logit_g <dbl>, logit_g.se <dbl>, g <dbl>
```

--------------------------------

### Example: generate osar residuals and plot QQ

Source: https://ianjonsen.github.io/aniMotum/reference/plot.osar.html

Fits a random walk SSM to the ellie dataset, computes one-step-ahead residuals with osar(), then plots the QQ residuals. The fit_ssm call is speed-optimized with se=FALSE and verbose=0.

```r
## generate a fG_ssm fit object (call is for speed only)
xs <- fit_ssm(ellie, spdf=FALSE, model = "rw", time.step=24, 
control = ssm_control(se = FALSE, verbose = 0))
#> 

res <- osar(xs) 

plot(res, type = "qq")
```

--------------------------------

### Example: fit_ssm and plot fitted locations

Source: https://ianjonsen.github.io/aniMotum/reference/plot.ssm_df.html

Fits a random walk model to the sese2 dataset (for speed) and plots fitted locations as 1-D time series on a single page. The fit_ssm call uses control = ssm_control(verbose = 0) to suppress output.

```r
## generate a ssm fit object (call is for speed only)
xs <- fit_ssm(sese2, spdf=FALSE, model = "rw", time.step=72, control = ssm_control(verbose = 0))
#> 
#> 

# plot fitted locations as 1-D timeseries on 1 page
plot(xs, what = "f", pages = 1)
```

--------------------------------

### sim_fit(x, what, reps, start, end, grad, beta, cpf, sim_only)

Source: https://ianjonsen.github.io/aniMotum/reference/sim_fit.html

Simulates animal tracks from a fitted SSM object using the rw or crw process models. Returns a fG_sim_fit object containing the simulated paths.

```APIDOC
## sim_fit(x, what, reps, start, end, grad, beta, cpf, sim_only)

### Description
Simulates animal tracks from a fitted SSM object using the rw or crw process models. Returns a fG_sim_fit object containing the simulated paths.

### Method
Function call

### Endpoint
sim_fit(x, what = c("fitted", "predicted"), reps = 1, start = NULL, end = NULL, grad = NULL, beta = c(-300, -300), cpf = FALSE, sim_only = FALSE)

### Parameters
#### Arguments
- **x** (ssm fit object) - Required - A `ssm` fit object with class `ssm_df`.
- **what** (character) - Optional - Simulate fitted (typically irregular in time) or predicted (typically regular in time) locations. Default is c("fitted", "predicted").
- **reps** (numeric) - Optional - Number of replicate tracks to simulate from an `ssm` model fit object. Default is 1.
- **start** (numeric vector) - Optional - A 2-element vector for the simulated track start location (lon,lat or x,y). Default is NULL.
- **end** (numeric vector) - Optional - A 2-element vector for the simulated track end location (lon,lat or x,y). Default is NULL.
- **grad** (SpatRaster) - Optional - A SpatRaster of x- and y-gradients as separate layers (see details). Default is NULL.
- **beta** (numeric vector) - Optional - A 2-element vector of parameters defining the potential function magnitude in x- and y-directions (ignored if `is.null(grad)`, ie. no potential function; see details). Default is c(-300, -300).
- **cpf** (logical) - Optional - Should simulated tracks return to their start point (ie. a central-place forager). Default is FALSE.
- **sim_only** (logical) - Optional - Do not include `ssm` estimated location in output (default is FALSE). Default is FALSE.

### Value
A `fG_sim_fit` object containing the paths simulated from a `ssm` fit object.

### Details
A potential function can be applied to the simulated paths to help avoid locations on land (or in water), using the `grad` and `beta` arguments. A coarse-resolution rasterStack of global x- and y-gradients of distance to land are provided. Stronger beta parameters result in stronger land (water) avoidance but may also introduce undesirable/unrealistic artefacts (zig-zags) in the simulated paths. See Brillinger et al. (2012) and `vignette("momentuHMM", package = "momentuHMM")` for more details on the use of potential functions for simulating constrained animal movements. WARNING: This application of potential functions to constrain simulated paths is experimental, likely to change in future releases, and NOT guaranteed to work enitrely as intended, especially if `cpf = TRUE`!

### References
Brillinger DR, Preisler HK, Ager AA, Kie J (2012) The use of potential functions in modelling animal movement. In: Guttorp P., Brillinger D. (eds) Selected Works of David Brillinger. Selected Works in Probability and Statistics. Springer, New York. pp. 385-409.

### Examples
```r
fit <- fit_ssm(ellie, model = "crw", time.step = 24)
#> fitting crw SSM to 1 tracks...
#> 
 pars:   1 1 0 -2.91153      
 pars:   0.38512 0.37191 -0.01692 -3.38813      
 pars:   -1.4595 -1.51236 -0.06769 -4.81792      
 pars:   -2.20058 -2.24383 -0.11527 -4.95732      
 pars:   -0.38346 -0.4056 -0.04615 -3.85456      
 pars:   -1.23664 -1.20844 -0.10327 -3.65984      
 pars:   -2.25711 -2.16871 -0.17159 -3.42693      
 pars:   -3.07868 -3.34599 -0.26131 -1.24778      
 pars:   -4.46619 -1.62751 -0.49092 0.12579      
 pars:   -3.18442 -2.86245 -0.30197 -0.80151      
 pars:   -3.40955 -3.30581 -0.49919 -0.40195      
 pars:   -3.06893 -3.16418 -1.05454 -0.36585      
 pars:   -3.5513 -2.81435 -1.34642 -0.44033      
 pars:   -3.30092 -3.01944 -1.10395 -0.39263      
 pars:   -3.27833 -3.03821 -1.38152 -0.39631      
 pars:   -3.23173 -2.97174 -2.21731 -0.40477      
 pars:   -3.28771 -3.08271 -5.28966 -0.34232      
 pars:   -3.30222 -3.07091 -5.31779 -0.38305      
 pars:   -3.30222 -3.07091 -5.31779 -0.38305      
trs <- sim_fit(fit, what = "predicted", reps = 3)
plot(trs)
```
```

--------------------------------

### Re-route predicted locations with route_path

Source: https://ianjonsen.github.io/aniMotum/reference/route_path.html

Example usage of route_path on a fitted ssm object. It first checks that pathroutr is installed, fits a correlated random walk model to the ellie dataset, re-routes the predicted locations, and extracts the re-routed path with grab. The output shows the fitting progress and a tibble of re-routed locations.

```R
# if 'pathroutr' is installed then ok to use route_path()
if(requireNamespace("pathroutr", quietly = TRUE)) {
  fit <- fit_ssm(ellie, vmax = 4, model = "crw", time.step = 24)
  fit <- route_path(fit, what = "predicted")
  grab(fit, what = "rerouted")
}
#> fitting crw SSM to 1 tracks...
#> 
 pars:   1 1 0 -2.91153      
 pars:   0.38512 0.37191 -0.01692 -3.38813      
 pars:   -1.4595 -1.51236 -0.06769 -4.81792      
 pars:   -2.20058 -2.24383 -0.11527 -4.95732      
 pars:   -0.38346 -0.4056 -0.04615 -3.85456      
 pars:   -1.23664 -1.20844 -0.10327 -3.65984      
 pars:   -2.25711 -2.16871 -0.17159 -3.42693      
 pars:   -3.07868 -3.34599 -0.26131 -1.24778      
 pars:   -4.46619 -1.62751 -0.49092 0.12579      
 pars:   -3.18442 -2.86245 -0.30197 -0.80151      
 pars:   -3.40955 -3.30581 -0.49919 -0.40195      
 pars:   -3.06893 -3.16418 -1.05454 -0.36585      
 pars:   -3.5513 -2.81435 -1.34642 -0.44033      
 pars:   -3.30092 -3.01944 -1.10395 -0.39263      
 pars:   -3.27833 -3.03821 -1.38152 -0.39631      
 pars:   -3.23173 -2.97174 -2.21731 -0.40477      
 pars:   -3.28771 -3.08271 -5.28966 -0.34232      
 pars:   -3.30222 -3.07091 -5.31779 -0.38305      
 pars:   -3.30222 -3.07091 -5.31779 -0.38305      
#> # A tibble: 108 × 8
#>    id    date                  lon   lat      x      y        x.se        y.se
#>    <chr> <dttm>              <dbl> <dbl>  <dbl>  <dbl>       <dbl>       <dbl>
#>  1 54591 2012-03-05 05:00:00  111. -66.4 12309. -9956.  0.00001000  0.00001000
#>  2 54591 2012-03-06 05:00:00  110. -66.4 12299. -9947. 17.4         0.00763   
#>  3 54591 2012-03-07 05:00:00  110. -66.4 12296. -9956. 25.1        19.2       
#>  4 54591 2012-03-08 05:00:00  110. -66.5 12298. -9970. 21.1        18.5       
#>  5 54591 2012-03-09 05:00:00  110. -66.5 12300. -9972.  8.11        1.22      
#>  6 54591 2012-03-10 05:00:00  110. -66.4 12300. -9955.  5.10        3.03      
#>  7 54591 2012-03-11 05:00:00  110. -66.4 12292. -9958. 18.1        14.3       
#>  8 54591 2012-03-12 05:00:00  110. -66.5 12285. -9962. 18.2         4.41      
#>  9 54591 2012-03-13 05:00:00  110. -66.4 12287. -9952.  5.83        4.87      
#> 10 54591 2012-03-14 05:00:00  110. -66.4 12291. -9939.  4.31        4.36      
#> # ℹ 98 more rows

```

--------------------------------

### Check R version with R.Version()

Source: https://ianjonsen.github.io/aniMotum/index.html

Check the installed R version to ensure it is at least 4.0.0 (preferably 4.2.0 or higher) before installing aniMotum.

```R
R.Version()
```

--------------------------------

### Use optim optimizer via ssm_control

Source: https://ianjonsen.github.io/aniMotum/articles/SSM_fitting.html

Shows how to select the 'optim' optimizer instead of the default 'nlminb' by passing a named list from ssm_control to the control argument of fit_ssm.

```r
fit <- fit_ssm(ellie, 
               model = "crw", 
               time.step = 24,
               control = ssm_control(optim = "optim",
                                     verbose = 0))
```

--------------------------------

### join() function signature

Source: https://ianjonsen.github.io/aniMotum/reference/join.html

Shows the usage signature for `join()`, including all arguments and their defaults. Use this to understand the function's parameters before calling it.

```r
join(
  ssm,
  mpm,
  what.ssm = "predicted",
  as_sf = FALSE,
  normalise = FALSE,
  group = FALSE
)
```

--------------------------------

### Custom date color palette

Source: https://ianjonsen.github.io/aniMotum/articles/Mapping.html

Uses hcl.colors() to specify a new color palette for displaying date-along-track. The example uses the 'Plasma' palette with 100 colors.

```R
map(fit, 
    what = "p", 
    aes = aes_lst(mp=FALSE, 
                  conf=FALSE, 
                  date_pal = hcl.colors(n=100, "Plasma")), 
    by.date = TRUE, by.id=FALSE)
```
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