### Install IsoplotR Development Version from GitHub

Source: https://github.com/pvermees/isoplotr/blob/master/README.md

Install the current development version of IsoplotR from Github using the remotes package. Ensure you have the remotes package installed first.

```r
install.packages('remotes')
remotes::install_github('pvermees/IsoplotR')
```

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

### Install IsoplotR Package

Source: https://context7.com/pvermees/isoplotr/llms.txt

Installs the stable version from CRAN or the development version from GitHub. Loads the library for use.

```r
install.packages('IsoplotR')

install.packages('remotes')
remotes::install_github('pvermees/IsoplotR')

library(IsoplotR)
```

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

### Load IsoplotR Library

Source: https://github.com/pvermees/isoplotr/blob/master/README.md

Load the IsoplotR package into your R session after installation to start using its functions.

```r
library(IsoplotR)
```

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

### Install IsoplotR from CRAN

Source: https://github.com/pvermees/isoplotr/blob/master/README.md

Use this command to install the most recent stable version of IsoplotR from CRAN.

```r
install.packages('IsoplotR')
```

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

### Get and Set Decay Constants and Isotopic Parameters

Source: https://context7.com/pvermees/isoplotr/llms.txt

Retrieve or override decay constants, isotopic ratios, and other parameters used in calculations. Supports JSON import/export.

```r
# Read current U-238 decay constant [value, 1-sigma error]
settings('lambda', 'U238')

# Override with a custom value
settings('lambda', 'U238', 1.55125e-10, 0.17e-12)

# Read the 238U/235U isotopic ratio
settings('iratio', 'U238U235')

# Set the significance level for confidence intervals
settings('alpha', 0.05)
```

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

### Perform Kernel Density Estimation (KDE)

Source: https://context7.com/pvermees/isoplotr/llms.txt

Generate adaptive kernel density estimates for age distributions, optionally overlaid with a histogram. Supports log transformation and multiple samples. Requires attaching example data.

```r
attach(examples)

kde(DZ, show.hist = TRUE, binwidth = 50)

kde(ArAr, log = TRUE)

Ages <- c(250, 260, 255, 480, 485, 1000, 1010)
kde(ages, from = 100, to = 1200)

kde(DZ, ncol = 2, nmodes = 3, sigdig = 3)

k <- kde(DZ, plot = FALSE)
str(k)   # list: x (ages), y (density), bw (bandwidth)
```

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

### Read Data and Plot Isochron

Source: https://github.com/pvermees/isoplotr/blob/master/README.md

Example of navigating to built-in data files, reading a CSV file using the 'Rb-Sr' method with format 1, and generating an isochron plot.

```r
setwd(system.file(package='IsoplotR')) # navigate to the built-in data files
RbSr <- read.data('RbSr1.csv',method='Rb-Sr',format=1)  
isochron(RbSr)
```

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

### Generate U-Th-He Ternary / Logratio Plots

Source: https://context7.com/pvermees/isoplotr/llms.txt

Visualize U-Th(-Sm)-He data on a logratio plot or ternary diagram and compute a compositional central age. Requires attaching example data.

```r
attach(examples)

helioplot(UThHe, logratio = TRUE)

helioplot(UThHe, logratio = FALSE)

helioplot(UThHe, model = 3)

helioplot(UThHe, levels = 1:nrow(UThHe$x))
```

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

### Perform Multidimensional Scaling (MDS) on Detrital Age Distributions

Source: https://context7.com/pvermees/isoplotr/llms.txt

Reduce pairwise distances between detrital age samples into a 2D configuration for visual comparison. Supports classical and nonmetric MDS, and different distance metrics. Requires attaching example data.

```r
attach(examples)

mds(DZ, classical = TRUE)

mds(DZ, classical = FALSE, nnlines = TRUE)

mds(DZ, shepard = TRUE)

mds(DZ, method = 'W2')
```

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

### Create Cumulative Age Distribution (CAD) plots

Source: https://context7.com/pvermees/isoplotr/llms.txt

Plot empirical cumulative distribution functions for one or more age datasets for direct visual comparison without smoothing. Requires attaching example data.

```r
attach(examples)

cad(ArAr)

cad(DZ, col = 'rainbow')

cad(DZ[1:2], verticals = TRUE, col = c('blue', 'red'))
```

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

### Generate Galbraith radial plots

Source: https://context7.com/pvermees/isoplotr/llms.txt

Visualize ages with different precisions on a Galbraith radial plot. Optionally overlay finite or continuous mixture models. Requires attaching example data.

```r
attach(examples)

radialplot(FT1)

radialplot(ArAr, transformation = 'log')

radialplot(UPb, type = 2, show.numbers = TRUE)

radialplot(FT1, k = 2)

radialplot(FT1, z0 = 100, from = 50, to = 300)
```

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

### Calculate Central Age for Overdispersed Data

Source: https://context7.com/pvermees/isoplotr/llms.txt

Fit a random effects model to fission track or U-Th-He data to decompose scatter into analytical uncertainty and geological dispersion. Requires attaching example data.

```r
attach(examples)

central(FT1)
# Returns: t (central age), s[t], disp (overdispersion), MSWD, p.value

central(UThHe)

age(FT1, central = TRUE)
age(UThHe, central = TRUE)
```

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

### Calculate Weighted Mean Age with Outlier Detection

Source: https://context7.com/pvermees/isoplotr/llms.txt

Compute a weighted mean from heteroscedastic ages using inverse-variance or random effects models. Applies modified Chauvenet criterion for outlier rejection. Requires attaching example data.

```r
attach(examples)

weightedmean(ArAr, random.effects = TRUE)

weightedmean(UPb, type = 2, detect.outliers = TRUE)

mat <- cbind(c(100, 102, 99, 150),   # ages
             c(2,   3,   2,   5))    # 1-sigma errors
weightedmean(mat, random.effects = FALSE)
```

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

### settings()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Allows saving and restoring decay constants and isotopic ratios as JSON for reproducible workflows.

```APIDOC
## settings()

### Description
Saves or loads the current settings (e.g., decay constants, isotopic ratios) to or from a JSON file.

### Parameters
#### Path Parameters
- **fname** (string) - Required - The file path for the JSON file to save or load settings from.

### Request Example
```r
# Export all settings to a JSON file
settings(fname = '/tmp/mySettings.json')

# Import settings from a JSON file
settings(fname = '/tmp/mySettings.json')
```
```

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

### settings()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Retrieves or sets decay constants, isotopic ratios, and other parameters used in geochronological calculations.

```APIDOC
## settings()

### Description
Retrieves or overrides the decay constants, isotopic ratios, molar masses, and fission track parameters used internally by all calculations. Supports import/export via JSON.

### Usage
settings(parameter, isotope, value = NULL, error = NULL)

### Parameters
- `parameter`: The parameter to get or set (e.g., 'lambda', 'iratio', 'alpha').
- `isotope`: The specific isotope or parameter name (e.g., 'U238', 'U238U235').
- `value`: The new value to set (optional).
- `error`: The error associated with the new value (optional).

### Examples
```r
# Read current U-238 decay constant [value, 1-sigma error]
settings('lambda', 'U238')

# Override with a custom value
settings('lambda', 'U238', 1.55125e-10, 0.17e-12)

# Read the 238U/235U isotopic ratio
settings('iratio', 'U238U235')

# Set the significance level for confidence intervals
settings('alpha', 0.05)
```
```

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

### kde()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Performs adaptive kernel density estimation for age distributions, with options for overlaying histograms and customizing plot appearance.

```APIDOC
## kde()

### Description
Generates adaptive kernel density estimates (Botev 2010 bandwidth + Abramson 1982 adaptive modifier) for age distributions, optionally overlaid with a histogram.

### Usage
kde(data, show.hist = FALSE, binwidth = NULL, log = FALSE, from = NULL, to = NULL, ncol = 1, nmodes = NULL, sigdig = NULL, plot = TRUE)

### Parameters
- `data`: The input age data (e.g., DZ, ArAr) or a numeric vector of ages.
- `show.hist`: Logical, whether to show a histogram overlay.
- `binwidth`: The binwidth for the histogram.
- `log`: Logical, whether to perform estimation on a log scale.
- `from`, `to`: Range for the density estimation.
- `ncol`: Number of columns for multiple sample plots.
- `nmodes`: Number of modes to highlight.
- `sigdig`: Number of significant digits for labels.
- `plot`: Logical, whether to generate a plot.

### Examples
```r
attach(examples)

# KDE of detrital zircon ages with histogram
kde(DZ, show.hist = TRUE, binwidth = 50)

# Ar-Ar KDE on log scale
kde(ArAr, log = TRUE)

# KDE from a numeric vector
ages <- c(250, 260, 255, 480, 485, 1000, 1010)
kde(ages, from = 100, to = 1200)

# Multiple samples in one panel; label top-3 modes
kde(DZ, ncol = 2, nmodes = 3, sigdig = 3)

# Capture KDE object without plotting
k <- kde(DZ, plot = FALSE)
str(k)   # list: x (ages), y (density), bw (bandwidth)
```
```

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

### Export and Import Settings with JSON

Source: https://context7.com/pvermees/isoplotr/llms.txt

Use the settings() function to export current geochronometer settings to a JSON file or import them from a JSON file. This is useful for reproducible workflows.

```r
settings(fname = '/tmp/mySettings.json')
```

```r
settings(fname = '/tmp/mySettings.json')
```

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

### age()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Calculates isotopic ages and propagates uncertainties for various geochronometers. It dispatches methods based on the input data class and supports different age calculation types.

```APIDOC
## `age()` — Calculate isotopic ages and propagate uncertainties

A generic S3 function dispatching on the class of the input object. Returns individual grain ages (type 1), concordia ages (type 2), discordia ages (type 3–5), isochron ages, or central ages, with configurable uncertainty output.

### Usage
```r
age(x, ...)
```

### Parameters
*   **x**: An object of a typed IsoplotR data class (e.g., `UPb`, `ArAr`) or a numeric vector for single ratio ages.
*   **type** (integer, optional): Specifies the type of age to calculate (e.g., 1 for individual grain ages, 2 for concordia ages, 3-5 for discordia ages).
*   **oerr** (integer, optional): Sets the type of output errors (e.g., 1 for 1-sigma, 2 for 2-sigma absolute errors).
*   **isochron** (boolean, optional): If TRUE, calculates an isochron age.
*   **i2i** (boolean, optional): If TRUE, derives the initial `40Ar/36Ar` ratio from the isochron intercept.
*   **central** (boolean, optional): If TRUE, calculates a central age.
*   **method** (string, optional): Specifies the geochronometer method when calculating age from a single ratio.

### Request Example
```r
attach(examples)

# U-Pb: individual grain ages (t.75, t.68, t.76, t.conc, disc)
tUPb <- age(UPb, type = 1, oerr = 2)         # 2-sigma absolute errors
head(tUPb)

# U-Pb concordia age from all grains
tconc <- age(UPb, type = 2)
print(tconc$age)   # [age, s[age]]

# U-Pb discordia (Ludwig maximum likelihood)
tdisc <- age(UPb, type = 3)

# Ar-Ar individual ages
tArAr <- age(ArAr, oerr = 1)

# Ar-Ar isochron age (i2i = initial 40Ar/36Ar from isochron intercept)
tiso <- age(ArAr, isochron = TRUE, i2i = TRUE)

# Fission track central age
tft <- age(FT1, central = TRUE)

# Single scalar ratio -> age (no object needed)
age(c(0.511, 0.001), method = 'U238-Pb206')  # returns [t68, s[t68]]

# U-Th-He age from raw measurements
age(c(238, 2, 232, 3, 4e-9, 1e-11), method = 'U-Th-He')
```
```

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

### age2ratio()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Converts ages (with uncertainties) into isotopic ratios for supported geochronometers, including error propagation.

```APIDOC
## age2ratio()

### Description
Predicts isotopic ratios from ages (with uncertainties) for any supported geochronometer, performing full error propagation.

### Parameters
#### Path Parameters
- **tt** (numeric or numeric vector) - Required - The age(s) in Ma.
- **st** (numeric) - Optional - The uncertainty in the age(s) in Ma. If not provided, no error propagation is performed.
- **ratio** (string) - Required - The isotopic ratio to calculate. Supported values include 'Pb206U238', 'Wetherill', 'Tera-Wasserburg', 'Ar40Ar39', 'Stacey-Kramers'.
- **J** (numeric) - Optional - The J-factor, required for 'Ar40Ar39' ratio.
- **sJ** (numeric) - Optional - The uncertainty in the J-factor, required for 'Ar40Ar39' ratio when uncertainty is needed.

### Request Example
```r
# Predict Pb206/U238 ratio at 500 Ma (±5 Ma)
age2ratio(tt = 500, st = 5, ratio = 'Pb206U238')

# Wetherill concordia coordinates at 1000 Ma
age2ratio(tt = 1000, st = 10, ratio = 'Wetherill')
# Returns list: $x (Pb207U235, Pb206U238), $cov (2x2 covariance)

# Tera-Wasserburg coordinates
age2ratio(tt = 1000, st = 10, ratio = 'Tera-Wasserburg')

# Ar-Ar ratio requiring a J-factor
age2ratio(tt = 65, st = 1, ratio = 'Ar40Ar39', J = 0.01, sJ = 0.0001)

# Stacey-Kramers Pb isotope evolution model
age2ratio(tt = c(500, 1000, 2000), ratio = 'Stacey-Kramers')
```

### Response
#### Success Response (200)
- **x** (numeric or list) - The calculated isotopic ratio(s) or coordinates.
- **cov** (matrix) - The covariance matrix of the calculated ratios, if uncertainties are provided.
```

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

### read.data()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Loads geochronological data from CSV files or R matrices into typed S3 classes for various geochronometers. It supports multiple data formats for each method.

```APIDOC
## `read.data()` — Load geochronological data from CSV or matrix

Parses a `.csv` file (or an R matrix) and casts it into one of IsoplotR's typed data classes. The `method` argument selects the geochronometer and `format` specifies the column layout (up to 12 formats for U-Pb alone).

### Usage
```r
read.data(file, method, format, ...)
```

### Parameters
*   **file**: Path to the CSV file or an R matrix containing the data.
*   **method** (string): The geochronometer to use (e.g., 'U-Pb', 'Rb-Sr', 'Ar-Ar', 'fissiontracks', 'detritals').
*   **format** (integer, optional): Specifies the column layout of the input data. Defaults to 1 if not provided.

### Request Example
```r
library(IsoplotR)
UPb <- read.data('UPb1.csv', method = 'U-Pb', format = 1)
RbSr <- read.data('RbSr1.csv', method = 'Rb-Sr', format = 1)
ArAr <- read.data('ArAr1.csv', method = 'Ar-Ar', format = 1)
FT <- read.data('FT1.csv', method = 'fissiontracks', format = 1)
DZ <- read.data('DZ.csv', method = 'detritals')

mat <- matrix(c(0.0753, 0.0002, 0.0511, 0.0003, 0.9),
              ncol = 5,
              dimnames = list(NULL, c('Pb207U235','errPb207U235',
                                     'Pb206U238','errPb206U238','rXY')))
UPb_mat <- read.data(mat, method = 'U-Pb', format = 1)
```
```

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

### isochron()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Fits isochrons to multi-aliquot data using various regression algorithms and computes isochron ages. It supports different regression models and handling of overdispersion.

```APIDOC
## `isochron()` — Fit isochrons and compute isochron ages

Fits regression lines through cogenetic multi-aliquot data using York (2004), Titterington, or Ludwig algorithms. Handles overdispersion via MSWD-inflation, total least squares, or geological scatter models.

### Usage
```r
isochron(x, ...)
```

### Parameters
*   **x**: An object of a typed IsoplotR data class (e.g., `RbSr`, `ArAr`) containing multi-aliquot data.
*   **i2i** (boolean, optional): If TRUE, derives the initial `40Ar/36Ar` ratio from the isochron intercept.
*   **inverse** (boolean, optional): If TRUE, fits an inverse isochron.

### Request Example
```r
attach(examples)

# Rb-Sr isochron
isochron(RbSr)
# Returns: age, s[age], initial 87Sr/86Sr, MSWD, p-value

# Ar-Ar inverse isochron (i2i = derive initial 40Ar/36Ar from intercept)
isochron(ArAr, i2i = TRUE, inverse = TRUE)
```
```

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

### Plot U-Pb Concordia Diagrams with concordia()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Plots U-Pb data on Wetherill or Tera-Wasserburg concordia diagrams, fits discordia lines, and computes concordia ages and MSWD. Ellipses can be colored by specific data levels.

```r
attach(examples)

# Wetherill concordia with error ellipses (default)
concordia(UPb, type = 1, show.ellipses = 1)

# Tera-Wasserburg concordia with discordia line
concordia(UPb, type = 2, exterr = TRUE)

# Colour ellipses by U concentration levels
concordia(UPb, type = 1,
          levels     = UPb$x[, 'Pb207U235'],
          clabel     = '207Pb/235U',
          ellipse.fill = c('white', 'red'))

# Suppress plot, just get concordia age
ca <- concordia_age(UPb, i = 1, exterr = FALSE)
print(ca$age)   # [t.conc, s[t.conc], MSWD, p-value]
```

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

### Predict Isotopic Ratios from Ages with age2ratio()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Converts ages (with uncertainties) to isotopic ratios for supported geochronometers, including error propagation. Specify the age (tt), its uncertainty (st), and the desired ratio.

```r
# Predict Pb206/U238 ratio at 500 Ma (±5 Ma)
age2ratio(tt = 500, st = 5, ratio = 'Pb206U238')
```

```r
# Wetherill concordia coordinates at 1000 Ma
age2ratio(tt = 1000, st = 10, ratio = 'Wetherill')
# Returns list: $x (Pb207U235, Pb206U238), $cov (2x2 covariance)
```

```r
# Tera-Wasserburg coordinates
age2ratio(tt = 1000, st = 10, ratio = 'Tera-Wasserburg')
```

```r
# Ar-Ar ratio requiring a J-factor
age2ratio(tt = 65, st = 1, ratio = 'Ar40Ar39', J = 0.01, sJ = 0.0001)
```

```r
# Stacey-Kramers Pb isotope evolution model
age2ratio(tt = c(500, 1000, 2000), ratio = 'Stacey-Kramers')
```

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

### Calculate Isotopic Ages with age()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Calculates various isotopic ages (grain, concordia, discordia, isochron, central) and propagates uncertainties. Can also calculate age from a single ratio.

```r
attach(examples)

# U-Pb: individual grain ages (t.75, t.68, t.76, t.conc, disc)
tUPb <- age(UPb, type = 1, oerr = 2)         # 2-sigma absolute errors
head(tUPb)

# U-Pb concordia age from all grains
tconc <- age(UPb, type = 2)
print(tconc$age)   # [age, s[age]]

# U-Pb discordia (Ludwig maximum likelihood)
tdisc <- age(UPb, type = 3)

# Ar-Ar individual ages
tArAr <- age(ArAr, oerr = 1)

# Ar-Ar isochron age (i2i = initial 40Ar/36Ar from isochron intercept)
tiso <- age(ArAr, isochron = TRUE, i2i = TRUE)

# Fission track central age
tft <- age(FT1, central = TRUE)

# Single scalar ratio -> age (no object needed)
age(c(0.511, 0.001), method = 'U238-Pb206')  # returns [t68, s[t68]]

# U-Th-He age from raw measurements
age(c(238, 2, 232, 3, 4e-9, 1e-11), method = 'U-Th-He')
```

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

### cad()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Plots empirical cumulative distribution functions (step functions) for age datasets, allowing direct visual comparison.

```APIDOC
## cad()

### Description
Plots empirical cumulative distribution functions (step functions) for one or more age datasets, enabling direct visual comparison without smoothing.

### Usage
cad(data, col = NULL, verticals = FALSE)

### Parameters
- `data`: The input age data (e.g., ArAr, DZ).
- `col`: Color(s) for the plot lines.
- `verticals`: Logical, whether to draw vertical lines at steps.

### Examples
```r
attach(examples)

# Single-sample CAD
cad(ArAr)

# Multi-sample detrital CAD with colour ramp
cad(DZ, col = 'rainbow')

# Compare two samples on the same plot
cad(DZ[1:2], verticals = TRUE, col = c('blue', 'red'))
```
```

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

### Fit isochrons and capture results

Source: https://context7.com/pvermees/isoplotr/llms.txt

Fit isochrons for various isotopic systems. Suppress plotting to capture results for further analysis.

```r
isochron(ReOs, model = 3)

isochron(UPb)

fit <- isochron(RbSr, plot = FALSE)
cat('Rb-Sr isochron age:', fit$age[1], '±', fit$age[2], 'Ma\n')
cat('MSWD:', fit$mswd, '\n')
```

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

### concordia()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Plots U-Pb concordia diagrams (Wetherill or Tera-Wasserburg) with error ellipses and discordia lines. It can also compute concordia ages and MSWD.

```APIDOC
## `concordia()` — Plot U-Pb concordia diagrams

Plots U-Pb data on Wetherill or Tera-Wasserburg concordia diagrams with error ellipses, fits discordia lines, and computes concordia ages and MSWD.

### Usage
```r
concordia(x, type, ...)
```

### Parameters
*   **x**: A `UPb` object containing U-Pb data.
*   **type** (integer): The type of concordia diagram to plot: 1 for Wetherill, 2 for Tera-Wasserburg.
*   **show.ellipses** (boolean, optional): If TRUE, displays error ellipses for each data point.
*   **exterr** (boolean, optional): If TRUE, includes external uncertainties in discordia line fitting.
*   **levels** (numeric vector, optional): Values to color ellipses by, e.g., `Pb207U235`.
*   **clabel** (string, optional): Label for the color scale legend.
*   **ellipse.fill** (vector of strings, optional): Colors to fill the ellipses.

### Request Example
```r
attach(examples)

# Wetherill concordia with error ellipses (default)
concordia(UPb, type = 1, show.ellipses = 1)

# Tera-Wasserburg concordia with discordia line
concordia(UPb, type = 2, exterr = TRUE)

# Colour ellipses by U concentration levels
concordia(UPb, type = 1,
          levels     = UPb$x[, 'Pb207U235'],
          clabel     = '207Pb/235U',
          ellipse.fill = c('white', 'red'))

# Suppress plot, just get concordia age
ca <- concordia_age(UPb, i = 1, exterr = FALSE)
print(ca$age)   # [t.conc, s[t.conc], MSWD, p-value]
```
```

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

### central()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Fits a random effects model to fission track or U-Th-He data to yield a central age and overdispersion parameter.

```APIDOC
## central()

### Description
Fits a random effects model to fission track or U-Th-He data, decomposing scatter into analytical uncertainty and true geological dispersion to yield a central age and overdispersion parameter.

### Usage
central(data, model = 1, central = TRUE)

### Parameters
- `data`: The input age data (e.g., FT1, UThHe).
- `model`: The model to use for fitting.
- `central`: Logical, whether to compute the central age.

### Returns
- `t`: Central age.
- `s[t]`: Standard deviation of the central age.
- `disp`: Overdispersion parameter.
- `MSWD`: Mean of the squared weighted deviations.
- `p.value`: P-value for the MSWD test.

### Examples
```r
attach(examples)

# Central age of fission track data
central(FT1)
# Returns: t (central age), s[t], disp (overdispersion), MSWD, p.value

# Central U-Th-He age using logratio (compositional) approach
central(UThHe)

# Via age() wrapper
age(FT1, central = TRUE)
age(UThHe, central = TRUE)
```
```

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

### helioplot()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Visualizes U-Th-He data on a logratio plot or ternary diagram and computes a compositional central age.

```APIDOC
## helioplot()

### Description
Visualises U-Th(-Sm)-He data on a logratio plot (`log[He/Th]` vs `log[U/He]`) or a ternary diagram, and computes a compositional (barycentric) central age.

### Usage
helioplot(data, logratio = TRUE, model = 1, levels = NULL, plot = TRUE)

### Parameters
- `data`: The input U-Th-He data.
- `logratio`: Logical, whether to use a logratio plot (TRUE) or ternary diagram (FALSE).
- `model`: The model to use for fitting.
- `levels`: Optional values for colouring points by grain index.
- `plot`: Logical, whether to generate a plot.

### Examples
```r
attach(examples)

# Logratio plot (default)
helioplot(UThHe, logratio = TRUE)

# Ternary diagram
helioplot(UThHe, logratio = FALSE)

# Model 3: add geological overdispersion term
helioplot(UThHe, model = 3)

# Colour points by grain index
helioplot(UThHe, levels = 1:nrow(UThHe$x))
```
```

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

### mds()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Performs multidimensional scaling on detrital age distributions to reduce pairwise distances into a 2D configuration for visual comparison.

```APIDOC
## mds()

### Description
Reduces a matrix of Kolmogorov-Smirnov (or Wasserstein-2) pairwise distances between detrital age samples into a 2D configuration for visual comparison.

### Usage
mds(data, classical = TRUE, nnlines = FALSE, shepard = FALSE, method = 'KS')

### Parameters
- `data`: The detrital age data (e.g., DZ).
- `classical`: Logical, whether to use classical MDS.
- `nnlines`: Logical, whether to draw nearest-neighbour lines.
- `shepard`: Logical, whether to generate a Shepard plot.
- `method`: The distance metric to use ('KS' for Kolmogorov-Smirnov, 'W2' for Wasserstein-2).

### Examples
```r
attach(examples)

# Classical MDS of detrital zircon samples
mds(DZ, classical = TRUE)

# Nonmetric MDS with nearest-neighbour lines
mds(DZ, classical = FALSE, nnlines = TRUE)

# Shepard plot to assess goodness of fit
mds(DZ, shepard = TRUE)

# Wasserstein-2 distance instead of KS
mds(DZ, method = 'W2')
```
```

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

### weightedmean()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Computes a weighted mean from heteroscedastic ages, with options for outlier detection and different weighting models.

```APIDOC
## weightedmean()

### Description
Computes a weighted mean from heteroscedastic ages using either ordinary inverse-variance weights or a random effects (lognormal overdispersion) model. Applies a modified Chauvenet criterion for outlier rejection.

### Usage
weightedmean(data, random.effects = TRUE, type = 1, detect.outliers = FALSE)

### Parameters
- `data`: The input age data (e.g., ArAr, UPb) or a two-column matrix [value, 1-sigma error].
- `random.effects`: Logical, whether to use a random effects model for overdispersion.
- `type`: Data type indicator (e.g., 2 for U-Pb).
- `detect.outliers`: Logical, whether to apply outlier detection.

### Examples
```r
attach(examples)

# Weighted mean of Ar-Ar ages; random effects overdispersion model
weightedmean(ArAr, random.effects = TRUE)

# U-Pb weighted mean (206Pb/238U ages), reject outliers
weightedmean(UPb, type = 2, detect.outliers = TRUE)

# From a two-column matrix [value, 1-sigma error]
mat <- cbind(c(100, 102, 99, 150),   # ages
             c(2,   3,   2,   5))    # 1-sigma errors
weightedmean(mat, random.effects = FALSE)
```
```

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

### Fit Isochrons with isochron()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Fits regression lines to cogenetic multi-aliquot data using various algorithms. Handles overdispersion and different regression models.

```r
attach(examples)

# Rb-Sr isochron
isochron(RbSr)
# Returns: age, s[age], initial 87Sr/86Sr, MSWD, p-value

# Ar-Ar inverse isochron (i2i = derive initial 40Ar/36Ar from intercept)
isochron(ArAr, i2i = TRUE, inverse = TRUE)
```

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

### Load Geochronological Data with read.data()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Reads geochronological data from CSV files or R matrices. Specifies the geochronometer and data format.

```r
library(IsoplotR)
setwd(system.file(package = 'IsoplotR'))  # navigate to built-in example files

# U-Pb data, Wetherill format (07/35, err, 06/38, err, rho)
UPb <- read.data('UPb1.csv', method = 'U-Pb', format = 1)

# Rb-Sr isochron data
RbSr <- read.data('RbSr1.csv', method = 'Rb-Sr', format = 1)

# Ar-Ar step-heating data
ArAr <- read.data('ArAr1.csv', method = 'Ar-Ar', format = 1)

# Fission track data (external detector method)
FT <- read.data('FT1.csv', method = 'fissiontracks', format = 1)

# Detrital zircon multi-sample dataset
DZ <- read.data('DZ.csv', method = 'detritals')

# From a matrix instead of a file
mat <- matrix(c(0.0753, 0.0002, 0.0511, 0.0003, 0.9),
              ncol = 5,
              dimnames = list(NULL, c('Pb207U235','errPb207U235',
                                     'Pb206U238','errPb206U238','rXY')))
UPb_mat <- read.data(mat, method = 'U-Pb', format = 1)
```

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

### isochron()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Fits isochron models to geochronological data. Supports different isotopic systems (Re-Os, U-Pb, Rb-Sr) and includes options for overdispersion and plotting.

```APIDOC
## isochron()

### Description
Fits isochron models to geochronological data, supporting various isotopic systems and model complexities.

### Usage
isochron(data, model = 1, plot = TRUE)

### Parameters
- `data`: The input geochronological data (e.g., ReOs, UPb, RbSr).
- `model`: The isochron model to use (e.g., 1 for standard, 3 for overdispersion).
- `plot`: Logical, whether to generate a plot of the isochron.

### Examples
```r
# Re-Os isochron, overdispersion model 3 (add geological scatter)
isochron(ReOs, model = 3)

# U-Pb isochron on Tera-Wasserburg coordinates
isochron(UPb)

# Suppress plot; capture results
fit <- isochron(RbSr, plot = FALSE)
cat('Rb-Sr isochron age:', fit$age[1], '±', fit$age[2], 'Ma\n')
cat('MSWD:', fit$mswd, '\n')
```
```

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

### radialplot()

Source: https://context7.com/pvermees/isoplotr/llms.txt

Generates a Galbraith radial plot for heteroscedastic age data, with options to overlay mixture models and customize plot appearance.

```APIDOC
## radialplot()

### Description
Visualises a set of ages with different precisions on Galbraith's radial plot, and optionally overlays finite or continuous mixture models.

### Usage
radialplot(data, transformation = NULL, type = 1, show.numbers = FALSE, k = NULL, z0 = NULL, from = NULL, to = NULL, plot = TRUE)

### Parameters
- `data`: The input age data (e.g., FT1, ArAr, UPb).
- `transformation`: Optional transformation to apply ('log').
- `type`: Plotting type (e.g., 2 for U-Pb).
- `show.numbers`: Logical, whether to show grain numbers.
- `k`: Number of components for mixture models.
- `z0`: Reference value for the radial axis.
- `from`, `to`: Axis limits for the radial plot.
- `plot`: Logical, whether to generate a plot.

### Examples
```r
attach(examples)

# Fission track radial plot with central age
radialplot(FT1)

# Ar-Ar radial plot with log transformation
radialplot(ArAr, transformation = 'log')

# U-Pb radial plot (206Pb/238U ages), show grain numbers
radialplot(UPb, type = 2, show.numbers = TRUE)

# Finite mixture model: identify 2 age components
radialplot(FT1, k = 2)

# Custom axis limits and reference age
radialplot(FT1, z0 = 100, from = 50, to = 300)
```
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.