Package 'MultiModalR'

Check and install required packages

Description

Check and install required packages

Usage

check_PACKS()

Value

Installs missing packages

Invisibly returns TRUE if all packages are available

---

Create output data frame

Description

Converts MCMC results to exact CSV format

Usage

create_MM_output(
  mcmc_result,
  y_original = NULL,
  group_original = NULL,
  main_class = "",
  max_groups = 5
)

Arguments

mcmc_result	Output from MM_MH() or MM_MH_dirichlet()
y_original	Original y values (if different from mcmc_result$y)
group_original	Original group assignments (optional)
main_class	Category/class name
max_groups	Maximum number of groups for output columns

Value

Data frame in CSV format

---

Create multimodal dummy dataset

Description

Generates the same dummy dataset used in the package. This is useful if users want to generate similar data with different parameters.

Usage

create_multimodal_dummy(
  seed = 5,
  n_categories = 9,
  n_per_group = 25,
  n_subgroups = 3
)

Arguments

seed	Random seed for reproducibility (default: 5)
n_categories	Number of categories (default: 9)
n_per_group	Number of observations per subgroup per category (default: 25)
n_subgroups	Number of subgroups per category (default: 3)

Value

A data frame with multimodal data

Examples

# Generate the default dataset
df <- create_multimodal_dummy()

# Generate with different parameters
df2 <- create_multimodal_dummy(seed = 12, n_categories = 6)

---

Parallel Bayesian mixture modeling using Markov Chain Monte Carlo (MCMC)

Description

Performs multimodal probability assignment using either: 1. Metropolis-Hastings-within-partial-Gibbs 2. Dirichlet-Multinomial

Usage

fuss_PARALLEL_mcmc(
  data,
  varCLASS,
  varY,
  varID,
  method = "sj-dpi",
  within = 1,
  maxNGROUP = 5,
  out_dir = NULL,
  n_workers = 3,
  n_iter = NULL,
  burnin = NULL,
  proposal_sd = 0.15,
  sj_adjust = 0.5,
  mcmc_method = "metropolis",
  dirichlet_alpha = 2
)

Arguments

data	Input data frame
varCLASS	Character, category variable name (required)
varY	Character, value variable name (required)
varID	Character, ID variable name (required)
method	Density estimator method ("sj-dpi", "bcv", "ucv", "nrd") (default: "sj-dpi")
within	Range parameter for grouping modes (default: 1.0)
maxNGROUP	Maximum number of groups (default: 5)
out_dir	Output directory for CSV files (if NULL, returns combined data frame)
n_workers	Number of parallel workers (default: 3)
n_iter	Number of MCMC iterations (default: 6000 for metropolis, 3000 for dirichlet)
burnin	Burn-in period (default: 2000 for metropolis, 1000 for dirichlet)
proposal_sd	Proposal standard deviation for component means (default: 0.15)
sj_adjust	Adjustment factor for bandwidth methods (default: 0.5, smaller -> more modes, higher -> fewer modes)
mcmc_method	"metropolis" or "dirichlet"(default: "metropolis")
dirichlet_alpha	Dirichlet concentration parameter (default: 2.0)

Value

Data frame (if out_dir is NULL) or writes CSV files

---

Density height-aware mode detection

Description

Returns mode estimates from FOUR different bandwidth methods. Each method may detect different numbers and locations of modes.

Usage

get_MODES_enhanced(y, adjust = 1, threshold = 1)

Arguments

y	Numeric vector
adjust	Bandwidth adjustment factor (affects "SJ", "nrd", "bcv" methods)
threshold	Relative threshold for significant peaks

Value

List with mode estimates from multiple methods including density heights

---

Density height-aware mode grouping

Description

Density height-aware mode grouping

Usage

group_MODES_enhanced(df, within = 0.1)

Arguments

df	data frame containing 'Est_Mode' and 'Density_Height' columns
within	numeric, range for grouping modes (default: 0.1)

Value

data frame with grouped modes

---

Fast MCMC for mixture models (Metropolis-Hastings-within-partial-Gibbs)

Description

Fast MCMC for mixture models (Metropolis-Hastings-within-partial-Gibbs)

Usage

MM_MH(
  y,
  grp,
  prior_means = NULL,
  ids,
  n_iter = 1000,
  burnin = 500,
  proposal_sd = 0.15,
  seed = NULL
)

Arguments

y	Numeric vector of data
grp	Number of mixture components
prior_means	Prior means for components (optional)
ids	Vector of IDs for validation (required)
n_iter	Number of MCMC iterations (default: 1000)
burnin	Burn-in period (default: 500)
proposal_sd	Proposal standard deviation for component means (default: 0.15)
seed	Random seed

Value

List with MCMC results

---

Dirichlet MCMC (identical interface to MM_MH)

Description

Dirichlet MCMC (identical interface to MM_MH)

Usage

MM_MH_dirichlet(
  y,
  grp,
  prior_means = NULL,
  ids,
  n_iter = 5000,
  burnin = 2000,
  proposal_sd = 0.15,
  dirichlet_alpha = 2,
  seed = NULL
)

Arguments

y	Numeric vector of data
grp	Number of mixture components
prior_means	Prior means for components
ids	Vector of IDs for validation
n_iter	Number of MCMC iterations
burnin	Burn-in period
proposal_sd	Proposal standard deviation
dirichlet_alpha	Dirichlet concentration parameter
seed	Random seed

Value

List with MCMC results (SAME FORMAT as MM_MH)

---

Multimodal Dummy Dataset

Description

A simulated dataset containing 9 categories each with 3 distinct subpopulations following truncated normal distributions. Ideal for testing multimodal mixture modeling.

A simulated dataset containing 9 categories (AA, BB, ..., II) each with 3 distinct subpopulations (Group 1, Group 2, Group 3) following truncated normal distributions.

Usage

data(multimodal_dummy)

multimodal_dummy

Format

A data frame with 675 rows and 4 columns

A data frame with 675 rows and 4 variables:

Category

Factor with 9 levels: AA, BB, CC, DD, EE, FF, GG, HH, II

Subpopulation

Factor with 3 levels: Group 1, Group 2, Group 3

Value

Numeric values between 5 and 10

ID

Unique identifier from 1 to 675

Details

This dataset is useful for demonstrating the capabilities of MultiModalR package. Each category contains three distinct subpopulations with overlapping but separable distributions, making it ideal for testing multimodal mixture modeling algorithms.

Source

Generated with set.seed(5) using truncnorm::rtruncnorm(). See create_multimodal_dummy for the generating function.

Examples

# Load the dataset
data(multimodal_dummy)

# View structure
str(multimodal_dummy)

# Summary statistics
summary(multimodal_dummy)

# Plot data
library(ggplot2)
ggplot(multimodal_dummy, aes(x = Value, fill = Subpopulation)) +
  geom_density(alpha = 0.5, color = NA) +
  facet_wrap(~Category) +
  theme_dark()

# Use with MultiModalR

library(MultiModalR)
results <- fuss_PARALLEL_mcmc(
  data = multimodal_dummy,
  varCLASS = "Category",
  varY = "Value",
  varID = "ID",
  n_workers = 3
)

---

Plot validation of subgroup assignments (handles both balanced and imbalanced data)

Description

Plot validation of subgroup assignments (handles both balanced and imbalanced data)

Usage

plot_VALIDATION(
  csv_dir,
  observed_df,
  subpop_col = "Subpopulation",
  value_col = "Value",
  id_col = "ID",
  pattern = "^df_"
)

Arguments

csv_dir	Directory containing CSV files from create_MM_output
observed_df	Original data frame with true subgroups
subpop_col	Character, name of the true subgroup column in observed_df (default: "Subpopulation")
value_col	Character, name of the value column in observed_df (default: "Value")
id_col	Character, name of the ID column in observed_df (default: "ID")
pattern	Pattern to match CSV files (default: "^df_")

Value

ggplot object showing validation results

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