Package 'llmimpute'

Extract the imputed data frame from an lmi_result

Description

Convenience S3 method allowing as.data.frame(result) to extract the imputed data frame from an lmi_result object directly.

Usage

## S3 method for class 'lmi_result'
as.data.frame(x, ...)

Arguments

x	An object of class lmi_result.
...	Currently unused. Included for S3 compatibility.

Value

The imputed data.frame.

Examples

df <- data.frame(x = c(1, NA, 3))
result <- lmi_impute_offline(df, verbose = FALSE)
clean  <- as.data.frame(result)

---

Diagnose missing data in a data frame

Description

Analyses a data frame and prints a report on missing values, column types, and the number of unique observed values per column. No API calls are made. Use this function before lmi_impute to preview what will be imputed and to choose an appropriate method.

Usage

lmi_diagnose(data, na_strings = NULL)

Arguments

data	A data.frame or tibble.
na_strings	Character vector of additional strings to treat as NA (e.g. c("N/A", "?", "-")). Default NULL.

Value

Invisibly returns a data.frame with one row per column and columns column, type, n_missing, pct_missing, n_unique.

See Also

lmi_impute, lmi_impute_offline

Examples

df <- data.frame(
  age    = c(25L, NA, 35L, 40L),
  income = c(50000, 60000, NA, 80000),
  edu    = c("BSc", NA, "MSc", "BSc"),
  stringsAsFactors = FALSE
)
lmi_diagnose(df)
lmi_diagnose(df, na_strings = "N/A")

---

Export imputed data and audit trail to files

Description

Writes the imputed data frame and the imputation audit trail to disk. Supports CSV (default) and RDS output formats. When flag_suspicious = TRUE was used in lmi_impute, the suspicious-values table is also written.

Usage

lmi_export(
  result,
  path = tempdir(),
  prefix = "llmimpute",
  format = c("csv", "rds"),
  overwrite = FALSE
)

Arguments

result	An object of class lmi_result returned by lmi_impute or lmi_impute_offline.
path	Character string. Output directory. Created recursively if it does not exist. Default is the current working directory ".".
prefix	Character string prepended to output file names. Default "llmimpute".
format	Character string. "csv" (default) or "rds".
overwrite	Logical. Overwrite existing files? Default FALSE.

Value

Invisibly returns a named character vector of the file paths written.

See Also

lmi_impute, lmi_impute_offline

Examples

df <- data.frame(
  age    = c(25L, NA, 35L),
  income = c(50000, 60000, NA),
  stringsAsFactors = FALSE
)
result <- lmi_impute_offline(df, verbose = FALSE)
## Not run: 
lmi_export(result, path = tempdir(), prefix = "my_study")

## End(Not run)

---

Impute missing values using LLM or built-in statistical methods

Description

Primary entry point for the llmimpute package. When an Anthropic API key is configured, missing values are filled using the Claude large language model, which reasons about each missing cell using the semantic meaning of column names, inter-column relationships, and domain knowledge. When no API key is available (or when offline = TRUE), the function transparently delegates to lmi_impute_offline, which runs entirely in base R without internet access.

Usage

lmi_impute(
  data,
  domain = c("general", "healthcare", "financial", "hr", "survey", "scientific"),
  offline = FALSE,
  offline_fallback = TRUE,
  offline_method = c("auto", "mean", "median", "mode", "locf", "nocb", "hotdeck", "pmm",
    "knn", "linear", "lasso", "ridge", "bayesian_ridge", "svr", "decision_tree",
    "random_forest", "gradient_boost", "missforest", "pca_impute", "softimpute"),
  na_strings = NULL,
  cols = NULL,
  confidence = TRUE,
  reasoning = TRUE,
  flag_suspicious = FALSE,
  max_rows = 50L,
  knn_k = 5L,
  seed = 42L,
  verbose = TRUE
)

Arguments

data	A data.frame or tibble containing the data to impute.
domain	Character string describing the data domain. Guides LLM reasoning. One of "general" (default), "healthcare", "financial", "hr", "survey", "scientific". Ignored in offline mode.
offline	Logical. If TRUE, always use the offline statistical engine regardless of API key availability. Default FALSE.
offline_fallback	Logical. If TRUE (default), switch to the offline engine when no API key is found instead of raising an error.
offline_method	Character. Offline imputation strategy passed to lmi_impute_offline. One of "auto" (default), "mean", "median", "mode", "locf", "nocb", "hotdeck", "pmm", "knn", "linear", "lasso", "ridge", "bayesian_ridge", "svr", "decision_tree", "random_forest", "gradient_boost", "missforest", "pca_impute", "softimpute".
na_strings	Character vector of additional strings to treat as NA, e.g. c("N/A", "?", "-"). Default NULL.
cols	Character vector of column names to impute. NULL imputes all columns with any missing values.
confidence	Logical. Include a confidence score (0-100) per imputed cell. Default TRUE.
reasoning	Logical. Include a one-sentence explanation per imputed cell. Default TRUE.
flag_suspicious	Logical. Ask the LLM to flag anomalous existing values (LLM mode only). Default FALSE.
max_rows	Integer. Maximum rows per API call chunk (LLM mode only). Default 50L.
knn_k	Integer. Neighbours for the "knn" offline method. Default 5L.
seed	Integer. Random seed for reproducible offline imputation. Default 42L.
verbose	Logical. Print progress messages. Default TRUE.

Value

An object of class lmi_result, a named list with:

data

The imputed data.frame.

imputations

data.frame audit trail: one row per imputed cell with columns row, col, original, imputed, confidence, reasoning.

suspicious

data.frame of flagged existing values (LLM mode, flag_suspicious = TRUE only). NULL otherwise.

summary

Named list of imputation statistics.

call

The matched call.

Engine selection

Situation	Behaviour
API key present, offline = FALSE	LLM imputation (Anthropic)
No key, offline_fallback = TRUE	Offline engine (silent)
No key, offline_fallback = FALSE	Error with guidance
offline = TRUE	Offline engine always

See Also

lmi_impute_offline, lmi_diagnose, lmi_export, print.lmi_result

Examples

# Offline imputation (works with no API key)
df <- data.frame(
  age    = c(25L, NA, 35L, 40L, NA),
  income = c(50000, 60000, NA, 80000, 55000),
  edu    = c("BSc", NA, "MSc", "BSc", "PhD"),
  stringsAsFactors = FALSE
)

result <- lmi_impute(df)
result$data
result$imputations
summary(result)

# Force a specific offline method
result2 <- lmi_impute(df, offline = TRUE, offline_method = "random_forest")

## Not run: 
# LLM mode requires a valid Anthropic API key
lmi_set_api_key()   # reads ANTHROPIC_API_KEY from environment
result3 <- lmi_impute(df, domain = "hr")

## End(Not run)

---

Impute missing values using built-in statistical and ML methods (no API required)

Description

Fully offline imputation using 20 methods from scratch in base R. No API key, no internet, no third-party packages needed.

Usage

lmi_impute_offline(
  data,
  method = c("auto", "mean", "median", "mode", "locf", "nocb", "hotdeck", "pmm", "knn",
    "linear", "lasso", "ridge", "bayesian_ridge", "svr", "decision_tree",
    "random_forest", "gradient_boost", "missforest", "pca_impute", "softimpute"),
  cols = NULL,
  na_strings = NULL,
  knn_k = 5L,
  n_trees = 50L,
  max_depth = 4L,
  shrinkage = 0.1,
  n_iter = 10L,
  lambda = 1,
  rank = 3L,
  seed = 42L,
  verbose = TRUE
)

Arguments

data	A data.frame or tibble.
method	One of: "auto", "mean", "median", "mode", "locf", "nocb", "hotdeck", "pmm", "knn", "linear", "lasso", "ridge", "bayesian_ridge", "svr", "decision_tree", "random_forest", "gradient_boost", "missforest", "pca_impute", "softimpute".
cols	Column names to impute. NULL = all missing columns.
na_strings	Extra strings treated as NA.
knn_k	Neighbours for knn. Default 5L.
n_trees	Trees for rf/gb/missforest. Default 50L.
max_depth	Tree depth. Default 4L.
shrinkage	Learning rate for gradient_boost. Default 0.1.
n_iter	Iterations for iterative methods. Default 10L.
lambda	Regularisation for ridge/lasso/bayesian_ridge. Default 1.0.
rank	SVD rank for pca_impute/softimpute. Default 3L.
seed	Random seed. Default 42L.
verbose	Print progress. Default TRUE.

Value

An object of class lmi_result.

See Also

lmi_impute, lmi_methods

Examples

df <- data.frame(
  age    = c(25, NA, 35, 40, NA),
  income = c(50000, 60000, NA, 80000, 55000),
  edu    = c("BSc", NA, "MSc", "BSc", "PhD"),
  stringsAsFactors = FALSE
)
lmi_impute_offline(df)
lmi_impute_offline(df, method = "random_forest")
lmi_impute_offline(df, method = "softimpute")

---

List all available offline imputation methods

Description

Prints all 20 methods grouped by category with usage guidance.

Usage

lmi_methods()

Value

Invisibly returns a data.frame of method metadata.

Examples

lmi_methods()

---

List recommended models for each supported LLM provider

Description

Prints recommended model identifiers for every provider supported by lmi_impute, including free-tier options. Use lmi_set_model to activate a model after choosing a provider with lmi_set_api_key.

Usage

lmi_models()

Value

Invisibly returns a character vector of all model identifiers across every provider.

See Also

lmi_set_model, lmi_providers, lmi_impute

Examples

lmi_models()

---

List all supported LLM providers

Description

Prints every LLM provider supported by lmi_impute, grouped into cloud APIs, local servers, and custom endpoints, with free-tier indicators and required environment variables.

Usage

lmi_providers()

Value

Invisibly returns a character vector of provider names.

See Also

lmi_set_api_key, lmi_models

Examples

lmi_providers()

---

Configure the API key and LLM provider for llmimpute

Description

Sets the API key and LLM provider used by LLM-mode imputation. Supports cloud APIs (Anthropic 'Claude', OpenAI, Google Gemini, Groq, Mistral, Cohere, OpenRouter, Together AI, Fireworks AI, DeepSeek, Perplexity, xAI Grok, AI21 Labs, Cerebras) and local servers (Ollama, LM Studio, Jan, llama.cpp, KoboldCpp, Text Generation WebUI). Any other OpenAI-compatible endpoint can be used via provider = "custom" with a base_url.

If no API key is configured, lmi_impute automatically falls back to the offline statistical engine. The package is fully functional without any API key.

Usage

lmi_set_api_key(
  api_key = NULL,
  provider = "anthropic",
  base_url = NULL,
  .session = TRUE
)

Arguments

api_key	Character string. Your API key. Not required for local providers (Ollama, LM Studio, Jan, llama.cpp, KoboldCpp, Text Generation WebUI). If NULL, the function reads from the provider's environment variable.
provider	Character string. Provider name. One of: "anthropic", "openai", "gemini", "groq", "mistral", "cohere", "openrouter", "together", "fireworks", "deepseek", "perplexity", "xai", "ai21", "cerebras", "ollama", "lmstudio", "jan", "llamacpp", "koboldcpp", "textgenwebui", or "custom". Default "anthropic".
base_url	Character string. Required only for "custom". For local providers you can override the default port here too, e.g. base_url = "http://localhost:9000/v1/chat/completions".
.session	Logical. Store for the current session only (default TRUE).

Value

Invisibly returns the API key string (or "" for keyless local providers).

See Also

lmi_providers, lmi_set_model, lmi_impute

Examples

## Not run: 
## Cloud APIs
lmi_set_api_key("sk-ant-...",  provider = "anthropic")
lmi_set_api_key("sk-...",      provider = "openai")
lmi_set_api_key("AIza...",     provider = "gemini")   # free tier
lmi_set_api_key("gsk_...",     provider = "groq")     # free tier
lmi_set_api_key("sk-or-...",   provider = "openrouter") # free models
lmi_set_api_key("...",         provider = "deepseek")
lmi_set_api_key("...",         provider = "cerebras") # free tier

## Local servers (no key needed)
lmi_set_api_key(provider = "ollama")
lmi_set_api_key(provider = "lmstudio")
lmi_set_api_key(provider = "jan")
lmi_set_api_key(provider = "llamacpp")
lmi_set_api_key(provider = "koboldcpp")
lmi_set_api_key(provider = "textgenwebui")

## Any OpenAI-compatible endpoint
lmi_set_api_key("mykey", provider = "custom",
                base_url = "http://my-server:8000/v1/chat/completions")

## Override local port
lmi_set_api_key(provider = "ollama",
                base_url = "http://localhost:11435/api/chat")

## End(Not run)

---

Set the LLM model used for imputation

Description

Sets or retrieves the model identifier used by lmi_impute in LLM mode. The default model is determined by the active provider. Use lmi_models to see recommended models per provider.

Usage

lmi_set_model(model = NULL)

lmi_get_model()

Arguments

model

Character string. A valid model identifier for the active provider. If NULL, returns the current model without changing it.

Value

Invisibly returns the active model string.

See Also

lmi_set_api_key, lmi_models

Examples

lmi_get_model()

## Not run: 
lmi_set_model("gpt-4o-mini")
lmi_set_model("gemini-1.5-flash")
lmi_set_model("llama-3.3-70b-versatile")
lmi_set_model("deepseek-chat")
lmi_set_model("llama3.2")        # Ollama local

## End(Not run)

---

Print an lmi_result object

Description

Displays a formatted summary of an imputation result in the console, including overall statistics, per-column imputation counts, and the first n imputed values with their confidence scores and reasoning.

Usage

## S3 method for class 'lmi_result'
print(x, n = 10L, ...)

Arguments

x	An object of class lmi_result returned by lmi_impute or lmi_impute_offline.
n	Integer. Number of individual imputation rows to display. Default 10L.
...	Currently unused. Included for S3 compatibility.

Value

Invisibly returns x.

Examples

df <- data.frame(
  age    = c(25L, NA, 35L),
  income = c(50000, 60000, NA),
  stringsAsFactors = FALSE
)
result <- lmi_impute_offline(df, verbose = FALSE)
print(result)

---

Summarise an lmi_result object

Description

Returns a data.frame summarising imputation counts and confidence statistics per column, suitable for further analysis or reporting.

Usage

## S3 method for class 'lmi_result'
summary(object, ...)

Arguments

object	An object of class lmi_result.
...	Currently unused. Included for S3 compatibility.

Value

A data.frame with columns column, n_imputed, mean_confidence, min_confidence, max_confidence. Returns NULL invisibly when no imputations were performed.

Examples

df <- data.frame(
  age    = c(25L, NA, 35L, 40L),
  income = c(50000, 60000, NA, 80000),
  stringsAsFactors = FALSE
)
result <- lmi_impute_offline(df, verbose = FALSE)
summary(result)

---