Analyze Imported Addon Data

Description

Run common analyses on data imported from ankiR Stats addon.

Usage

analyze_addon_import(data)

Arguments

Value

A list with analysis results

Examples

## Not run: 
data <- import_addon_export("ankir_export.json")
analysis <- analyze_addon_import(data)
analysis$retention_trend

## End(Not run)

A/B Comparison

Description

Compare retention, efficiency, and performance across different note types, deck settings, card formats, or custom groups.

Usage

anki_ab_comparison(
  path = NULL,
  profile = NULL,
  by = "note_type",
  min_reviews = 100
)

Arguments

Value

A tibble with comparison statistics

Examples

## Not run: 
# Compare note types
comp <- anki_ab_comparison(by = "note_type")

# Compare decks
comp <- anki_ab_comparison(by = "deck")

## End(Not run)

Backlog Calculator

Description

Calculate how long it would take to clear your review backlog at different review rates, and project backlog growth if you stop studying.

Usage

anki_backlog_calculator(path = NULL, profile = NULL, new_cards_per_day = 0)

Arguments

Value

A list with backlog analysis and projections

Examples

## Not run: 
backlog <- anki_backlog_calculator()
backlog$current
backlog$scenarios

## End(Not run)

Get Anki base path

Description

Returns the default Anki2 directory for the current platform.

Usage

anki_base_path()

Value

Character string path to Anki2 directory

Examples

## Not run: 
anki_base_path()

## End(Not run)

Benchmark against FSRS averages

Description

Compare your statistics against typical FSRS users (based on published research).

Usage

anki_benchmark(path = NULL, profile = NULL)

Arguments

Value

A tibble with your stats vs benchmarks

Examples

## Not run: 
anki_benchmark()

## End(Not run)

Find optimal review times

Description

Analyzes retention and performance by hour of day and day of week to identify when you learn best.

Usage

anki_best_review_times(path = NULL, profile = NULL, min_reviews = 50)

Arguments

Value

A list with optimal time analysis

Examples

## Not run: 
times <- anki_best_review_times()
times$best_hours
times$best_days

## End(Not run)

Get buried cards

Description

Get buried cards

Usage

anki_buried(path = NULL, profile = NULL, include_notes = FALSE)

Arguments

Value

A tibble of buried cards

Examples

## Not run: 
buried <- anki_buried()

## End(Not run)

Detect Burnout Warning Signs

Description

Analyzes study patterns to detect potential burnout indicators: declining retention, increasing response time, shorter sessions, more "Again" presses, decreased consistency.

Usage

anki_burnout_detection(
  path = NULL,
  profile = NULL,
  days = 30,
  baseline_days = 90
)

Arguments

Value

A list with burnout indicators and recommendations

Examples

## Not run: 
burnout <- anki_burnout_detection()
burnout$risk_level
burnout$indicators

## End(Not run)

Analyze card complexity

Description

Measures card complexity based on field content, media usage, etc.

Usage

anki_card_complexity(path = NULL, profile = NULL)

Arguments

Value

A tibble with card complexity metrics

Examples

## Not run: 
complexity <- anki_card_complexity()

## End(Not run)

Card Content Analysis

Description

Analyze card content: word count, cloze density, complexity score, and correlations with retention.

Usage

anki_card_content(path = NULL, profile = NULL, sample_size = 1000)

Arguments

Value

A list with content analysis

Examples

## Not run: 
content <- anki_card_content()
content$summary
content$complexity_retention

## End(Not run)

Generate card recommendations

Description

Generates actionable recommendations for improving your collection: cards to unsuspend, leeches to rewrite, near-duplicates to merge, etc.

Usage

anki_card_recommendations(path = NULL, profile = NULL)

Arguments

Value

A list with categorized recommendations

Examples

## Not run: 
recs <- anki_card_recommendations()
recs$leeches_to_rewrite
recs$cards_to_unsuspend

## End(Not run)

Read cards from Anki collection

Description

Read cards from Anki collection

Usage

anki_cards(path = NULL, profile = NULL)

Arguments

Value

A tibble of cards with columns: cid, nid, did, type, queue, due, ivl, reps, lapses

Examples

## Not run: 
anki_cards()

## End(Not run)

Read cards with FSRS-6 parameters

Description

Extracts cards along with their FSRS (Free Spaced Repetition Scheduler) memory state parameters. FSRS-6 stores stability, difficulty, and a per-card decay parameter in the card's data field.

Usage

anki_cards_fsrs(path = NULL, profile = NULL)

Arguments

Value

A tibble of cards with FSRS parameters:

• cid - Card ID

• nid - Note ID

• did - Deck ID

• type - Card type (0=new, 1=learning, 2=review, 3=relearning)

• queue - Queue (-1=suspended, 0=new, 1=learning, 2=review, 3=day learning, 4=preview)

• due - Due date/position

• ivl - Current interval in days

• reps - Number of reviews

• lapses - Number of lapses

• stability - FSRS stability (S) in days

• difficulty - FSRS difficulty (D), range 1-10

• desired_retention - Target retention rate

• decay - FSRS-6 decay parameter (w20), typically 0.1-0.8

Examples

## Not run: 
cards_fsrs <- anki_cards_fsrs()
# Calculate current retrievability
cards_fsrs$retrievability <- fsrs_retrievability(
  stability = cards_fsrs$stability,
  days_elapsed = as.numeric(Sys.Date() - as.Date("1970-01-01")) -
                 cards_fsrs$due / 86400,
  decay = cards_fsrs$decay
)

## End(Not run)

Get cards with full joined data

Description

Returns cards joined with notes, decks, and models for complete analysis.

Usage

anki_cards_full(path = NULL, profile = NULL)

Arguments

Value

A tibble with card data joined with note content, deck names, and model info

Examples

## Not run: 
cards_full <- anki_cards_full()

## End(Not run)

Cohort Analysis (Vintage Analysis)

Description

Compare card performance by when they were added. Cards added in the same period form a "cohort" and their learning outcomes are compared.

Usage

anki_cohort_analysis(
  path = NULL,
  profile = NULL,
  cohort_size = "month",
  min_cards = 20
)

Arguments

Value

A tibble with cohort statistics

Examples

## Not run: 
cohorts <- anki_cohort_analysis()
cohorts

## End(Not run)

Open an Anki collection

Description

Opens an Anki collection database and returns an object with methods to access notes, cards, decks, note types, and review logs.

Usage

anki_collection(path = NULL, profile = NULL)

Arguments

Value

An anki_collection object with methods:

• notes() - Get all notes

• cards() - Get all cards

• revlog() - Get review log

• decks() - Get deck information

• models() - Get note types (models)

• tables() - List all tables

• close() - Close database connection

Examples

## Not run: 
col <- anki_collection()
col$notes()
col$decks()
col$close()

## End(Not run)

Compare retention by card age

Description

Analyze how retention varies by how long you've been studying cards.

Usage

anki_compare_by_age(path = NULL, profile = NULL)

Arguments

Value

A tibble with retention by card age

Examples

## Not run: 
anki_compare_by_age()

## End(Not run)

Compare performance by deck difficulty

Description

Compare performance by deck difficulty

Usage

anki_compare_deck_difficulty(path = NULL, profile = NULL)

Arguments

Value

A tibble ranking decks by difficulty metrics

Examples

## Not run: 
anki_compare_deck_difficulty()

## End(Not run)

Compare statistics between decks

Description

Side-by-side comparison of deck statistics.

Usage

anki_compare_decks(path = NULL, profile = NULL, decks = NULL)

Arguments

Value

A tibble with comparative statistics

Examples

## Not run: 
anki_compare_decks()
anki_compare_decks(decks = c("Medical", "Anatomy"))

## End(Not run)

Compare Forecast Methods

Description

Run multiple forecasting methods and compare their predictions.

Usage

anki_compare_forecasts(
  path = NULL,
  profile = NULL,
  days_ahead = 30,
  methods = c("holt", "arima", "seasonal", "monte_carlo")
)

Arguments

Value

A list with comparison results

Examples

## Not run: 
comp <- anki_compare_forecasts(days_ahead = 14)
comp$summary

## End(Not run)

Compare Two Specific Groups

Description

Detailed statistical comparison between two groups (decks, note types, etc.)

Usage

anki_compare_groups(path = NULL, profile = NULL, group_a, group_b, by = "deck")

Arguments

Value

A list with detailed comparison statistics

Examples

## Not run: 
comp <- anki_compare_groups("Basic", "Cloze", by = "note_type")
comp$winner
comp$differences

## End(Not run)

Compare two time periods

Description

Compare study statistics between two time periods.

Usage

anki_compare_periods(
  path = NULL,
  profile = NULL,
  period1 = NULL,
  period2 = NULL,
  period_names = c("Period 1", "Period 2")
)

Arguments

Value

A tibble comparing the two periods

Examples

## Not run: 
# Compare this month vs last month
anki_compare_periods(
  period1 = c("2024-01-01", "2024-01-31"),
  period2 = c("2024-02-01", "2024-02-29"),
  period_names = c("January", "February")
)

## End(Not run)

Analyze study consistency

Description

Measures how consistent your study habits are.

Usage

anki_consistency(path = NULL, profile = NULL, days = 90)

Arguments

Value

A list with consistency metrics

Examples

## Not run: 
anki_consistency()

## End(Not run)

Analyze topic coverage

Description

Shows percentage complete, mature, and retained by topic (tag or subdeck).

Usage

anki_coverage_analysis(
  path = NULL,
  profile = NULL,
  by = "tag",
  pattern = NULL,
  min_cards = 10
)

Arguments

Value

A tibble with coverage analysis per topic

Examples

## Not run: 
coverage <- anki_coverage_analysis()
coverage <- anki_coverage_analysis(pattern = "Anatomy")

## End(Not run)

Launch interactive Anki dashboard

Description

Opens a Shiny dashboard with comprehensive collection analytics.

Usage

anki_dashboard(path = NULL, profile = NULL)

Arguments

Value

Opens a Shiny app in the browser

Examples

## Not run: 
anki_dashboard()

## End(Not run)

Get path to Anki database

Description

Returns the full path to an Anki collection database file.

Usage

anki_db_path(profile = NULL, base_path = NULL)

Arguments

Value

Character string path to collection.anki2 or collection.anki21

Examples

## Not run: 
anki_db_path()
anki_db_path("User 1")

## End(Not run)

Read decks from Anki collection

Description

Read decks from Anki collection

Usage

anki_decks(path = NULL, profile = NULL)

Arguments

Value

A tibble of decks with columns: did, name

Examples

## Not run: 
anki_decks()

## End(Not run)

Get cards due for review

Description

Get cards due for review

Usage

anki_due(path = NULL, profile = NULL, days_ahead = 0)

Arguments

Value

A tibble of due cards

Examples

## Not run: 
due_today <- anki_due()
due_week <- anki_due(days_ahead = 7)

## End(Not run)

Find cards with empty fields

Description

Identifies cards that have empty required fields.

Usage

anki_empty_cards(path = NULL, profile = NULL)

Arguments

Value

A tibble of cards with empty fields

Examples

## Not run: 
empty <- anki_empty_cards()

## End(Not run)

Track exam readiness

Description

Projects whether you'll complete cards before an exam, at what retention level. Useful for medical board exam preparation.

Usage

anki_exam_readiness(
  path = NULL,
  profile = NULL,
  target_date,
  deck_pattern = NULL,
  new_cards_per_day = 20,
  target_retention = 0.9
)

Arguments

Value

A list with exam readiness analysis

Examples

## Not run: 
readiness <- anki_exam_readiness(target_date = "2024-06-15", deck_pattern = "Step1")

## End(Not run)

Export to Anki-importable format

Description

Export to Anki-importable format

Usage

anki_export_importable(data, file, tags = NULL)

Arguments

Value

Invisibly returns number of cards

Examples

## Not run: 
cards <- data.frame(front = c("Q1", "Q2"), back = c("A1", "A2"))
anki_export_importable(cards, "new_cards.txt")

## End(Not run)

Export review history

Description

Exports the full review log to CSV.

Usage

anki_export_revlog(
  file,
  path = NULL,
  profile = NULL,
  include_card_info = FALSE
)

Arguments

Value

Invisibly returns the exported data

Examples

## Not run: 
anki_export_revlog("my_reviews.csv")

## End(Not run)

Parse note fields into columns

Description

Splits the field content (separated by \x1f) into separate columns.

Usage

anki_field_contents(path = NULL, profile = NULL, model_id = NULL)

Arguments

Value

A tibble with nid and separate columns for each field

Examples

## Not run: 
fields <- anki_field_contents()

## End(Not run)

Search cards by content similarity

Description

Find cards with similar content to a given card or text.

Usage

anki_find_similar(
  query,
  path = NULL,
  profile = NULL,
  n = 10,
  method = "tfidf",
  within_deck = FALSE
)

Arguments

Value

A tibble with similar cards and similarity scores

Examples

## Not run: 
# Find cards similar to card ID 1234567890
anki_find_similar(1234567890)

# Find cards similar to specific text
anki_find_similar("mitochondria powerhouse")

## End(Not run)

Fit forgetting curve from review data

Description

Fits a forgetting curve to actual review data and compares it to the theoretical FSRS curve. Can analyze individual cards or aggregate data.

Usage

anki_fit_forgetting_curve(
  path = NULL,
  profile = NULL,
  cid = NULL,
  min_reviews = 5,
  max_cards = 1000
)

Arguments

Value

A list with fitted curve parameters and comparison data

Examples

## Not run: 
curve <- anki_fit_forgetting_curve()
plot(curve$data$days_elapsed, curve$data$observed_retention, type = "p")
lines(curve$data$days_elapsed, curve$data$fitted_retention, col = "blue")

## End(Not run)

Get forecast of upcoming reviews

Description

Predicts how many reviews will be due each day.

Usage

anki_forecast(path = NULL, profile = NULL, days = 30)

Arguments

Value

A tibble with daily forecast

Examples

## Not run: 
forecast <- anki_forecast()

## End(Not run)

Enhanced Time Series Forecasting

Description

Improved forecasting with ARIMA, seasonal patterns, and workload ceilings.

Usage

anki_forecast_enhanced(
  path = NULL,
  profile = NULL,
  metric = "reviews",
  days_ahead = 30,
  method = "auto",
  confidence = 0.95,
  workload_ceiling = NULL
)

Arguments

Value

A tibble with forecast results

Examples

## Not run: 
fc <- anki_forecast_enhanced("reviews", days_ahead = 30)
plot(fc$date, fc$forecast, type = "l")

## End(Not run)

Monte Carlo Forecasting

Description

Forecast future reviews using Monte Carlo simulation with bootstrapping. Unlike statistical methods (ARIMA, Holt-Winters), this approach:

• Makes no distributional assumptions

• Preserves day-of-week patterns naturally

• Handles irregular study habits (missed days, catch-up sessions)

• Provides empirical confidence intervals

Usage

anki_forecast_monte_carlo(
  path = NULL,
  profile = NULL,
  days_ahead = 30,
  n_sim = 1000,
  method = "weekday",
  block_size = 7,
  include_trend = TRUE,
  seed = NULL
)

Arguments

Value

A list with forecast distribution, summary statistics, and simulation data

Examples

## Not run: 
mc <- anki_forecast_monte_carlo(days_ahead = 30, n_sim = 1000)

# Summary
mc$summary

# Probability of >100 reviews on day 7
mc$prob_above(day = 7, threshold = 100)

# Full simulation matrix
dim(mc$simulations)  # n_sim x days_ahead

## End(Not run)

Gamification Stats

Description

Calculate XP, level, achievements, and progress toward goals based on your Anki review history. Makes studying more engaging!

Usage

anki_gamification(
  path = NULL,
  profile = NULL,
  xp_per_review = 10,
  xp_per_correct = 5,
  xp_per_streak = 25
)

Arguments

Value

A list with XP, level, achievements, and stats

Examples

## Not run: 
stats <- anki_gamification()
stats$level
stats$achievements

## End(Not run)

Collection health check

Description

Performs a comprehensive health check on your Anki collection, identifying common issues and problems.

Usage

anki_health_check(path = NULL, profile = NULL)

Arguments

Value

A list with health check results

Examples

## Not run: 
health <- anki_health_check()

## End(Not run)

Get review data formatted for calendar heatmaps

Description

Get review data formatted for calendar heatmaps

Usage

anki_heatmap_data(path = NULL, profile = NULL, year = NULL)

Arguments

Value

A tibble with date and count columns suitable for heatmap visualization

Examples

## Not run: 
heatmap_data <- anki_heatmap_data()
# Use with ggplot2:
# ggplot(heatmap_data, aes(week, weekday, fill = reviews)) + geom_tile()

## End(Not run)

Detect card interference

Description

Find cards that are often confused with each other, based on similar failure patterns or content similarity combined with poor retention.

Usage

anki_interference_analysis(
  path = NULL,
  profile = NULL,
  min_lapses = 3,
  time_window_days = 7,
  max_pairs = 50
)

Arguments

Value

A tibble of potentially interfering card pairs

Examples

## Not run: 
interference <- anki_interference_analysis()

## End(Not run)

Track card learning progression over time

Description

Shows how cards have progressed through intervals over time.

Usage

anki_learning_curve(
  path = NULL,
  profile = NULL,
  card_ids = NULL,
  n_cards = 100
)

Arguments

Value

A tibble with review history per card

Examples

## Not run: 
curve <- anki_learning_curve()

## End(Not run)

Analyze learning efficiency

Description

Calculate how much "real learning" is happening vs time spent on failed reviews. Measures the ratio of successful retention to total study time.

Usage

anki_learning_efficiency(
  path = NULL,
  profile = NULL,
  days = 30,
  by_deck = FALSE
)

Arguments

Value

A tibble with efficiency metrics

Examples

## Not run: 
eff <- anki_learning_efficiency()
eff <- anki_learning_efficiency(days = 90, by_deck = TRUE)

## End(Not run)

Learning Velocity Analysis

Description

Track learning rate over time: cards learned per day, time to graduation, acceleration or deceleration of learning.

Usage

anki_learning_velocity(path = NULL, profile = NULL, period = "month")

Arguments

Value

A list with velocity metrics and trends

Examples

## Not run: 
velocity <- anki_learning_velocity()
velocity$current
velocity$trend

## End(Not run)

Find leech cards (high lapse count)

Description

Leeches are cards that you keep forgetting. By default, Anki marks cards as leeches after 8 lapses.

Usage

anki_leeches(path = NULL, profile = NULL, threshold = 8, include_notes = FALSE)

Arguments

Value

A tibble of leech cards ordered by lapses

Examples

## Not run: 
leeches <- anki_leeches()
leeches <- anki_leeches(threshold = 5)

## End(Not run)

Find cards with very long content

Description

Identifies cards that might be too complex.

Usage

anki_long_cards(path = NULL, profile = NULL, threshold = 2000)

Arguments

Value

A tibble of long cards

Examples

## Not run: 
long_cards <- anki_long_cards()

## End(Not run)

Get mature cards (interval >= 21 days)

Description

Get mature cards (interval >= 21 days)

Usage

anki_mature(path = NULL, profile = NULL, min_interval = 21)

Arguments

Value

A tibble of mature cards

Examples

## Not run: 
mature <- anki_mature()

## End(Not run)

List media files in collection

Description

Returns all media files stored in the collection's media folder.

Usage

anki_media_list(path = NULL, profile = NULL)

Arguments

Value

A tibble with filename, size, and extension

Examples

## Not run: 
media <- anki_media_list()

## End(Not run)

Find missing media references

Description

Identifies media files referenced in notes but not present in the media folder.

Usage

anki_media_missing(path = NULL, profile = NULL)

Arguments

Value

A tibble with missing filenames and the notes referencing them

Examples

## Not run: 
missing <- anki_media_missing()

## End(Not run)

Get media folder path

Description

Get media folder path

Usage

anki_media_path(path = NULL, profile = NULL)

Arguments

Value

Character string path to collection.media folder

Examples

## Not run: 
anki_media_path()

## End(Not run)

Get media statistics

Description

Get media statistics

Usage

anki_media_stats(path = NULL, profile = NULL)

Arguments

Value

A list with media statistics

Examples

## Not run: 
stats <- anki_media_stats()

## End(Not run)

Find unused media files

Description

Identifies media files that are not referenced in any note.

Usage

anki_media_unused(path = NULL, profile = NULL)

Arguments

Value

A tibble of unused media files

Examples

## Not run: 
unused <- anki_media_unused()

## End(Not run)

Read note types (models) from Anki collection

Description

Read note types (models) from Anki collection

Usage

anki_models(path = NULL, profile = NULL)

Arguments

Value

A tibble of note types with columns: mid, name, flds (list of field names), tmpls (list of template names)

Examples

## Not run: 
anki_models()

## End(Not run)

Get monthly summary statistics

Description

Get monthly summary statistics

Usage

anki_monthly_summary(path = NULL, profile = NULL, months = 12)

Arguments

Value

A tibble with monthly statistics

Examples

## Not run: 
anki_monthly_summary()

## End(Not run)

Get new cards (never reviewed)

Description

Get new cards (never reviewed)

Usage

anki_new(path = NULL, profile = NULL, deck = NULL)

Arguments

Value

A tibble of new cards

Examples

## Not run: 
new_cards <- anki_new()

## End(Not run)

Read notes from Anki collection

Description

Read notes from Anki collection

Usage

anki_notes(path = NULL, profile = NULL)

Arguments

Value

A tibble of notes with columns: nid, mid, tags, flds, sfld

Examples

## Not run: 
anki_notes()

## End(Not run)

Plot difficulty distribution

Description

Histogram of card difficulties (FSRS).

Usage

anki_plot_difficulty(path = NULL, profile = NULL, by_deck = FALSE)

Arguments

Value

A ggplot2 object

Examples

## Not run: 
anki_plot_difficulty()

## End(Not run)

Plot review forecast

Description

Shows predicted upcoming review workload.

Usage

anki_plot_forecast(path = NULL, profile = NULL, days = 30)

Arguments

Value

A ggplot2 object

Examples

## Not run: 
anki_plot_forecast()

## End(Not run)

Plot forgetting curve comparison

Description

Creates a visualization comparing your fitted forgetting curve to FSRS defaults.

Usage

anki_plot_forgetting_curve(curve_data)

Arguments

Value

A ggplot object

Examples

## Not run: 
curve <- anki_fit_forgetting_curve()
anki_plot_forgetting_curve(curve)

## End(Not run)

Plot review heatmap calendar

Description

Creates a calendar heatmap showing review activity.

Usage

anki_plot_heatmap(
  path = NULL,
  profile = NULL,
  year = NULL,
  colors = c("#ebedf0", "#9be9a8", "#40c463", "#30a14e", "#216e39")
)

Arguments

Value

A ggplot2 object

Examples

## Not run: 
anki_plot_heatmap()
anki_plot_heatmap(year = 2024)

## End(Not run)

Plot reviews by hour of day

Description

Plot reviews by hour of day

Usage

anki_plot_hours(path = NULL, profile = NULL, days = 90)

Arguments

Value

A ggplot2 object

Examples

## Not run: 
anki_plot_hours()

## End(Not run)

Plot interval distribution

Description

Histogram of card intervals.

Usage

anki_plot_intervals(path = NULL, profile = NULL, log_scale = TRUE)

Arguments

Value

A ggplot2 object

Examples

## Not run: 
anki_plot_intervals()

## End(Not run)

Plot Monte Carlo Forecast

Description

Visualize Monte Carlo forecast with confidence bands.

Usage

anki_plot_monte_carlo(
  mc_forecast,
  show_bands = c("95", "80"),
  show_simulations = 20,
  cumulative = FALSE
)

Arguments

Value

A ggplot2 object

Examples

## Not run: 
mc <- anki_forecast_monte_carlo(days_ahead = 30)
anki_plot_monte_carlo(mc)
anki_plot_monte_carlo(mc, cumulative = TRUE)

## End(Not run)

Plot retention over time

Description

Shows how retention rate has changed over time.

Usage

anki_plot_retention(path = NULL, profile = NULL, days = 90, window = 7)

Arguments

Value

A ggplot2 object

Examples

## Not run: 
anki_plot_retention()
anki_plot_retention(days = 365, window = 14)

## End(Not run)

Plot stability distribution

Description

Histogram of FSRS stability values.

Usage

anki_plot_stability(path = NULL, profile = NULL)

Arguments

Value

A ggplot2 object

Examples

## Not run: 
anki_plot_stability()

## End(Not run)

Plot reviews by day of week

Description

Plot reviews by day of week

Usage

anki_plot_weekdays(path = NULL, profile = NULL, days = 90)

Arguments

Value

A ggplot2 object

Examples

## Not run: 
anki_plot_weekdays()

## End(Not run)

List Anki profiles

Description

Returns the names of all Anki profiles found in the Anki2 directory.

Usage

anki_profiles(base_path = NULL)

Arguments

Value

Character vector of profile names

Examples

## Not run: 
anki_profiles()

## End(Not run)

Generate progress report

Description

Generates a shareable HTML or Markdown progress report with charts.

Usage

anki_progress_report(
  path = NULL,
  profile = NULL,
  output_path = "anki_progress.html",
  format = "html",
  period = "month",
  title = "Anki Progress Report"
)

Arguments

Value

Invisibly returns the report content

Examples

## Not run: 
anki_progress_report(output_path = "my_progress.html", period = "month")

## End(Not run)

Analyze card quality metrics

Description

Comprehensive quality analysis combining multiple metrics.

Usage

anki_quality_report(path = NULL, profile = NULL)

Arguments

Value

A list with quality metrics

Examples

## Not run: 
quality <- anki_quality_report()

## End(Not run)

Quick Collection Summary

Description

Get a one-liner overview of your Anki collection.

Usage

anki_quick_summary(path = NULL, profile = NULL, print = TRUE)

Arguments

Value

Invisibly returns a list with summary stats

Examples

## Not run: 
anki_quick_summary()

## End(Not run)

Generate collection summary report

Description

Creates a summary of your Anki collection statistics.

Usage

anki_report(path = NULL, profile = NULL)

Arguments

Value

A list with collection statistics

Examples

## Not run: 
report <- anki_report()
print(report)

## End(Not run)

Analyze response time by card properties

Description

Shows how response time varies by difficulty, interval, etc.

Usage

anki_response_time(path = NULL, profile = NULL, days = 90)

Arguments

Value

A tibble with response time analysis

Examples

## Not run: 
anki_response_time()

## End(Not run)

Analyze response time outliers

Description

Find reviews with suspicious response times (too fast or too slow).

Usage

anki_response_time_outliers(
  path = NULL,
  profile = NULL,
  min_time_ms = 500,
  max_time_ms = 120000
)

Arguments

Value

A tibble with outlier reviews

Examples

## Not run: 
outliers <- anki_response_time_outliers()

## End(Not run)

Calculate retention by content type

Description

Break down retention by card characteristics: cloze vs basic, with/without media, short vs long content.

Usage

anki_retention_by_type(path = NULL, profile = NULL, days = 30)

Arguments

Value

A list with retention breakdowns by different content types

Examples

## Not run: 
ret_type <- anki_retention_by_type()
ret_type$by_card_type
ret_type$by_media

## End(Not run)

Calculate actual retention rate from review history

Description

Calculates the proportion of reviews that were successful (not "Again").

Usage

anki_retention_rate(path = NULL, profile = NULL, days = 30, by_deck = FALSE)

Arguments

Value

A tibble with retention statistics

Examples

## Not run: 
retention <- anki_retention_rate()
retention <- anki_retention_rate(days = 90, by_deck = TRUE)

## End(Not run)

Retention Stability Analysis

Description

Analyze how stable your retention is over time.

Usage

anki_retention_stability(path = NULL, profile = NULL, window = 7)

Arguments

Value

A tibble with retention stability metrics

Review Quality Score

Description

Detects low-quality reviews: pattern clicking, rushed reviews, suspicious timing patterns, and other indicators of disengaged studying.

Usage

anki_review_quality(
  path = NULL,
  profile = NULL,
  days = 30,
  min_time_ms = 800,
  max_time_ms = 60000
)

Arguments

Value

A list with quality metrics and flagged reviews

Examples

## Not run: 
quality <- anki_review_quality()
quality$score
quality$issues

## End(Not run)

Read review log from Anki collection

Description

Read review log from Anki collection

Usage

anki_revlog(path = NULL, profile = NULL)

Arguments

Value

A tibble of review log entries with columns: rid, cid, ease, ivl, time, review_date

Examples

## Not run: 
anki_revlog()

## End(Not run)

Calculate spaced repetition ROI

Description

Estimate the return on investment of your spaced repetition practice. Calculates knowledge half-life extension per minute of study.

Usage

anki_roi_analysis(path = NULL, profile = NULL)

Arguments

Value

A list with ROI analysis

Examples

## Not run: 
roi <- anki_roi_analysis()

## End(Not run)

Anki Schema Version Detection

Description

Detect the Anki database schema version to help debug compatibility issues.

Usage

anki_schema_version(path = NULL, profile = NULL)

Arguments

Value

A list with schema information

Examples

## Not run: 
schema <- anki_schema_version()

## End(Not run)

Search cards like Anki's browser

Description

Searches cards using a simplified version of Anki's search syntax. Supports: deck:name, tag:name, is:new, is:due, is:suspended, is:buried, is:learn, is:review, prop:ivl>N, prop:lapses>N, prop:reps>N

Usage

anki_search(query, path = NULL, profile = NULL)

Arguments

Value

A tibble of matching cards

Examples

## Not run: 
# Find suspended cards
anki_search("is:suspended")

# Find cards in a specific deck
anki_search("deck:Default")

# Find cards with many lapses
anki_search("prop:lapses>5")

# Combine conditions
anki_search("deck:Medical is:review prop:ivl>30")

## End(Not run)

Enhanced Search for Anki Cards

Description

Advanced search functionality supporting additional search operators beyond the basic anki_search().

Usage

anki_search_enhanced(
  pattern,
  path = NULL,
  profile = NULL,
  case_sensitive = FALSE
)

Arguments

Details

Supported search operators:

• added:N - Cards added in last N days

• rated:N - Cards rated in last N days

• rated:N:ease - Cards rated with specific ease (1-4) in last N days

• note:type - Cards with specific note type name

• card:N - Card template number (0-indexed)

• ivl:N - Cards with interval >= N days

• ⁠ivl:<N⁠ - Cards with interval < N days

• prop:ease>N - Cards with ease factor > N (e.g., prop:ease>2.5)

• prop:lapses>N - Cards with lapses > N

• prop:reps>N - Cards with reps > N

• re:pattern - Regex search in card content

• OR - OR operator between terms

• Standard operators: ⁠deck:⁠, ⁠tag:⁠, ⁠is:⁠, ⁠flag:⁠

Value

A tibble with matching cards

Examples

## Not run: 
# Cards added in last 7 days
anki_search_enhanced("added:7")

# Cards rated "Again" in last 3 days
anki_search_enhanced("rated:3:1")

# Leeches with high lapses
anki_search_enhanced("is:leech prop:lapses>5")

# Regex search
anki_search_enhanced("re:^The\\s+")

# OR search
anki_search_enhanced("deck:German OR deck:Spanish")

## End(Not run)

Analyze study sessions

Description

Identifies and analyzes discrete study sessions based on gaps between reviews.

Usage

anki_session_analysis(
  path = NULL,
  profile = NULL,
  gap_minutes = 30,
  min_session_reviews = 5
)

Arguments

Value

A list with session analysis

Examples

## Not run: 
sessions <- anki_session_analysis()

## End(Not run)

Analyze study sessions

Description

Identifies study sessions and calculates session statistics. A session is defined as reviews with gaps less than session_gap minutes.

Usage

anki_session_stats(path = NULL, profile = NULL, session_gap = 30, days = 90)

Arguments

Value

A tibble with session statistics

Examples

## Not run: 
sessions <- anki_session_stats()

## End(Not run)

Analyze sibling card effects

Description

Analyzes how sibling cards (cards from the same note) affect each other's retention. For example, does seeing the forward card help with the reverse?

Usage

anki_sibling_analysis(path = NULL, profile = NULL, max_gap_days = 7)

Arguments

Value

A list with sibling analysis

Examples

## Not run: 
siblings <- anki_sibling_analysis()

## End(Not run)

Find similar/duplicate cards

Description

Uses text similarity to find potential duplicate cards.

Usage

anki_similar_cards(
  path = NULL,
  profile = NULL,
  threshold = 0.8,
  max_comparisons = 50000
)

Arguments

Value

A tibble of similar card pairs

Examples

## Not run: 
dupes <- anki_similar_cards(threshold = 0.9)

## End(Not run)

Simulate a study session

Description

Given a time budget, predict how many cards you'll review, expected retention, and workload distribution.

Usage

anki_simulate_session(
  path = NULL,
  profile = NULL,
  minutes = 30,
  include_new = TRUE
)

Arguments

Value

A tibble with session simulation

Examples

## Not run: 
sim <- anki_simulate_session(minutes = 30)

## End(Not run)

Calculate daily review statistics

Description

Calculate daily review statistics

Usage

anki_stats_daily(path = NULL, profile = NULL, from = NULL, to = NULL)

Arguments

Value

A tibble with daily statistics

Examples

## Not run: 
daily <- anki_stats_daily()
daily <- anki_stats_daily(from = "2024-01-01")

## End(Not run)

Calculate per-deck statistics

Description

Calculate per-deck statistics

Usage

anki_stats_deck(path = NULL, profile = NULL)

Arguments

Value

A tibble with deck statistics

Examples

## Not run: 
stats <- anki_stats_deck()

## End(Not run)

Calculate current review streak

Description

Calculate current review streak

Usage

anki_streak(path = NULL, profile = NULL)

Arguments

Value

A list with current_streak, longest_streak, and streak_history

Examples

## Not run: 
streak <- anki_streak()
streak$current_streak
streak$longest_streak

## End(Not run)

Advanced Streak Analytics

Description

Detailed streak analysis including best streaks, recovery time after breaks, weekend vs weekday patterns, and streak predictions.

Usage

anki_streak_analytics(path = NULL, profile = NULL)

Arguments

Value

A list with detailed streak statistics

Examples

## Not run: 
streaks <- anki_streak_analytics()
streaks$current
streaks$history

## End(Not run)

Create study plan

Description

Generates a daily study plan based on exam date and current progress.

Usage

anki_study_plan(
  path = NULL,
  profile = NULL,
  target_date,
  daily_minutes = 60,
  deck_pattern = NULL
)

Arguments

Value

A list with study plan

Examples

## Not run: 
plan <- anki_study_plan(target_date = "2024-06-15", daily_minutes = 90)

## End(Not run)

Generate study priority list

Description

Identifies which topics/decks should be prioritized based on progress and retention.

Usage

anki_study_priorities(path = NULL, profile = NULL, by = "tag", n = 10)

Arguments

Value

A tibble with prioritized topics

Examples

## Not run: 
priorities <- anki_study_priorities(n = 10)

## End(Not run)

Quick collection summary

Description

Returns a one-liner overview of your Anki collection.

Usage

anki_summary(path = NULL, profile = NULL)

Arguments

Value

A tibble with key metrics

Examples

## Not run: 
anki_summary()

## End(Not run)

Get suspended cards

Description

Get suspended cards

Usage

anki_suspended(path = NULL, profile = NULL, include_notes = FALSE)

Arguments

Value

A tibble of suspended cards

Examples

## Not run: 
suspended <- anki_suspended()

## End(Not run)

Analyze tag usage

Description

Provides detailed tag statistics and identifies orphan/unused tags.

Usage

anki_tag_analysis(path = NULL, profile = NULL)

Arguments

Value

A list with tag analysis

Examples

## Not run: 
tags <- anki_tag_analysis()

## End(Not run)

Extract unique tags with counts

Description

Parses tags from all notes and returns unique tags with their frequency.

Usage

anki_tags(path = NULL, profile = NULL)

Arguments

Value

A tibble with columns: tag, count

Examples

## Not run: 
tags <- anki_tags()

## End(Not run)

Analyze reviews by hour of day

Description

Shows when you study most during the day.

Usage

anki_time_by_hour(path = NULL, profile = NULL, days = 90)

Arguments

Value

A tibble with hourly statistics

Examples

## Not run: 
anki_time_by_hour()

## End(Not run)

Analyze reviews by day of week

Description

Shows which days you study most.

Usage

anki_time_by_weekday(path = NULL, profile = NULL, days = 90)

Arguments

Value

A tibble with daily statistics

Examples

## Not run: 
anki_time_by_weekday()

## End(Not run)

Convert Anki timestamp to date

Description

Converts Anki's millisecond-epoch timestamps to Date objects.

Usage

anki_timestamp_to_date(x)

Arguments

Value

Date object

Examples

# Convert a typical Anki timestamp
anki_timestamp_to_date(1368291917470)

Convert Anki timestamp to datetime

Description

Converts Anki's millisecond-epoch timestamps to POSIXct datetime objects.

Usage

anki_timestamp_to_datetime(x)

Arguments

Value

POSIXct datetime object in local timezone

Examples

# Convert a typical Anki timestamp
anki_timestamp_to_datetime(1368291917470)

Export deck to CSV

Description

Exports cards from a deck to a CSV file with note content.

Usage

anki_to_csv(
  deck,
  file = NULL,
  path = NULL,
  profile = NULL,
  include_html = FALSE
)

Arguments

Value

Invisibly returns the exported data

Examples

## Not run: 
anki_to_csv("Default")
anki_to_csv("Medical", file = "medical_cards.csv")

## End(Not run)

Export collection report to HTML

Description

Export collection report to HTML

Usage

anki_to_html(file, path = NULL, profile = NULL)

Arguments

Value

Invisibly returns the file path

Examples

## Not run: 
anki_to_html("report.html")

## End(Not run)

Export collection as JSON

Description

Full collection export as structured JSON for web dashboards or custom apps.

Usage

anki_to_json(
  path = NULL,
  profile = NULL,
  output_path = "anki_collection.json",
  include_revlog = TRUE,
  include_content = TRUE
)

Arguments

Value

Invisibly returns the exported data structure

Examples

## Not run: 
anki_to_json(output_path = "collection.json")

## End(Not run)

Export deck to Markdown format

Description

Exports cards to Markdown flashcard format.

Usage

anki_to_markdown(
  deck,
  file = NULL,
  path = NULL,
  profile = NULL,
  format = c("obsidian", "logseq", "basic")
)

Arguments

Value

Invisibly returns the number of cards exported

Examples

## Not run: 
anki_to_markdown("Medical")

## End(Not run)

Export to Mochi format

Description

Exports cards to Mochi's JSON import format.

Usage

anki_to_mochi(
  path = NULL,
  profile = NULL,
  output_path = "mochi_cards.json",
  deck_pattern = NULL
)

Arguments

Value

Invisibly returns the number of cards exported

Examples

## Not run: 
anki_to_mochi(output_path = "mochi_import.json")

## End(Not run)

Export to Obsidian Spaced Repetition plugin format

Description

Exports cards in a format compatible with the Obsidian Spaced Repetition plugin.

Usage

anki_to_obsidian_sr(
  path = NULL,
  profile = NULL,
  output_dir = ".",
  model_id = NULL,
  deck_pattern = NULL,
  include_scheduling = FALSE
)

Arguments

Value

Invisibly returns the number of cards exported

Examples

## Not run: 
anki_to_obsidian_sr(output_dir = "~/obsidian/vault/flashcards")

## End(Not run)

Export deck to Org-mode format

Description

Exports cards to Emacs Org-mode flashcard format (org-drill compatible).

Usage

anki_to_org(
  deck,
  file = NULL,
  path = NULL,
  profile = NULL,
  include_scheduling = FALSE
)

Arguments

Value

Invisibly returns the number of cards exported

Examples

## Not run: 
anki_to_org("Medical")
anki_to_org("Vocabulary", file = "vocab.org")

## End(Not run)

Export deck to SuperMemo Q&A format

Description

Export deck to SuperMemo Q&A format

Usage

anki_to_supermemo(deck, file = NULL, path = NULL, profile = NULL)

Arguments

Value

Invisibly returns the number of cards exported

Examples

## Not run: 
anki_to_supermemo("Medical")

## End(Not run)

Today's activity summary

Description

Returns what happened today in your Anki studies.

Detailed breakdown of today's Anki activity.

Usage

anki_today(path = NULL, profile = NULL)

anki_today(path = NULL, profile = NULL)

Arguments

Value

A list with today's stats

A list with today's activity breakdown

Examples

## Not run: 
anki_today()

## End(Not run)
## Not run: 
anki_today()

## End(Not run)

Detect anomalies in review patterns

Description

Identifies unusual days (very high or low activity).

Usage

anki_ts_anomalies(path = NULL, profile = NULL, threshold = 2)

Arguments

Value

A tibble with anomalous days

Examples

## Not run: 
anomalies <- anki_ts_anomalies()

## End(Not run)

Calculate autocorrelation of review patterns

Description

Identifies cyclical patterns in studying.

Usage

anki_ts_autocorrelation(path = NULL, profile = NULL, max_lag = 30)

Arguments

Value

A tibble with autocorrelation values

Examples

## Not run: 
acf_data <- anki_ts_autocorrelation()
# Peaks at lag 7 indicate weekly patterns

## End(Not run)

Decompose time series into trend, seasonal, and residual

Description

Uses classical decomposition on review data.

Usage

anki_ts_decompose(
  path = NULL,
  profile = NULL,
  metric = "reviews",
  frequency = 7
)

Arguments

Value

A decomposition object (or list with components)

Examples

## Not run: 
dec <- anki_ts_decompose()
plot(dec)

## End(Not run)

Forecast future reviews using simple methods

Description

Uses exponential smoothing or linear trend for forecasting.

Usage

anki_ts_forecast(path = NULL, profile = NULL, days = 30, method = "linear")

Arguments

Value

A tibble with forecasted values

Examples

## Not run: 
forecast <- anki_ts_forecast(days = 30)

## End(Not run)

Analyze interval progression over time

Description

Tracks how card intervals change over time periods.

Usage

anki_ts_intervals(path = NULL, profile = NULL, by = "week")

Arguments

Value

A tibble with interval statistics over time

Examples

## Not run: 
ts <- anki_ts_intervals()
plot(ts$date, ts$median_ivl, type = "l")

## End(Not run)

Analyze learning rate (new cards learned)

Description

Tracks how many new cards are introduced and learned over time.

Usage

anki_ts_learning(path = NULL, profile = NULL, by = "week")

Arguments

Value

A tibble with learning statistics over time

Examples

## Not run: 
ts <- anki_ts_learning()

## End(Not run)

Analyze card maturation over time

Description

Tracks cumulative mature cards over time.

Usage

anki_ts_maturation(
  path = NULL,
  profile = NULL,
  by = "week",
  mature_threshold = 21
)

Arguments

Value

A tibble with maturation statistics

Examples

## Not run: 
ts <- anki_ts_maturation()

## End(Not run)

Plot time series with trend line

Description

Plot time series with trend line

Usage

anki_ts_plot(ts_data, y_col, title = NULL)

Arguments

Value

A ggplot object

Examples

## Not run: 
ts <- anki_ts_intervals()
anki_ts_plot(ts, "median_ivl", "Median Interval Over Time")

## End(Not run)

Analyze retention over time

Description

Tracks retention rate changes over time periods.

Usage

anki_ts_retention(path = NULL, profile = NULL, by = "week")

Arguments

Value

A tibble with retention statistics over time

Examples

## Not run: 
ts <- anki_ts_retention()

## End(Not run)

Analyze FSRS stability over time

Description

Tracks how memory stability evolves.

Usage

anki_ts_stability(path = NULL, profile = NULL, by = "week")

Arguments

Value

A tibble with stability statistics over time

Examples

## Not run: 
ts <- anki_ts_stability()

## End(Not run)

Analyze workload trends

Description

Tracks review workload over time.

Usage

anki_ts_workload(path = NULL, profile = NULL, by = "week")

Arguments

Value

A tibble with workload statistics over time

Examples

## Not run: 
ts <- anki_ts_workload()

## End(Not run)

Find weak areas by tag or deck

Description

Identifies tags or subdecks with the lowest retention or highest lapse rates.

Usage

anki_weak_areas(path = NULL, profile = NULL, n = 10, by = "tag", days = 90)

Arguments

Value

A tibble of weak areas

Examples

## Not run: 
weak <- anki_weak_areas(n = 10, by = "tag")

## End(Not run)

Workload Projection (Rough Estimate)

Description

Project future review workload with scenario analysis. This provides rough ballpark estimates for planning purposes, not accurate FSRS simulation.

Usage

anki_workload_projection(
  path = NULL,
  profile = NULL,
  days = 30,
  scenarios = NULL
)

Arguments

Details

This function uses simplified heuristics to estimate workload:

• New cards reviewed ~4x during learning phase

• Mature card reviews based on recent average

• Retention affects review frequency

For accurate FSRS simulation, use Anki's built-in simulator (Tools > FSRS > Simulate) or the FSRS Helper add-on, which implement the full FSRS algorithm.

This function is useful for:

• Quick "what if" scenario comparisons

• Rough planning estimates

• Programmatic analysis in R

Value

A tibble with workload projections for each scenario

Examples

## Not run: 
proj <- anki_workload_projection(days = 30)

## End(Not run)

Convert date to Anki timestamp

Description

Converts a Date or POSIXct to Anki's millisecond-epoch format.

Usage

date_to_anki_timestamp(x)

Arguments

Value

Numeric timestamp in milliseconds since Unix epoch

Examples

date_to_anki_timestamp(Sys.Date())

Compare FSRS parameters

Description

Compares your optimized FSRS parameters against defaults and community averages.

Usage

fsrs_compare_parameters(path = NULL, profile = NULL)

Arguments

Value

A list with parameter comparison

Examples

## Not run: 
comp <- fsrs_compare_parameters()

## End(Not run)

Calculate current retrievability for all cards

Description

Computes the current probability of recall for all FSRS-enabled cards.

Usage

fsrs_current_retrievability(path = NULL, profile = NULL)

Arguments

Value

A tibble with cards and their current retrievability

Examples

## Not run: 
r <- fsrs_current_retrievability()
# Cards with low retrievability need review soon
r[r$retrievability < 0.8, ]

## End(Not run)

Analyze FSRS decay parameter distribution

Description

In FSRS-6, each card can have its own decay parameter. This analyzes the distribution across your collection.

Usage

fsrs_decay_distribution(path = NULL, profile = NULL, by_deck = FALSE)

Arguments

Value

A tibble with decay distribution statistics

Examples

## Not run: 
decay <- fsrs_decay_distribution()

## End(Not run)

Analyze FSRS difficulty distribution

Description

Analyze FSRS difficulty distribution

Usage

fsrs_difficulty_distribution(path = NULL, profile = NULL, by_deck = FALSE)

Arguments

Value

A tibble with difficulty statistics

Examples

## Not run: 
diff_dist <- fsrs_difficulty_distribution()

## End(Not run)

Export reviews for external FSRS analysis

Description

Export reviews for external FSRS analysis

Usage

fsrs_export_reviews(file, path = NULL, profile = NULL, format = "csv")

Arguments

Value

Invisibly returns the exported data

Examples

## Not run: 
fsrs_export_reviews("reviews.csv")

## End(Not run)

Calculate forgetting index

Description

Estimates the percentage of cards currently below target retention.

Usage

fsrs_forgetting_index(path = NULL, profile = NULL, target_retention = 0.9)

Arguments

Value

A list with forgetting analysis

Examples

## Not run: 
fsrs_forgetting_index()

## End(Not run)

Import review data from CSV for analysis

Description

Import review data from CSV for analysis

Usage

fsrs_from_csv(
  file,
  date_col = "date",
  rating_col = "rating",
  card_col = "card_id",
  date_format = "%Y-%m-%d"
)

Arguments

Value

A tibble with standardized review data

Examples

## Not run: 
reviews <- fsrs_from_csv("reviews.csv", date_col = "date", rating_col = "grade")

## End(Not run)

Get FSRS parameters from Anki deck config

Description

Extracts the FSRS parameters stored in Anki's deck configuration.

Usage

fsrs_get_parameters(path = NULL, profile = NULL)

Arguments

Value

A list with FSRS parameters or NULL if not found

Examples

## Not run: 
params <- fsrs_get_parameters()

## End(Not run)

Calculate next interval for desired retention

Description

Given a stability value and desired retention, calculates how many days until the card should be reviewed.

Usage

fsrs_interval(stability, desired_retention = 0.9, decay = 0.5)

Arguments

Details

Derived from the retrievability formula by solving for t:

I(r, S) = S / factor \cdot (r^{-1/decay} - 1)

Value

Interval in days until next review

Examples

## Not run: 
# When should a card with 30-day stability be reviewed for 90% retention?

## End(Not run)
## Not run: 
\dontrun{
fsrs_interval(stability = 30, desired_retention = 0.9)
}

## End(Not run)

Calculate memory state for cards

Description

Calculates the current FSRS memory state (stability, difficulty, retrievability) for all cards in the collection.

Usage

fsrs_memory_states(path = NULL, profile = NULL, include_projection = FALSE)

Arguments

Value

A tibble with memory states

Examples

## Not run: 
states <- fsrs_memory_states()

## End(Not run)

Prepare review data for r-fsrs optimizer

Description

Converts Anki review history to the format expected by the r-fsrs package for parameter optimization.

Usage

fsrs_prepare_for_optimizer(path = NULL, profile = NULL, min_reviews = 3)

Arguments

Value

A list of review items suitable for r-fsrs

Examples

## Not run: 
# Prepare data for r-fsrs
items <- fsrs_prepare_for_optimizer()

# Use with r-fsrs (if installed)
# library(fsrs)
# params <- compute_parameters(items)

## End(Not run)

Calculate retrievability for FSRS cards

Description

Computes the current probability of recall using the FSRS-6 power forgetting curve formula. This is the formula used by Anki 24.11+ with FSRS-6.

Usage

fsrs_retrievability(stability, days_elapsed, decay = 0.5)

Arguments

Details

The FSRS-6 forgetting curve formula is:

R(t, S) = (1 + factor \cdot t / S)^{-decay}

where factor = 0.9^(-1/decay) - 1.

When t = S, retrievability equals 90% by definition.

Value

Retrievability (R), probability of recall between 0 and 1

Examples

# Card with 30-day stability reviewed 15 days ago
## Not run: 
fsrs_retrievability(stability = 30, days_elapsed = 15)

## End(Not run)

Analyze FSRS stability distribution

Description

Analyze FSRS stability distribution

Usage

fsrs_stability_distribution(path = NULL, profile = NULL, by_deck = FALSE)

Arguments

Value

A tibble with stability statistics

Examples

## Not run: 
stab_dist <- fsrs_stability_distribution()

## End(Not run)

Import ankiR Stats Addon Export

Description

Import data exported from the ankiR Stats Anki addon. This allows analyzing Anki data in R without direct database access.

Usage

import_addon_export(path)

Arguments

Value

A list containing cards, revlog, daily_stats, and summary

Examples

## Not run: 
data <- import_addon_export("ankir_export.json")
data$summary
data$cards
data$daily_stats

## End(Not run)

Plot time series decomposition

Description

Plot time series decomposition

Usage

## S3 method for class 'anki_decomposition'
plot(x, ...)

Arguments

Value

A ggplot object (if ggplot2 available) or base R plots

Print Gamification Stats

Description

Print Gamification Stats

Usage

## S3 method for class 'anki_gamification'
print(x, ...)

Arguments

Print Monte Carlo Forecast

Description

Print Monte Carlo Forecast

Usage

## S3 method for class 'anki_mc_forecast'
print(x, ...)

Arguments