Running the benchmark | A raw bench::mark result | Summary table | Speed | Memory | Reading the results

Introduction | How test selection works | Descriptive statistics | Assumption checks | One numerical variable | Two independent groups | Paired measurements | More than two groups | Post-hoc comparisons | Factorial designs | Repeated measurements (numeric) | Categorical outcomes | Association between two categorical variables | One-sample proportion | Multinomial goodness of fit | Paired categorical measurements | Repeated categorical measurements (3+ time points) | Correlation | Regression | Linear regression | Logistic regression | Survival analysis | Log-rank test | Cox proportional hazards regression | Diagnostic test accuracy and agreement | Diagnostic accuracy | ROC / AUC | Inter-rater agreement (Cohen's kappa) | Intraclass correlation coefficient | Outlier screening | Effect-size reference | Magnitude labels | Sample-size planning | Continuous endpoints | Binary endpoints | Survival endpoints | Ordinal endpoints | Bioequivalence | Precision-based sample size | Cluster-randomized adjustment and dropout | Summary of supported settings | References

Overview | Succinct Use | Characterizing the Missingness Structure | Imputer Registry | Simulating Missingness for Benchmarking | Missing completely at random | Missing at random | Missing not at random | Quick Visual Checks | The Chained Imputation Algorithm | Implemented Update Equations | Mean, Median, Mode, and Naive | Normal Linear Draw | Predictive Mean Matching | Logistic and Polytomous Regression | Donor Methods: knn, hotdeck, famd | Learner-Backed Imputers | Running the Imputation | Tuning Imputers | Super Learner Imputation | Evaluating Imputation Quality | Diagnostic Plotting | Pooling Across Imputations | Pooling Regression Models: pool_coxph() and pool_glm() | Limitations and Future Work | References

The mapping | Side-by-side | What changed on purpose

What it computes | Example | Inference | Assumptions

1. Mechanics on a simulated example | 2. Worked example: METABRIC predicted curves (from the paper) | Where the data come from | Cluster the predicted curves | Out-of-sample assignment | Compare against a scalar-risk baseline | Stability | Tips and troubleshooting

What densemlp is | Fitting a single model | Architecture options | Internal ensembles | Cross-validation | Hyperparameter tuning | Survival outcomes | Variable importance | Visualizing training | Learn more

Why a functional style | The core: fmap() and friends | floop() is deprecated | Programming with functionals | Function factories | Composition and reduction | Parameter sweeps with fmapn() | Safe iteration | Reproducible randomness | Parallelism and progress | functionals and purrr | Performance versus purrr

Fit one model per group | Fit many model specifications | Cross-validation | Bootstrap with reproducible streams | A benchmark over learners | Note on non-forkable models

Introduction | Core verbs | Subsetting rows and columns: subset() | Picking and dropping columns: pick(), drop() | Creating and modifying columns: transform(), within() | Renaming columns: renamecols() | Aggregation and grouping | Grouped summaries: aggregate() | Counting rows: count(), propcount() | Named summaries with summaries() | Splitting into groups: split() and applyby() | First/last row per group: firstby(), lastby() | Joining tables | Standard joins: merge() | Semi- and anti-joins: semimerge(), antimerge() | Key-matching helpers: matchedkeys(), unmatchedkeys(), joinrelationship() | Cross joins: crossmerge() | Rolling and nearest-key joins: rollingmerge(), nearestmerge() | Interval overlap joins: overlapmerge() | Updating values from another table: updatemerge() | Conditional joins: nonequimerge(), rangemerge() | Set operations and table comparison | Row-level set operations: unionrows(), intersectrows(), diffrows() | Comparing two tables: equalrows(), equaldata(), sameschema(), compareschema(), changedcols() | Comparing two snapshots of the same table: addedrows(), removedrows(), changedrows() | Row/column binding: rbindfill(), cbind() | Reshaping | Long and wide formats: tolong(), towide() | Base R reshape, stack, and unstack: reshape(), stack(), unstack() | Splitting and combining text columns: separate(), unite() | Transposing: transpose() | Exploration and EDA | Shape and types: dims(), types(), nrows(), ncols() | Peeking at data: headtail(), preview() | Descriptive statistics: describe(), profile() | Frequency tables: freq() | Table 1-style summaries: summarytab() | Missingness: missingness() | Comparing two tables at a glance: compare() | Data cleaning and validation | Assertions that stop on failure: assert*() | Finding rows/values that fail a check: invalidrows(), invalidvalues(), outofrange() | Missing-data helpers: missingrows(), keepmissing(), omitmissing(), missingindicator() | Filling in missing combinations: completegrid(), expandrows() | Recoding values: naif(), nato(), blanktona(), natoblank(), replacevalues(), replacewhere(), replacecols() | Deduplication: uniquerows(), removeduplicates(), duplicaterows(), duplicatekeys(), duplicatenames() | Column-name hygiene: cleannames(), repairnames(), renamewith(), commonnames() | Filling in missing values within groups: filldown(), fillup(), fillboth() | Row and column utilities | Column metadata: colnames(), rownames(), classes(), uniques(), cardinality(), constants(), emptycols() | Finding blank rows: emptyrows() | Reordering columns: move(), firstcols(), lastcols() | Row subsets: firstrows(), lastrows(), samplerows(), samplefrac(), reverse() | Sorting rows: orderrows() | Row position helpers: rownumber() | Row-wise reductions across columns: rowmin(), rowmax(), rowany(), rowall(), rowcount(), rowfirst(), rowlast(), rowapply() | Applying a function to selected columns: applycols(), convertcols() | String manipulation | Whitespace and case: trim(), squish(), lower(), upper(), titlecase(), sentencecase(), textlen() | Substrings and padding: left(), right(), middle(), truncate(), padleft(), padright(), padcenter() | Pattern matching: containstext(), matchestext(), startswith(), endswith(), countmatch(), locate(), locateall() | Extraction: extract(), extractall(), extractnum(), extractint(), extractbetween() | Replace and remove: replacetext(), removetext(), removeall(), replaceall() | Split and join: splittext(), splitfirst(), splitlast(), jointext(), collapsetext() | Blank and encoding helpers: isblank(), removeaccents(), normalizeunicode(), normalizeencoding(), transliterate() | String distance and similarity: textdist(), nearesttext(), similartext() | Classification predicates: isalpha(), isalphanumeric(), isnumerictext(), isintegertext(), isemail(), isurl() | Recoding and grouping values: recode(), collapsevalues(), casewhen(), collapselevels(), lump(), reorderlevels(), expandlevels() | Parsing text into typed values | Dates and times | Extracting components | Arithmetic and sequences: adddays(), addweeks(), addmonths(), addyears(), datediff(), dateseq(), betweendates() | Rounding dates: floordate(), ceilingdate(), rounddate() | Numeric transforms and rolling windows | Rescaling and standardizing: rescale(), standardize(), center(), winsorize() | Binning: quantilegroup() | Period-over-period change and rank: percentchange(), percentrank(), denserank() | Cumulative helpers: cumcount(), cumedist(), cumavg() | Lag/lead and differencing: difference(), lagvalue(), leadvalue() | Rolling window functions: rollmean(), rollsum(), rollmin(), rollmax(), rollmedian(), rollsd(), rollvar(), rollprod(), rollapply() | Functional helpers | Closing notes

The canonical compact core | Namespaces stay explicit

Overview | A first simulation | The sim_func contract | Reproducibility | When a replication fails | Resuming a long run | From replicates to a results table | Rounding | The performance-measure functions | Numerical diagnostics | Visual diagnostics | Case study: robust location estimators | Practical notes | Reference

A compact pipeline | Values from the calling function | Grouped summaries | Joins | Validation in a pipeline | Explicit package calls

Introduction | Notation and data organization | Pooled models | Fixed-effects models | Gaussian response | Poisson response | Negative binomial response | Binomial response | Random-effects models | Covariance estimation and confidence intervals | Model assessment | Hurdle models for panel counts | Computational validation | Model scope | References

Introduction | The Royston-Parmar model | Spline basis | Linear predictor and scales | Log-likelihood | Analytic gradient | Fitting API and output | Prediction and plots | Time-varying effect vs. time-varying covariate | Time-varying effect (non-proportional hazards) | Time-varying covariate | Diagnostics | Validation against rstpm2 and flexsurv | Speed benchmark | What is delegated to C++ | Benchmark | Session info

Introduction | Data preparation | Numeric data | Mixed-type data | Clustering algorithms | K-means | PAM | Hierarchical clustering | DBSCAN | Gaussian mixture models | K-prototypes | KMM (K-Mixed-Modes) | Accessors | Validation | Internal metrics | External metrics | Grid search | Per-cluster silhouette widths | Exploration | Prediction | Consensus meta-clustering | How it works | Usage | Validation and exploration of the consensus | Interpretation | The unsupervised interpretation problem | Global feature importance | Local explanations (LIME) | Ceteris paribus profiles | Complete pipeline example | References

Objectives | Split search | Parallelism | What's not yet implemented

Predictions and evaluation | Formula interface

Cox objective (objective = "cox", the default) | AFT objective (objective = "aft") | Piecewise-exponential objective (objective = "pexp") | Concordance | Missing values

Matrix interface | Predictions | Formula interface | Evaluation and importance | Partial dependence

Predictions and evaluation | Formula interface | Early stopping

Purpose | Continuous endpoints | Parallel superiority | Paired superiority | Repeated measures (3+ time points) | Non-inferiority and equivalence | Binary endpoints | Paired (discordant-pairs) superiority | Survival endpoints | Required events | Converting events to a total sample size | Uniform accrual | Ordinal endpoints | Bioequivalence | Precision-based sample size | Rare-prevalence planning: Wilson and exact confidence intervals | Unequal allocation for two proportions | Cluster-randomized adjustment | Dropout adjustment and reporting | Summary of supported settings | Reference

Purpose | sample_size_continuous() | design = "parallel" | objective = "superiority" | objective = "noninferiority" | objective = "equivalence" | design = "paired" | design = "repeated" | sample_size_binary() | objective = "superiority", method = "pooled" | objective = "superiority", method = "anticipated" | design = "paired" / design = "repeated" (n_time = 2) | sample_size_survival() | objective = "superiority", method = "exponential" | objective = "superiority", method = "ph_only" | Converting events to a total sample size | Uniform accrual | sample_size_ordinal() | sample_size_bioequivalence() | method = "iterative_tost" (default) | method = "normal_approx" | sample_size_precision() | sample_size_cluster_adjust() | sample_size() dispatcher | sample_size_adjust_dropout() | Methods on sample_size objects | References

Purpose | What "validated" means here | Two defects found and fixed | Bioequivalence sample size: iterative_tost was not exact | Cohen's kappa: the hypothesis test used the wrong standard error | Everything else checked, with nothing found | What this does and does not guarantee

Overview | Core Quantities | Real Trial-Based CEA Example | Bootstrap Uncertainty | Net Monetary Benefit and CEAC | Deterministic Sensitivity Analysis | Reproducibility Checklist | References

Overview | Single imputation | A note on complete() | Multiple imputation | Per-column adaptive tuning | Why this matters for larger data | Options at a glance

Getting started

Overview | Fit a model | Generate predictions | Evaluate the fitted model | Cross-validation | Tune hyper-parameters | Plot fitted curves