Missing data are very frequently found in datasets. Base R provides a few options to handle them using computations that involve only observed data ( na.rm = TRUE in functions mean, var, ... or use = complete.obs|na.or.complete|pairwise.complete.obs in functions cov, cor, ...). The base package stats also contains the generic function na.action that extracts information of the NA action used to create an object.

These basic options are complemented by many packages on CRAN, which we structure into main topics:

• Exploration of missing data
• Likelihood based approaches
• Single imputation
• Multiple imputation
• Weighting methods
• Specific types of data
• Specific application fields

In addition to the present task view, this reference website on missing data might also be helpful.

If you think that we missed some important packages in this list, please contact the maintainer.

Exploration of missing data

• Manipulation of missing data is implemented in the packages sjmisc and sjlabelled. memisc also provides defineable missing values, along with infrastruture for the management of survey data and variable labels.
• Missing data patterns can be identified and explored using the packages mi, dlookr, wrangle, DescTools, and naniar.
• Graphics that describe distributions and patterns of missing data are implemented in VIM (which has a Graphical User Interface, VIMGUI, currently archived on CRAN) and naniar (which abides by tidyverse principles).
• Tests of the MAR assumption (versus the MCAR assumption) are implemented in MissMech (a non parametric test). RBtest proposes a regression based approach to test the missing data mechanism.
• Evaluation with simulations can be performed using the function ampute of mice.

Likelihood based approaches

• Methods based on the Expectation Maximization (EM) algorithm are implemented in norm (using the function em.norm for multivariate Gaussian data), in cat (function em.cat for multivariate categorical data), in mix (function em.mix for multivariate mixed categorical and continuous data). These packages also implement Bayesian approaches (with Imputation and Posterior steps) for the same models (functions da. XXX for norm, cat and mix) and can be used to obtain imputed complete datasets or multiple imputations (functions imp. XXX for norm, cat and mix), once the model parameters have been estimated. imputeR is a Multivariate Expectation-Maximization (EM) based imputation framework that offers several different algorithms, including Lasso, tree-based models or PCA. In addition, TestDataImputation implements imputation based on EM estimation (and other simpler imputation methods) that are well suited for dichotomous and polytomous tests with item responses.
• Full Information Maximum Likelihood (also known as "direct maximum likelihood" or "raw maximum likelihood") is available in lavaan, OpenMx and rsem, for handling missing data in structural equation modeling.
• Bayesian approaches for handling missing values in model based clustering with variable selection is available in VarSelLCM. The package also provides imputation using the posterior mean.
• Missing values in mixed-effect models and generalized linear models are supported in the packages mdmb, icdGLM and JointAI, the last one being based on a Bayesian approach. brlrmr also handles MNAR values in response variable for logistic regression using an EM approach. ui implements uncertainty intervals for linear and probit regressions when the outcome is missing not at random.
• Missing data in item response models is implemented in TAM, mirt and ltm.
• Robust covariance estimation is implemented in the package GSE. Robust location and scatter estimation and robust multivariate analysis with missing data are implemented in rrcovNA.

Single imputation

• The simplest method for missing data imputation is imputation by mean (or median, mode, ...). This approach is available in many packages among which ForImp, Hmisc, and dlookr that contain various proposals for imputing with the same value all missing instances of a variable.
• k-nearest neighbors is a popular method for missing data imputation that is available in many packages including DMwR, impute, VIM, GenForImp and yaImpute (with many different methods for kNN imputation, including a CCA based imputation). wNNSel implements a kNN based method for imputation in large dimensional datasets. isotree uses a similar approach that is based on similarities between samples to impute missing data with isolation forests.
• hot-deck imputation is implemented in hot.deck, FHDI and VIM (function hotdeck). StatMatch uses hot-deck imputation to impute surveys from an external dataset. impimp also uses the notion of "donor" to impute a set of possible values, termed "imprecise imputation".
• Other regression based imputations are implemented in VIM (linear regression based imputation in the function regressionImp). In addition, simputation is a general package for imputation by any prediction method that can be combined with various regression methods, and works well with the tidyverse. WaverR imputes data using a weighted average of several regressions. iai tunes optimal imputation based on knn, tree or SVM.
• Based on random forest in missForest.
• Based on copula in CoImp and in sbgcop (semi-parametric Bayesian copula imputation). The latter supports multiple imputation.
• PCA/Singular Value Decomposition/matrix completion is implemented in the package missMDA for numerical, categorical and mixed data. Heterogeneous missingness in a high-dimensional PCA is also addressed in primePCA. softImpute contains several methods for iterative matrix completion, as well as filling, rsparse and denoiseR for numerical variables, mimi that uses low rank assumptions to impute mixed datasets, and ECLRMC performs ensemble correlation based low rank matrix completion that accounts for correlation among samples. The package pcaMethods offers some Bayesian implementation of PCA with missing data. NIPALS (based on SVD computation) is implemented in the packages mixOmics (for PCA and PLS), ade4, nipals and plsRglm (for generalized model PLS). As a generalization, tensorBF implements imputation in 3-way tensor data. ddsPLS implements a multi-block imputation method based on PLS in a supervised framework. ROptSpace proposes a matrix completion method under low-rank assumption and collective matrix factorization for imputation using Bayesian matrix completion for groups of variables (binary, quantitative, Poisson). Imputation for groups is also available in missMDA in the function imputeMFA.
• Imputation of clustered data using k-means is implemented in ClustImpute.
• Imputation for non-parametric regression by wavelet shrinkage is implemented in CVThresh using solely maximization of the h-likelihood.
• mi and VIM also provide diagnostic plots to evaluate the quality of imputation.

Multiple imputation

Some of the above mentioned packages can also handle multiple imputations.

• Amelia implements Bootstrap multiple imputation using EM to estimate the parameters, for quantitative data it imputes assuming a Multivariate Gaussian distribution. In addition, AmeliaView is a GUI for Amelia, available from the Amelia web page . NPBayesImputeCat also implements multiple imputation by joint modelling for categorical variables with a Bayesian approach.
• mi, mice and smcfcs implement multiple imputation by Chained Equations. smcfcs extends the models covered by the two previous packages. miceFast provides an alternative implementation of mice imputation methods using object oriented style programming and C++. miceMNAR imputes MNAR responses under Heckman selection model for use with mice. bootImpute performs bootstrap based imputations and analyses of these imputations to use with mice or smcfcs. miceRanger performs multiple imputation by chained equations using random forests.
• missMDA implements multiple imputation based on SVD methods.
• hot.deck implements hot-deck based multiple imputation.
• Multilevel imputation : Multilevel multiple imputation is implemented in hmi, jomo, mice, miceadds, micemd, mitml, and pan.
• Qtools and miWQS implement multiple imputation based on quantile regression.
• lodi implements the imputation of observed values below the limit of detection (LOD) via censored likelihood multiple imputation (CLMI).
• BaBooN implements a Bayesian bootstrap approach for discrete data imputation that is based on Predictive Mean Matching (PMM).
• accelmissing provides multiple imputation with the zero-inflated Poisson lognormal model for missing count values in accelerometer data.

In addition, mitools provides a generic approach to handle multiple imputation in combination with any imputation method.

Weighting methods

• Computation of weights for observed data to account for unobserved data by Inverse Probability Weighting (IPW) is implemented in ipw. IPW is also used for quantile estimations and boxplots in IPWboxplot.
• Doubly Robust Inverse Probability Weighted Augmented GEE Estimator with missing outcome is implemented in CRTgeeDR.

Specific types of data

• Longitudinal data / time series and censored data : Imputation for time series is implemented in imputeTS and imputePSF. Other packages, such as forecast, spacetime, timeSeries, xts, prophet, stlplus or zoo, are dedicated to time series but also contain some (often basic) methods to handle missing data (see also TimeSeries). To help fill down missing values for time series, the padr and tsibble packages provide methods for imputing implicit missing values. Imputation of time series based on Dynamic Time Warping is implemented in DTWBI for univariate time series and in DTWUMI or in FSMUMI for multivariate ones. naniar also imputes data below the range for exploratory graphical analysis with the function impute_below. TAR implements an estimation of the autoregressive threshold models with Gaussian noise and of positive-valued time series with a Bayesian approach in the presence of missing data. swgee implements a probability weighted generalized estimating equations method for longitudinal data with missing observations and measurement error in covariates based on SIMEX. icenReg performs imputation for censored responses for interval data. imputeTestbench proposes tools to benchmark missing data imputation in univariate time series. On a related topic, imputeFin handles imputation of missing values in financial time series using AR models or random walk.
• Spatial data : Imputation for spatial data is implemented in phylin using interpolation with spatial distance weights or kriging. gapfill is dedicated to satellite data. Geostatistical interpolation of data with irregular spatial support is implemented in rtop and in areal that estimates values for overlapping but incongruent polygon features. Estimation and prediction for spatio-temporal data with missing values is implemented in StempCens with a SAEM approach that approximates EM when the E-step does not have an analytic form.
• Spatio-temporal data : Imputation for spatio-temporal data is implemented in the package cutoffR using different methods as knn and SVD and in CircSpaceTime for circular data using kriging. Similarly, reddPrec imputes missing values in daily precipitation time series accross different locations.
• Graphs/networks : Imputation for graphs/networks is implemented in the package dils to impute missing edges. PST provides a framework for analyzing Probabilistic Suffix Trees, including functions for learning and optimizing VLMC (variable length Markov chains) models from sets of individual sequences possibly containing missing values. missSBM imputes missing edges in Stochastic Block models and cassandRa predicts possible missing links with different stochastic network models.
• Imputation for contingency tables is implemented in lori that can also be used for the analysis of contingency tables with missing data.
• Imputation for compositional data (CODA) is implemented in robCompositions (based on kNN or EM approaches) and in zCompositions (various imputation methods for zeros, left-censored and missing data).
• Imputation for diffusion processes is implemented in DiffusionRimp by imputing missing sample paths with Brownian bridges.
• Imputation for meta-analyses of binary outcomes is provided in metasens.
• experiment handles missing values in experimental design such as randomized experiments with missing covariate and outcome data, matched-pairs design with missing outcome.
• cdparcoord provides tools to handle missing values in parallel coordinates settings.

Specific application fields

• Genomics : Imputation for dropout events ( i.e. , under-sampling of mRNA molecules) in single-cell RNA-Sequencing data is implemented in DrImpute and Rmagic. RNAseqNet uses hot-deck imputation to improve RNA-seq network inference with an auxiliary dataset.
• Epidemiology : powerlmm implements power calculation for time x treatment effects in the presence of dropouts and missing data in mixed linear models and pseval evaluates principal surrogates in a single clinical trial in the presence of missing counterfactual surrogate responses. idem provides missing data imputation with a sensitivity analysis strategy to handle the unobserved functional outcomes not due to death. sievePH implements continuous, possibly multivariate, mark-specific hazard ratio with missing values in multivariate marks using an IPW approach.
• Causal inference : Causal inference with interactive fixed-effect models is available in gsynth with missing values handled by matrix completion. MatchThem matches multiply imputed datasets using several matching methods, and provides users with the tools to estimate causal effects in each imputed datasets.
• Scoring : Basic methods (mean, median, mode, ...) for imputing missing data in scoring datasets are proposed in scorecardModelUtils.
• Preference models : Missing data in preference models are handled with a Composite Link approach that allows for MCAR and MNAR patterns to be taken into account in prefmod.
• Health economy : missingHE implements models for health economic evaluations with missing outcome data.
• Administrative records / Surveys : fastLink provides a Fellegi-Sunter probabilistic record linkage that allows for missing data and the inclusion of auxiliary information. EditImputeCont provides imputation methods for continuous microdata under linear constraints with a Bayesian approach.
• Regression and classification : eigenmodel handles missing values in regression models for symmetric relational data. randomForest and StratifiedRF handle missing values in predictors for random forest. mipred handles prediction in generalized linear models and Cox prediction models with multiple imputation of predictors and misaem handles missing data in logistic regression. psfmi provides a framework for model selection for various linear models in multiply imputed datasets. naivebayes provides an efficient implementation of the naive Bayes classifier in the presence of missing data. plsRbeta implements PLS for beta regression models with missing data in the predictors.
• Clustering biclustermd handles missing data in biclustering. RMixtComp fits various mixture models in the presence of missing data.
• Tests for two-sample paired missing data are implemented in robustrank.
• robustrao computes the Rao-Stirling diversity index (a well-established bibliometric indicator to measure the interdisciplinarity of scientific publications) with data containing uncategorized references.