This CRAN task view contains a list of packages that includes
methods typically used in official statistics and survey methodology.
Many packages provide functionality for more than one of the topics listed
below. Therefore this list is not a strict categorization and packages can be
listed more than once. Certain data import/export facilities regarding to often used statistical software tools
like SPSS, SAS or Stata are mentioned in the end of the task view.
Complex Survey Design: Sampling and Sample Size Calculation
 Package sampling includes many different algorithms (Brewer, Midzuno, pps, systematic, Sampford, balanced
(cluster or stratified) sampling via the cube method, etc.) for
drawing survey samples and calibrating the design weights.
 R package surveyplanning includes tools for sample survey planning, including sample size calculation, estimation of expected precision for the estimates of totals, and calculation of optimal sample size allocation.

Package simFrame includes a fast (compiled CCode) version of
Midzuno sampling.

The pps package contains functions to select samples using pps
sampling. Also stratified simple random sampling is possible as well as to
compute joint inclusion probabilities for Sampford's method of pps sampling.

Package stratification allows univariate stratification of survey
populations with a generalisation of the LavalleeHidiroglou method.

Package SamplingStrata offers an approach for choosing the best
stratification of a sampling frame in a multivariate and multidomain setting,
where the sampling sizes in each strata are determined in order to satisfy accuracy
constraints on target estimates.
To evaluate the distribution of target variables in different strata, information of the sampling frame,
or data from previous rounds of the same survey, may be used.

The package BalancedSampling selects balanced and spatially balanced probability samples in multidimensional spaces with any prescribed inclusion probabilities. It also includes the local pivot method, the cube and local cube method and a few more methods.

Package gridsample selects PSUs within userdefined strata using gridded population data, given desired numbers of sampled households within each PSU. The population densities used to create PSUs are drawn from rasters

Package PracTools contains functions for sample size calculation for survey samples using stratified or clustered one, two, and threestage sample designs as well as functions to compute variance components for multistage designs and sample sizes in twophase designs.
Complex Survey Design: Point and Variance Estimation and Model Fitting

Package survey works with survey samples. It allows to specify a complex survey design (stratified sampling design, cluster sampling, multistage sampling and pps
sampling with or without replacement). Once
the given survey design is specified within the function
svydesign()
, point and variance estimates can be computed.
The resulting object can be used to estimate (HorvitzThompson) totals, means,
ratios and quantiles for domains or the whole survey sample, and to apply
regression models. Variance estimation for means, totals and ratios can be
done either by Taylor linearization or resampling (BRR, jackkife, bootstrap
or userdefined).
 The methods from the survey package are called from package
srvyr using the dplyr syntax, i.e., piping, verbs like
group_by
and summarize
, and other dplyrinspired
syntactic style when calculating summary statistics on survey data.

Package convey extends package survey  see the topic about indicators below.

Package laeken provides functions to estimate certain Laeken
indicators (atriskofpoverty rate, quintile share ratio, relative median
riskofpoverty gap, Gini coefficient) including their variance for domains
and strata using a calibrated bootstrap.

Package simFrame allows to compare (userdefined) point and
variance estimators in a simulation environment. It provides a framework for comparing
different point and variance estimators under different survey designs as
well as different conditions regarding missing values, representative and
nonrepresentative outliers.

The lavaan.survey package provides a wrapper function for packages survey and lavaan.
It can be used for
fitting structural equation models (SEM) on samples from complex designs. Using
the design object functionality from package survey, lavaan objects are refit
(corrected) with the
lavaan.survey()
function of package lavaan.survey.
This allows for the incorporation of clustering, stratification, sampling weights,
and finite population corrections into a SEM analysis. lavaan.survey()
also accommodates
replicate weights and multiply imputed datasets.

Package vardpoor allows to calculate linearisation of several nonlinear population statistics, variance estimation of sample surveys by the ultimate cluster method, variance estimation for longitudinal and crosssectional measures and measures of change for any stage cluster sampling designs.

The package rpms fits a linear model to survey data in each node obtained by recursively partitioning the data. The algorithm
accounts for onestage of stratification and clustering as well as unequal probability of selection.

Package svyPVpack extends package survey. This package deals with data
which stem from survey designs and has been created to handle data from large scale
assessments like PISA, PIAAC etc..

Package weights provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, ChiSquared statistics, histograms and ttests.
Complex Survey Design: Calibration

Package survey allows for poststratification, generalized
raking/calibration, GREG estimation and trimming of weights.

The
calib()
function in package sampling allows to
calibrate for nonresponse (with response homogeneity groups) for stratified
samples.

The
calibWeights()
function in package laeken is a
possible faster (depending on the example) implementation of parts of calib()
from package
sampling.

The
calibSample()
function in package simPop is potential faster than the previous two mentioned functions, and it provides more userfriendlyness. calibVars()
can be used to construct a matrix of binary variables for calibration. calibPop()
is used to calibrate population person within household data using a simulated annealing approach.

Package icarus focuses on calibration and reweighting in survey sampling and was designed to provide a familiar setting in R for user of the SAS macro Calmar.

Package reweight allows for calibration of survey weights for
categorical survey data so that the marginal distributions of certain
variables fit more closely to those from a given population, but does not
allow complex sampling designs.

The package CalibrateSSB include a function to calculate weights and estimates for panel data with nonresponse.

Package Frames2 allows point and interval estimation in dual frame surveys. When two probability samples (one from each frame) are drawn. Information collected is suitably combined to get estimators of the parameter of interest.
Editing and Visual Inspection of Microdata
Editing tools:

Package validate includes rule management and data validation and package validatetools is checking and simplifying sets of validation rules.

Package errorlocate includes error localisation based on the principle of Fellegi and Holt. It supports categorical and/or numeric data and linear equalities, inequalities and conditional rules. The package includes a configurable backend for MIPbased error localization.

Package editrules convert readable linear (in)equalities into matrix form.

Package deducorrect depends on package editrules and applies deductive correction of simple rounding, typing and
sign errors based on balanced edits. Values are changed so that the given balanced edits are fulfilled. To determine which values are changed the Levensteinmetric is applied.

The package rspa implements functions to minimally
adjust numerical records so they obey (in)equation restrictions.

Package SeleMix can be used for selective editing for continuous scaled data.
A mixture model (Gaussian contamination model) based on response(s) y and a depended set of covariates is fit to the data to
quantify the impact of errors to the estimates.

Package rrcovNA provides robust location and scatter estimation and robust
principal component analysis with high breakdown point for
incomplete data. It is therefore
applicable to find representative and nonrepresentative outliers.
Visual tools:

Package VIM is designed to visualize missing values
using suitable plot methods. It can be used to analyse the structure of missing values in microdata using univariate, bivariate, multiple and multivariate plots where the
information of missing values
from specified variables are highlighted in selected variables.
It also comes with a graphical user interface.

Package tabplot provides the tableplot visualization method, which is used to profile or explore large statistical datasets.
Up to a dozen of variables are shown columnwise as bar charts (numeric variables) or stacked bar charts (factors).
Key aspects of the analysis with tableplots are the smoothness of a data distribution,
the selective occurrence of missing values, and the distribution of correlated variables.

Package treemap provide treemaps. A treemap is a spacefilling visualization of aggregates of data with
hierarchical structures. Colors can be used to relate to highlight differences between comparable aggregates.
Imputation
A distinction between iterative modelbased methods, knearest neighbor methods
and miscellaneous methods is made. However, often the criteria for using a
method depend on the scale of the data, which in official statistics are
typically a mixture of continuous, semicontinuous, binary, categorical and
count variables. In addition, measurement errors may corrupt nonrobust imputation methods.
Note that only few imputation methods can deal with mixed types of variables and only few methods account for robustness issues.
EMbased Imputation Methods:

Package mi provides iterative EMbased multiple Bayesian
regression imputation of missing values and model checking of the regression
models used. The regression models for each variable can also be
userdefined. The data set may consist of continuous, semicontinuous,
binary, categorical and/or count variables.

Package mice provides iterative EMbased multiple regression
imputation. The data set may consist of continuous, binary, categorical
and/or count variables.

Package mitools provides tools to perform analyses and combine
results from multiplyimputed datasets.

Package Amelia provides multiple imputation where first bootstrap
samples with the same dimensions as the original data are drawn, and then
used for EMbased imputation. It is also possible to impute longitudinal
data. The package in addition comes with a graphical user interface.

Package VIM provides EMbased multiple imputation (function
irmi()
) using robust estimations, which allows to adequately
deal with data including outliers. It can handle data consisting of
continuous, semicontinuous, binary, categorical and/or count variables.

Single imputation methods are included or called from other packages by the package simputation. It supports regression (standard, Mestimation, ridge/lasso/elasticnet), hotdeck methods (powered by VIM), randomForest, EMbased, and iterative randomForest imputation.

Package mix provides iterative EMbased multiple regression
imputation. The data set may consist of continuous, binary or categorical
variables, but methods for semicontinuous variables are missing.

Package pan provides multiple imputation for multivariate panel or
clustered data.

Package norm provides EMbased multiple imputation for
multivariate normal data.

Package cat provides EMbased multiple imputation for multivariate
categorical data.

Package MImix provides tools to combine results for
multiplyimputed data using mixture approximations.

Package robCompositions provides iterative modelbased imputation
for compositional data (function
impCoda()
).

Package missForest uses the functionality of the randomForest to impute missing values in an iterative singleimputation fashion. It can deal with almost any kind of variables except semicontinuous ones. Even the underlying bootstrap approach of random forests ensures that from multiple runs one can get multiple imputations but the additional uncertainty of imputation is only considered when choosing the random forest method of package mice.
Nearest Neighbor Imputation Methods

Package VIM provides an implementation of the popular
sequential and random (within a domain) hotdeck algorithm.

VIM also provides a fast knearest neighbor (knn) algorithm which can be used for large data sets.
It uses a modification of the Gower Distance for numerical, categorical, ordered, continuous and semicontinuous variables.

Package yaImpute performs popular nearest neighbor routines for
imputation of continuous variables where different metrics and methods can be
used for determining the distance between observations.

Package robCompositions provides knn imputation for
compositional data (function
impKNNa()
) using the Aitchison
distance and adjustment of the nearest neighbor.

Package rrcovNA provides an algorithm for (robust) sequential imputation (function
impSeq()
and impSeqRob()
by minimizing the determinant of the covariance of the augmented data matrix. It's application is limited to continuous scaled data.

Package impute on Bioconductor impute provides knn imputation of continuous
variables.
Copulabased Imputation Methods:

The S4 class package CoImp imputes multivariate missing data by using conditional copula functions. The imputation procedure is semiparametric: the margins are nonparametrically estimated through local likelihood of lowdegree polynomials while a range of different parametric models for the copula can be selected by the user. The missing values are imputed by drawing observations from the conditional density functions by means of the Hit or Miss Monte Carlo method. It works either for a matrix of continuous scaled variables or a matrix of discrete distributions.
Miscellaneous Imputation Methods:

Package missMDA allows to impute incomplete continuous variables
by principal component analysis (PCA) or categorical variables by multiple
correspondence analysis (MCA).

Package mice (function
mice.impute.pmm()
) and
Package Hmisc (function aregImpute()
) allow
predictive mean matching imputation.

Package VIM allows to visualize the structure of missing values
using suitable plot methods. It also comes with a graphical user interface.
Statistical Disclosure Control
Data from statistical agencies and other institutions are in its raw form
mostly confidential and data providers have to be ensure confidentiality by
both modifying the original data so that no statistical units can be
reidentified and by guaranteeing a minimum amount of information loss.

Package sdcMicro can be used for the generation of confidential
(micro)data, i.e. for the generation of public and scientificuse files.
The package also comes with a graphical user interface.

Package sdcTable can be used to provide confidential (hierarchical) tabular data. It includes the HITAS and the HYPERCUBE technique and uses linear programming packages (Rglpk and lpSolveAPI) for solving (a large amount of) linear programs.

An interface to the package sdcTable is provided by package easySdcTable.
Seasonal Adjustment and Forecasting
For a more general view on time series methodology we refer to the
TimeSeries task view. Only very specialized time series packages related to complex surveys are discussed here.

Decomposition of time series can be done with the function
decompose()
, or more advanced by using the function
stl()
, both from the basic stats package.
Decomposition is also possible with the StructTS()
function,
which can also be found in the stats package.

Many powerful tools can be accessed via packages x12 and x12GUI and package seasonal. x12 provides a wrapper function for the X12 binaries, which have to be installed first. It uses
with a S4class interface for batch processing of multiple time series. x12GUI provides a graphical user interface for
the X12Arima seasonal adjustment software.
Less functionality but with the support of SEATS Spec is supported by package seasonal.

Given the large pool of individual forecasts in surveytype forecasting, forecast combination techniques from package GeomComb can be useful. It can also handle missing values in the time series.
Statistical Matching and Record Linkage

Package StatMatch provides functions to perform statistical
matching between two data sources sharing a number of common variables. It
creates a synthetic data set after matching of two data sources via a
likelihood approach or via hotdeck.

Package RecordLinkage provides functions for linking and
deduplicating data sets.

Package MatchIt allows nearest neighbor matching, exact matching, optimal matching and full matching amongst
other matching methods. If two data sets have to be matched, the data must come as one data frame including a factor
variable which includes information about the membership of each observation.

Package stringdist can calculate various string distances based on edits (dameraulevenshtein, hamming, levenshtein, optimal sting alignment), qgrams (qgram, cosine, jaccard distance) or heuristic metrics (jaro, jarowinkler).

Package XBRL allows the extraction of business financial information from XBRL Documents.
Small Area Estimation

Package sae include functions for small area estimation, for example, direct estimators, the empirical best predictor and composite estimators.

Package emdi includes further functionality for supporting the user even beyond estimation, for example, for performing model diagnostic analyses, visualizing, and exporting the results for further processing. It includes buildin functionality for transformating variables and includes bootstrap methods for variance estimation. It also includes export to Excel and applies parallel computing in an automatized manner.

Package rsae provides functions to estimate the parameters of the basic unitlevel
small area estimation (SAE) model (aka nested error regression model)
by means of maximum likelihood (ML) or robust ML. On the basis of the estimated parameters, robust predictions of the areaspecific
means are computed (incl. MSE estimates; parametric bootstrap).
The current version (rsae 0.4x) does not allow for categorical independent variables.

Package nlme provides facilities to fit Gaussian linear and nonlinear mixedeffects models and
lme4 provides facilities to fit linear and generalized linear mixedeffects model, both used in
small area estimation.

The hbsae package provides functions to compute small area estimates based on a basic area or unitlevel model.
The model is fit using restricted maximum likelihood, or in a hierarchical Bayesian way. Auxilary information can be either
counts resulting from categorical variables or means from continuous population information.

With package JoSAE point and variance estimation for the generalized regression (GREG) and a unit level
empirical best linear unbiased prediction EBLUP estimators can be made at domain level. It basically provides wrapper functions to the nlme package
that is used to fit the basic random effects models.

The package BayesSAE also allows for Bayesian methods range from the basic FayHerriot model to its improvement such as YouChapman models, unmatched models, spatial models and so on.
Indices, Indicators, Tables and Visualisation of Indicators

Package laeken provides functions to estimate popular
riskofpoverty and inequality indicators (atriskofpoverty rate, quintile
share ratio, relative median riskofpoverty gap, Gini coefficient).
In addition, standard and robust methods for tail modeling of Pareto
distributions are provided for semiparametric estimation of indicators
from continuous univariate distributions such as income variables.

Package convey estimates variances on indicators of income concentration and poverty using familiar linearized and replicationbased designs created by the survey package such as the Gini coefficient, Atkinson index, atriskofpoverty threshold, and more than a dozen others.

Package ineq computes various inequality measures (Gini, Theil,
entropy, among others), concentration measures (Herfindahl, Rosenbluth), and poverty
measures (Watts, Sen, SST, and Foster). It also computes and draws empirical and theoretical
Lorenz curves as well as Pen's parade. It is not designed to deal with sampling weights directly
(these could only be emulated via
rep(x, weights)
).
 Package IC2 include three inequality indices:
extended Gini, Atkinson and Generalized Entropy. It can deal with sampling weights and
subgroup decomposition is supported.
 Package DHS.rates estimates key indicators (especially fertility rates) and their variances for the Demographic and Health Survey (DHS) data.

Functions
priceIndex()
from package micEconIndex allows to
estimate the Paasche, the Fisher and the Laspeyres price indices. For estimating quantities (of goods, for example), function quantityIndex()
might be your friend.

Package tmap offers a layerbased way to make thematic maps, like choropleths and bubble maps.

Package rworldmap outline how to map country referenced data and
support users in visualising their own data. Examples are given, e.g., maps for the world bank and UN. It provides also new ways to visualise maps.

Package rrcov3way provides robust methods for multiway data analysis, applicable also for compositional data.

Package robCompositions methods for compositional tables including statistical tests.
Microsimulation

Using package simPop one can simulate populations from surveys based on auxiliary data with modelbased methods or synthetic reconstruction methods. Hiercharical and cluster structures (such as households) can be considered as well as the methods takes account for samples collected based on complex sample designs. Calibration tools (iterative proportional fitting, iterative proportional updating) and combinatorial optimization tools (simulated annealing) are also available. The code is optimized for fast computations. The package based on a S4 class implementation. The simulated population can serve as basis data for microsimulation studies.

The MicSim package includes methods for microsimulations. Given a initial population, mortality rates, divorce rates, marriage rates, education changes, etc. and their transition matrix can be defined and included for the simulation of future states of the population. The package does not contain compiled code but functionality to run the microsimulation in parallel is provided.

Package sms provides facilities to simulate microdata from given areabased macrodata. Simulated annealing is used to best satisfy the available description of an area.
For computational issues, the calculations can be run in parallel mode.

Package synthpop using regression tree methods to simulate synthetic data from given data. It is suitable to produce synthetic data when the data have no hierarchical and cluster information (such as households) as well as when the data does not collected with a complex sampling design.

Package saeSim Tools for the simulation of data in the context of small area estimation.
Additional Packages and Functionalities
Various additional packages are available that provides certain functionality useful in official statistics and survey methodology.

The questionr package contains a set of functions to make the processing and analysis of surveys easier. It provides interactive shiny apps and addins for data recoding, contingency tables, dataset metadata handling, and several convenience functions.
Data Import and Export:

Package SAScii imports ASCII files directly into R using only a SAS input script, which
is parsed and converted into arguments for a read.fwf call. This is useful whenever SAS scripts for importing data
are already available.

The foreign package includes tools for reading data from SAS Xport (function
read.xport()
), Stata (function read.dta()
), SPSS (function read.spss()
) and various other formats. It provides facilities to write file to various formats, see function write.foreign()
.

Also the package haven imports and exports SAS, Stata and SPSS (function
read.spss()
) files. The package is more efficient for loading heavy data sets and it handles the labelling of variables and values in an advanced manner.

Also the package Hmisc provides tools to read data sets from SPSS (function
spss.get()
) or Stata (function stata.get()
).

The pxR package provides a set of functions for reading
and writing PCAxis files, used by different statistical
organizations around the globe for dissemination of their (multidimensional) tables.

With package prevR and it's function
import.dhs()
it is possible to directly imports
data from the Demographic Health Survey.

Function
describe()
from package questionr describes the variables of a dataset that might include labels imported with the foreign or memisc packages.

Package OECD searches and extracts data from the OECD.

Package Rilostat contains
tools to download data from the international labour organisation database together with search and manipulation utilities. It can also import ilostat data that are available on their data base in SDMX format.

Access to Finnish open government data is provided by package sorvi

Tools to download data from the Eurostat database together with search and manipulation utilities are included in package eurostat.

Package acs downloads, manipulates, and presents the American Community Survey and decennial data from the US Census.

Access to data published by INEGI, Mexico's official statistics agency, is supported by package inegiR

Package cbsodataR provides access to Statistics Netherlands' (CBS) open data API.

A wrapper for the U.S. Census Bureau APIs that returns data frames of Census data and metadata is implemented in package censusapi.
Misc:

Package samplingbook includes sampling procedures from the book
'Stichproben. Methoden und praktische Umsetzung mit R' by Goeran Kauermann
and Helmut Kuechenhoff (2010).

Package SDaA is designed to reproduce results from Lohr, S. (1999)
'Sampling: Design and Analysis, Duxbury' and includes the data sets from this
book.

The main contributions of
samplingVarEst are Jackknife alternatives for variance estimation
of unequal probability with one or two stage designs.

Package memisc includes tools for the management of survey data,
graphics and simulation.

Package anesrake provides a comprehensive system for selecting
variables and weighting data to match the specifications of the American
National Election Studies.

Package spsurvey includes facilities for spatial survey design and
analysis for equal and unequal probability (stratified) sampling.

The FFD package is designed to calculate optimal sample sizes of a population of animals
living in herds for surveys to substantiate freedom from disease.
The criteria of estimating the sample sizes take the herdlevel clustering of
diseases as well as imperfect diagnostic tests into account and select the samples
based on a twostage design. Inclusion probabilities are not considered in the estimation.
The package provides a graphical user interface as well.

mipfp provides multidimensional iterative proportional fitting to calibrate ndimensional arrays given target marginal tables.

Package MBHdesign provides spatially balanced designs from a set of (contiguous) potential sampling locations
in a study region.

Package quantification provides different functions for quantifying qualitative survey data. It supports the CarlsonParkin method, the regression approach, the balance approach and the conditional expectations method.

BIFIEsurvey includes tools for survey statistics in educational assessment
including data with replication weights (e.g. from bootstrap).

surveybootstrap includes tools for using different kinds of bootstrap for estimating sampling variation using complex survey data.

Package surveyoutliers winsorize values of a variable of interest.

The package univOutl includes various methods for detecting univariate outliers, e.g. the HidiroglouBerthelot method.

Package extremevalues is designed to detect univariate outliers based on modeling the bulk distribution.

Package RRreg implements univariate and multivariate analysis (correlation, linear, and logistic regression) for several variants of the randomized response technique, a survey method for eliminating response biases due to social desirability.

Package RRTCS includes randomized response techniques for complex surveys.

Package panelaggregation aggregates business tendency survey data (and other qualitative surveys) to time series at various aggregation levels.

Package surveydata makes it easy to keep track of metadata from surveys, and to easily extract columns with specific questions.

RcmdrPlugin.sampling includes tools for sampling in official statistical surveys. It includes tools for calculating sample sizes and selecting samples using various sampling designs.

Package mapStats does automated calculation and visualization of survey data statistics on a colorcoded map.