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: General Comments

Package
sampling
includes many different algorithms for
drawing survey samples and calibrating the design weights.

Package
survey
can also handle moderate data sets and is the
standard package for dealing with already drawn survey samples in R. Once
the given survey design is specified within the function
svydesign(), point and variance estimates can be computed.

Package
simFrame
is designed for performing simulation
studies in official statistics. 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.
Complex Survey Design: Details

Package
survey
allows to specify a complex survey design
(stratified sampling design, cluster sampling, multistage sampling and pps
sampling with or without replacement) for an already drawn survey sample in
order to compute accurate point and variance estimates.

Various algorithms for drawing a sample are implemented in package
sampling
(Brewer, Midzuno, pps, systematic, Sampford, balanced
(cluster or stratified) sampling via the cube method, etc.).

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.
Complex Survey Design: Point and Variance Estimation

Package
survey
allows to specify a complex survey design. 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).

Package
EVER
provides the estimation of variance for complex
designs by deleteagroup jackknife replication for (HorvitzThompson)
totals, means, absolute and relative frequency distributions, contingency
tables, ratios, quantiles and regression coefficients even for domains.

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 stratas based on bootstrap resampling.

Package
simFrame
allows to compare (userdefined) point and
variance estimators in a simulation environment.

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 accomodates
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 crossectional measures and measures of change for any stage cluster sampling designs.
Complex Survey Design: Calibration

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

Package
EVER
provide facilities (function
kottcalibrate()) to calibrate either on a total number of units
in the population, on mariginal distributions or joint distributions of
categorical variables, or on totals of quantitative variables.

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.

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.
Editing and Visual Inspection of Microdata
Editing tools:

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.

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 multiplyimputated 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 longitudial
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.

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()).
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 semicontinous 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
predicitve 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 guaranting 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.
Seasonal Adjustment
For general time series methodology we refer to the
TimeSeries
task view.

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.
Statistical Record Matching

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 aproach or via hotdeck.

Package
MatchIt
allows nearest neighbor matching, exact matching, optimal matching and full matching amonst
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.
Small Area Estimation

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.
Indices and Indicators 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
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.

Function
priceIndex()
from package
micEcon
allows to
estimate the Paasche, the Fisher and the Laspeyres price indices.

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.
Microsimulation

The
MicSim
package includes methods for microsimulations. Given a initial population, mortality rates, divorce rates, marriage rates, eduction 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.
Additional Packages and Functionalities
Various additional packages are available that provides certain functionality useful in official statistics and survey methodology.
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 facilites to write file to various formats, see function
write.foreign().

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 disemination 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.
Sampling Techniques:

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 one or two stage designs.

Package
TeachingSampling
includes functionality for sampling
designs and parameter estimation in finite populations.

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

Package
odfWeave.survey
provides support for
odfWeave
for the
survey
package.

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.