<CRANTaskView>

<name>OfficialStatistics</name>
<topic>Official Statistics &amp; Survey Methodology</topic>
<maintainer email="matthias.templ@gmail.com">Matthias Templ</maintainer>
<version>2016-06-28</version>
  
<info> 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.
 

<p><strong>Complex Survey Design: General Comments</strong></p>
<ul>
  <li> Package <pkg>sampling</pkg> includes many different algorithms for 
  drawing survey samples and calibrating the design weights.  
  </li>
  <li> Package <pkg>survey</pkg> 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 
  <code>svydesign()</code>, point and variance estimates can be computed.
  </li>
  <li> Package <pkg>simFrame</pkg> 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 
  non-representative outliers.
  </li>
</ul>



<p><strong>Complex Survey Design: Details</strong></p>
<ul>
  <li>
  Package <pkg>survey</pkg> allows to specify a complex survey design 
  (stratified sampling design, cluster sampling, multi-stage sampling and pps 
  sampling with or without replacement) for an already drawn survey sample in 
  order to compute accurate point and variance estimates.
  </li>
  <li>
  Various algorithms for drawing a sample are implemented in package 
  <pkg>sampling</pkg> (Brewer, Midzuno, pps, systematic, Sampford, balanced 
  (cluster or stratified) sampling via the cube method, etc.).
  </li>
  <!--
  <li>
  Package <pkg>simFrame</pkg> includes a fast (compiled C-Code) version of 
  Midzuno sampling. 
  </li>
  -->
  <li>
  The <pkg>pps</pkg> 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. 
  </li>
  <li>
  Package <pkg>stratification</pkg> allows univariate stratification of survey 
  populations with a generalisation of the Lavallee-Hidiroglou method.
  </li>
  <li>
  Package <pkg>SamplingStrata</pkg> 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.
  </li>
</ul>



<p><strong>Complex Survey Design: Point and Variance Estimation</strong></p>
<ul>
  <li>
  Package <pkg>survey</pkg> allows to specify a complex survey design. The 
  resulting object can be used to estimate (Horvitz-Thompson-) 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 user-defined).
  </li>
  <li>
Package <pkg>convey</pkg> extends package <pkg>survey</pkg> -- see the topic about indicators below.
  </li>
    <li>
  Package <pkg>laeken</pkg> provides functions to estimate certain Laeken 
  indicators (at-risk-of-poverty rate, quintile share ratio, relative median 
  risk-of-poverty gap, Gini coefficient) including their variance for domains 
  and stratas using a calibrated bootstrap. 
  </li>
  <li>
  Package <pkg>simFrame</pkg> allows to compare (user-defined) point and 
  variance estimators in a simulation environment. 
  </li>
  <li>
  The <pkg>lavaan.survey</pkg> package provides a wrapper function for packages <pkg>survey</pkg> and <pkg>lavaan</pkg>. 
  It can be used for 
  fitting structural equation models (SEM) on samples from complex designs. Using 
  the design object functionality from package <pkg>survey</pkg>, lavaan objects are re-fit 
  (corrected) with the <code>lavaan.survey()</code> function of package <pkg>lavaan.survey</pkg>.
  This allows for the incorporation of clustering, stratification, sampling weights, 
and finite population corrections into a SEM analysis. <code>lavaan.survey()</code> also accomodates 
replicate weights and multiply imputed datasets.
  </li>
 <li>
Package <pkg>vardpoor</pkg> 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. 
 </li>
</ul>



<p><strong>Complex Survey Design: Calibration</strong></p>
<ul>
  <li>
  Package <pkg>survey</pkg> allows for post-stratification, generalized 
  raking/calibration, GREG estimation and trimming of weights. 
  </li>
  <li>
  The <code>calib()</code> function in package <pkg>sampling</pkg> allows to 
  calibrate for nonresponse (with response homogeneity groups) for stratified 
  samples.
  </li>
  <li>
  The <code>calibWeights()</code> function in package <pkg>laeken</pkg> is a 
  possible faster (depending on the example) implementation of parts of <code>calib()</code> from package 
  <pkg>sampling</pkg>. 
  </li>
  <li>
  The <code>calibSample()</code> function in package <pkg>simPop</pkg> is potential faster than the previous two mentioned functions, and it provides more user-friendlyness. <code>calibVars()</code> can be used to construct a matrix of binary variables for calibration. <code>calibPop()</code> is used to calibrate population person within household data using a simulated annealing approach.
  </li>
  <li>
  Package <pkg>icarus</pkg> focuses on calibration and reweighting in survey sampling and was designed to provide a familiar setting in R for user of the SAS macro <tt>Calmar</tt>.
  </li>
  <li>
  Package <pkg>reweight</pkg> 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. 
  </li>
</ul>

<p><strong>Editing and Visual Inspection of Microdata</strong></p>
<p>Editing tools:</p>
<ul>
  <li> 
    Package <pkg>editrules</pkg> convert readable linear (in)equalities into matrix form.
  </li>
  <li> 
Package <pkg>deducorrect</pkg> depends on package <pkg>editrules</pkg> 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 Levenstein-metric is applied.
  </li>
  <li>
  Package <pkg>SeleMix</pkg> 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.
  </li>
  <li>
  Package <pkg>rrcovNA</pkg> 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 non-representative outliers.
  </li>
</ul>  
<p>Visual tools:</p>
<ul>
  <li> 
    Package <pkg>VIM</pkg> 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.  
   </li>
     <li> 
   Package <pkg>tabplot</pkg>  provides the tableplot visualization method, which is used to profile or explore large statistical datasets. 
   Up to a dozen of variables are shown column-wise 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. 
      </li>
   	<li>
   	Package <pkg>treemap</pkg> provide treemaps. A treemap is a space-filling visualization of aggregates of data with
    hierarchical structures. Colors can be used to relate to highlight differences between comparable aggregates.
    </li>
</ul>
   
<p><strong>Imputation</strong></p>
A distinction between iterative model-based methods, k-nearest 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, semi-continuous, binary, categorical and 
count variables. In addition, measurement errors may corrupt non-robust imputation methods.
Note that only few imputation methods can deal with mixed types of variables and only few methods account for robustness issues.
<p>EM-based Imputation Methods:</p>
<ul>
  <li>
  Package <pkg>mi</pkg> provides iterative EM-based multiple Bayesian 
  regression imputation of missing values and model checking of the regression 
  models used.  The regression models for each variable can also be
  user-defined.  The data set may consist of continuous, semi-continuous, 
  binary, categorical and/or count variables.
  </li>
  <li>
  Package <pkg>mice</pkg> provides iterative EM-based multiple regression 
  imputation.  The data set may consist of continuous, binary, categorical 
  and/or count variables. 
  </li>
  <li>
  Package <pkg>mitools</pkg> provides tools to perform analyses and combine 
  results from multiply-imputated datasets.
  </li>
  <li>
  Package <pkg>Amelia</pkg> provides multiple imputation where first bootstrap 
  samples with the same dimensions as the original data are drawn, and then 
  used for EM-based imputation.  It is also possible to impute longitudial 
  data.  The package in addition comes with a graphical user interface. 
  </li>
  <li>
  Package <pkg>VIM</pkg> provides EM-based multiple imputation (function 
  <code>irmi()</code>) using robust estimations, which allows to adequately 
  deal with data including outliers.  It can handle data consisting of 
  continuous, semi-continuous, binary, categorical and/or count variables.
  </li>
  <li>
  Package <pkg>mix</pkg> provides iterative EM-based multiple regression 
  imputation.  The data set may consist of continuous, binary or categorical 
  variables, but methods for semi-continuous variables are missing.
  </li>
  <li>
  Package <pkg>pan</pkg> provides multiple imputation for multivariate panel or 
  clustered data.
  </li>
  <li>
  Package <pkg>norm</pkg> provides EM-based multiple imputation for 
  multivariate normal data.
  </li>
  <li>
  Package <pkg>cat</pkg> provides EM-based multiple imputation for multivariate 
  categorical data.
  </li>
  <li>
  Package <pkg>MImix</pkg> provides tools to combine results for 
  multiply-imputed data using mixture approximations.
  </li>
  <li>
  Package <pkg>robCompositions</pkg> provides iterative model-based imputation 
  for compositional data (function <code>impCoda()</code>). 
  </li>
</ul>

<p>Nearest Neighbor Imputation Methods</p>
<ul>
  <li>
  Package <pkg>VIM</pkg> provides an implementation of the popular 
  sequential and random (within a domain) hot-deck algorithm.  
  </li> 
  <li>
  <pkg>VIM</pkg> also provides a fast k-nearest 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 semi-continous variables.
  </li>
  <li>
  Package <pkg>yaImpute</pkg> performs popular nearest neighbor routines for 
  imputation of continuous variables where different metrics and methods can be 
  used for determining the distance between observations.
  </li>
  <li>
  Package <pkg>robCompositions</pkg> provides knn imputation for 
  compositional data (function <code>impKNNa()</code>) using the Aitchison 
  distance and adjustment of the nearest neighbor. 
  </li>
  <li> 
  Package <pkg>rrcovNA</pkg> provides an algorithm for (robust) sequential imputation (function 
  <code>impSeq()</code> and <code>impSeqRob()</code>  
  by minimizing the determinant of the covariance of the augmented data matrix. It's application is limited to continuous scaled data.
  </li>
  <li>
  Package <bioc>impute</bioc> on Bioconductor impute provides knn imputation of continuous 
  variables.
  </li>
</ul>


<p>Copula-based Imputation Methods: </p>
<ul>
  <li>
The S4 class package <pkg>CoImp</pkg> imputes multivariate missing data by using conditional copula functions. The imputation procedure is semiparametric: the margins are non-parametrically estimated through local likelihood of low-degree 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.
  </li>
</ul>

<p>Miscellaneous Imputation Methods:</p>
<ul>
  <li>
  Package <pkg>missMDA</pkg> allows to impute incomplete continuous variables 
  by principal component analysis (PCA) or categorical variables by multiple 
  correspondence analysis (MCA).
  </li>
  <li>
  Package <pkg>mice</pkg> (function <code>mice.impute.pmm()</code>) and 
  Package <pkg>Hmisc</pkg> (function <code>aregImpute()</code>) allow 
  predicitve mean matching imputation.
  </li>
  <li>
  Package <pkg>VIM</pkg> allows to visualize the structure of missing values 
  using suitable plot methods.  It also comes with a graphical user interface.
  </li>
</ul>


<p><strong>Statistical Disclosure Control</strong></p>
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 
re-identified and by guaranting a minimum amount of information loss.
<ul>
  <li>
  Package <pkg>sdcMicro</pkg> can be used for the generation of confidential 
  (micro)data, i.e. for the generation of public- and scientific-use files.  
  The package also comes with a graphical user interface.
  </li>
<!--  <li>
  Package <pkg>simPop</pkg> simulates synthetic, confidential, close-to-reality populations for 
  surveys based on sample data. Such population data can then be used for 
  extensive simulation studies in official statistics, using <pkg>simFrame</pkg> for example. See also for more details in the microsimulation section.
  </li> -->
  <li>
  Package <pkg>sdcTable</pkg>  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.
  </li>
</ul>

<p><strong>Seasonal Adjustment</strong></p>
For general time series methodology we refer to the 
<view>TimeSeries</view> task view. 
<ul>
  <li>
  Decomposition of time series can be done with the function 
  <code>decompose()</code>, or more advanced by using the function 
  <code>stl()</code>, both from the basic stats package.  
  Decomposition is also possible with the <code>StructTS()</code> function, 
  which can also be found in the stats package.
  </li>
  <li>
  Many powerful tools can be accessed via packages <pkg>x12</pkg> and <pkg>x12GUI</pkg> and package <pkg>seasonal</pkg>. <pkg>x12</pkg> provides a wrapper function for the <a href="http://www.census.gov/srd/www/x12a/"> X12 binaries</a>, which have to be installed first. It uses  
  with a S4-class interface for batch processing of multiple time series. <pkg>x12GUI</pkg> provides a graphical user interface for 
  the X12-Arima seasonal adjustment software. 
Less functionality but with the support of SEATS Spec is supported by package <pkg>seasonal</pkg>. 
  </li>
</ul>

<p><strong>Statistical Matching and Record Linkage</strong></p>
<ul>
  <li>
  Package <pkg>StatMatch</pkg> 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 hot-deck.
  </li>
  <li>
  Package <pkg>RecordLinkage</pkg> provides functions for linking and 
  deduplicating data sets. 
  </li>
  <li>
  Package <pkg>MatchIt</pkg> 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.
  </li>
  <li>
  Package <pkg>stringdist</pkg> can calculate various string distances based on edits (damerau-levenshtein, hamming, levenshtein, optimal sting alignment), qgrams (q-gram, cosine, jaccard distance) or heuristic metrics (jaro, jaro-winkler). 
  </li>
</ul>  

<p><strong>Small Area Estimation</strong></p>
<ul>
  <li>
	Package <pkg>rsae</pkg> provides functions to estimate the parameters of the basic unit-level 
	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 area-specific 
	means are computed (incl. MSE estimates; parametric bootstrap). 
	The current version (rsae 0.4-x) does not allow for categorical independent variables.
  </li>
  <li>
  Package <pkg>nlme</pkg> provides facilities to fit Gaussian linear and nonlinear mixed-effects models and 
  <pkg>lme4</pkg> provides facilities to fit linear and generalized linear mixed-effects model, both used in 
  small area estimation. 
  </li>
  <li>
  The <pkg>hbsae</pkg> package provides functions to compute small area estimates based on a basic area or unit-level 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. 
  </li>
  <li>
  With package <pkg>JoSAE</pkg> 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 <pkg>nlme</pkg> package 
that is used to fit the basic random effects models.
  </li>
</ul> 
 
<p><strong>Indices and Indicators and Visualisation of Indicators</strong></p>
<ul>
  <li>
  Package <pkg>laeken</pkg> provides functions to estimate popular 
  risk-of-poverty and inequality indicators (at-risk-of-poverty rate, quintile 
  share ratio, relative median risk-of-poverty gap, Gini coefficient). 
  In addition, standard and robust methods for tail modeling of Pareto 
  distributions are provided for semi-parametric estimation of indicators
  from continuous univariate distributions such as income variables.
  </li>
  <li>
  Package <pkg>convey</pkg> estimates variances on indicators of income concentration and poverty using familiar linearized and replication-based designs created by the <pkg>survey</pkg> package such as the Gini coefficient, Atkinson index, at-risk-of-poverty threshold, and more than a dozen others.
  </li>
  <li>
  Package <pkg>ineq</pkg> 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 <code>rep(x, weights)</code>). 
  </li>
  <li> Package <pkg>IC2</pkg> include three inequality indices: 
  extended Gini, Atkinson and Generalized Entropy. It can deal with sampling weights and 
  subgroup decomposition is supported.
  </li>
  <li>
  Function <code>priceIndex()</code> from package <pkg>micEcon</pkg> allows to 
  estimate the Paasche, the Fisher and the Laspeyres price indices.
  </li>
  <li>
 Package <pkg>tmap</pkg> offers a layer-based way to make thematic maps, like choropleths and bubble maps.
  </li>
  <li>
 Package <pkg>rworldmap</pkg> 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.
  </li>
</ul>


 
<p><strong>Microsimulation</strong></p>
<ul>
<li>
Using package <pkg>simPop</pkg> one can simulate populations from surveys based on auxiliary data with model-based 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.
</li>
  <li>
The <pkg>MicSim</pkg> 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.
  </li>
  <li>
Package <pkg>sms</pkg> provides facilities to simulate micro-data from given area-based macro-data. Simulated annealing is used to best satisfy the available description of an area. 
For computational issues, the calculations can be run in parallel mode.
  </li>
  <li>
Package <pkg>synthpop</pkg> 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.
  </li>
  <li>
Package <pkg>saeSim</pkg> Tools for the simulation of data in the context of small area estimation. 
  </li>
</ul>
  
<p><strong>Additional Packages and Functionalities</strong></p>
Various additional packages are available that provides certain functionality useful in official statistics and survey methodology.
<p>Data Import and Export:</p>
<ul>
  <li>
  Package <pkg>SAScii</pkg> 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.
  </li> 
<li>
The <pkg>foreign</pkg> package includes tools for reading data from SAS Xport (function <code>read.xport()</code>), Stata (function <code>read.dta()</code>), SPSS (function <code>read.spss()</code>) and various other formats. It provides facilites to write file to various formats, see function <code>write.foreign()</code>.
</li>
<li>
Also the package <pkg>Hmisc</pkg> provides tools to read data sets from SPSS (function 
<code>spss.get()</code>)  or Stata (function <code>stata.get()</code>). 
</li>
  <li>
  The <pkg>pxR</pkg> package provides a set of functions for reading
and writing PC-Axis files, used by different statistical
organizations around the globe for disemination of their (multidimensional) tables.
  </li>
<li>
With package <pkg>prevR</pkg> and it's function <code>import.dhs()</code> it is possible to directly imports 
data from the Demographic Health Survey.
</li>
<li>
Function <code>describe()</code> from package <pkg>questionr</pkg> describes the variables of a dataset that might include labels imported with the foreign or memisc packages.
</li>
</ul>

<p>Sampling Techniques:</p>
<ul>
  <li>
  Package <pkg>samplingbook</pkg> includes sampling procedures from the book 
  'Stichproben. Methoden und praktische Umsetzung mit R' by Goeran Kauermann 
  and Helmut Kuechenhoff (2010).
  </li> 
  <li>
  Package <pkg>SDaA</pkg> is designed to reproduce results from Lohr, S. (1999) 
  'Sampling: Design and Analysis, Duxbury' and includes the data sets from this 
  book.
  </li>
    <li>
  The main contributions of
 <pkg>samplingVarEst</pkg> are Jackknife alternatives for variance estimation
 of unequal probability with one or two stage designs. 
  </li>
  <li>
  Package <pkg>TeachingSampling</pkg> includes functionality for sampling 
  designs and parameter estimation in finite populations.
  </li>
  <li>
  Package <pkg>memisc</pkg> includes tools for the management of survey data, 
  graphics and simulation.
  </li>
  <li>
  Package <pkg>odfWeave.survey</pkg> provides support for <pkg>odfWeave</pkg> 
  for the <pkg>survey</pkg> package.
  </li>
  <li>
  Package <pkg>spsurvey</pkg> includes facilities for spatial survey design and 
  analysis for equal and unequal probability (stratified) sampling.
  </li>
  <li>
  The <pkg>FFD</pkg> 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 herd-level clustering of 
  diseases as well as imperfect diagnostic tests into account and select the samples 
  based on a two-stage design. Inclusion probabilities are not considered in the estimation. 
  The package provides a graphical user interface as well. 
  </li>
  <li>
  <pkg>mipfp</pkg> provides multidimensional iterative proportional fitting to calibrate n-dimensional arrays given target marginal tables.
  </li>
</ul>  
  

  
</info>

<packagelist>
  <pkg>Amelia</pkg>
  <pkg>cat</pkg>
  <pkg>CoImp</pkg>
  <pkg>convey</pkg>
  <pkg>deducorrect</pkg>
  <pkg>editrules</pkg>
  <pkg>FFD</pkg>
  <pkg>foreign</pkg>
  <pkg>hbsae</pkg>
  <pkg>Hmisc</pkg>
  <pkg>IC2</pkg>
  <pkg>icarus</pkg>
  <pkg>ineq</pkg>
  <pkg>JoSAE</pkg>
  <pkg>laeken</pkg>
  <pkg>lavaan</pkg>
  <pkg>lavaan.survey</pkg>
  <pkg>lme4</pkg>
  <pkg>MatchIt</pkg>
  <pkg>memisc</pkg>
  <pkg>mi</pkg>
  <pkg>mice</pkg>
  <pkg>micEcon</pkg>
  <pkg>MicSim</pkg>
  <pkg>MImix</pkg>
  <pkg>mipfp</pkg>
  <pkg>missMDA</pkg>
  <pkg>mitools</pkg>
  <pkg>mix</pkg>
  <pkg>nlme</pkg>
  <pkg>norm</pkg>
  <pkg>odfWeave</pkg>
  <pkg>odfWeave.survey</pkg>
  <pkg>pan</pkg>
  <pkg>pps</pkg>
  <pkg>prevR</pkg>
  <pkg>pxR</pkg> 
  <pkg>questionr</pkg> 
  <pkg>RecordLinkage</pkg>
  <pkg>reweight</pkg>
  <pkg>robCompositions</pkg>
  <pkg>rrcovNA</pkg>
  <pkg>rsae</pkg>
  <pkg>saeSim</pkg>
  <pkg>sampling</pkg>
  <pkg>samplingbook</pkg>
  <pkg>SamplingStrata</pkg>
  <pkg>samplingVarEst</pkg>
  <pkg>SAScii</pkg>
  <pkg>SDaA</pkg>
  <pkg>sdcMicro</pkg>
  <pkg>sdcTable</pkg>
  <pkg>seasonal</pkg>
  <pkg>SeleMix</pkg>
  <pkg>simFrame</pkg>
  <pkg>simPop</pkg>
  <pkg>sms</pkg>
  <pkg>spsurvey</pkg>
  <pkg>StatMatch</pkg>
  <pkg>stratification</pkg>
  <pkg>stringdist</pkg>
  <pkg priority="core">survey</pkg>
  <pkg>synthpop</pkg>
  <pkg>tabplot</pkg>
  <pkg>TeachingSampling</pkg>
  <pkg>tmap</pkg>
  <pkg>treemap</pkg>
  <pkg>vardpoor</pkg>
  <pkg>VIM</pkg>
  <pkg>rworldmap</pkg>
  <pkg>x12</pkg>
  <pkg>x12GUI</pkg>
  <pkg>yaImpute</pkg>
</packagelist>

<links>
  <view>TimeSeries</view>
  <view>SocialSciences</view>
  <bioc>impute</bioc>
  <a href="http://www.R-project.org/conferences/useR-2008/tutorials/gomez.html">useR!2008 Tutorial: Small Area Estimation</a>
</links>

</CRANTaskView>
