An introduction         rpf - Response Probability Functions
ChenThissen1997         Computes local dependence indices for all pairs
                        of items
Class rpf.1dim          The base class for 1 dimensional response
                        probability functions.
Class rpf.1dim.drm      Unidimensional dichotomous item models (1PL,
                        2PL, and 3PL).
Class rpf.1dim.graded   The base class for 1 dimensional graded
                        response probability functions.
Class rpf.1dim.grm      The unidimensional graded response item model.
Class rpf.1dim.lmp      Unidimensional logistic function of a monotonic
                        polynomial.
Class rpf.base          The base class for response probability
                        functions.
Class rpf.mdim          The base class for multi-dimensional response
                        probability functions.
Class rpf.mdim.drm      Multidimensional dichotomous item models (M1PL,
                        M2PL, and M3PL).
Class rpf.mdim.graded   The base class for multi-dimensional graded
                        response probability functions.
Class rpf.mdim.grm      The multidimensional graded response item
                        model.
Class rpf.mdim.mcm      The multiple-choice response item model (both
                        unidimensional and multidimensional models have
                        the same parameterization).
Class rpf.mdim.nrm      The nominal response item model (both
                        unidimensional and multidimensional models have
                        the same parameterization).
EAPscores               Compute EAP scores
LSAT6                   Description of LSAT6 data
LSAT7                   Description of LSAT7 data
SitemFit                Compute the S fit statistic for a set of items
SitemFit1               Compute the S fit statistic for 1 item
as.IFAgroup             Convert an OpenMx MxModel object into an IFA
                        group
bestToOmit              Identify the columns with most missing data
compressDataFrame       Compress a data frame into unique rows and
                        frequencies
crosstabTest            Monte-Carlo test for cross-tabulation tables
expandDataFrame         Expand summary table of patterns and
                        frequencies
fromFactorLoading       Convert factor loadings to response function
                        slopes
fromFactorThreshold     Convert factor thresholds to response function
                        intercepts
itemOutcomeBySumScore   Produce an item outcome by observed sum-score
                        table
kct                     Knox Cube Test dataset
logit                   Transform from [0,1] to the reals
multinomialFit          Multinomial fit test
observedSumScore        Compute the observed sum-score
omitItems               Omit the given items
omitMostMissing         Omit items with the most missing data
orderCompletely         Order a data.frame by missingness and all
                        columns
ordinal.gamma           Compute the ordinal gamma association statistic
ptw2011.gof.test        Compute the P value that the observed and
                        expected tables come from the same distribution
read.flexmirt           Read a flexMIRT PRM file
rpf.1dim.fit            Calculate item and person Rasch fit statistics
rpf.1dim.moment         Calculate cell central moments
rpf.1dim.residual       Calculate residuals
rpf.1dim.stdresidual    Calculate standardized residuals
rpf.dLL                 Item parameter derivatives
rpf.dTheta              Item derivatives with respect to the location
                        in the latent space
rpf.drm                 Create a dichotomous response model
rpf.grm                 Create a graded response model
rpf.id_of               Convert an rpf item model name to an ID
rpf.info                Map an item model, item parameters, and person
                        trait score into a information vector
rpf.lmp                 Create logistic function of a monotonic
                        polynomial (LMP) model
rpf.logprob             Map an item model, item parameters, and person
                        trait score into a probability vector
rpf.mcm                 Create a multiple-choice response model
rpf.mean.info           Find the point where an item provides mean
                        maximum information
rpf.mean.info1          Find the point where an item provides mean
                        maximum information
rpf.modify              Create a similar item specification with the
                        given number of factors
rpf.nrm                 Create a nominal response model
rpf.numParam            Length of the item parameter vector
rpf.numSpec             Length of the item model vector
rpf.ogive               The ogive constant
rpf.paramInfo           Retrieve a description of the given parameter
rpf.prob                Map an item model, item parameters, and person
                        trait score into a probability vector
rpf.rescale             Rescale item parameters
rpf.rparam              Generates item parameters
rpf.sample              Randomly sample response patterns given a list
                        of items
science                 Liking for Science dataset
stripData               Strip data and scores from an IFA group
sumScoreEAP             Compute the sum-score EAP table
sumScoreEAPTest         Conduct the sum-score EAP distribution test
tabulateRows            Tabulate data.frame rows
toFactorLoading         Convert response function slopes to factor
                        loadings
toFactorThreshold       Convert response function intercepts to factor
                        thresholds
write.flexmirt          Write a flexMIRT PRM file
