Changes in mirt 1.2

MAJOR CHANGES

  - `fitIndices()` replaced with `M2()` function, and currently limited to
  only dichotomous items of class 'dich'

  - `bfactor()` default SE.type set to 'crossprod' rather than 'SEM'

  - generalized partial credit models now display fixed scoring coefs

  - `TOL` convergence criteria moved outside of the `technical` input to
  its own argument

  - `restype` argument to `residuals()` changed to `type` to be more consistent
  with the package

  - removed `fitted()` since `residuals(model, type = 'exp')` gives essentially
  the same output

  - mixedmirt has `SE` set to `TRUE` by default to help construct a more
  accurate information matrix

  - if not specified, S-EM `TOL` dropped to `1e-6` in the EM, and `SEtol =
  .001` for each parameter to better approximate the information matrix

NEW FEATURES

  - two new `SE.type` inputs: 'Louis' and 'sandwich' for computing Louis'
  1982 computation of the observed information matrix, and for the sandwich
  estimate of the covariance matrix

  - `as.data.frame` logical option for `coef()` to convert list to a
  row-stacked data.frame

  - `type = 'scorecontour'` added to `plot()` for a contour plot with the
  expected total scores

  - `type = 'infotrace'` added to `itemplot()` to plot trace lines and
  information on the same plot, and `type = 'tracecontour'` for a contour
  plot using trace lines (suggested by Armi Lantano)

  - `mirt.model()` support for multi-line inputs

  - new `type = 'LDG2'` input for `residuals()` to compute local dependence
  stat based on G2 instead of X2, and `type = 'Q3'` added as well

  - S-EM computation of the information matrix support for latent parameters,
  which previously was only effective when estimation item-level parameters. A
  technical option has also been added to force the information matrix to
  be symmetric (default is set to `TRUE` for better numerical stability)

  - new `empirical.CI` argument in `itemfit()` used when plotting confidence
  intervals for dichotomous items (suggested by Okan Bulut)

  - `printSE` argument can now be passed to `coef()` for printing the
  standard errors instead of confidence intervals. As a consequence, `rawug`
  is automatically set to `TRUE` (suggested by Olivia Bertelli)

  - second-order test and condition number added to estimated objects when
  an information matrix is computed

  - `tables` argument can be passed to `residuals()` to return all observed
  and expected tables used in computing the LD statistics

BUG FIXES

  - using `scores.only = TRUE` for multipleGroup objects returns the correct
  person ordering (reported by Mateusz Zoltak)

  - `read.mirt()` crash fix for multiple group analyses objects (reported
  by Felix Hansen)

  - updated math for `SE.type = 'crossprod'`

  - bias correction for standard errors in models that include equality
  constraints

Changes in mirt 1.1

NEW FEATURES

  - `facet_items` argument added to plot() to control whether seperate plots
  should be constructed for each item or to merge them onto a single plot

  - three dimensional models supported in `itemplot()` for types `trace`,
  `score`, `info`, and `SE`

  - new DIF() function to quicky calculate common differential item functioning
  routines, similar to how IRTLRDIF worked. Supports likelihood ratio testings
  as well as the Wald approach, and includes forward and backword sequential
  DIF searching methods

  - added a `shiny = TRUE` option to `itemplot()` to run the interactive
  shiny applet.  Useful for instructive purposes, as well as understanding
  how the internal parameters of mirt behave

  - `type = 'trace'` and `type = 'infotrace'` support added to `plot`
  generic for multiple group objects

  - `fscores(..., method = 'EAPsum')` returns observed and expected values,
  along with general fit statistics that are printed to the console and
  returned as a 'fit' attribute

  - removed multinomial constant in log-likelihood since it has no influence
  on nested model comparisons

  - `SE.type = 'crossprod'` and `Fisher` added for computing the parameter
  information matrix based on the variance of the Fisher scoring vector and
  complete Fisher information matrix, respectively

  - `customPriorFun` input to technical list now available for utilizing
  user defined prior distribution functions in the EM algorithm

  - empirical histogram estimation now available in `mirt()` and
  `multipleGroup()` for unidimensional models. Additional plot `type =
  'empiricalhist'` also added to the `plot()` generic

  - reimplement `read.mirt()` with better consistency checking between the
  `plink` package

BUG FIXES

  - starting values for `multipleGroup()` now returns proper estimated
  parameter information from the `invariance` input argument

  - remove `as.integer()` in MultipleGroup df slot

  - pass proper item type when using custom pattern calles in `fscores()`

  - return proper object in personfit when gpcm models used

Changes in mirt 1.0

NEW FEATURES

  - `GenRandomPars` logical argument now supported in the `technical = list()`
  input. This will generate random starting values for freely estimated
  parameters, and can be helpful to determine if obtained solutions are
  local minimums

  - seperate `free_var` and `free_cov` invariance options available in
  multipleGroup

  - new `CONSTRAIN` and `CONSTRAINB` arguments in `mirt.model()` syntax for
  specifying equality constraints explicitly for parameters accross items
  and groups. Also the `PRIOR = ...` specification was brought back and uses
  a similar format as the new CONSTRAIN options

  - `plot(..., type = 'trace')` now supports polytomous and dichotomous
  tracelines, and `type = 'infotrace'` has a better y-axis range

  - removed the '1PL' itemtype since the name was too ambiguous. Still
  possible to obtain however by applying slope constraints to the 2PL/graded
  response models

  - `plot()` contains a which.items argument to specify which items to plot
  in aggregate type, such as `'infotrace'` and `'trace'`

  - `fitIndicies()` will return `CFI.M2` and `TLI.M2` if the argument
  `calcNull = TRUE` is passed. CFI stats also normed to fall between 0 and 1

  - data.frame returned from `mod2values()` and `pars = 'values'` now contains
  a column indicating the internal item class

  - use `ginv()` from MASS package to improve accuracy in `fitIndices()`
  calculation of M2

BUG FIXES

  - fix error thrown in `PLCI.mirt` when parameter value is equal to the bound

  - fix the global df values, and restrict G2 statistic when tables are
  too sparse

Changes in mirt 0.9.0

NEW FEATURES

  - `PLCI.mirt()` function added for computing profiled likelihood standard
  errors. Currently only applicable to unidimensional models

  - prior distributions returned in the `pars = 'values'` data.frame along
  with the input parameters, and can be edited and returned as well

  - full.scores option for `residuals()` to compute residuals for each row
  in the original data

  - `bfactor()` can include an additional model argument for modeling two-tier
  structures introduced by Cai (2010), and now supports a `'group'` input
  for multiple group analyses

  - added a general Ramsey (1975) acceleration to EM estimation by default. Can
  be disable with `accelerate = FALSE` (and is done so automatically when
  estimating SEM standard errors)

  - renamed response.vector to response.pattern in `fscores()`, and now
  supports matrix input for computing factor scores on larger data sets
  (suggested by Felix Hansen)

  - total.info logical added to `iteminfo()` to return either total item
  information or information from each category

  - `mirt.model()` supports the so-called Q-matrix input format, along with
  a matrix input for the covariance terms

  - MH-RM algorithm now accessible by passing `mirt(..., method = 'MHRM')`,
  and `confmirt()` function removed completely. `confmirt.model()` also
  renamed to `mirt.model()`

  - support for polynomial and interaction terms in EM estimation

  - lognormal priors may now be passed to parprior

  - iterative computations in `fscores()` can now be run in parallel
  automatically following a `mirtCluster()` definition

  - `mirtCluster()` function added to make utilizing parallel cores more
  convenient. Globally removed the cl argument for multi-core objects

  - updated documentation for data sets by adding relevant examples, and
  added Bock1997 data set for replicating table 3 in van der Linden, W. J. &
  Hambleton, R. K. (1997) Handbook of modern item response theory

  - general speed improvements in all functions

BUG FIXES

  - WLE estimation fixed and now estimates extreme response patterns

  - exploratory starting values no longer crash in datasets with a huge
  number of NAs, which caused standard deviations to be zero

  - math fix for beta priors

Changes in mirt 0.8.0

NEW FEATURES

  - support for random effect predictors now available in `mixedmirt()`,
  along with a `randef()` function for computing MAP predictions for the
  random parameters

  - EAPsum support in `fscores()` for mixed item types

  - for consistency with current IRT software (rather than TESTFACT and
  POLYFACT), the scaling constant has been set to D = 1 and fixed at this value

  - nominal.highlow option added to specify which categories are the highest
  and lowest in nominal models.  Often provide better numerical stability
  when utilized. Default is still to use the highest and lowest categories

  - increase number of draws in the Monte Carlo calculation of the
  log-likelihood from 3000 to 5000

  - when itemtype all equal 'Rasch' or 'rsm' models the latent variance
  parameter(s) are automatically freed and estimated

  - `mixedmirt()` more supportive of user defined R formulas, and now includes
  an internal 'items' argument to create the item design matrix used to
  estimate the intercepts. More closely mirrors the results from lme4 for Rasch
  models as well (special thanks to Kevin Joldersma for testing and debugging)

  - `drop.zeros` option added to extract.item and itemplot to reduce
  dimensionality of factor structures that contain slopes equal to zero

  - EM tolerance (TOL argument) default dropped to .0001 (originally .001)

  - `type = 'score'` and `type = 'infoSE'` added to `plot()` generic for
  expected total score and joint test standard error/information

  - custom latent mean and covariance matrix can be passed to `fscores()`
  for EAP, MAP, and EAPsum methods.  Also applies to `personfit()` and
  `itemfit()` diagnostics

  - scores.only option to `fscores()` for returning just the estimated
  factor scores

  - bfactor can include NA values in the model to omit the estimation of
  specific factors for the corresponding item

BUG FIXES

  - limiting values in z.outfit and z.infit statistics for small sample sizes
  (fix suggested by Mike Linacre)

  - missing data gradient bug fix in MH-RM for dichotomous item models

  - global df fix for multidimensional confirmatory models

  - SEM information matrix computed with more accuracy (M-step was not
  identical to original EM), and fixed when equality constrains are imposed

Changes in mirt 0.7.0

NEW FEATURES

  - new `'#PLNRM'` models to fit Suh & Bolt (2010) nested logistic models

  - `'large'` option added to estimation functions. Useful when the
  datasets being analysed are very large and organizing the data becomes a
  computationally burdensome task that should be avoided when fitting new
  models. Also, overall faster handling of datasets

  - `plot()`, `fitted()`, and `residuals()` generic support added for
  MultipleGroup objects

  - CFI and X2 model statistics added, and output now includes fit stats
  w.r.t. both G2 and X2

  - z stats added for itemfit/personfit infit and outfit statistics

  - supplemented EM ('SEM') added for calculating information matrix from EM
  history. By default the TOL value is dropped to help make the EM iterations
  longer and more stable. Supports parallel computing

  - added return empirical reliability (`returnER`) option to `fscores()`

  - `plot()` supports individual item information trace lines on the same
  graph (dichotomous items only) with the option `type = 'infotrace'`

  - `createItem()` function available for defining item types that can be
  passed to estimation functions.  This can be used to model items not
  available in the package (or anywhere for that matter) with the EM or
  MHRM. Derivatives are computed numerically by default using the numDeriv
  package for defining item types on the fly

  - Mstep in EM moved to quasi-Newton instead of my home grown MV
  Newton-Raphson approach. Gives more stability during estimation when
  the Hessian is ill-conditioned, and will provide an easier front-end for
  defining user rolled IRT models

BUG FIXES

  - small bias fix in Hessian and gradients in `mirt()` implementation
  causing the likelihood to not always be increasing near maximum

  - fix input to `itemplot()` when object is a list of model objects

  - fixed implementation of infit and outfit Rasch statistics

  - order of nominal category intercepts were sometimes backwards. Fixed now

  - S_X2 collapsed cells too much and caused negative df

  - `response.vector` input now supports NA inputs (reported by Neil Rubens)

Changes in mirt 0.6.0

NEW FEATURES

  - S-X2 statistic computed automatically for unidimensional models via
  itemfit()

  - EAP for sum-scores added to fscores() with method = 'EAPsum'. Works with
  full.scores option as well

  - improve speed of estimation in multipleGroup() when latent means/variances
  are estimated

  - multipleGroup(invariance = '') can include item names to specify which
  items are to be considered invariant accross groups. Useful for anchoring
  and DIF testing

  - type = 'trace' option added to plot() to display all item trace lines
  on a single graph (dichotomous items only)

  - default estimation method in multipleGroup() switched to 'EM'

  - boot.mirt() function added for computing bootstrapped standard errors
  with via the boot package (which supports parallel computing as well), as
  well as a new option SE.type = '' for choosing between Bock and Lieberman
  or MHRM type information matrix computations

  - indexing items in itemplot, itemfit, and extract.item can be called
  using either a number or the original item name

  - added probtrace() function for front end users to generate probability
  trace functions from models

  - plotting item tracelines with only two categories now omits the lowest
  category (as is more common)

  - parallel option passed to calcLogLik to compute Monte Carlo log-likelihood
  more quickly. Can also be passed down the call stack from confmirt,
  multipleGroup, and mixedmirt

  - Confidence envelopes option added to itemplot() for trace lines and
  information plots

  - lbound and ubound parameter bounds are now available to the user for
  restricting the parameter estimation space

  - mod2values() function added to convert an estimated mirt model into the
  appropriate data.frame used to determine parameter estimation characteristics
  (starting values, group names, etc)

  - added imputeMissing() function to impute missing values given an
  estimated mirt model. Useful for checking item and person fit diagnostics
  and obtaining overall model fit statistics

  - allow for Rasch itemtype in multidimensional confirmatory models

  - oblimin the new default exploratory rotation (suggested by Dave Flora)

  - more flexible calculation of M2 statistic in fitIndicies(), with user
  prompt option if the internal variables grow too large and cause time/RAM
  problems

BUG FIXES

  - read.mirt() fixed when objects contain standard errors (didn't properly
  line up before)

  - mixedmirt() fix when COV argument supplied (reported by Aaron Kaat)

  - fix for multipleGroup when independent groups don't contain all potential
  response options (reported by Scot McNary)

  - prevent only using 'free_means' and 'free_varcov' in multipleGroup
  since this would not be identified without further constraints (reported
  by Ken Beath)


Changes in mirt 0.5.0


  - all dichotomous, graded rating scale, (generalized) partial credit,
  rating scale, and nominal models have been better optimized

  - wald() will now support information matrices that contain constrained
  parameters

  - confmirt.model() can accept a string inputs, which may be useful for
  knitr/sweave documents since the scan() function tends to hang

  - multipleGroup() now has the logical options bfactor = TRUE to use the
  dimensional reduction algorithm for when the factor pattern is structured
  like a bifactor model

  - new fitIndices() function added to compute additional model fit statistics
  such as M2

  - testinfo() function added for test information

  - lower bound parameters under more stringent control during estimation
  and are bounded to never be higher than .6

  - infit and outfit stats in itemfit() now work for Rasch partial credit
  and rating scale models

  - Rasch rating scale models can now be estimated with potential rsm.blocks
  (same as grsm model).  "Generalized" rating scale models can also be
  estimated, though this requires manipulating the starting values directly

  - added AICc and sample size adjusted BIC (SABIC) information statistics

  - new mixedmirt() function for estimating IRT models with person and
  item level (e.g., LLTM) covariates.  Currently only supports fixed effect
  predictors, but random effect predictors are being developed

  - more structured output when using the anova() generic

  - standard errors no longer slightly positively biased when parameter
  constraints are imposed

Changes in mirt 0.4.2

  - item probability functions now only permit permissible values, and models
  may converge even when the log-likelihood decreases during estimation. In
  the EM if the model does not have a strictly increasing log-likelihood
  then a warning message will be printed

  - infit and outfit statistics are now only applicable to Rasch models
  (as they should be), and in itemfit/personfit() a 'method' argument has
  been added to specify which factor score estimates should be used

  - read.mirt() re-added into the package to allow for translating estimated
  models into a format usable by the plink package

  - test standard error added to plot() generic using type = 'SE', and
  expected score plot added to itemplot() using type = 'score'

  - weighted likelihood estimation (WLE) factor scores now available (without
  standard errors)

  - removed the allpars option to coef() generics and only return a named
  list with the (possibly rotated) item and group coefficients

  - information functions slightly positively biased due to logistic constant
  adjustment, fixed for all models. Also, information functions are now
  available for almost all item response models (mcm items missing)

  - constant (D) used in estimating logistic functions can now be modified
  (default is still 1.702)

  - partcomp models recently broken, fixed now

  - more than one parameter can now be passed to parprior to make specifying
  identical priors more convenient

Changes in mirt 0.4.1

  - relative efficiency plots added to itemplot(). Works directly for
  multipleGroup analysis and for comparing different item types (e.g.,
  1PL vs 2PL) can be wrapped into a named list

  - infit and outfit statistics added to personfit() and itemfit()

  - empirical reliability printed for each dimension when fscores(...,
  fulldata = FALSE) called

  - better system to specify fixed/free parameters and starting values using
  pars = 'values'. Should allow for much better simulation based work

  - graded model type rating scale added (Muraki, 1990) with optional
  estimation 'blocks'. Use itemtype = 'grsm', and the grsm.block option

  - for multipleGroup(), optional input added to change the current freely
  estimated parameters to values of a previously computed model. This will
  save needless iterations in the EM and MHRM since these parameters should
  be much closer to the new ML estimates

  - itemplot() supports multipleGroup objects now

  - analytical derivatives much more stable, although some are not yet
  optimized

  - estimation bug fix in bfactor(), and slight bias fix in mirt() estimation
  (introduced in version 0.4.0 when multipleGroup() added)

  - updated documentation and beamer slide show included for some background
  on MIRT and some of the packages capabilities

  - labels added to coef() when standard errors not computed. Also allpars =
  TRUE is now the default

  - kernel estimation moved entirely to one method. Much easier to maintain
  and guarantees consistency across methods (i.e., no more quasi-Newton
  algorithms used)

Changes in mirt 0.4.0

  - Added itemfit() and personfit() functions for uni and multidimensional
  models. Within itemfit empirical response curves can also be plotted for
  unidimensional models

  - Wrapped itemplot() and fscores() into S3 function for better
  documentation. Also response curve now are all contained in individual plots

  - Added free.start list option for all estimation functions. Allows a
  quicker way to specify free and fixed parameters

  - Added iteminfo() and extract.item() to calculate the item information
  and extract desired items

  - Multiple group estimation available with the multipleGroup() function. Uses
  the EM and MHRM as the estimation engines. The MHRM seems to be faster at
  two factors+ though and naturally should be more accurate, therefore it
  is set as the default

  - wald() function added for testing linear constraints. Useful in situations
  for testing sets of parameters rather than estimating a new model for a
  likelihood ratio test

  - Methods that use the MHRM can now estimate the nominal, gpcm, mcm,
  and 4PL models

  - fscores computable for multiple group objects and in general play nicer
  with missing data (reported by Judith Conijn). Also, using the options
  full.scores = TRUE has been optimized with Rcpp

  - Oblique rotation bug fix for fscores and coef (reported by Pedro
  A. Barbetta)

  - Added the item probability equations in the ?mirt documentation for
  reference

  - General bug fixes as usual that were spawned from all the added
  features. Overall, stay frosty.

Changes in mirt 0.3.1

  - Individual classes now correspond to the type of methods: ExploratoryClass,
  ConfirmatoryClass, and MultipleGroupClass

  - plot and itemplot now works for confmirt objects

  - mirt can now make use of confmirt.model specified objects and hence be
  confirmatory as well

  - stochastic estimation of factor scores removed entirely, now only
  quadrature based methods for all objects. Also, bfactor returned objects now
  will estimate all the factors scores instead of just the general dimension

  - Standard errors for mirt now automatically calculated (borrowed from
  running a tweaked MHRM run)

Changes in mirt 0.3.0

  - radically changed the underlying mechanisms for the estimation functions
  and in doing so have decided that polymirt() was redundant and could be
  replaced completely by calling confmirt(data, number_of_factors). The
  reason for the change was to facilitate a wider range or MIRT models
  and to allow for easier extensions to future multiple group analysis and
  multilevel modelling

  - new univariate and MV models are available, including the
  1-4 parameter logistic generalized partial credit, nominal,
  and multiple choice models. These are called by specifying a
  character vector called 'itemtype' of length nitems with the options
  '2PL','3PL','4PL','graded','gpcm', 'nominal', or 'mcm'; use 'PC2PL' and
  'PC3PL' for partially-compensatory items. If itemtype = '1PL' or 'Rasch',
  then the 1-parameter logistic/1-parameter ordinal or Rasch/partial credit
  models are estimated for all the data. The default assumes that items are
  either '2PL' or 'graded', as before.

  - flexible user defined linear equality restrictions may be imposed on
  all estimation functions, so too can prior parameter distributions, start
  values, and choice of which parameters to estimate. These all follow these
  general 2 steps:

    1) Call the function as you would normally would but use, for example,
       mirt(data, 1, startvalues = 'index') to return the start values as
       they are indexed
    2) Edit them as you please (without changing the structure), then input
    them back into
       the function as mirt(data, 1, startvalues = editedstartvalues).

  This is true for the parprior (MAP priors), constrain (linear equality
  constraints), and freepars (parameters freely estimated), each with
  their own little quirk. All inputs are lists with named parameters for
  easy identification and manipulation. Note that this means that the partial
  credit model and Rasch models may be calculated as well by modifying either
  the start values and constraints accordingly (e.g., constrain all slopes
  to be equal to 1/1.702 and not freely estimated for the classical Rasch
  model, or all equal but estimated for the 1PL model)

  - number of confmirt.model() options decreased due to the new way to specify
  item types, startvalues, prior parameter distributions, and constraints

  - plink package has not kept up with item information curves, so I'll
  implement my own for now.  Replaced plink item plots from 'itemplots'
  function with ones that I rolled

  - package descriptions and documentation updated

  - coef() now prints slightly different output, with the new option 'allpars
  = TRUE' to display all the item and group parameters, returned as a list

  - simdata() updated to support new item types

  - more accurate standard errors for MAP and ML factor scores, and specific
  factors in bfactorClass objects can now be estimated for all methods

Changes in mirt 0.2.6-1

  - dropped the ball and had lots of bug fixes this round. Future commits
  will avoid this problem by utilizing the testthat package to test code
  extensively before release

  - internal change in confmirt function to move MHRM engine outside the
  function for better maintenance

  - theta_angle added to mirt and polymirt plots for changing the viewing
  angle w.r.t theta_1

  - null model no longer calculated when missing data present

  - fixed item slope models estimated in mirt() with associated standard errors

Changes in mirt 0.2.6

  - null model computed, allowing for model statistics such as TLI

  - documentation changes

  - many back end technical details about estimation moved to technical lists

  - support for all GPArotation methods and options, including Target rotations

  - polymirt() uses confmirt() estimation engine

  - 4PL support for mirt() and bfactor(), treating the upper bound as fixed

  - coef() now has a rotate option for returning rotated IRT parameters

Changes in mirt 0.2.5

  - Fixed translation bug in the C++ code from bfactor() causing illegal
  vector length throw

  - Fixed fscores() bug when using polychotomous items for mirt() and bfactor()

  - pass rotate='rotation' from mirt and polymirt to override default
  'varimax' rotation at estimation time (suggested by Niels Waller)

  - RMSEA, G^2, and p set to NaN instead of internal placeholder when there
  are missing data

  - df adjusted when missing data present

  - oblique rotations return invisible factor correlation matrix

Changes in mirt 0.2.4

  - degrees of freedom correctly adjusted when using noncompensatory items

  - confmirtClass reorganized to work with S4 methods, now work more
  consistently with methods.

  - fixed G^2 and log-likelihood in logLik() when product terms included

  - bugfix in drawThetas when noncompensatory items used

Changes in mirt 0.2.3

  - bugfixes for fscores, itemplot, and generic functions

  - read.mirt() added for creating a suitable plink object

  - mirt() and bfactor() can now accommodate polychotomous items using an
  ordinal IRT scheme

  - itemplot() now makes use of the handy plink package plots, giving a good
  deal of flexibility.

  - Generic plot()'s now use lattice plots extensively

Changes in mirt 0.2.2

  - Ported src code into Rcpp for future tweaking.

  - Added better fitted() function when missing data exist (noticed by
  Erin Horn)

Changes in mirt 0.2.1

  - ML estimation of factor scores for mirt and bfactor

  - RMSEA statistic added for all fitted models

  - Nonlinear polynomial estimation specification for confmirt models,
  now with more consistent returned labels

  - Provide better identification criteria for confmirt() (suggested by
  Hendrik Lohse)

Changes in mirt 0.2.0

  - parameter standard errors added for mirt() (1 factor only) and bfactor()
  models

  - bfactor() values that are ommited are recoded to NA in summary and coef
  for better viewing

  - 'technical' added for confmirt function, allowing for various tweaks
  and varying beta prior weights

  - product relations added for confmirt.model(). Specified by enclosing in
  brackets and using an asterisk

  - documentation fixes with roxygenize

Changes in mirt 0.1.20

  - allow lower bound beta priors to vary over items (suggested by James Lee)

Changes in mirt 0.1.6

  - bias fix for mirt() function (noticed by Pedro Barbetta)
