Changes in Version 1.2-2
o Add running time to output of resample() function
o Added a note and references on the normalizability of MGMs
o Added function plotRes() to plot summaries of sampling distributions resampled with the resample() function
o The consecutiveness of measurements in mvar() and tvmvar() models now can be specified alternatively to the consec argument with the arguments beepvar and dayvar. This is tailored to the typical time stamps of ecological momentary assessment (EMA) studies, where the consecutiveness is defined by the number of notification on a given day (beepvar) and the number of that given day (dayvar).
o New function showInteraction() retrieves details of interactions from mgm model objects
o The print method now also shows number of rows in the VAR design matrix compared to the number of rows of the original data matrix.
o All estimation functions require a matrix object as data input to avoid problems with properties of data.frame objects
o Corrected row-order in dataset "symptom_data"
o In mVAR models we use an adjusted sample size that is equal to the number of rows in the design matrix. The adjusted sample size can be smaller than nrow(data) - max(lags) if measurements are not consecutive, or if non-uniform weighting is used. In earlier versions the sample size was only adjusted for non-uniform weighting. The adjusted sample size is used in the additional thresholding of parameter estimates (see ?mgm or ?mvar).
o Added function resample() that fits any model class to a specified number of bootstrap samples and provides the bootstrapped sampling distribution in an array. For time series data, the block bootstrap is used, where the number of blocks can be specified.
Changes in Version 1.2-1
o Added consec argument to predict.mgm(), which allows prediction for VAR models in time series with unequal time intervals by excluding time points that are not preceded by enough measurements (see ?mvar)
Changes in Version 1.2-0
o The whole package has been rewritten from scratch to reduce susceptibility to bugs and to allow new features
o Every function now has many input checks and associated informative error messages
o Major syntax changes: mgmfit() -> mgm(), var.mgm() -> mvar(), tv.mgmfit() -> tvmgm() and tv_var.mgm() -> tvmvar()
o The names of function arguments are now consistent across the package and therefore had to be changed considerably
o There is no more missing argument in the estimation functions
o All estimation functions allow to search a sequence of the (alpha) elastic net paramter, using the EBIC or cross-validation; so far alpha was fixed to 1
o mgm() and tvmgm() now presents all paramters involved in higher (than pairwise) order interactions in the output; this includes a factor-graph representation that is easy to visualize
o The d parameter for the largest order of interaction in the neighborhood of a given node has been replaced with the k parameter, the largest order of interaction in the whole graph. Note that d = k - 1
o All estimation functions allow the standard parameterization for categorical variables, but also an overparameterization. This is necessary to correctly identify higher order interactions between categorical variables.
o The sampling functions mgmsampler() and tvmgmsampler() were extended to k-order MGMs (before only pairwise / k = 2)
o mvar() and tvmvar() now allow the specification of any number of lags
o The new functions mvarsampler() and tvmvarsampler() now allow to sample from mVAR models any number of lags
o There is no more function provided for resampling. Instead, we provide an interface with the bootnet package.
o The function bwSelect() allows the selection of an optimal bandwidth parameter for timer-varying MGM or mVAR models using cross-validation
o predict.mgm() now allows two different ways to predict from time-varying models, see ?predict.mgm
o Fixed bug in mgmsampler() that was present in binary-Gaussian graphs
o Fixed bug in predict.mgm() which caused the prediction of incorrect category labels in some situations
o Fixed bug in mgm() which did not use the weight-argument in case of lambdaSel = 'CV'
o Default for argument lambdaSel changed from 'EBIC' to 'CV'
Changes in Version 1.1-7
o The predict() function now returns the predicted probabilities in addition to the predicted category for categorical variables
o Added a message for all estimation functions indicating where edge weights (if defined) can be found: fitobject$signs
o Added a startup message with a link to report bugs
o The predict() function now computes predicted values and a prediction error for each variable in the graph
o The print() function now returns a small summary of the model type when printing a mgm object
o Added subsampling scheme to evaluate edge-stability for non-time-varying models (MGM and mixed VAR)
o Added summary() & plot() for the bootstrap object to summarize edge-stability
o Added argument 'binary.sign': If binary.sign=TRUE, the sign of the interactions of all binary variables coded (0,1) with other binary variables and continuous variables will be returned in the sign matrix fit$signs