lgpr 1.1 Unreleased

1.1.5

  • Add more imports to NAMESPACE per a rstan developer’s recommendation.

1.1.4

  • New vignette about mathematical description of models.
  • Prediction and function posterior computation at P points where P is larger than number of data points should be now much faster and take less memory, as P x P matrices are not computed.

1.1.3

  • First CRAN release.

1.1.1-1.1.2

  • Only small patches in documentation etc. in order to conform to CRAN policies

1.1.0

  • Adds prior_pred() for prior predictive sampling and sample_param_prior() for sampling from the parameter prior.
  • Adds read_proteomics_data() function.
  • Relax data type checking, to require that they only inherit from factor or numeric. Allow also tibbles and data.tables to be passed as data.
  • Adds more methods for lgpfit and lgpmodel objects, see their documentation.
  • Lot of improvements internally. Kernel computations in functions like pred() should take a lot less memory now. Two separete main Stan models now. One for latent GP (signal where f is sampled) and other for GP with marginalized f.
  • Improved documentation.
  • Improve verbose messages to user.

lgpr 1.0 Unreleased

1.0.13

  • Fix bug that ignored the group_by argument in get_teff_obs() and caused at least new_x() to not work if the subject identifier variable was called something else than id (see issue #22).

1.0.12

  • Add more informative error message if trying to specify a model like y ~ age + id | age, which should be y ~ age + age | id, i.e. the continuous covariate on the left of | and categorical on the right.
  • New startup message that prints also rstan version
  • Update citation information

1.0.11

  • Add the c_hat_pred argument to pred(), to be used when f has been sampled and c_hat is not constant. Previously, c_hat = 0 was used in all prediction points, which did not make sense in all cases.

1.0.10

  • Allow setting group_by = NA in plot_pred(), plot_components() and new_x() to avoid grouping in plots.
  • Allow setting color_by as the same factor as group_by.
  • Fix bug which caused an error when trying to define a separate prior for parameters of the same type.

1.0.9

  • Internal change for more effective computation of function (component) posterior variances.

1.0.8

  • Add option do_yrng which controls whether to do draws from the predictive distribution. This was previously always done if sample_f was TRUE. That is now considered a bug because it is unnecessary work if the y_rng draws are not needed. So the default is now do_yrng = FALSE, since do_yrng = TRUE can cause errors with the negative binomial model due to numerical problems (see here). These problems should be addressed in a future release to allow more stable prior and posterior predictive sampling.

1.0.7

  • Small documentation update.

1.0.6

  • Fix bug in get_pred(), which was caused by not adding the GP mean to the sampled signal. This was causing postprocessing functions like relevances() and plot_pred() to give erroneous results if the GP mean was not a vector of zeros and sample_f = TRUE.
  • Small edits in documentation and verbose information messages.

1.0.5

  • Make plot_pred() work with any response variable name (fixes issue #12).
  • Avoid adding ggplot2::color_scale_manual() if number of colors > 5 (fixes issue #11).

1.0.4

Edit type checking to work more generally on all systems (fixes issue #5).

1.0.3

Fix CITATION to point to new preprint.

1.0.2

Added RcppParallel dependency explicitly.

1.0.1

Added warning if using default prior for input warping steepness.

1.0.0

New features

  • More general modeling options, allowing more mixing of different types of kernels/options
  • Prior and posterior predictive checks using ppc(), which interfaces to bayesplot.

Changes and improvements

  • Formula syntax where | indicates interaction terms.
  • Alternative advanced formula syntax with gp(), gp_warp(), zerosum() etc.
  • Beta binomial observation model.
  • Categorical covariates must now be specified as factors in data, and don’t have to be numeric.
  • Component relevance assessment is now separated from model fitting into the relevances() function and selection into select().
  • Easier prior specification with normal(), log_normal(), student_t() etc.
  • Better prediction and plotting functionality with get_pred(), pred(), plot_pred(), and plot_f().
  • Extensive argument checking (see check_positive_all() etc.) to give users informative error messages

Automated testing

  • Thorough unit tests using test_that.
  • C++ versions of the Stan model functions are now exposed to package namespace and also tested.

lgpr 0.33 Unreleased

  • First release.