sharp version 1.4.4

• Update references with published articles

sharp version 1.4.3

• Add sparse K means from the R package sparcl
• Allow for missing values in proportions for more flexibility

sharp version 1.4.2

• Removed functions depending on regsem (removed from CRAN)
• Fixed the use of packages in Suggests in the examples

sharp version 1.4.1

• Added package vignette
• Used Ridge regression calibrated by cross validation instead of unpenalised regression in Refit(), ExplanatoryPerformance() and Incremental()
• Added new S3 class structural_model
• Fixed inclusion of unpenalised predictors in Incremental()
• Fixed clustering of rows in Clustering()

sharp version 1.4.0

• Updated the stability score used by default (n_cat=NULL), previous score can be used with n_cat=3
• Added new functions for structural equation modelling including StructuralModel(), PenalisedSEM(), PenalisedOpenMx(), PenalisedLinearSystem(), LavaanModel(), LavaanMatrix(), OpenMxModel(), OpenMxMatrix() and LinearSystemMatrix()
• Added new function CART() for classification and regression trees
• Added the option to run randomised or adaptive lasso in PenalisedRegression()
• Fixed a bug when running multinomial lasso with predictors with null variance in the subsamples
• Fixed a bug where additional parameters in … were used in glm.control() within Refit()

sharp version 1.3.0

• Added new functions for consensus clustering including Clustering(), Clusters(), ConsensusMatrix(), ClusteringPerformance() and more
• Added new print(), plot() and summary() functions
• Updated plotting functions
• Fixed parallelisation using argument n_cores in main functions
• Remove duplicated messages in ExplanatoryPerformance()
• Allow for factor ydata in VariableSelection() and related functions

sharp version 1.2.1

• Updated examples for use with fake 1.3.0
• Fixed requirements on input data format in Refitting()
• Added resampling argument in Explanatory()
• Added optional beep at the end of the run in main functions
• Increased igraph vertex size in Graph() and plot()

sharp version 1.2.0

• Added the functions Ensemble() and EnsemblePredictions() to build and predict from an ensemble model for VariableSelection()
• Added S3 classes including coef() and predict() for VariableSelection()
• Renamed Recalibrate() as Refit()
• Fixed use of CPSS in GraphicalModel()
• Fixed maximisation of the contrast
• Simulation functions now added to the companion R package fake

sharp version 1.1.0

First release of stability selection methods and simulation models.