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sharp version 1.4.6

• Update the vignette

sharp version 1.4.5

• Allow for alternative optimisation methods implemented in nloptr
• Update parallelisation, now using the future package
• Fix the formatting of continuous outcome in VariableSelection()
• Update the vignette

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

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

sharp version 1.4.1

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

sharp version 1.4.0

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

sharp version 1.3.0

• Add new functions for consensus clustering including Clustering(), Clusters(), ConsensusMatrix(), ClusteringPerformance() and more
• Add new print(), plot() and summary() functions
• Update plotting functions
• Fix 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

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

sharp version 1.2.0

• Add the functions Ensemble() and EnsemblePredictions() to build and predict from an ensemble model for VariableSelection()
• Add S3 classes including coef() and predict() for VariableSelection()
• Rename Recalibrate() as Refit()
• Fix use of CPSS in GraphicalModel()
• Fix maximisation of the contrast
• Add simulation functions to the companion R package fake

sharp version 1.1.0

First release of stability selection methods and simulation models.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.