- Make API about specifing loss function's type more consistent. See the changes of
RSAVS_Further_Improve
for more details. - Internally, add functions
Draw_Mu_Path
andDraw_Beta_Path
to visualize the solution path regarding to subgroup identification and variable selection namely. - Add support to choose whether to use
double log likelihood
when computing the modified BIC. - Add a parameter
update_mu
toRSAVS_Path
andRSAVS_Path_PureR
so one can choose whether to update the mu vector into meaningful subgroup structure during the computation of solution plain. - Add a parameter
omp_zsw
toRSAVS_Path
andRSAVS_Solver
which controls number of threads during the update ofz
,s
andw
via OpenMP. - Add a parameter
s_v2
toRSAVS_Path
andRSAVS_Solver
which indicates whether to use the faster implementation to updates_vec
andq2_vec
.
- Tidy up some scirpt.
- Add a vignette about applying the proposed method onto the
iris
dataset. It demonstrates the unsupervised nature of the proposed method. - Add
RSI
andRSI_DAC
internally. - Add
RSAVS_Simple_Path
internally. This is a wrapper function for a simplified search over the solution plane.
-
Tidy up cpp source files to make it compatible with newer version Eigen.
-
Update and add more detailed dependency info into DESCRIPTION.
- Parameters
lam1_length
andlam2_length
in functionRSAVS_LargeN
are renamed tolam1_len
andlam2_len
. - Possible choices for parameter
loss_type
are standardized to the same format. Currently they are"L1"
,"L2"
and"Huber"
.
- Add pure R implementation of RSAVS. Refer to
RSAVS_Path_PureR
andRSAVS_Solver_PureR
for more details. - Add cpp implementation of RSAVS. The detailed algorithm uses the original design, which might be unstable when dealing with large scale dataset but is more flexible. Refer to
RSAVS_Path
andRSAVS_Solver
for more details. - By default,
lam1_vec
is now in order from small to big. - The newly added
RSAVS_Path
andRSAVS_Path_PureR
now supports progress bar via packageprogressr
.
- [done] Finish the vignette about the detail design of the internal ADMM algorithm
- Clean up the code
- [done] Add functionality for smaller dataset
- Add RSI/LLA algorithm
- Add a vignette about why lasso is not suitable for subgroup identificaion in this algorithm
- Add student performance data into the package.
- Add a vignette about using this package to analyze the student performance dataset
- Update the website layout of this package
- Add a vignette about the detail design of the internal ADMM algorithm
- Clean up the code
- Add functionality for smaller dataset
- Add RSI/LLA algorithm
- Initial release of the package.