Package index • SelectBoost

All functions

rerr() ridge_logistic() BIC_glmnetB() AICc_glmnetB(): AICc and BIC for glmnet logistic models

net_confidence net_confidence_.5 net_confidence_thr: Confidence indices

M Net Net_inf_C: Simulated Cascade network and inference

auto.analyze(): Find limits for selectboost analysis

autoboost(): Autoboost

autoboost.res.x: Autoboost lasso diabetes first order.

autoboost.res.x.adapt: Autoboost adaptative lasso diabetes first order.

autoboost.res.x2: Autoboost lasso diabetes second order.

autoboost.res.x2.adapt: Autoboost adaptative lasso diabetes second order.

boost.normalize() boost.compcorrs() boost.correlation_sign() boost.findgroups() boost.Xpass() boost.adjust() boost.random() boost.apply() boost.select(): Boost step by step functions

fastboost(): Fastboost

fastboost.res.x: Fastboost lasso diabetes first order.

fastboost.res.x.adapt: Fastboost adaptative lasso diabetes first order.

fastboost.res.x2: Fastboost lasso diabetes second order.

fastboost.res.x2.adapt: Fastboost adaptative lasso diabetes second order.

force.non.inc(): Non increasing post processinng step for selectboost analysis

group_func_1(): Generate groups by thresholding.

group_func_2(): Generate groups using community analysis.

plot(<matrix>): Miscellaneous plot functions

network.confidence-class: Network confidence class.

plot(<selectboost>): Plot selectboost object

plot(<summary.selectboost>): Plot a summary of selectboost results

plot(<network.confidence>,<ANY>): plot_Selectboost_cascade

test.seq_C test.seq_PL test.seq_PL2 test.seq_PL2_W test.seq_PL2_tW test.seq_PSel test.seq_PSel.5 test.seq_PSel.e2 test.seq_PSel.5.e2 test.seq_PSel_W test.seq_robust test.seq_PB test.seq_PB_095_075 test.seq_PB_075_075 test.seq_PB_W sensitivity_C sensitivity_PL sensitivity_PL2 sensitivity_PL2_W sensitivity_PL2_tW sensitivity_PSel sensitivity_PSel.5 sensitivity_PSel.e2 sensitivity_PSel.5.e2 sensitivity_PSel_W sensitivity_robust sensitivity_PB sensitivity_PB_095_075 sensitivity_PB_075_075 sensitivity_PB_W predictive_positive_value_C predictive_positive_value_PL predictive_positive_value_PL2 predictive_positive_value_PL2_W predictive_positive_value_PL2_tW predictive_positive_value_PSel predictive_positive_value_PSel.5 predictive_positive_value_PSel.e2 predictive_positive_value_PSel.5.e2 predictive_positive_value_PSel_W predictive_positive_value_robust predictive_positive_value_PB predictive_positive_value_PB_095_075 predictive_positive_value_PB_075_075 predictive_positive_value_PB_W F_score_C F_score_PL F_score_PL2 F_score_PL2_W F_score_PL2_tW F_score_PSel F_score_PSel.5 F_score_PSel.e2 F_score_PSel.5.e2 F_score_PSel_W F_score_robust F_score_PB F_score_PB_095_075 F_score_PB_075_075 F_score_PB_W nv_C nv_PL nv_PL2 nv_PL2_W nv_PL2_tW nv_PSel nv_PSel.5 nv_PSel.e2 nv_PSel.5.e2 nv_PSel_W nv_robust nv_PB nv_PB_095_075 nv_PB_075_075 nv_PB_W: Simulations for reverse-engineering

selectboost(): Selectboost_cascade

simulation_cor() simulation_X() simulation_DATA() compsim(): Miscellaneous simulation functions

summary(<selectboost>): Summarize a selectboost analysis

trajC0(): Plot trajectories

lasso_cv_glmnet_bin_min() lasso_cv_glmnet_bin_1se() lasso_glmnet_bin_AICc() lasso_glmnet_bin_BIC() lasso_cv_lars_min() lasso_cv_lars_1se() lasso_cv_glmnet_min() lasso_cv_glmnet_min_weighted() lasso_cv_glmnet_1se() lasso_cv_glmnet_1se_weighted() lasso_msgps_Cp() lasso_msgps_AICc() lasso_msgps_GCV() lasso_msgps_BIC() enetf_msgps_Cp() enetf_msgps_AICc() enetf_msgps_GCV() enetf_msgps_BIC() lasso_cascade(): Variable selection functions

lasso_msgps_all() enet_msgps_all() alasso_msgps_all() alasso_enet_msgps_all() lasso_cv_glmnet_all_5f() spls_spls_all() varbvs_linear_all() lasso_cv_glmnet_bin_all() lasso_glmnet_bin_all() splsda_spls_all() sgpls_spls_all() varbvs_binomial_all(): Variable selection functions (all)