Function reference • vicar

All functions
`adjust_by_t()`	Returns adjusted sebetahat's based on t likelihood so that we can use a normal likelihood.
`ash_ruv2()`	Use control genes to estimate hidden confounders and variance inflation parameter, then run ASH.
`ash_ruv4()`	Use control genes to estimate hidden confounders and variance inflation parameter, then run ASH.
`ash_wrap()`	Wrapper for `ash.workhorse` for use in `caw`.
`back_elbo()`	The Evidence lower bound.
`back_fix()`	Fixed point iteration for BACKWASH.
`back_obj()`	Objective function for BACKWASH.
`back_update_phi()`	Update for the "g" hyperparameter.
`back_update_pi()`	Update for the prior mixing proportions.
`back_update_qbeta()`	Update for the variational density of beta
`back_update_v()`	Update for the variational density of v.
`back_update_xi()`	Update for the variance scaling parameter.
`backwash()`	BACKWASH: Bayesian Adjustment for Confounding Knitted With Adaptive SHrinkage.
`backwash_second_step()`	Second step of the backwash procedure.
`bfa_gd_gibbs()`	Fast Gibbs sampler for Bayesian factor analysis.
`bfa_gs()`	Bayesian factor analysis used in Gerard and Stephens (2021).
`bfa_gs_linked()`	Simple Bayesian low rank matrix decomposition.
`bfa_gs_linked_gibbs()`	Fast Gibbs sampler for Bayesian factor analysis.
`bfa_gs_linked_gibbs_r()`	R implementation of `bfa_gs_linked_gibbs`.
`bfa_wrapper()`	Wrapper for the bfa package.
`bfl()`	Simple Bayesian low rank matrix decomposition.
`brent_obj_norm()`	Objective function for updating variance inflation parameter during EM for mixtures of normals prior.
`bsvd()`	Gibbs sampler for Bayesian SVD.
`calc_lfsr()`	Empirical estimate of lfsr based on posterior samples.
`calc_lfsr_g()`	Same as `calc_lfsr` except with a prior specification.
`calc_mean_g()`	Calculate moments of pointmass rv's.
`calc_quantiles_g()`	Calculate quantiles of pointmass rv's.
`caw()`	Iterative procedure for confounder correction with a procedure that returns lfdrs.
`clean_my_p()`	Use the distribution of control genes' p-values to adjust all of the p-values.
`clean_my_stats()`	Quantile normalize stats to their theoretical distributions.
`cruv4()`	RUV4's second step.
`cruv4_multicov()`	RUV4's second step.
`dnormalmix()`	Density of a mixture of normals
`dt_wrap()`	Wrapper for dt with a non-zero mean and non-1 scale parameter.
`em_miss()`	EM algorithm for factor analysis with missing block matrix.
`em_miss_fix()`	Fixed point iteration for em algorithm with missing block.
`em_miss_fix_fast()`	Faster version of `em_miss_fix`.
`em_miss_obj()`	The objective function for em with a missing block.
`em_miss_obj_fast()`	A faster version of em_miss_obj.
`fa_ml()`	Wrapper for cate's fa.em function.
`fa_out_test()`	Test output form fa_func.
`fa_tester()`	Tests whether a user-specified factor analysis function is appropriate for use in `vruv4`, `ruv3`, `mouthwash`, or `backwash`.
`fa_tester_ruvb()`	Test whether a user-specified function is compatible with `ruvb`.
`fix_caw()`	A fixed point iteration for CAW.
`fix_caw_wrapper()`	A wrapper for `fix_caw` so that I can use SQUAREM.
`gdfa()`	Old version of Bayesian factor analysis.
`get_grid_var()`	Default way to set grid of variances.
`hard_impute()`	My version of hard imputation that begins at the ruv estimates
`hier_fun()`	Hierarchical prior density function as described in Gerard and Stephens (2021)
`impute_block()`	Constructs an overall matrix, then applies a given imputation function.
`impute_ruv_reproduce()`	Reproduce RUV2, RUV3, and RUV4 with RUVimpute.
`initialize_mixing_prop()`	Function for initializing mixing proportions.
`knn_wrapper()`	Wrapper for impute.knn
`missforest_wrapper()`	Wrapper for missForest package.
`mouthwash()`	MOUTHWASH: Maximize Over Unobservables To Help With Adaptive SHrinkage.
`mouthwash_coordinate()`	Coordinate ascent for optimizing t likelihood with uniform mixtures.
`mouthwash_second_step()`	The second step of MOUTHWASH.
`mouthwash_z_grad()`	Gradient wrt z of `uniform_mix_llike`.
`normal_mix_fix()`	A fixed point iteration for updating the mixing proportions and the confounders associated with the covariates of interest when using a mixture of normals prior.
`normal_mix_fix_wrapper()`	Wrapper for `normal_mix_fix` so that I can use SQUAREM.
`normal_mix_llike()`	Penalized MOUTHWASH likelihood when using a mixture of normals.
`normal_mix_llike_wrapper()`	Wrapper for `normal_mix_llike` so that I can use it in SQUAREM.
`normal_prior()`	A basic normal prior density function.
`pca_2step()`	PCA when first `vr` rows have a variance multiplicatively different from the rest of the rows.
`pca_naive()`	Basic PCA.
`pca_ruv2()`	PCA when first `vr` rows have a variance multiplicatively different from the rest of the rows.
`pcaruv2_fix()`	Fix point for mle in pca_ruv2.
`pcaruv2_obj()`	The objective function for mle in pca_ruv2.
`plot(<backwash>)`	Plotting method for `backwash`.
`plot(<mouthwash>)`	Plotting method for `moutwash`.
`plot(<ruvb>)`	Plotting method for `ruvb`.
`prior_fun_wrapper()`	Wrapper for `prior_fun` so that can be called in apply.
`pt_wrap()`	Wrapper for pt with a non-zero mena and non-1 scale parameter.
`ptdiff()`	More stable way to calculate differences in T cdf's.
`ptdiff_mat()`	More stable way to calculate differences in T cdfs when input is a matrix.
`ptdiff_mat_log()`	Log version of `ptdiff_mat`.
`qmle_obj()`	Objective function for quasi-mle approach.
`qmle_obj_basic()`	Basic no optimized code objective function.
`qmle_ruv2()`	Quasi-mle when first `vr` rows of `Y` have variances which differ by a scale factor from the rest of the rows.
`qmle_ruv2_lambda_grid()`	Quasi-mle when first `vr` rows of `Y` have variances which differ by a scale factor from the rest of the rows.
`qr_ident()`	An identified QR decomposition.
`rmixnorm()`	Draw from a mixture of normals.
`rnormalmix()`	Random draw from a mixture of normals.
`rotate_model()`	QR rotation to independent models.
`rubin_copy()`	Very inefficient copy of Rubin and Thayer iteration mostly meant for debugging.
`ruv3()`	Removing Unwanted Variation 3.
`ruvb()`	Bayesian version of Removing Unwanted Variation.
`ruvem()`	Same as ruvimpute but only does ruvem and comes up with estimates of standard errors.
`ruvimpute()`	General imputation framework.
`sim_gtex`	A simulated RNA-seq dataset.
`smell_my_p()`	Wrapper for a Kolmogorov-Smirnov test using the p-values from the control genes.
`smell_my_stats()`	Wrapper for a Kolmogorov-Smirnov test using the statistics from the control genes.
`softimpute_wrapper()`	A wrapper for using the softImpute function from the softImpute package.
`tregress_em()`	EM algorithm to find regression coefficients using t-likelihood when variances are known up to scale.
`tregress_fix()`	Fixed point iteration for EM algorithm for regression with t-errors where the variance is known up to scale.
`tregress_obj()`	The likelihood for regression with t-errors where the variance is known up to scale.
`tregress_obj_wrapper()`	Wrapper for `tregress_obj`.
`unif_int_grad()`	Gradient wrt z2 of intermediate function in EM.
`unif_int_obj()`	Intermediate objective function in EM.
`uniform_mix_fix()`	Fixed point iteration when using t errors and a mixture of uniforms prior.
`uniform_mix_fix_wrapper()`	Wrapper for `uniform_mix_fix`, mostly so I can use SQUAREM.
`uniform_mix_llike()`	Log-likelihood when using t errors and mixture of uniforms prior.
`uniform_mix_llike_wrapper()`	Wrapper for `uniform_mix_llike`, mostly for the SQUAREM package.
`update_f()`	Utility function for updating F in `gdfa`.
`update_lambda()`	Wrapper for `optim` and `qmle_obj`.
`update_sig_alpha()`	Fast update for sig_diag and alpha.
`update_sig_alpha_basic()`	Basic no optimized code update for sig_diag and alpha.
`vicar`	`vicar`: Various Ideas for Confounder Adjustment in Regression.
`vruv2()`	Calibrated RUV2.
`vruv2_old()`	Old version of Calibrated RUV2 that doesn't really work.
`vruv4()`	Calibrated RUV4 where the control genes are used to estimate hidden confounders and a variance inflation parameter.
`vruvinv()`	Variance inflated RUV-inverse.

Reference

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