Function reference • quantspace

All functions
`addMissingSpecColumns()`	Add missing columns for specification
`all_match()`	check if all values of two vectors match
`avg_spacing()`	Computes means for various slices of a regression_spec by betas product.
`bin_along_range()`	Bin a variable along a range
`bootstrapRows()`	Function that fully bootstraps rows
`capture_output()`	Capture print output
`check_algorithm()`	Check if algorithm exists
`coef(<qs>)`	Method for getting coefficients from fitted qs model
`collapse_correctly()`	helper function, collapse using correct method
`columns_match_vector()`	Check which rows of a matrix match a whole vector
`csrToDgc()`	Convert SparseM row compressed matrix to Matrix dgC matrix
`cub_root()`	Find cube root of the form ax^3 + bx^2 + cx + d=0
`cub_root_deriv()`	Derivative of a cubic root
`cub_root_select()`	Find cube root of the form ax^3 + bx^2 + cx + d=0
`cub_root_select_rconics()`	Calculate cubic roots using the Rconics package
`defcombine()`	helper function, borrowed from foreach
`denseMatrixToSparse()`	Convert matrix to a SparseM csr matrix
`distributional_effects()`	Calculate distributional effects
`do_matched_call()`	run function only with arguments that match the arguments for f
`.onAttach()`	Set bootstrap cores on load + welcome messages
`ensureSpecFullRank()`	Ensure that a regression specification is full rank
`eval_CDF()`	Evaluate CDF given quantiles and residuals
`eval_PDF()`	Evaluate PDF given quantiles and residuals
`eval_Quantiles()`	Evaluate CDF given quantiles and residuals
`eval_density_R()`	A function to evaluate the quantile or density of given data based on normal distribution
`fitQuantileRegression()`	Estimate a single quantile regression
`fit_approx_quantile_model()`	Compute quantile regression via accelerated gradient descent using Huber approximation, warm start based on data subset
`fit_lasso()`	Fit a quantile regression w/ a lasso penalty
`fit_penalize_approx_quantile_model()`	Compute quantile regression via accelerated gradient descent using Huber approximation, warm start based on data subset
`getColNums()`	Get column numbers given starting values and regression specification
`getCores()` `setCores()`	Get user defined cores
`getRank()`	Computes the rank of a matrix. Outputs consistent results across various matrix types.
`getRows()`	Function which gets resampled rows
`getWeights()`	Function that returns the correct weights for weighted bootstrap
`get_intercept()`	Finds which column of X has the intercept term
`get_marginal_effects()`	Calculates the marginal effects of an N x p matrix (wide-format) of qreg coefficients
`get_strata()`	Get clusters for subsampling given a formula
`get_underlying()`	Get the data inside the s4 slot of this sparse matrix class
`glob_obs_mat()`	Glob observations w/ residuals above a certain magnitude
`glob_obs_vec()`	Glob observations w/ residuals above a certain magnitude
`inv()`	Inverse of a matrix, but catches the error
`lasso_cv_search()`	Search for optimal lambda via cross-validation
`leaveRows()`	Function that doesn't reorder rows (for weighted bootstrap)
`makePlan()`	Make a plan for the future parallel backend
`make_se_mat()`	Make matrix into a "standard error" matrix
`map_parallel()`	Map a function along a list in parallel
`map_rows_parallel()`	Map a function along rows of a matrix or data.frame
`marginal_effects()`	Get all marginal effects of variables in the fit
`matrixTocsc()`	Convert Matrix Sparse Matrix to SparseM row compressed matrix
`matrixTocsr()`	Convert Matrix Sparse Matrix to SparseM row compressed matrix
`me()`	Get marginal effects at a set of levels for the covariates
`make_penalized_blots()` `mice.impute.qs()`	Imputation function to be used with the mice package
`na_if_null()`	return NA if argument is null
`pad_strings()`	Pad vector of strings based on longest length
`plot(<distributional_effects>)`	Visualize distributional effects
`post_processed_grad_descent()`	Smoothed Quantile Regression with Post-Processing
`predict(<qs>)`	Predict quantiles given fitted spacings model
`print(<qs>)`	Print qs summary
`print(<qs_me>)`	Print method for quantspace marginal effects
`print(<qs_summary>)`	Print qs summary
`printWarnings()`	Generate warning messages for regression model
`q_spline_R()`	Computes the tail parameters and the second derivatives given quantiles
`qr_drop_colinear_columns()`	Drop Colinear Columns from dense matrix
`qs()`	Compute quantile regressions via quantile spacings
`qs_control()`	Control quantreg_spacing parameters
`quad_form()`	Quadratic form of the cubic polynomial
`quantreg_spacing()`	Lower level function which calculates the quantile spacing regression coefficients
`randomly_assign()`	Randomly assign fold ids
`regressResiduals()`	Runs quantile regression on residuals of the model (calculates spaces around jstar quantile)
`reorder_coefficients()`	Reorder coefficients in case intercept wasn't the first term
`repMat()`	For copying matrices as in Matlab (works for sparse matrices)
`rescale_coefficients()`	Rescale coefficients estimated on scaled data to match unscaled data
`residuals(<qs>)`	Method for getting residuals from fitted qs model
`rho()`	Function for calculating Pseudo-R^2 values Evaluates the check objective function (possibly weighted) for QREG
`round_if()`	Round if x is numeric, otherwise don't
`rq.fit.agd()`	Quantile Regression approximated w/ huber loss
`rq.fit.br()`	Version that complies with more general requirements
`rq.fit.lasso()`	Quantile Regression w/ Lasso Penalty
`rq.fit.post_lasso()`	Quantile Regression w/ Lasso Penalty
`rq.fit.sfn()`	Version that complies with more general requirements
`rq.fit.sfn_start_val()`	Sparse Regression Quantile Fitting with Weights
`rq.fit.two_pass()`	Quantile regression approximated w/ huber loss followed by post-processing
`scale_for_lasso()`	Scale matrix for lasso regression
`se_control()`	Control standard_errors parameters
`seq_quant()`	For sequencing along probabilities
`spDiag()`	Create sparse diagonal matrix with vector x on diagonal
`spSums()`	Return column sums of matrix
`spacings_to_quantiles()`	Compute quantiles given parameter coefficients and data
`sparse_qr_drop_colinear_columns()`	Drop Colinear Columns from sparse matrix
`splint_R()`	A function to conduct the interpolation given data and fitted quantiles
`resample_qs()` `weighted_bootstrap()` `bootstrap()` `subsample()` `standard_errors()`	Computes standard errors for the quantile regression spacing method using subsampling.
`subsampleRows()`	Function that fully bootstraps rows
`summary(<qs>)`	creates a table of summary output for a qs object

Reference

All functions