Standardization-Based Effect Estimation with Optional Prior Covariance Adjustment
(Internal) Replace standard errors for moderator effect estimates with...
(Internal) Perform checks on formula for creation of StudySpecificatio...
Add new variables to a model frame from a teeMod object
(Internal) Fallback brute force method to locate data in the call st...
(Internal) Find dichotomy formulas in the call stack
(Internal) Product of
(Internal) Extract specified type from new data set
(Internal) Locate data in call stack
(Internal) Bread matrix of design-based variance
(Internal) Meat matrix of design-based variance
(Internal) Design-based variance for models with covariance adjustment
(Internal) Design-based variance for models without covariance adjustm...
(Internal) Removes the forcing column entirely from a `StudySpecificat...
(Internal) Rename columns to strip function calls
(Internal) Return ID's used to order observations in the covariance ad...
(Internal) Return ID's used to order observations in the direct adjust...
(Internal) show helper for PreSandwichLayer/SandwichLayer
(Internal) Checks newdata/by argument for specification accessors
(Internal) Use by to update StudySpecification with new variable n...
(Internal) Add columns for merging covariance adjustment and direct ad...
(Internal) Updates spec@call's formula with the currently defined va...
(Internal) Rename cluster/unitid/uoa in a formula to unit_of_assignmen...
(Internal) Replaces type columns in specification with new
(Internal) Validate a dichotomy against other dichotomies found in the...
(Internal) Worker function for weight calculation
Summarizing teeMod objects
Valid Weights
Variance/Covariance for teeMod objects
Compute variance-covariance matrix for fitted teeMod model
Generate Direct Adjusted Weights for Treatment Effect Estimation
(Internal) Modeling weights with an accompanying StudySpecification
WeightedStudySpecification subsetting
WeightedStudySpecification Operations
Extract Weights from WeightedStudySpecification
(Internal) Extracts treatment as binary vector
(Internal) A few checks to ensure by= is valid
(Internal) Applies dichotomy to treatment
Group-center akin to Stata's areg
Convert lm object into teeMod
Convert a PreSandwichLayer to a SandwichLayer with a `StudySpecifi...
Obtain Treatment from StudySpecification
Adjust residuals for both-sides absorption
Extract bread matrix from a teeMod model fit
Concatenate weights
Convert object to data.frame or produce meaningful error
(Internal) Extract empirical estimating equations from a teeMod mode...
(Internal) Compute as part of CR2 variance e...
Confidence intervals with standard errors provided by vcov.teeMod()
Covariance adjustment of teeMod model estimates
Return ..uoa.. column
(Internal) Helper function for design-based meat matrix calculation
(Internal) Helper function for design-based meat matrix calculation
(Internal) Helper function for design-based meat matrix calculation
(Internal) Aggregate weights and outcomes to cluster level
(Internal) Align the dimensions and rows of direct adjustment and cova...
(Internal) Compute the degrees of freedom of a contrast of a sandwich ...
Compute residuals for a teeMod object with leave-one-out estimates o...
Produce confidence intervals for linear models
(Internal) Ensures replacement column for StudySpecificationis a `da...
(Internal) Helper function for design-based meat matrix calculation
(Internal) Helper function for design-based meat matrix calculation
Design-based estimating equations contributions
(Internal) Expand treatment variable from a StudySpecificationto a d...
(Internal) Compute the degrees of freedom of a contrast of a sandwich ...
(Internal) Calculate grave{phi}
(Internal) Locate a StudySpecification in the call stack
(Internal) Expand unit of assignment level weights to the level of the...
(Internal) Get covariance adjustments and their gradient with respect ...
Make a dataframe that links units of assignment with clusters
Make ID's to pass to the cluster argument of vcov_tee()
(Internal) Merge multiple block IDs
(Internal) Merge data.frames ensuring order of first data.frame is...
(Internal) Create a new StudySpecification object.
(Internal) Order observations used to fit a teeMod model and a prior...
(Internal) Helper function for design-based meat matrix calculation
(Internal) Bias correct residuals contributing to standard errors of a...
Extract empirical estimating equations from a teeMod model fit
Extract empirical estimating equations from a glmbrob model fit
Check whether treatment stored in a StudySpecification object is bin...
Test equality of two StudySpecification objects
Identify fine strata
Linear Model for Intention To Treat
Generate matrix of estimating equations for lmrob() fit
(Internal) model predictions with some model artifacts, as S4 object
Show a PreSandwichLayer or SandwichLayer
PreSandwichLayer and SandwichLayer subsetting
(Internal) Estimate components of the sandwich covariance matrix retur...
(Internal) model predictions with more model artifacts, as S4 object
Show a StudySpecification
Show a teeMod
Show a WeightedStudySpecification
Check for variable agreement within units of assignment
Table of elements from a StudySpecification
Convert StudySpecification between types
Accessors and Replacers for StudySpecification objects
Generates a StudySpecification object with the given specifications.
StudySpecification Structure Information
Summarizing StudySpecification objects
Extract Variable Names from StudySpecification
Special terms in StudySpecification creation formula
The Prognostic Regression Offsets with Propagation of ERrors (for Treatment Effect Estimation) package facilitates direct adjustment for experiments and observational studies that is compatible with a range of study designs and covariance adjustment strategies. It uses explicit specification of clusters, blocks and treatment allocations to furnish probability of assignment-based weights targeting any of several average treatment effect parameters, and for standard error calculations reflecting these design parameters. For covariance adjustment of its Hajek and (one-way) fixed effects estimates, it enables offsetting the outcome against predictions from a dedicated covariance model, with standard error calculations propagating error as appropriate from the covariance model.
Useful links