augsynth

https://rdrr.io/github/ebenmichael/augsynth/
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augsynth is an R package for augmented synthetic control methods that enables causal inference with...
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### Predict ATT or Average Outcome Under Control

Source: https://rdrr.io/github/ebenmichael/augsynth/man/predict.augsynth_multiout.html

This is the S3 method for the `augsynth_multiout` class. Use this to get predictions for ATT or average outcome under control from an `augsynth_multiout` object. Optional arguments include 'att' to specify whether to return ATT or average outcome under control.

```R
predict(object, ...)
```

--------------------------------

### predict.augsynth

Source: https://rdrr.io/github/ebenmichael/augsynth/man/predict.augsynth.html

S3 method for class 'augsynth' to get prediction of ATT or average outcome under control.

```APIDOC
## predict.augsynth

### Description
Get prediction of ATT or average outcome under control

### Usage
```R
predict(object, att = FALSE, ...)
```

### Arguments
* `object` (augsynth): augsynth object
* `att` (logical): If TRUE, return the ATT, if FALSE, return imputed counterfactual. Defaults to FALSE.
* `...` (any): Optional arguments.

### Value
Vector of predicted post-treatment control averages
```

--------------------------------

### predict.augsynth_multiout

Source: https://rdrr.io/github/ebenmichael/augsynth/man/predict.augsynth_multiout.html

Get prediction of ATT or average outcome under control. This is an S3 method for the augsynth_multiout class.

```APIDOC
## predict(object, ...)

### Description
Get prediction of ATT or average outcome under control 

### Arguments
`object` |  augsynth_multiout object  
`...` |  Optional arguments, including 
  * "att"Whether to return the ATT or average outcome under control

### Value
Vector of predicted post-treatment control averages
```

--------------------------------

### Predict augsynth object

Source: https://rdrr.io/github/ebenmichael/augsynth/man/predict.augsynth.html

This is the S3 method for the 'augsynth' class. Use this function to get predictions from an 'augsynth' object. Set `att = TRUE` to return the ATT, or `att = FALSE` to return imputed counterfactuals.

```R
predict(object, att = F, ...)
```

--------------------------------

### Get Non-Missing Outcome Donors in R

Source: https://rdrr.io/github/ebenmichael/augsynth/man/get_nona_donors.html

Use this function to filter donor units that have complete outcome data, which is a prerequisite for certain causal inference methods when treated units have outcomes.

```r
get_nona_donors(X, y, trt, n_lags, n_leads, time_cohort)
```

--------------------------------

### make_V_matrix

Source: https://rdrr.io/github/ebenmichael/augsynth/man/make_V_matrix.html

This function constructs a V matrix. It takes a time point 't0' and a vector 'V' as input. If 'V' is null, it handles it appropriately.

```APIDOC
## make_V_matrix

### Description
Make a V matrix from a vector (or null)

### Usage
```R
make_V_matrix(t0, V)
```

### Parameters
* **t0**: A numeric value representing a time point.
* **V**: A numeric vector or NULL. This vector is used to construct the V matrix.
```

--------------------------------

### print.augsynth_multiout

Source: https://rdrr.io/github/ebenmichael/augsynth/man/print.augsynth_multiout.html

This is the S3 method for the 'augsynth_multiout' class, used for printing its output.

```APIDOC
## print.augsynth_multiout

### Description
Print function for augsynth 

### Usage
```
## S3 method for class 'augsynth_multiout'
print(x, ...)

```

### Arguments
`x` |  augsynth_multiout object  
`...` |  Optional arguments  
```

--------------------------------

### Print Method for Multisynth Objects

Source: https://rdrr.io/github/ebenmichael/augsynth/man/print.multisynth.html

This is the S3 method for the 'multisynth' class. Use it to print or display the contents of a multisynth object. Optional arguments can be passed for further customization.

```R
## S3 method for class 'multisynth'
print(x, att_weight = NULL, ...)
```

--------------------------------

### print.multisynth

Source: https://rdrr.io/github/ebenmichael/augsynth/man/print.multisynth.html

This is the S3 method for printing a multisynth object. It allows users to view the details of a generated multisynth object.

```APIDOC
## Print function for multisynth

### Description
Print function for multisynth 

### Usage
```R
## S3 method for class 'multisynth'
print(x, att_weight = NULL, ...)

```

### Arguments
`x` |  multisynth object  
`...` |  Optional arguments  
```

--------------------------------

### Print Method for augsynth_multiout Objects

Source: https://rdrr.io/github/ebenmichael/augsynth/man/print.augsynth_multiout.html

This is the S3 method for the 'augsynth_multiout' class. It is used to print the object.

```r
## S3 method for class 'augsynth_multiout'
print(x, ...)
```

--------------------------------

### Check Data for Multisynth

Source: https://rdrr.io/github/ebenmichael/augsynth/man/check_data_stag.html

Use this function to check if your data is suitable for running multisynth. It takes formatted data and parameters for fixed effects and treatment effect estimation.

```R
check_data_stag(wide, fixedeff, n_leads, n_lags)
```

--------------------------------

### Plotting Summary of Augsynth Multi-Output Results

Source: https://rdrr.io/github/ebenmichael/augsynth/man/plot.summary.augsynth_multiout.html

This is the S3 method for plotting the summary of a multi-output augsynth object. Use this to visualize treatment effects and uncertainty intervals. Set `inf = TRUE` to display uncertainty intervals and `plt_avg = TRUE` to plot the average of outcomes.

```R
plot(x, inf = F, plt_avg = F, ...)
```

--------------------------------

### plot.summary.multisynth

Source: https://rdrr.io/github/ebenmichael/augsynth/man/plot.summary.multisynth.html

Plot function for summary function for multisynth. This is an S3 method for the 'summary.multisynth' class.

```APIDOC
## S3 method for class 'summary.multisynth'
plot(x, inf = T, levels = NULL, label = T, weights = FALSE, ...)

### Arguments
`x` |  summary object  
`inf` |  Whether to plot confidence intervals  
`levels` |  Which units/groups to plot, default is every group  
`label` |  Whether to label the individual levels  
`weights` |  Whether to plot the weights, default = FALSE  
`...` |  Optional arguments  
```

--------------------------------

### check_data_stag

Source: https://rdrr.io/github/ebenmichael/augsynth/man/check_data_stag.html

Checks if the data is ready for multisynth analysis by verifying its structure and parameters.

```APIDOC
## check_data_stag

### Description
Check that we can actually run multisynth on the data

### Usage
```R
check_data_stag(wide, fixedeff, n_leads, n_lags)
```

### Arguments
* `wide` (output of `format_data_stag`): The input data in wide format.
* `fixedeff` (boolean): Whether to include a unit fixed effect in the analysis.
* `n_leads` (numeric): The number of post-treatment periods to estimate effects for. Defaults to the number of post-treatment periods for the last treated unit.
* `n_lags` (numeric): The number of pre-treatment periods to balance. Defaults to balancing all periods.
```

--------------------------------

### Make a V matrix from a vector (or null) in R

Source: https://rdrr.io/github/ebenmichael/augsynth/man/make_V_matrix.html

Use this function to construct a V matrix. It takes a vector 't0' and a matrix 'V' as input.

```r
make_V_matrix(t0, V)
```

--------------------------------

### print.augsynth

Source: https://rdrr.io/github/ebenmichael/augsynth/man/print.augsynth.html

Prints the augsynth object. This is an S3 method for the 'augsynth' class.

```APIDOC
## print.augsynth

### Description
Print function for augsynth 

### Usage
```R
## S3 method for class 'augsynth'
print(x, ...)

```

### Arguments
`x` |  augsynth object  
`...` |  Optional arguments  
```

--------------------------------

### Predict Average Outcome under Control or ATT

Source: https://rdrr.io/github/ebenmichael/augsynth/man/predict.multisynth.html

This S3 method for 'multisynth' objects calculates predictions. Set `att = TRUE` to return the ATT, or `att = FALSE` (default) to return the imputed counterfactual. Optional weights can be provided for averaging or bootstrap.

```R
predict(object, att = F, att_weight = NULL, bs_weight = NULL, ...)
```

--------------------------------

### Print Augsynth Object

Source: https://rdrr.io/github/ebenmichael/augsynth/man/print.augsynth.html

This is the S3 method for printing augsynth objects. It takes the object 'x' and optional arguments.

```r
print(x, ...)
```

--------------------------------

### Fit Staggered Synth

Source: https://rdrr.io/github/ebenmichael/augsynth/man/multisynth.html

This function fits staggered synthetic controls. It requires a formula, unit and time identifiers, and the panel data. Optional arguments control the estimation of post-treatment effects, pre-treatment period balancing, regularization, and the inclusion of fixed effects.

```R
multisynth(
  form,
  unit,
  time,
  data,
  n_leads = NULL,
  n_lags = NULL,
  nu = NULL,
  lambda = 0,
  V = NULL,
  fixedeff = TRUE,
  n_factors = 0,
  scm = T,
  time_cohort = F,
  how_match = "knn",
  cov_agg = NULL,
  eps_abs = 1e-04,
  eps_rel = 1e-04,
  verbose = FALSE,
  ...
)
```

--------------------------------

### jackknife_se_single

Source: https://rdrr.io/github/ebenmichael/augsynth/man/jackknife_se_single.html

Estimates standard errors for a single Augmented Synthetic Control Method (ASCM) using the jackknife method. This is useful for ridge-augmented synthetic control.

```APIDOC
## jackknife_se_single

### Description
Estimate standard errors for single ASCM with the jackknife Do this for ridge-augmented synth 

### Usage
```R
jackknife_se_single(ascm)
```

### Arguments
* `ascm` (Fitted augsynth object) - A fitted augsynth object.

### Value
A list containing:
* `att` (Vector) - Vector of ATT estimates.
* `se` (Vector) - Standard error estimate.
* `lb` (Vector) - Lower bound of the confidence interval.
* `ub` (Vector) - Upper bound of the confidence interval.
* `alpha` (Numeric) - Level of the confidence interval.
```

--------------------------------

### Plotting Multisynth Results

Source: https://rdrr.io/github/ebenmichael/augsynth/man/plot.multisynth.html

This S3 method is used to plot the results of a multisynth analysis. It allows customization of inference type, confidence intervals, level labeling, and weight visualization.

```R
plot(
  x,
  inf_type = "bootstrap",
  inf = T,
  levels = NULL,
  label = T,
  weights = FALSE,
  ...
)
```

--------------------------------

### plot.summary.augsynth

Source: https://rdrr.io/github/ebenmichael/augsynth/man/plot.summary.augsynth.html

This is the S3 method for plotting the summary of an augsynth object. It can optionally display confidence intervals.

```APIDOC
## Plot function for summary function for augsynth

### Description
Plot function for summary function for augsynth 

### Usage
```R
## S3 method for class 'summary.augsynth'
plot(x, inf = T, ...)

```

### Arguments
`x` |  Summary object  
---|---
`inf` |  Boolean, whether to plot confidence intervals  
`...` |  Optional arguments  
```

--------------------------------

### plot.multisynth

Source: https://rdrr.io/github/ebenmichael/augsynth/man/plot.multisynth.html

This function generates a plot for a 'multisynth' object, visualizing the results of a synthetic control analysis. It supports various customization options for inference, confidence intervals, labeling, and plotting weights.

```APIDOC
## plot.multisynth

### Description
Plot function for multisynth 

### Usage
```R
plot(x, inf_type = "bootstrap", inf = T, levels = NULL, label = T, weights = FALSE, ...)
```

### Arguments
* `x` (multisynth object): The synthetic control object to be plotted.
* `inf_type` (string): Type of inference to perform. Options are "bootstrapWild" (default) or "jackknife".
* `inf` (boolean): Whether to compute and plot confidence intervals. Defaults to TRUE.
* `levels` (vector): Which units/groups to plot. Defaults to all groups.
* `label` (boolean): Whether to label the individual levels. Defaults to TRUE.
* `weights` (boolean): Whether to plot the weights. Defaults to FALSE.
* `...` (any): Optional arguments to be passed to the plotting function.
```

--------------------------------

### plot.augsynth_multiout

Source: https://rdrr.io/github/ebenmichael/augsynth/man/plot.augsynth_multiout.html

Plots the results from a summary.augsynth_multiout object, allowing visualization of treatment effects and uncertainty intervals.

```APIDOC
## plot.augsynth_multiout

### Description
Plot function for summary function for augsynth 

### Usage
```R
## S3 method for class 'augsynth_multiout'
plot(x, inf = T, plt_avg = F, ...)

```

### Arguments
`x` |  summary.augsynth_multiout object  
---|---
`inf` |  Boolean, whether to plot uncertainty intervals, default TRUE  
`plt_avg` |  Boolean, whether to plot the average of the outcomes, default FALSE  
`...` |  Optional arguments for summary function  
```

--------------------------------

### Plotting summary.multisynth objects in R

Source: https://rdrr.io/github/ebenmichael/augsynth/man/plot.summary.multisynth.html

Use this function to visualize the results of a multisynth analysis. Customize the plot by controlling confidence intervals, selected levels, labels, and weights.

```r
plot(x, inf = T, levels = NULL, label = T, weights = FALSE, ...)
```

--------------------------------

### Plotting augsynth_multiout Objects

Source: https://rdrr.io/github/ebenmichael/augsynth/man/plot.augsynth_multiout.html

Use this S3 method to plot the results of an augsynth_multiout object. Control the display of uncertainty intervals and the average outcome line using the 'inf' and 'plt_avg' arguments, respectively. Optional arguments can be passed to the underlying summary function.

```R
plot(x, inf = T, plt_avg = F, ...)
```

--------------------------------

### predict.multisynth

Source: https://rdrr.io/github/ebenmichael/augsynth/man/predict.multisynth.html

S3 method for class 'multisynth'. Retrieves predictions from a fitted multisynth object.

```APIDOC
## predict.multisynth

### Description
Get prediction of average outcome under control or ATT

### Usage
```R
predict(object, att = FALSE, att_weight = NULL, bs_weight = NULL, ...)
```

### Arguments
- `object` (Fit multisynth object): The fitted multisynth object from which to generate predictions.
- `att` (logical, optional): If TRUE, return the ATT. If FALSE, return the imputed counterfactual. Defaults to FALSE.
- `att_weight` (numeric vector, optional): Weights to place on individual units/cohorts when averaging.
- `bs_weight` (numeric vector, optional): Weight to perturb units by for weighted bootstrap.
- `...` (Optional arguments): Additional optional arguments.

### Value
Matrix of predicted post-treatment control outcomes for each treated unit.
```

--------------------------------

### plot.augsynth

Source: https://rdrr.io/github/ebenmichael/augsynth/man/plot.augsynth.html

Generates a plot for an augsynth object. Allows customization for confidence intervals and cross-validation visualization.

```APIDOC
## plot.augsynth

### Description
Plot function for augsynth 

### Usage
```R
plot(x, inf = T, cv = F, ...)
```

### Arguments
* `x` (Augsynth object) - The augsynth object to be plotted.
* `inf` (Boolean) - If TRUE, displays confidence intervals around the point estimates.
* `cv` (Boolean) - If TRUE, plots cross-validation MSE against the hyper-parameter. Otherwise, plots effects.
* `...` - Optional arguments to be passed to the plotting function.
```

--------------------------------

### Plotting Augsynth Results

Source: https://rdrr.io/github/ebenmichael/augsynth/man/plot.augsynth.html

This is the S3 method for plotting augsynth objects. Use it to visualize treatment effects, confidence intervals, or cross-validation results.

```R
plot(x, inf = T, cv = F, ...)
```

--------------------------------

### conformal_inf_multiout

Source: https://rdrr.io/github/ebenmichael/augsynth/man/conformal_inf_multiout.html

Performs conformal inference to compute p-values and point-wise confidence intervals.

```APIDOC
## conformal_inf_multiout

### Description
Conformal inference procedure to compute p-values and point-wise confidence intervals 

### Usage
```R
conformal_inf_multiout(
  ascm_multi,
  alpha = 0.05,
  stat_func = NULL,
  type = "iid",
  q = 1,
  ns = 1000,
  grid_size = 1,
  lin_h0 = NULL
)
```

### Arguments
* `ascm_multi`: Fitted 'augsynth' object
* `alpha` (numeric): Confidence level (default is 0.05)
* `stat_func` (function): Function to compute test statistic (default is NULL)
* `type` (character): Either "iid" for iid permutations or "block" for moving block permutations (default is "iid")
* `q` (numeric): The norm for the test static '((sum(x ^ q))) ^ (1/q)' (default is 1)
* `ns` (numeric): Number of resamples for "iid" permutations (default is 1000)
* `grid_size` (numeric): Number of grid points to use when inverting the hypothesis test (default is 1, so only to test joint null)
* `lin_h0` (numeric): Linear hypothesis for the null (default is NULL)

### Value
List that contains:
  * `att`: Vector of ATT estimates
  * `heldout_att`: Vector of ATT estimates with the time period held out
  * `se`: Standard error, always NA but returned for compatibility
  * `lb`: Lower bound of 1 - alpha confidence interval
  * `ub`: Upper bound of 1 - alpha confidence interval
  * `p_val`: p-value for test of no post-treatment effect
  * `alpha`: Level of confidence interval
```

--------------------------------

### Estimate Standard Errors with Jackknife

Source: https://rdrr.io/github/ebenmichael/augsynth/man/jackknife_se_single.html

Use this function to estimate standard errors for a single augmented synthetic control model (ASCM), especially when using ridge-augmented synthetic control. It requires a fitted `augsynth` object as input.

```R
jackknife_se_single(ascm)
```

--------------------------------

### Plotting Summary of Augsynth Results

Source: https://rdrr.io/github/ebenmichael/augsynth/man/plot.summary.augsynth.html

This S3 method is used to plot the results of the summary function for an 'augsynth' object. Set `inf` to `FALSE` to exclude confidence intervals from the plot.

```R
plot(x, inf = T, ...)
```

--------------------------------

### Fit Augmented SCM with multiple outcomes

Source: https://rdrr.io/github/ebenmichael/augsynth/man/augsynth_multiout.html

Use this function to fit an Augmented SCM model when you have multiple outcome variables. It supports different imputation functions for control outcomes and methods for combining multiple outcomes.

```R
augsynth_multiout(
  form,
  unit,
  time,
  t_int,
  data,
  progfunc = c("Ridge", "None"),
  scm = T,
  fixedeff = FALSE,
  cov_agg = NULL,
  combine_method = "avg",
  ...
)
```

--------------------------------

### Conformal Inference Function Signature

Source: https://rdrr.io/github/ebenmichael/augsynth/man/conformal_inf.html

This is the function signature for conformal_inf. It takes a fitted 'augsynth' object and several optional parameters to control the inference procedure.

```R
conformal_inf(
  ascm,
  alpha = 0.05,
  stat_func = NULL,
  type = "iid",
  q = 1,
  ns = 1000,
  grid_size = 50
)


```

--------------------------------

### Conformal Inference Function Signature

Source: https://rdrr.io/github/ebenmichael/augsynth/man/conformal_inf_multiout.html

Defines the signature for the conformal_inf_multiout function, specifying its arguments and their default values. Use this function for computing p-values and confidence intervals with multi-output augsynth objects.

```R
conformal_inf_multiout(
  ascm_multi,
  alpha = 0.05,
  stat_func = NULL,
  type = "iid",
  q = 1,
  ns = 1000,
  grid_size = 1,
  lin_h0 = NULL
)
```

--------------------------------

### conformal_inf Function

Source: https://rdrr.io/github/ebenmichael/augsynth/man/conformal_inf.html

Computes p-values and point-wise confidence intervals using conformal inference.

```APIDOC
## conformal_inf

### Description
Conformal inference procedure to compute p-values and point-wise confidence intervals

### Usage
```R
conformal_inf(
  ascm,
  alpha = 0.05,
  stat_func = NULL,
  type = "iid",
  q = 1,
  ns = 1000,
  grid_size = 50
)
```

### Arguments
- `ascm` (Fitted 'augsynth' object): The fitted augsynth object.
- `alpha` (numeric, optional): The confidence level. Defaults to 0.05.
- `stat_func` (function, optional): A function to compute the test statistic.
- `type` (character, optional): Type of permutations. Either "iid" for iid permutations or "block" for moving block permutations. Defaults to "block".
- `q` (numeric, optional): The norm for the test static '((sum(x ^ q))) ^ (1/q)'. Defaults to 1.
- `ns` (numeric, optional): Number of resamples for "iid" permutations. Defaults to 1000.
- `grid_size` (numeric, optional): Number of grid points to use when inverting the hypothesis test. Defaults to 50.

### Value
A list containing:
- `att` (numeric vector): Vector of ATT estimates.
- `heldout_att` (numeric vector): Vector of ATT estimates with the time period held out.
- `se` (numeric): Standard error (always NA but returned for compatibility).
- `lb` (numeric vector): Lower bound of the 1 - alpha confidence interval.
- `ub` (numeric vector): Upper bound of the 1 - alpha confidence interval.
- `p_val` (numeric): p-value for the test of no post-treatment effect.
- `alpha` (numeric): The level of the confidence interval.
```

--------------------------------

### augsynth_multiout

Source: https://rdrr.io/github/ebenmichael/augsynth/man/augsynth_multiout.html

Fits Augmented SCM with multiple outcomes. This function allows for the imputation of control outcomes using various methods and can incorporate unit fixed effects.

```APIDOC
## augsynth_multiout

### Description
Fit Augmented SCM with multiple outcomes

### Usage
```R
augsynth_multiout(
  form,
  unit,
  time,
  t_int,
  data,
  progfunc = c("Ridge", "None"),
  scm = TRUE,
  fixedeff = FALSE,
  cov_agg = NULL,
  combine_method = "avg",
  ...
)
```

### Arguments
* `form`: outcome ~ treatment | auxillary covariates
* `unit`: Name of unit column
* `time`: Name of time column
* `t_int`: Time of intervention
* `data`: Panel data as dataframe
* `progfunc`: What function to use to impute control outcomes Ridge=Ridge regression (allows for standard errors), None=No outcome model
* `scm`: Whether the SCM weighting function is used
* `fixedeff`: Whether to include a unit fixed effect, default F
* `cov_agg`: Covariate aggregation functions, if NULL then use mean with NAs omitted
* `combine_method`: How to combine outcomes: ‘concat' concatenates outcomes and 'avg' averages them, default: ’avg'
* `...`: optional arguments for outcome model

### Value
augsynth object that contains:
  * "weights": Ridge ASCM weights
  * "l2_imbalance": Imbalance in pre-period outcomes, measured by the L2 norm
  * "scaled_l2_imbalance": L2 imbalance scaled by L2 imbalance of uniform weights
  * "mhat": Outcome model estimate
  * "data": Panel data as matrices
```

--------------------------------

### conformal_inf_linear

Source: https://rdrr.io/github/ebenmichael/augsynth/man/conformal_inf_linear.html

Conformal inference procedure to compute a confidence interval for a linear in time effect.

```APIDOC
## conformal_inf_linear

### Description
Conformal inference procedure to compute a confidence interval for a linear in time effect

### Usage
```R
conformal_inf_linear(
  ascm,
  alpha = 0.05,
  stat_func = NULL,
  type = "iid",
  q = 1,
  ns = 1000,
  grid_size = 50
)
```

### Arguments
`ascm` | Fitted 'augsynth' object
`alpha` | Confidence level
`stat_func` | Function to compute test statistic
`type` | Either "iid" for iid permutations or "block" for moving block permutations; default is "iid"
`q` | The norm for the test static '((sum(x ^ q))) ^ (1/q)'
`ns` | Number of resamples for "iid" permutations
`grid_size` | Number of grid points to use when inverting the hypothesis test

### Value
List that contains:
  * "att"Vector of ATT estimates
  * "heldout_att"Vector of ATT estimates with the time period held out
  * "se"Standard error, always NA but returned for compatibility
  * "lb"Lower bound of 1 - alpha confidence interval
  * "ub"Upper bound of 1 - alpha confidence interval
  * "p_val"p-value for test of no post-treatment effect
  * "alpha"Level of confidence interval
```

--------------------------------

### multisynth Function

Source: https://rdrr.io/github/ebenmichael/augsynth/man/multisynth.html

Fits staggered synthetic controls. This function estimates the effects of a treatment in a staggered adoption setting by constructing synthetic controls for each treated unit.

```APIDOC
## multisynth

### Description
Fit staggered synth 

### Usage
```
multisynth(
  form, 
  unit, 
  time, 
  data, 
  n_leads = NULL, 
  n_lags = NULL, 
  nu = NULL, 
  lambda = 0, 
  V = NULL, 
  fixedeff = TRUE, 
  n_factors = 0, 
  scm = T, 
  time_cohort = F, 
  how_match = "knn", 
  cov_agg = NULL, 
  eps_abs = 1e-04, 
  eps_rel = 1e-04, 
  verbose = FALSE, 
  ...
)
```

### Arguments
`form` |  outcome ~ treatment | weighting covariates | approximate matching covaraites | exact matching covariates 
  * outcomeName of the outcome of interest 
  * treatmentName of the treatment assignment variable 
  * weighting covariatesAuxiliary covariates to weight on 
  * approximate matching covariatesAuxiliary covariates to approximately match one before weighting 
  * exact matching covariatesAuxiliary covariates to exactly match on before weighting 

If covariates are time-varying, their average value before the first unit is treated will be used. This can be changed by supplying a custom aggregation function to cov_agg.  
`unit` |  Name of unit column  
`time` |  Name of time column  
`data` |  Panel data as dataframe  
`n_leads` |  How long past treatment effects should be estimated for, default is number of post treatment periods for last treated unit  
`n_lags` |  Number of pre-treatment periods to balance, default is to balance all periods  
`nu` |  Fraction of balance for individual balance  
`lambda` |  Regularization hyperparameter, default = 0  
`V` |  Scaling matrix for synth optimization, default NULL is identity  
`fixedeff` |  Whether to include a unit fixed effect, default TRUE  
`n_factors` |  Number of factors for interactive fixed effects, setting to NULL fits with CV, default is 0  
`scm` |  Whether to fit scm weights  
`time_cohort` |  Whether to average synthetic controls into time cohorts, default FALSE  
`cov_agg` |  Covariate aggregation function  
`eps_abs` |  Absolute error tolerance for osqp  
`eps_rel` |  Relative error tolerance for osqp  
`verbose` |  Whether to print logs for osqp  
`...` |  Extra arguments  

### Value
multisynth object that contains: 
  * "weights"weights matrix where each column is a set of weights for a treated unit 
  * "data"Panel data as matrices 
  * "imbalance"Matrix of treatment minus synthetic control for pre-treatment time periods, each column corresponds to a treated unit 
  * "global_l2"L2 imbalance for the pooled synthetic control 
  * "scaled_global_l2"L2 imbalance for the pooled synthetic control, scaled by the imbalance for unitform weights 
  * "ind_l2"Average L2 imbalance for the individual synthetic controls 
  * "scaled_ind_l2"Average L2 imbalance for the individual synthetic controls, scaled by the imbalance for unitform weights 
  * "n_leads", "n_lags"Number of post treatment outcomes (leads) and pre-treatment outcomes (lags) to include in the analysis 
  * "nu"Fraction of balance for individual balance 
  * "lambda"Regularization hyperparameter 
  * "scm"Whether to fit scm weights 
  * "grps"Time periods for treated units 
  * "y0hat"Pilot estimates of control outcomes 
  * "residuals"Difference between the observed outcomes and the pilot estimates 
  * "n_factors"Number of factors for interactive fixed effects 

```

--------------------------------

### plot.summary.augsynth_multiout

Source: https://rdrr.io/github/ebenmichael/augsynth/man/plot.summary.augsynth_multiout.html

Plots the results of a synthetic control analysis for multiple outcomes.

```APIDOC
## S3 method for class 'summary.augsynth_multiout'
plot(x, inf = F, plt_avg = F, ...)

### Arguments
`x` | summary.augsynth_multiout object
`inf` | Boolean, whether to plot uncertainty intervals, default TRUE
`plt_avg` | Boolean, whether to plot the average of the outcomes, default FALSE
```

--------------------------------

### Conformal Inference for Linear Time Effect - R

Source: https://rdrr.io/github/ebenmichael/augsynth/man/conformal_inf_linear.html

Use this function to compute a confidence interval for a linear in time effect using conformal inference. It requires a fitted 'augsynth' object and allows customization of the confidence level, test statistic function, permutation type, and resampling parameters.

```r
conformal_inf_linear(
  ascm,
  alpha = 0.05,
  stat_func = NULL,
  type = "iid",
  q = 1,
  ns = 1000,
  grid_size = 50
)
```

--------------------------------

### get_nona_donors

Source: https://rdrr.io/github/ebenmichael/augsynth/man/get_nona_donors.html

Retrieves donors that do not have missing outcomes where treated units have outcomes.

```APIDOC
## get_nona_donors

### Description
Get donors that don't have missing outcomes where treated units have outcomes 

### Usage
```R
get_nona_donors(X, y, trt, n_lags, n_leads, time_cohort)
```

### Arguments
* `X`: A matrix or data frame of covariates.
* `y`: A vector of outcomes.
* `trt`: A vector indicating treatment assignment.
* `n_lags`: The number of lagged periods to consider.
* `n_leads`: The number of lead periods to consider.
* `time_cohort`: A vector indicating the time cohort for each unit.
```
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