aic()

Calculates Aikike Inforation Criteria for fitted FCR model, used for selecting optimal number of groups

bic()

Calculates Bayesian Inforation Criteria for fitted FCR model, used for selecting optimal number of groups

coef()

Extract coefficients from fitted model

coefnames()

Returns names of coefficients from fitted model

confint()

Returns lower and upper confidence interval for fitted model, for specified significance level (default = 0.95)

distribution()

Calculates distribution of weighted coefficients from fitted model

Arguments

• results::FCRModel Model type from fcr output
• index::Integer Column index of variable in X matrix to calculate coefficient distibution for (defaults to 1)

fit()

Fits the FCR model

Arguments

- `df`: name of dataframe (if missing, data must be passed as arrays)
- `y`: column name or array holding values of the dependent variable (required)
- `X`: a list of column names or a matrix holding values of the independent variable(s) with heterogeneous coefficients (required)
- `Z`: a list of column names or a matrix holding values of the independent variable(s) with homogeneous coefficients
- `G`: number of groups (required)
- `m`: regularization parameter (greater than 1), where group assignment becomes binary as m approaches 1 (default = 1.5)
- `unit`: column name or array with unit identifier (if panel structure)
- `time`: column name or array with time indicators (if panel structure)
- `startvals`: number of starting values for the minimization routine (default = 100)
- `cores`: number of parallel workers (default = 1)

predict()

Obtain predicted values of the dependent variable from the fitted model, using modal group membership

residuals()

Get the vector of residuals from the fitted model

stderror()

Standard errors from fitted model

summarize()

Summarizes results from fitted model

vcov()

Variance covariance matrix from fitted model

weights()

Calculate group weights from fitted model, using modal group membership