Package 'capybara' reference manual

Title:	Fast and Memory Efficient Fitting of Linear Models with High-Dimensional Fixed Effects
Description:	Fast and user-friendly estimation of generalized linear models with multiple fixed effects and cluster the standard errors. The method to obtain the estimated fixed-effects coefficients is based on Stammann (2018) <doi:10.48550/arXiv.1707.01815>, Gaure (2013) <doi:10.1016/j.csda.2013.03.024>, Berge (2018) <https://ideas.repec.org/p/luc/wpaper/18-13.html>, and Correia et al. (2020) <doi: 10.1177/1536867X20909691>. This implementation is described in Vargas Sepulveda (2025) <doi:10.1371/journal.pone.0331178>.
Authors:	Mauricio Vargas Sepulveda [aut, cre] (ORCID: <https://orcid.org/0000-0003-1017-7574>), Joao Santos Silva [ths], Yoto Yotov [ctb]
Maintainer:	Mauricio Vargas Sepulveda <[email protected]>
License:	Apache License (>= 2)
Version:	2.0.0
Built:	2026-06-15 15:53:30 UTC
Source:	https://github.com/cran/capybara

Generalized Linear Models (GLMs) with high-dimensional k-way fixed effects

Description

Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding GLM. The package is based on the algorithm described in Stammann (2018). It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. This package is a ground up rewrite with multiple refactors, optimizations, and new features compared to the original package alpaca. In its current state, the package is stable and future changes will be limited to bug fixes and improvements, but not to altering the functions' arguments or outputs.

Author(s)

Maintainer: Mauricio Vargas Sepulveda [email protected] (ORCID)

Authors:

Mauricio Vargas Sepulveda [email protected] (ORCID)

Other contributors:

Joao Santos Silva [thesis advisor]
Yoto Yotov [contributor]

Broom Integration

Description

The provided broom methods do the following:

augment: Takes the input data and adds additional columns with the fitted values and residuals.
glance: Extracts the deviance, null deviance, and the number of observations.'
tidy: Extracts the estimated coefficients and their standard errors.

Usage

## S3 method for class 'feglm'
augment(x, newdata = NULL, ...)

## S3 method for class 'felm'
augment(x, newdata = NULL, ...)

## S3 method for class 'feglm'
glance(x, ...)

## S3 method for class 'felm'
glance(x, ...)

## S3 method for class 'feglm'
tidy(x, conf_int = FALSE, conf_level = 0.95, ...)

## S3 method for class 'felm'
tidy(x, conf_int = FALSE, conf_level = 0.95, ...)
## S3 method for class 'feglm'
augment(x, newdata = NULL, ...)

## S3 method for class 'felm'
augment(x, newdata = NULL, ...)

## S3 method for class 'feglm'
glance(x, ...)

## S3 method for class 'felm'
glance(x, ...)

## S3 method for class 'feglm'
tidy(x, conf_int = FALSE, conf_level = 0.95, ...)

## S3 method for class 'felm'
tidy(x, conf_int = FALSE, conf_level = 0.95, ...)

Arguments

x

A fitted model object.

newdata

Optional argument to use data different from the data used to fit the model.

...

Additional arguments passed to the method.

conf_int

Logical indicating whether to include the confidence interval.

conf_level

The confidence level for the confidence interval.

Value

A tibble with the respective information for the augment, glance, and tidy methods.

Examples

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

fit <- fepoisson(ltrade ~ ldist, ross2004_subset,
  control = fit_control(keep_data = TRUE)
)

broom::augment(fit)
broom::glance(fit)
broom::tidy(fit)

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

fit <- fepoisson(ltrade ~ ldist, ross2004_subset,
  control = fit_control(keep_data = TRUE)
)

broom::augment(fit)
broom::glance(fit)
broom::tidy(fit)

Autoplot method for feglm objects

Description

Extracts the estimated coefficients and their confidence intervals.

Usage

## S3 method for class 'feglm'
autoplot(object, ...)

## S3 method for class 'felm'
autoplot(object, ...)
## S3 method for class 'feglm'
autoplot(object, ...)

## S3 method for class 'felm'
autoplot(object, ...)

Arguments

object

A fitted model object.

...

Additional arguments passed to the method. In this case, the additional argument is conf_level, which is the confidence level for the confidence interval.

Value

A ggplot object with the estimated coefficients and their confidence intervals.

Examples

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

fit <- fepoisson(ltrade ~ ldist | ctry1, ross2004_subset)

autoplot(fit, conf_level = 0.99)

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

fit <- felm(ltrade ~ ldist | ctry1, ross2004_subset)

autoplot(fit, conf_level = 0.99)

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

fit <- fepoisson(ltrade ~ ldist | ctry1, ross2004_subset)

autoplot(fit, conf_level = 0.99)

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

fit <- felm(ltrade ~ ldist | ctry1, ross2004_subset)

autoplot(fit, conf_level = 0.99)

Separation Example Datasets

Description

Nonexistence of estimates of Poisson models across different statistical packages.

Usage

correia2019
correia2019

Format

`correia2019`

A list of data frames with three elements:

example1: Data frame used to show lack of convergence
example2: Data frame used to show lack of convergence with step-halving
fe1: Data frame with fixed effects used with the 'alpaca' package

Source

'correia2019' GitHub repository (https://github.com/sergiocorreia/correia2019/blob/master/guides/nonexistence_benchmarks.md#r-packages)

Generate fixed effects tables

Description

Generate fixed effects tables

Usage

fe_table(
  ...,
  n = 5,
  coef_digits = 3,
  latex = FALSE,
  model_names = NULL,
  caption = NULL,
  label = NULL,
  position = "htbp"
)
fe_table(
  ...,
  n = 5,
  coef_digits = 3,
  latex = FALSE,
  model_names = NULL,
  caption = NULL,
  label = NULL,
  position = "htbp"
)

Arguments

...

One or more model objects of felm or feglm class fitted with return_fe = TRUE in control.

n

Number of levels to show per fixed effect dimension. Use Inf to show all levels. The default is 5.

coef_digits

Number of digits for fixed effect coefficients. The default is 3.

latex

Whether to output as LaTeX code. The default is FALSE.

model_names

Optional vector of custom model names.

caption

Optional caption for the table (LaTeX only).

label

Optional label for cross-referencing (LaTeX only).

position

LaTeX float position specifier (LaTeX only). The default is "htbp".

Value

A formatted fixed effects table of class summary_table.

Examples

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

m1 <- felm(ltrade ~ ldist | ctry1, ross2004_subset)
m2 <- fepoisson(ltrade ~ ldist | ctry1, ross2004_subset)

fe_table(m1, m2, model_names = c("Linear", "Poisson"))
ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

m1 <- felm(ltrade ~ ldist | ctry1, ross2004_subset)
m2 <- fepoisson(ltrade ~ ldist | ctry1, ross2004_subset)

fe_table(m1, m2, model_names = c("Linear", "Poisson"))

GLM fitting with high-dimensional k-way fixed effects

Description

feglm can be used to fit generalized linear models with many high-dimensional fixed effects. The term fixed effect means having one intercept for each level in each category.

Usage

feglm(
  formula = NULL,
  data = NULL,
  family = gaussian(),
  weights = NULL,
  vcov = NULL,
  beta_start = NULL,
  eta_start = NULL,
  offset = NULL,
  control = NULL
)
feglm(
  formula = NULL,
  data = NULL,
  family = gaussian(),
  weights = NULL,
  vcov = NULL,
  beta_start = NULL,
  eta_start = NULL,
  offset = NULL,
  control = NULL
)

Arguments

formula

an object of class "formula": a symbolic description of the model to be fitted. formula must be of type response ~ slopes | fixed_effects | cluster.

data

an object of class "data.frame" containing the variables in the model. The expected input is a dataset with the variables specified in formula and a number of rows at least equal to the number of variables in the model.

family

the link function to be used in the model. Similar to glm.fit this has to be the result of a call to a family function. Default is gaussian(). See family for details of family functions.

weights

an optional string with the name of the prior weights variable in data.

vcov

an optional character string specifying the type of variance-covariance estimator. One of "iid" (default OLS, ignore cluster part of formula), "hetero" (heteroskedastic-robust HC0, computed in C++ - no cluster variable needed), "cluster" (one-way sandwich using the cluster variable in the formula), "m-estimator" (M-estimator one-way sandwich), or "dyadic" (Cameron-Miller dyadic sandwich; requires two entity variables in the third part of the formula). When NULL (default), the type is inferred from the formula: if a cluster variable is present the standard sandwich is used, otherwise the inverse Hessian (IID) is returned.

beta_start

an optional vector of starting values for the structural parameters in the linear predictor. Default is $\boldsymbol{\beta} = \mathbf{0}$ .

eta_start

an optional vector of starting values for the linear predictor.

offset

an optional formula or numeric vector specifying an a priori known component to be included in the linear predictor. If a formula, it should be of the form ~ variable.

control

a named list of parameters for controlling the fitting process. See fit_control for details.

Details

If feglm does not converge this is often a sign of linear dependence between one or more regressors and a fixed effects category. In this case, you should carefully inspect your model specification.

Value

A named list of class "feglm". The list contains the following fifteen elements:

coefficients

a named vector of the estimated coefficients

eta

a vector of the linear predictor

weights

a vector of the weights used in the estimation

hessian

a matrix with the numerical second derivatives

deviance

the deviance of the model

null_deviance

the null deviance of the model

conv

a logical indicating whether the model converged

iter

the number of iterations needed to converge

nobs

a named vector with the number of observations used in the estimation indicating the dropped and perfectly predicted observations

fe_levels

a named vector with the number of levels in each fixed effects

nms_fe

a list with the names of the fixed effects variables

formula

the formula used in the model

data

the data used in the model after dropping non-contributing observations

family

the family used in the model

control

the control list used in the model

vcov_type

a character string indicating the variance-covariance type used: "iid", "hetero", "cluster", "m-estimator", or "dyadic"

References

Gaure, S. (2013). "OLS with Multiple High Dimensional Category Variables". Computational Statistics and Data Analysis, 66.

Marschner, I. (2011). "glm2: Fitting generalized linear models with convergence problems". The R Journal, 3(2).

Stammann, A., F. Heiss, and D. McFadden (2016). "Estimating Fixed Effects Logit Models with Large Panel Data". Working paper.

Stammann, A. (2018). "Fast and Feasible Estimation of Generalized Linear Models with High-Dimensional k-Way Fixed Effects". ArXiv e-prints.

Examples

# check the felm examples for the details about clustered standard errors
ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

fit <- feglm(ltrade ~ ldist | ctry1, ross2004_subset, family = poisson())

summary(fit)
# check the felm examples for the details about clustered standard errors
ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

fit <- feglm(ltrade ~ ldist | ctry1, ross2004_subset, family = poisson())

summary(fit)

LM fitting with high-dimensional k-way fixed effects

Description

feglm can be used to fit linear models with many high-dimensional fixed effects. The estimation procedure is based on unconditional maximum likelihood and can be interpreted as a “weighted demeaning” approach.

Usage

felm(formula = NULL, data = NULL, weights = NULL, vcov = NULL, control = NULL)
felm(formula = NULL, data = NULL, weights = NULL, vcov = NULL, control = NULL)

Arguments

formula

an object of class "formula": a symbolic description of the model to be fitted. formula must be of type response ~ slopes | fixed_effects | cluster.

data

weights

an optional string with the name of the prior weights variable in data.

vcov

control

a named list of parameters for controlling the fitting process. See fit_control for details.

Value

A named list of class "felm". The list contains the following eleven elements:

coefficients

a named vector of the estimated coefficients

fitted_values

a vector of the estimated dependent variable

weights

a vector of the weights used in the estimation

hessian

a matrix with the numerical second derivatives

null_deviance

the null deviance of the model

nobs

a named vector with the number of observations used in the estimation indicating the dropped and perfectly predicted observations

fe_levels

a named vector with the number of levels in each fixed effect

nms_fe

a list with the names of the fixed effects variables

formula

the formula used in the model

data

the data used in the model after dropping non-contributing observations

control

the control list used in the model

References

Gaure, S. (2013). "OLS with Multiple High Dimensional Category Variables". Computational Statistics and Data Analysis, 66.

Marschner, I. (2011). "glm2: Fitting generalized linear models with convergence problems". The R Journal, 3(2).

Stammann, A., F. Heiss, and D. McFadden (2016). "Estimating Fixed Effects Logit Models with Large Panel Data". Working paper.

Stammann, A. (2018). "Fast and Feasible Estimation of Generalized Linear Models with High-Dimensional k-Way Fixed Effects". ArXiv e-prints.

Examples

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

# Model with fixed effects
fit <- felm(ltrade ~ ldist | ctry1, ross2004_subset)
summary(fit)

ross2004_subset <- ross2004[ross2004$year %in% c(1994, 1999), ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

# Model without fixed effects but with clustered standard errors
# Note: Use 0 to indicate no fixed effects when specifying clusters
fit <- felm(ltrade ~ ldist | 0 | year, ross2004_subset)
summary(fit)

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

# Model with fixed effects
fit <- felm(ltrade ~ ldist | ctry1, ross2004_subset)
summary(fit)

ross2004_subset <- ross2004[ross2004$year %in% c(1994, 1999), ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

# Model without fixed effects but with clustered standard errors
# Note: Use 0 to indicate no fixed effects when specifying clusters
fit <- felm(ltrade ~ ldist | 0 | year, ross2004_subset)
summary(fit)

Negative Binomial model fitting with high-dimensional k-way fixed effects

Description

A routine that uses the same internals as feglm.

Usage

fenegbin(
  formula = NULL,
  data = NULL,
  weights = NULL,
  beta_start = NULL,
  eta_start = NULL,
  init_theta = NULL,
  link = c("log", "identity", "sqrt"),
  offset = NULL,
  control = NULL
)
fenegbin(
  formula = NULL,
  data = NULL,
  weights = NULL,
  beta_start = NULL,
  eta_start = NULL,
  init_theta = NULL,
  link = c("log", "identity", "sqrt"),
  offset = NULL,
  control = NULL
)

Arguments

formula

an object of class "formula": a symbolic description of the model to be fitted. formula must be of type response ~ slopes | fixed_effects | cluster.

data

weights

an optional string with the name of the prior weights variable in data.

beta_start

an optional vector of starting values for the structural parameters in the linear predictor. Default is $\boldsymbol{\beta} = \mathbf{0}$ .

eta_start

an optional vector of starting values for the linear predictor.

init_theta

an optional initial value for the theta parameter (see glm.nb).

link

the link function. Must be one of "log", "sqrt", or "identity".

offset

an optional formula or numeric vector specifying an a priori known component to be included in the linear predictor. If a formula, it should be of the form ~ variable.

control

a named list of parameters for controlling the fitting process. See fit_control for details.

Value

A named list of class "feglm". The list contains the following eighteen elements:

coefficients

a named vector of the estimated coefficients

eta

a vector of the linear predictor

weights

a vector of the weights used in the estimation

hessian

a matrix with the numerical second derivatives

deviance

the deviance of the model

null_deviance

the null deviance of the model

conv

a logical indicating whether the model converged

iter

the number of iterations needed to converge

theta

the estimated theta parameter

iter_outer

the number of outer iterations

conv_outer

a logical indicating whether the outer loop converged

nobs

a named vector with the number of observations used in the estimation indicating the dropped and perfectly predicted observations

fe_levels

a named vector with the number of levels in each fixed effects

nms_fe

a list with the names of the fixed effects variables

formula

the formula used in the model

data

the data used in the model after dropping non-contributing observations

family

the family used in the model

control

the control list used in the model

Examples

# check the felm examples for the details about clustered standard errors

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

fit <- fenegbin(ltrade ~ ldist | ctry1, ross2004_subset)

summary(fit)

# check the felm examples for the details about clustered standard errors

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

fit <- fenegbin(ltrade ~ ldist | ctry1, ross2004_subset)

summary(fit)

Poisson model fitting high-dimensional with k-way fixed effects

Description

A wrapper for feglm with family = poisson().

Usage

fepoisson(
  formula = NULL,
  data = NULL,
  weights = NULL,
  vcov = NULL,
  beta_start = NULL,
  eta_start = NULL,
  offset = NULL,
  control = NULL
)
fepoisson(
  formula = NULL,
  data = NULL,
  weights = NULL,
  vcov = NULL,
  beta_start = NULL,
  eta_start = NULL,
  offset = NULL,
  control = NULL
)

Arguments

formula

an object of class "formula": a symbolic description of the model to be fitted. formula must be of type response ~ slopes | fixed_effects | cluster.

data

weights

an optional string with the name of the prior weights variable in data.

vcov

beta_start

an optional vector of starting values for the structural parameters in the linear predictor. Default is $\boldsymbol{\beta} = \mathbf{0}$ .

eta_start

an optional vector of starting values for the linear predictor.

offset

an optional formula or numeric vector specifying an a priori known component to be included in the linear predictor. If a formula, it should be of the form ~ variable.

control

a named list of parameters for controlling the fitting process. See fit_control for details.

Value

A named list of class "feglm".

Examples

# check the feglm examples for the details about clustered standard errors

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

fit <- fepoisson(ltrade ~ ldist, ross2004_subset)

summary(fit)

# check the feglm examples for the details about clustered standard errors

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

fit <- fepoisson(ltrade ~ ldist, ross2004_subset)

summary(fit)

Asymmetric Poisson Pseudo-Maximum Likelihood (APPML) Estimation

Description

Fits an asymmetric Poisson pseudo-maximum likelihood model with high-dimensional fixed effects using expectile regression. This approach extends standard PPML by allowing different weights for positive and negative residuals, enabling estimation of conditional expectiles rather than the conditional mean.

Usage

fepoisson_asymmetric(
  formula = NULL,
  data = NULL,
  weights = NULL,
  beta_start = NULL,
  eta_start = NULL,
  offset = NULL,
  control = NULL
)
fepoisson_asymmetric(
  formula = NULL,
  data = NULL,
  weights = NULL,
  beta_start = NULL,
  eta_start = NULL,
  offset = NULL,
  control = NULL
)

Arguments

formula

an object of class "formula": a symbolic description of the model to be fitted. formula must be of type response ~ slopes | fixed_effects | cluster.

data

weights

an optional string with the name of the prior weights variable in data.

beta_start

an optional vector of starting values for the structural parameters in the linear predictor. Default is $\boldsymbol{\beta} = \mathbf{0}$ .

eta_start

an optional vector of starting values for the linear predictor.

offset

an optional formula or numeric vector specifying an a priori known component to be included in the linear predictor. If a formula, it should be of the form ~ variable.

control

a named list of parameters for controlling the fitting process. See fit_control for details.

Details

The APPML estimator minimizes an asymmetric loss function based on expectiles. For a given expectile $\tau$ , observations with negative residuals receive weight $\tau$ while observations with positive residuals receive weight $1 - \tau$ . The algorithm iteratively:

Computes residuals from the current fit
Updates weights as $w_i = |\tau - \mathbf{1}(r_i < 0)|$
Re-fits the weighted Poisson model
Checks convergence using $(b - b_{old})' V^{-1} (b - b_{old}) < \epsilon$

The expectile parameter is specified via control = fit_control(expectile = ...). When expectile = 0.5, the estimator is equivalent to standard PPML. Values below 0.5 estimate lower conditional expectiles (more sensitive to small values), while values above 0.5 estimate upper conditional expectiles (more sensitive to large values).

Value

A named list of class "feglm" containing:

coefficients

named vector of estimated coefficients

vcov

variance-covariance matrix of coefficients

eta

linear predictor

fitted_values

fitted values from the final iteration

residuals

residuals from the final fit

weights

observation weights used in final fit

appml_weights

asymmetric weights used in APPML algorithm

deviance

the deviance of the model

null_deviance

the null deviance of the model

conv

logical indicating whether inner GLM converged

conv_outer

logical indicating whether APPML outer loop converged

iter

number of inner iterations

iter_outer

number of outer APPML iterations

expectile

the expectile value used

objective_function

final value of the convergence criterion

negative_residuals_share

proportion of negative residuals in final fit

nobs

a named vector with the number of observations

fe_levels

a named vector with the number of levels in each fixed effect

nms_fe

a list with the names of the fixed effects variables

formula

the formula used in the model

family

the family used in the model (Poisson)

control

the control list used in the model

References

Newey, W. K., & Powell, J. L. (1987). Asymmetric least squares estimation and testing. Econometrica, 55(4), 819-847.

Examples

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

# Lower expectile (10th) - more weight on negative residuals
fit10 <- fepoisson_asymmetric(
  ltrade ~ ldist | ctry1, ross2004_subset,
  control = fit_control(expectile = 0.1)
)

summary(fit10)

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

# Lower expectile (10th) - more weight on negative residuals
fit10 <- fepoisson_asymmetric(
  ltrade ~ ldist | ctry1, ross2004_subset,
  control = fit_control(expectile = 0.1)
)

summary(fit10)

Set `feglm` Control Parameters

Description

Set and change parameters used for fitting feglm, felm, and fenegbin. Termination conditions are similar to glm.

Usage

fit_control(
  dev_tol = 1e-06,
  center_tol = 1e-06,
  collin_tol = 1e-08,
  step_halving_factor = 0.5,
  alpha_tol = 1e-05,
  iter_max = 50L,
  iter_center_max = 10000L,
  iter_inner_max = 50L,
  iter_alpha_max = 10000L,
  step_halving_memory = 0.9,
  max_step_halving = 2L,
  start_inner_tol = 1e-05,
  grand_acc_period = 4L,
  centering = "berge",
  sep_tol = 1e-08,
  sep_zero_tol = 1e-08,
  sep_mu_tol = 1e-06,
  sep_max_iter = 200L,
  sep_simplex_max_iter = 2000L,
  sep_use_relu = TRUE,
  sep_use_simplex = TRUE,
  sep_use_mu = TRUE,
  return_fe = TRUE,
  keep_tx = FALSE,
  keep_data = FALSE,
  return_hessian = FALSE,
  check_separation = TRUE,
  init_theta = 0,
  vcov_type = NULL,
  expectile = NULL,
  expectile_tol = 1e-12,
  expectile_iter_max = 50L,
  expectile_glm_iter_max = NULL,
  expectile_trace = FALSE
)
fit_control(
  dev_tol = 1e-06,
  center_tol = 1e-06,
  collin_tol = 1e-08,
  step_halving_factor = 0.5,
  alpha_tol = 1e-05,
  iter_max = 50L,
  iter_center_max = 10000L,
  iter_inner_max = 50L,
  iter_alpha_max = 10000L,
  step_halving_memory = 0.9,
  max_step_halving = 2L,
  start_inner_tol = 1e-05,
  grand_acc_period = 4L,
  centering = "berge",
  sep_tol = 1e-08,
  sep_zero_tol = 1e-08,
  sep_mu_tol = 1e-06,
  sep_max_iter = 200L,
  sep_simplex_max_iter = 2000L,
  sep_use_relu = TRUE,
  sep_use_simplex = TRUE,
  sep_use_mu = TRUE,
  return_fe = TRUE,
  keep_tx = FALSE,
  keep_data = FALSE,
  return_hessian = FALSE,
  check_separation = TRUE,
  init_theta = 0,
  vcov_type = NULL,
  expectile = NULL,
  expectile_tol = 1e-12,
  expectile_iter_max = 50L,
  expectile_glm_iter_max = NULL,
  expectile_trace = FALSE
)

Arguments

dev_tol

tolerance level for the first stopping condition of the maximization routine. The stopping condition is based on the relative change of the deviance in iteration $r$ and can be expressed as follows: $|dev_{r} - dev_{r - 1}| / (0.1 + |dev_{r}|) < tol$ . The default is 1.0e-06.

center_tol

tolerance level for the stopping condition of the centering algorithm. The stopping condition is based on the relative change of the centered variable similar to the 'lfe' package. The default is 1.0e-05.

collin_tol

tolerance level for detecting collinearity. The default is 1.0e-08.

step_halving_factor

numeric indicating the factor by which the step size is halved to iterate towards convergence. This is used to control the step size during optimization. The default is 0.5.

alpha_tol

tolerance for fixed effects (alpha) convergence. The default is 1.0e-05.

iter_max

integer indicating the maximum number of iterations in the maximization routine. The default is 50L.

iter_center_max

integer indicating the maximum number of iterations in the centering algorithm. The default is 10000L.

iter_inner_max

integer indicating the maximum number of iterations in the inner loop of the centering algorithm. The default is 50L.

iter_alpha_max

maximum iterations for fixed effects computation. The default is 10000L.

step_halving_memory

numeric memory factor for step-halving algorithm. Controls how much of the previous iteration is retained. The default is 0.9.

max_step_halving

maximum number of post-convergence step-halving attempts. The default is 2.

start_inner_tol

starting tolerance for inner solver iterations. The default is 1.0e-05.

grand_acc_period

integer indicating the period (in iterations) for grand acceleration in the centering algorithm. Grand acceleration applies a second-level Irons-Tuck extrapolation on the overall convergence trajectory. Lower values (e.g., 4-10) may speed up convergence for difficult problems. Set to a very large value (e.g., 10000) to effectively disable. The default is 4L.

centering

character string indicating the centering algorithm to use for demeaning fixed effects. "stammann" (default) uses alternating projections with Gauss-Seidel sweeps plus Irons-Tuck and grand acceleration on coefficient vectors. Each iteration updates each fixed-effect dimension in sequence. "berge" uses a fixed-point reformulation as described in Berge (2018): all FE updates are composed into a single map $F = f_T \circ f_I$ , reducing the problem to finding $\beta^* = F(\beta^*)$ . The Irons and Tuck (1969) acceleration is then applied to the composed iteration. Both methods use warm-starting and grand acceleration.

sep_tol

tolerance for separation detection. The default is 1.0e-08.

sep_zero_tol

tolerance for treating values as zero in separation detection. The default is 1.0e-08.

sep_mu_tol

tolerance for mu-based separation detection during IRLS iterations. Observations with y == 0 and eta <= log(sep_mu_tol) are flagged as separated. Based on ppmlhdfe's mu separation method. The default is 1.0e-06.

sep_max_iter

maximum iterations for ReLU separation detection algorithm. The default is 200L.

sep_simplex_max_iter

maximum iterations for simplex separation detection algorithm. The default is 2000L.

sep_use_relu

logical indicating whether to use the ReLU algorithm for separation detection. The default is TRUE.

sep_use_simplex

logical indicating whether to use the simplex algorithm for separation detection. The default is TRUE.

sep_use_mu

logical indicating whether to use mu-based separation detection during IRLS iterations. This catches observations where predicted values become extremely small (suggesting perfect prediction of zeros). Following ppmlhdfe methodology. The default is TRUE.

return_fe

logical indicating if the fixed effects should be returned. This can be useful when fitting general equilibrium models where skipping the fixed effects for intermediate steps speeds up computation. Set it to FALSE to minimize memory usage. The default is TRUE.

keep_tx

logical indicating if the centered regressor matrix should be stored. The default is FALSE to minimize memory usage.

keep_data

logical indicating if the filtered data should be stored in the result object. Required for predict() methods. Set to TRUE when planning to use prediction functions. The default is FALSE to minimize memory usage for production/benchmark use.

return_hessian

logical indicating if the Hessian matrix should be returned. The Hessian is a P*P matrix used to compute the variance-covariance matrix. The default is FALSE to minimize memory usage (vcov is still computed and returned).

check_separation

logical indicating whether to perform separation detection for Poisson models. When TRUE (default), observations with perfect prediction are automatically detected and excluded from estimation. Set to FALSE to skip this check and speed up computation when separation is known not to be an issue. The default is TRUE.

init_theta

Initial value for the negative binomial dispersion parameter (theta). The default is 0.0.

vcov_type

Optional character string specifying the type of variance-covariance estimator to be used. When NULL (default), the covariance matrix is the inverse Hessian (IID) when no cluster variable is present, or a clustered sandwich when one is. Other values: "hetero" - heteroskedastic-robust HC0 sandwich (no cluster variable needed); "m-estimator" - one-way M-estimator sandwich (cluster variable required); "m-estimator-dyadic" - dyadic-robust Cameron-Miller sandwich (two entity columns required in the third part of the formula like z ~ x + y | fe | cl1 + cl2).

expectile

numeric value between 0 and 1 (exclusive) specifying the expectile for asymmetric Poisson pseudo-maximum likelihood (APPML) estimation. When NULL (default), standard symmetric estimation is performed. Values below 0.5 give more weight to negative residuals (lower quantiles), while values above 0.5 give more weight to positive residuals (upper quantiles). For example, expectile = 0.1 estimates the 10th expectile, expectile = 0.5 is equivalent to standard Poisson PML, and expectile = 0.9 estimates the 90th expectile.

expectile_tol

tolerance level for the stopping condition of the expectile iteration algorithm.

The convergence criterion uses a hybrid approach: the algorithm converges when the squared norm of coefficient changes $\|b - b_{old}\|^2$ is below a threshold computed as the maximum of an absolute floor (1e-14) and a relative tolerance scaled by the L2 norm of coefficients. Specifically: $\text{threshold} = \max(10^{-14}, (\text{tol} \cdot \|b\|_2)^2)$ . This hybrid criterion ensures numerical robustness across different compiler optimizations (e.g., FMA on macOS) and handles both cases with small and large coefficients appropriately. The default is 1.0e-12.

expectile_iter_max

integer indicating the maximum number of iterations for the expectile reweighting algorithm. The default is 50L.

expectile_glm_iter_max

integer indicating the maximum number of inner GLM (IRLS) iterations per APPML outer iteration. When NULL (default), the value of iter_max is used, meaning the inner GLM runs to full convergence before APPML weights are updated. Setting this to 1L enables a single-step mode where APPML asymmetric weights are updated after every Newton step, which typically reduces the total number of iterations needed.

expectile_trace

logical indicating whether to print iteration information during expectile estimation. The default is FALSE.

Value

A named list of control parameters.

Memory-Efficient vs Full Models

By default, fit_control() returns "thin" model objects with minimal memory footprint:

keep_data = FALSE - data not stored
return_fe = FALSE - fixed effects not stored
return_hessian = FALSE - Hessian not stored
keep_tx = FALSE - centered matrix not stored

Examples

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

felm(ltrade ~ ldist | ctry1, ross2004_subset,
  control = fit_control(dev_tol = 1e-10, center_tol = 1e-10)
)

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

felm(ltrade ~ ldist | ctry1, ross2004_subset,
  control = fit_control(dev_tol = 1e-10, center_tol = 1e-10)
)

Subset of WTO data from Ross (2004)

Description

Data from Ross (2004) used to show the different variance-covariance estimators from Cameron and Miller (2014).

Usage

ross2004
ross2004

Format

`ross2004`

A data frame with 234,597 rows and 22 columns:

ltrade: Log of bilateral trade between i and j at time t
bothin: Binary variable which is unity if both i and j are GATT/WTO members at t
onein: Binary variable which is unity if either i or j is a GATT/WTO member at t
gsp: Binary variable which is unity if i was a GSP beneficiary of j or vice versa at t
ldist: Log of distance between i and j
lrgdp: Log of real GDP
lrgdppc: Log of real GDP per capita
regional: Binary variable which is unity if i and j are in the same region
custrict: Binary variable which is unity if i and j are in the same customs union
comlang: Binary variable which is unity if i and j share a common language
border: Binary variable which is unity if i and j share a border
landl: Number of landlocked countries in the country-pair (0, 1, or 2)
island: Number of island nations in the pair (0, 1, or 2)
lareap: Log of the area of the country (in square kilometers)
comcol: Binary variable which is unity if i and j were ever colonies after 1945 with the same colonizer
curcol: Binary variable which is unity if i and j are colonies at time t
colony: Binary variable which is unity if i ever colonized j or vice versa
comctry: Binary variable which is unity if i and j remained part of the same nation during the sample (e.g., France and Guadeloupe)
ctry1: Country 1 ISO-3 code
ctry2: Country 2 ISO-3 code
pair: Country pair undirected dyads
year: Year of observation

Source

Do We Really Know That the WTO Increases Trade? (DOI: 10.1257/000282804322970724)

Recompute Sandwich Variance-Covariance Matrix

Description

Recompute the variance-covariance matrix for a fitted model using a different clustering structure or covariance type. This allows changing the vcov estimator without re-fitting the model, provided the model was fit with keep_tx = TRUE and return_hessian = TRUE in the control parameters.

Usage

sandwich_vcov(
  object,
  cluster1 = NULL,
  cluster2 = NULL,
  type = c("hetero", "clustered", "m-estimator", "dyadic", "m-estimator-dyadic"),
  ...
)
sandwich_vcov(
  object,
  cluster1 = NULL,
  cluster2 = NULL,
  type = c("hetero", "clustered", "m-estimator", "dyadic", "m-estimator-dyadic"),
  ...
)

Arguments

object

a fitted model object of class "feglm" or "felm".

cluster1

a vector or factor for the first clustering variable. Required for "clustered" and "dyadic" types.

cluster2

a vector or factor for the second clustering variable. Required for "dyadic" type.

type

character string specifying the covariance type. One of:

"hetero": heteroskedasticity-robust (no clustering, also known as "HC0")
"clustered" or "m-estimator": one-way cluster-robust
"dyadic" or "m-estimator-dyadic": dyadic cluster-robust for network/trade data

...

additional arguments (currently ignored).

Details

The model must be fit with fit_control(keep_tx = TRUE, return_hessian = TRUE) to store the centered design matrix, Hessian, and working residuals needed for vcov recomputation. Note that keep_data = TRUE is NOT required - residuals are stored automatically when keep_tx = TRUE.

For dyadic clustering (used in gravity/trade models), cluster1 and cluster2 represent the two entity dimensions (e.g., exporter and importer). The function handles the full dyadic correlation structure including cross-entity correlations.

Value

A named matrix of covariance estimates.

References

Cameron, C., J. Gelbach, and D. Miller (2011). "Robust Inference With Multiway Clustering". Journal of Business & Economic Statistics 29(2).

Cameron, C. and D. Miller (2014). "Robust Inference for Dyadic Data". Unpublished manuscript.

Examples

# Refitting models

ross2004_s1 <- ross2004[ross2004$year == 1994, ]
ross2004_s1 <- ross2004_s1[ross2004_s1$ltrade >
  quantile(ross2004_s1$ltrade, 0.75), ]

ross2004_s2 <- ross2004[ross2004$year == 1999, ]
ross2004_s2 <- ross2004_s2[ross2004_s2$ltrade >
  quantile(ross2004_s2$ltrade, 0.75), ]

ross2004_subset <- rbind(ross2004_s1, ross2004_s2)

fepoisson(
  ltrade ~ ldist | ctry1, ross2004_subset,
  control = fit_control(vcov_type = "hetero")
)

fepoisson(
  ltrade ~ ldist | ctry1, ross2004_subset,
  control = fit_control(vcov_type = "m-estimator")
)

# Reusing models

# Store required components
fit <- fepoisson(
  ltrade ~ ldist | ctry1, ross2004_subset,
  control = fit_control(keep_tx = TRUE, return_hessian = TRUE)
)

# Heteroskedastic-robust HC0 sandwich (no cluster variable needed)
sandwich_vcov(fit, type = "hetero")

#' One-way cluster
sandwich_vcov(fit, cluster1 = ross2004_subset$year, type = "clustered")

# Refitting models

ross2004_s1 <- ross2004[ross2004$year == 1994, ]
ross2004_s1 <- ross2004_s1[ross2004_s1$ltrade >
  quantile(ross2004_s1$ltrade, 0.75), ]

ross2004_s2 <- ross2004[ross2004$year == 1999, ]
ross2004_s2 <- ross2004_s2[ross2004_s2$ltrade >
  quantile(ross2004_s2$ltrade, 0.75), ]

ross2004_subset <- rbind(ross2004_s1, ross2004_s2)

fepoisson(
  ltrade ~ ldist | ctry1, ross2004_subset,
  control = fit_control(vcov_type = "hetero")
)

fepoisson(
  ltrade ~ ldist | ctry1, ross2004_subset,
  control = fit_control(vcov_type = "m-estimator")
)

# Reusing models

# Store required components
fit <- fepoisson(
  ltrade ~ ldist | ctry1, ross2004_subset,
  control = fit_control(keep_tx = TRUE, return_hessian = TRUE)
)

# Heteroskedastic-robust HC0 sandwich (no cluster variable needed)
sandwich_vcov(fit, type = "hetero")

#' One-way cluster
sandwich_vcov(fit, cluster1 = ross2004_subset$year, type = "clustered")

Generate formatted regression tables

Description

Generate formatted regression tables

Usage

summary_table(
  ...,
  coef_digits = 3,
  se_digits = 3,
  stars = TRUE,
  latex = FALSE,
  model_names = NULL,
  caption = NULL,
  label = NULL,
  position = "htbp"
)
summary_table(
  ...,
  coef_digits = 3,
  se_digits = 3,
  stars = TRUE,
  latex = FALSE,
  model_names = NULL,
  caption = NULL,
  label = NULL,
  position = "htbp"
)

Arguments

...

One or more model objects of felm or feglm class.

coef_digits

Number of digits for coefficients. The default is 3.

se_digits

Number of digits for standard errors. The default is 3.

stars

Whether to include significance stars. The default is TRUE.

latex

Whether to output as LaTeX code. The default is FALSE.

model_names

Optional vector of custom model names

caption

Optional caption for the table (LaTeX only)

label

Optional label for cross-referencing (LaTeX only)

position

LaTeX float position specifier (LaTeX only). The default is "htbp".

Value

A formatted table

Examples

ross2004_subset <- ross2004[ross2004$year == 1999 &
  ross2004$ltrade > quantile(ross2004$ltrade, 0.75), ]

m1 <- felm(ltrade ~ ldist | ctry1, ross2004_subset)
m2 <- fepoisson(ltrade ~ ldist | ctry1, ross2004_subset)

summary_table(m1, m2, model_names = c("Linear", "Poisson"))
ross2004_subset <- ross2004[ross2004$year == 1999 &
  ross2004$ltrade > quantile(ross2004$ltrade, 0.75), ]

m1 <- felm(ltrade ~ ldist | ctry1, ross2004_subset)
m2 <- fepoisson(ltrade ~ ldist | ctry1, ross2004_subset)

summary_table(m1, m2, model_names = c("Linear", "Poisson"))

Update a fitted `feglm` model

Description

S3 method for update() that understands the |-separated formula syntax used by feglm(). Uses Formula::update.Formula() for proper handling of multi-part formulas. Identical semantics to update.felm().

Usage

## S3 method for class 'feglm'
update(object, formula. = . ~ ., vcov = NULL, family = NULL, ...)
## S3 method for class 'feglm'
update(object, formula. = . ~ ., vcov = NULL, family = NULL, ...)

Arguments

object

A fitted feglm object.

formula.

Update formula; only the segments you want to change need to differ from .. Examples:

. ~ . | country + year - change FE, keep regressors.
. ~ . | . | ctry1 + ctry2 - keep FE, change cluster.
. ~ . - bothin | year - drop a regressor, keep FE.

vcov

Optional new vcov value (e.g. "cluster"). If omitted the original value is reused.

family

Optional new family (e.g. binomial()). If omitted the original family is reused.

...

Additional arguments forwarded to felm().

Value

A refitted feglm object.

Update a fitted `felm` model

Description

S3 method for update() that understands the |-separated formula syntax used by felm(). R's built-in stats::update.formula() breaks on these formulas because the | parts look like factor arithmetic. This method uses the Formula::update.Formula() method which correctly handles multi-part formulas.

The . placeholder works as usual:

. ~ . - keep the current response and RHS regressors.
The second | segment replaces (or keeps, if .) the fixed-effects.
The third | segment replaces (or keeps, if .) the cluster variables.

Usage

## S3 method for class 'felm'
update(object, formula. = . ~ ., vcov = NULL, ...)
## S3 method for class 'felm'
update(object, formula. = . ~ ., vcov = NULL, ...)

Arguments

object

A fitted felm object.

formula.

Update formula; only the segments you want to change need to differ from .. Examples:

. ~ . | country + year - change FE, keep regressors.
. ~ . | . | ctry1 + ctry2 - keep FE, change cluster.
. ~ . - bothin | year - drop a regressor, keep FE.

vcov

Optional new vcov value (e.g. "cluster"). If omitted the original value is reused.

...

Additional arguments forwarded to felm().

Value

A refitted felm object.

Update a `felm_formula` object

Description

S3 method for update() on felm_formula objects. Uses Formula::update.Formula() to properly handle multi-part formulas with |.

The . placeholder behaves as in stats::update.formula():

. ~ . - keep current response and RHS unchanged.
Second | segment - replaces (or keeps with .) the fixed effects.
Third | segment - replaces (or keeps with .) the cluster variables.

Usage

## S3 method for class 'felm_formula'
update(object, formula., ...)
## S3 method for class 'felm_formula'
update(object, formula., ...)

Arguments

object

A felm_formula object.

formula.

Update specification, e.g. . ~ . | . | ctry1 + ctry2.

...

Ignored.

Value

A new felm_formula object.

Covariance matrix for GLMs

Description

Covariance matrix for the estimator of the structural parameters from objects returned by feglm. The covariance is computed during model fitting - either the inverse Hessian (default) or the sandwich estimator if cluster variables are specified in the formula.

Usage

## S3 method for class 'feglm'
vcov(object, ...)
## S3 method for class 'feglm'
vcov(object, ...)

Arguments

object

an object of class "feglm".

...

additional arguments (currently ignored).

Value

A named matrix of covariance estimates.

References

Cameron, C., J. Gelbach, and D. Miller (2011). "Robust Inference With Multiway Clustering". Journal of Business & Economic Statistics 29(2).

Examples

# Model with clustering - returns sandwich covariance
ross2004_s1 <- ross2004[ross2004$year == 1994, ]
ross2004_s1 <- ross2004_s1[ross2004_s1$ltrade >
  quantile(ross2004_s1$ltrade, 0.75), ]

ross2004_s2 <- ross2004[ross2004$year == 1999, ]
ross2004_s2 <- ross2004_s2[ross2004_s2$ltrade >
  quantile(ross2004_s2$ltrade, 0.75), ]

ross2004_subset <- rbind(ross2004_s1, ross2004_s2)

fit <- fepoisson(ltrade ~ ldist + border | ctry1 | year, ross2004_subset)

vcov(fit)

# Model with clustering - returns sandwich covariance
ross2004_s1 <- ross2004[ross2004$year == 1994, ]
ross2004_s1 <- ross2004_s1[ross2004_s1$ltrade >
  quantile(ross2004_s1$ltrade, 0.75), ]

ross2004_s2 <- ross2004[ross2004$year == 1999, ]
ross2004_s2 <- ross2004_s2[ross2004_s2$ltrade >
  quantile(ross2004_s2$ltrade, 0.75), ]

ross2004_subset <- rbind(ross2004_s1, ross2004_s2)

fit <- fepoisson(ltrade ~ ldist + border | ctry1 | year, ross2004_subset)

vcov(fit)

Covariance matrix for LMs

Description

Covariance matrix for the estimator of the structural parameters from objects returned by felm. The covariance is computed during model fitting - either the inverse Hessian (default) or the sandwich estimator if cluster variables are specified in the formula.

Usage

## S3 method for class 'felm'
vcov(object, ...)
## S3 method for class 'felm'
vcov(object, ...)

Arguments

object

an object of class "felm".

...

additional arguments (currently ignored).

Value

A named matrix of covariance estimates.

References

Cameron, C., J. Gelbach, and D. Miller (2011). "Robust Inference With Multiway Clustering". Journal of Business & Economic Statistics 29(2).

Examples

# Model with clustering - returns sandwich covariance
ross2004_s1 <- ross2004[ross2004$year == 1994, ]
ross2004_s1 <- ross2004_s1[ross2004_s1$ltrade >
  quantile(ross2004_s1$ltrade, 0.75), ]

ross2004_s2 <- ross2004[ross2004$year == 1999, ]
ross2004_s2 <- ross2004_s2[ross2004_s2$ltrade >
  quantile(ross2004_s2$ltrade, 0.75), ]

ross2004_subset <- rbind(ross2004_s1, ross2004_s2)

fit <- felm(ltrade ~ ldist | ctry1 | year, ross2004_subset)

vcov(fit)

# Model with clustering - returns sandwich covariance
ross2004_s1 <- ross2004[ross2004$year == 1994, ]
ross2004_s1 <- ross2004_s1[ross2004_s1$ltrade >
  quantile(ross2004_s1$ltrade, 0.75), ]

ross2004_s2 <- ross2004[ross2004$year == 1999, ]
ross2004_s2 <- ross2004_s2[ross2004_s2$ltrade >
  quantile(ross2004_s2$ltrade, 0.75), ]

ross2004_subset <- rbind(ross2004_s1, ross2004_s2)

fit <- felm(ltrade ~ ldist | ctry1 | year, ross2004_subset)

vcov(fit)

Package 'capybara'

Help Index

Generalized Linear Models (GLMs) with high-dimensional k-way fixed effects

Description

Author(s)

See Also

Broom Integration

Description

Usage

Arguments

Value

Examples

Autoplot method for feglm objects

Description

Usage

Arguments

Value

Examples

Separation Example Datasets

Description

Usage

Format

correia2019

Source

Generate fixed effects tables

Description

Usage

Arguments

Value

Examples

GLM fitting with high-dimensional k-way fixed effects

Description

Usage

Arguments

Details

Value

References

Examples

LM fitting with high-dimensional k-way fixed effects

Description

Usage

Arguments

Value

References

Examples

Negative Binomial model fitting with high-dimensional k-way fixed effects

Description

Usage

Arguments

Value

Examples

Poisson model fitting high-dimensional with k-way fixed effects

Description

Usage

Arguments

Value

Examples

Asymmetric Poisson Pseudo-Maximum Likelihood (APPML) Estimation

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Set feglm Control Parameters

Description

Usage

Arguments

Value

Memory-Efficient vs Full Models

See Also

Examples

Subset of WTO data from Ross (2004)

Description

Usage

Format

ross2004

Source

`correia2019`

Set `feglm` Control Parameters

`ross2004`

Update a fitted `feglm` model

Update a fitted `felm` model

Update a `felm_formula` object