diff --git a/man/covprod.Rd b/man/covprod.Rd
index 04c922356458ef97e771abb6d8bde393f952bdc2..9542cd7328b1c4d5f01dd42a36492bbe59e39555 100644
--- a/man/covprod.Rd
+++ b/man/covprod.Rd
@@ -14,7 +14,11 @@ covprod(A, res, W)
 \item{W}{A matrix of weights.}
 }
 \description{
-Compute the cross-covariance matrix between covariance and regression parameters.
-Equation (11) of Bonat and Jorgensen (2015).
+Compute the cross-covariance matrix between covariance
+    and regression parameters.  Equation (11) of Bonat and Jorgensen
+    (2015).
+}
+\author{
+Wagner Hugo Bonat
 }
 
diff --git a/man/mc_bias_corrected_std.Rd b/man/mc_bias_corrected_std.Rd
index 0a1b6d2178540985421090b607f1315829d920f0..2283397d4a06d3058d3dbc6e4e0a7ae8e6c5f7db 100644
--- a/man/mc_bias_corrected_std.Rd
+++ b/man/mc_bias_corrected_std.Rd
@@ -9,15 +9,21 @@ mc_bias_corrected_std(object, id)
 \arguments{
 \item{object}{An object of mcglm class.}
 
-\item{id}{a vector which identifies the clusters. The length and order of id should be the
-same as the number of observations. Data are assumed to be sorted so that observations on a cluster
-are contiguous rows for all entities in the formula.}
+\item{id}{a vector which identifies the clusters. The length and
+order of id should be the same as the number of
+observations. Data are assumed to be sorted so that observations
+on a cluster are contiguous rows for all entities in the formula.}
 }
 \value{
-A matrix. Note that the function assumes that the data are in the correct order.
+A matrix. Note that the function assumes that the data are in
+    the correct order.
 }
 \description{
-Compute bias-corrected standard error for regression parameters in the context
-of clustered observations. It is also robust and has improved finite sample properties.
+Compute bias-corrected standard error for regression
+    parameters in the context of clustered observations. It is also
+    robust and has improved finite sample properties.
+}
+\author{
+Wagner Hugo Bonat
 }
 
diff --git a/man/mc_build_C.Rd b/man/mc_build_C.Rd
index ba76088e0d8a7f499dd4933e8534151cf4c33b7a..5c7823df235ec9b49e8d2cbf15caf44a52579a2b 100644
--- a/man/mc_build_C.Rd
+++ b/man/mc_build_C.Rd
@@ -11,7 +11,8 @@ mc_build_C(list_mu, list_Ntrial, rho, list_tau, list_power, list_Z, list_sparse,
 \arguments{
 \item{list_mu}{A list with values of the mean.}
 
-\item{list_Ntrial}{A list with the number of trials. Usefull only for binomial responses.}
+\item{list_Ntrial}{A list with the number of trials. Usefull only for
+binomial responses.}
 
 \item{rho}{Vector of correlation parameters.}
 
@@ -19,28 +20,38 @@ mc_build_C(list_mu, list_Ntrial, rho, list_tau, list_power, list_Z, list_sparse,
 
 \item{list_power}{A list with values for the power parameters.}
 
-\item{list_Z}{A list of matrix to be used in the matrix linear predictor.}
+\item{list_Z}{A list of matrix to be used in the matrix linear
+predictor.}
 
 \item{list_sparse}{A list with Logical.}
 
-\item{list_variance}{A list specifying the variance function to be used for each response variable.}
+\item{list_variance}{A list specifying the variance function to be
+used for each response variable.}
 
-\item{list_covariance}{A list specifying the covariance function to be used for each response variable.}
+\item{list_covariance}{A list specifying the covariance function to be
+used for each response variable.}
 
-\item{list_power_fixed}{A list of Logical specifying if the power parameters are fixed or not.}
+\item{list_power_fixed}{A list of Logical specifying if the power
+parameters are fixed or not.}
 
 \item{compute_C}{Logical. Compute or not the C matrix.}
 
-\item{compute_derivative_beta}{Logical. Compute or not the derivative of C with respect to regression parameters.}
+\item{compute_derivative_beta}{Logical. Compute or not the derivative
+of C with respect to regression parameters.}
 
-\item{compute_derivative_cov}{Logical. Compute or not the derivative of C with respect the covariance parameters.}
+\item{compute_derivative_cov}{Logical. Compute or not the derivative
+    of C with respect the covariance parameters.}
 }
 \value{
-A list with the inverse of the C matrix and the derivatives of the C matrix with respect to
-rho, power and tau parameters.
+A list with the inverse of the C matrix and the derivatives of
+    the C matrix with respect to rho, power and tau parameters.
 }
 \description{
-This function builds the joint variance-covariance matrix using the Generalized
-Kronecker product and its derivatives with respect to rho, power and tau parameters.
+This function builds the joint variance-covariance matrix
+    using the Generalized Kronecker product and its derivatives with
+    respect to rho, power and tau parameters.
+}
+\author{
+Wagner Hugo Bonat
 }
 
diff --git a/man/mc_build_bdiag.Rd b/man/mc_build_bdiag.Rd
index 091f765704f3a134b6c0a83c37e92bd576386a3a..c931eee2e3331bb14558eb82e896f829f0f2a32d 100644
--- a/man/mc_build_bdiag.Rd
+++ b/man/mc_build_bdiag.Rd
@@ -9,16 +9,21 @@ mc_build_bdiag(n_resp, n_obs)
 \arguments{
 \item{n_resp}{A numeric specifyng the number of response variables.}
 
-\item{n_obs}{A numeric specifying the number of observations in the data set.}
+\item{n_obs}{A numeric specifying the number of observations in the
+data set.}
 }
 \value{
 A list of zero matrices.
 }
 \description{
-Build a block-diagonal matrix of zeros. Such functions is used when computing
-the derivatives of the Cholesky decomposition of C.
+Build a block-diagonal matrix of zeros. Such functions
+    is used when computing the derivatives of the Cholesky
+    decomposition of C.
 }
 \details{
 It is an internal function.
 }
+\author{
+Wagner Hugo Bonat
+}
 
diff --git a/man/mc_build_omega.Rd b/man/mc_build_omega.Rd
index 31da4325a2b15cb49b22fbd64ab025dd9da614dd..3022c922caa92c69c38098903643e1315701a102 100644
--- a/man/mc_build_omega.Rd
+++ b/man/mc_build_omega.Rd
@@ -11,14 +11,21 @@ mc_build_omega(tau, Z, covariance_link, sparse = FALSE)
 
 \item{Z}{A list of matrices.}
 
-\item{covariance_link}{String specifing the covariance link function: identity, inverse, expm.}
+\item{covariance_link}{String specifing the covariance link function:
+identity, inverse, expm.}
 
-\item{sparse}{Logical force to use sparse matrix representation 'dsCMatrix'.}
+\item{sparse}{Logical force to use sparse matrix representation
+'dsCMatrix'.}
 }
 \value{
-A list with the \eqn{\Omega} matrix its inverse and derivatives with respect to \eqn{\tau}.
+A list with the \eqn{\Omega} matrix its inverse and
+    derivatives with respect to \eqn{\tau}.
 }
 \description{
-This function build \eqn{\Omega} matrix according the covariance link function.
+This function build \eqn{\Omega} matrix according the
+    covariance link function.
+}
+\author{
+Wagner Hugo Bonat
 }
 
diff --git a/man/mc_build_sigma.Rd b/man/mc_build_sigma.Rd
index 7178e91e154d0745941ad946c080fc23cd41da27..644ab33178a18c7c6e0e4adde4a8ec6ae2399ec6 100644
--- a/man/mc_build_sigma.Rd
+++ b/man/mc_build_sigma.Rd
@@ -8,41 +8,51 @@ mc_build_sigma(mu, Ntrial = 1, tau, power, Z, sparse, variance, covariance,
   power_fixed, compute_derivative_beta = FALSE)
 }
 \arguments{
-\item{mu}{A numeric vector. In general the output from \code{\link{mc_link_function}}.}
+\item{mu}{A numeric vector. In general the output from
+\code{\link{mc_link_function}}.}
 
-\item{Ntrial}{A numeric vector, or NULL or a numeric specifing the number of trials in the binomial
-experiment. It is usefull only when using variance = binomialP or binomialPQ. In the other cases
+\item{Ntrial}{A numeric vector, or NULL or a numeric specifing the
+number of trials in the binomial experiment. It is usefull only
+when using variance = binomialP or binomialPQ. In the other cases
 it will be ignored.}
 
 \item{tau}{A numeric vector.}
 
-\item{power}{A numeric or numeric vector. It should be one number for all variance functions except
-binomialPQ, in that case the argument specifies both p and q.}
+\item{power}{A numeric or numeric vector. It should be one number for
+all variance functions except binomialPQ, in that case the
+argument specifies both p and q.}
 
 \item{Z}{A list of matrices.}
 
 \item{sparse}{Logical.}
 
-\item{variance}{String specifing the variance function: constant, tweedie, poisson_tweedie,
-binomialP or binomialPQ.}
+\item{variance}{String specifing the variance function: constant,
+tweedie, poisson_tweedie, binomialP or binomialPQ.}
 
-\item{covariance}{String specifing the covariance function: identity, inverse or expm.}
+\item{covariance}{String specifing the covariance function: identity,
+inverse or expm.}
 
-\item{power_fixed}{Logical if the power parameter is fixed at initial value (TRUE). In the case
-power_fixed = FALSE the power parameter will be estimated.}
+\item{power_fixed}{Logical if the power parameter is fixed at initial
+value (TRUE). In the case power_fixed = FALSE the power parameter
+will be estimated.}
 
-\item{compute_derivative_beta}{Logical. Compute or not the derivative with respect to regression parameters.}
+\item{compute_derivative_beta}{Logical. Compute or not the derivative
+with respect to regression parameters.}
 }
 \value{
-A list with the Cholesky decomposition of \eqn{\Sigma}, \eqn{\Sigma^{-1}} and the derivative
-of \eqn{\Sigma} with respect to the power and tau parameters.
+A list with the Cholesky decomposition of \eqn{\Sigma},
+    \eqn{\Sigma^{-1}} and the derivative of \eqn{\Sigma} with respect
+    to the power and tau parameters.
 }
 \description{
-This function builds a variance-covariance matrix, based on the variance function and
-omega matrix.
+This function builds a variance-covariance matrix, based
+    on the variance function and omega matrix.
+}
+\author{
+Wagner Hugo Bonat
 }
 \seealso{
-\code{\link{mc_link_function}}, \code{\link{mc_variance_function}},
-\code{\link{mc_build_omega}}.
+\code{\link{mc_link_function}},
+    \code{\link{mc_variance_function}}, \code{\link{mc_build_omega}}.
 }
 
diff --git a/man/mc_build_sigma_between.Rd b/man/mc_build_sigma_between.Rd
index 033a43401ae5a489c0255d5e82b4cc9cd9d9ba28..a5469f8b8cb49662e3a94daea2750fae0e69d87e 100644
--- a/man/mc_build_sigma_between.Rd
+++ b/man/mc_build_sigma_between.Rd
@@ -14,13 +14,16 @@ mc_derivative_sigma_between(n_resp)
 
 \item{n_resp}{A numeric.}
 
-\item{inverse}{Logical}
+\item{inverse}{Logical.}
 }
 \value{
 A list with sigmab and its derivatives with respect to rho.
 }
 \description{
-This function builds the correlation matrix between response variable, its inverse and
-derivatives.
+This function builds the correlation matrix between
+    response variable, its inverse and derivatives.
+}
+\author{
+Wagner Hugo Bonat
 }
 
diff --git a/man/mc_core_pearson.Rd b/man/mc_core_pearson.Rd
index f7b1bcf3a0af821a74c9f4f9603f8e6024755da6..8c48d3524f86fd92263a9fe52df98d9ff28080ae 100644
--- a/man/mc_core_pearson.Rd
+++ b/man/mc_core_pearson.Rd
@@ -22,4 +22,7 @@ Core of the Pearson estimating function.
 \details{
 It is an internal function.
 }
+\author{
+Wagner Hugo Bonat
+}
 
diff --git a/man/mc_correction.Rd b/man/mc_correction.Rd
index a24379d92bdde9919386b3324992026b9f905ceb..1a744733fb6e4389e684124ccfdd264484457c49 100644
--- a/man/mc_correction.Rd
+++ b/man/mc_correction.Rd
@@ -9,20 +9,28 @@ mc_correction(D_C, inv_J_beta, D, inv_C)
 \arguments{
 \item{D_C}{A list of matrices.}
 
-\item{inv_J_beta}{A matrix. In general it is computed based on the output of the
-\code{[mcglm]{mc_quasi_score}}.}
+\item{inv_J_beta}{A matrix. In general it is computed based on the
+output of the \code{[mcglm]{mc_quasi_score}}.}
 
-\item{D}{A matrix. In general it is the output of the \link{mc_link_function}.}
+\item{D}{A matrix. In general it is the output of the
+\link{mc_link_function}.}
 
-\item{inv_C}{A matrix. In general the output of the \link{mc_build_C}.}
+\item{inv_C}{A matrix. In general the output of the
+\link{mc_build_C}.}
 }
 \value{
-A vector with the correction terms to be used on the Pearson estimating function.
+A vector with the correction terms to be used on the Pearson
+    estimating function.
 }
 \description{
-Compute the correction term associated with the Pearson estimating function.
+Compute the correction term associated with the Pearson
+    estimating function.
 }
 \details{
-It is an internal function useful inside the fitting algorithm.
+It is an internal function useful inside the fitting
+    algorithm.
+}
+\author{
+Wagner Hugo Bonat
 }
 
diff --git a/man/mc_cross_sensitivity.Rd b/man/mc_cross_sensitivity.Rd
index 68f9247eb227014e1436b70942780ba807aec226..ede94bfa205d49ef40aa9931fa9fa670b523406c 100644
--- a/man/mc_cross_sensitivity.Rd
+++ b/man/mc_cross_sensitivity.Rd
@@ -12,13 +12,19 @@ mc_cross_sensitivity(Product_cov, Product_beta,
 
 \item{Product_beta}{A list of matrices.}
 
-\item{n_beta_effective}{Numeric. Effective number of regression parameters.}
+\item{n_beta_effective}{Numeric. Effective number of regression
+parameters.}
 }
 \value{
-The cross-sensitivity matrix. Equation (10) of Bonat and Jorgensen (2015).
+The cross-sensitivity matrix. Equation (10) of Bonat and
+    Jorgensen (2015).
 }
 \description{
-Compute the cross-sensitivity matrix between regression and covariance parameters.
-Equation 10 of Bonat and Jorgensen (2015).
+Compute the cross-sensitivity matrix between regression
+    and covariance parameters.  Equation 10 of Bonat and Jorgensen
+    (2015).
+}
+\author{
+Wagner Hugo Bonat
 }
 
diff --git a/man/mc_cross_variability.Rd b/man/mc_cross_variability.Rd
index 219c0aa9932b80011328f04608ce19b59924c6cf..a976a2afb0c85be2d483ec07ebdc3c4e428ff3d4 100644
--- a/man/mc_cross_variability.Rd
+++ b/man/mc_cross_variability.Rd
@@ -16,9 +16,14 @@ mc_cross_variability(Product_cov, inv_C, res, D)
 \item{D}{A matrix.}
 }
 \value{
-The cross-variability matrix between regression and covariance parameters.
+The cross-variability matrix between regression and
+    covariance parameters.
 }
 \description{
-Compute the cross-variability matrix between covariance and regression parameters.
+Compute the cross-variability matrix between covariance
+    and regression parameters.
+}
+\author{
+Wagner Hugo Bonat
 }
 
diff --git a/man/mc_derivative_C_rho.Rd b/man/mc_derivative_C_rho.Rd
index b683cf6f5d980f0928d8d895fc70537d9b61469a..854631347d9edefc7033f256692f70ab070b88d9 100644
--- a/man/mc_derivative_C_rho.Rd
+++ b/man/mc_derivative_C_rho.Rd
@@ -20,9 +20,14 @@ mc_derivative_C_rho(D_Sigmab, Bdiag_chol_Sigma_within,
 A matrix.
 }
 \description{
-Compute the derivative of the C matrix with respect to the correlation parameters rho.
+Compute the derivative of the C matrix with respect to
+    the correlation parameters rho.
 }
 \details{
-It is an internal function used to build the derivatives of the C matrix.
+It is an internal function used to build the derivatives of
+    the C matrix.
+}
+\author{
+Wagner Hugo Bonat
 }
 
diff --git a/man/mc_derivative_cholesky.Rd b/man/mc_derivative_cholesky.Rd
index 7d7264c14e13e83826bb79a78052c74d1024684e..d6e5b22d14b3d61a5033e9aaf73567b44069e552 100644
--- a/man/mc_derivative_cholesky.Rd
+++ b/man/mc_derivative_cholesky.Rd
@@ -17,9 +17,13 @@ mc_derivative_cholesky(derivada, inv_chol_Sigma, chol_Sigma)
 A list of matrix.
 }
 \description{
-This function compute the derivative of the Cholesky decomposition.
+This function compute the derivative of the Cholesky
+    decomposition.
 }
 \details{
 It is an internal function.
 }
+\author{
+Wagner Hugo Bonat
+}
 
diff --git a/man/mc_derivative_expm.Rd b/man/mc_derivative_expm.Rd
index ff8de831d5603c0fb7168b4d1ef772e4b6c1f557..478c8bff8ece46a474b8e7ac2cada564551f4604 100644
--- a/man/mc_derivative_expm.Rd
+++ b/man/mc_derivative_expm.Rd
@@ -23,15 +23,20 @@ mc_derivative_expm(dU, UU, inv_UU, Q, n = dim(UU)[1], sparse = FALSE)
 A matrix.
 }
 \description{
-Compute the derivative of the exponential-matrix covariance link function.
+Compute the derivative of the exponential-matrix
+    covariance link function.
 }
 \details{
-Many arguments required by this function are provide by the \code{link[mcglm]{mc_dexpm}}.
-The argument dU is the derivative of the U matrix with respect to the models parameters. It should
-be computed by the user.
+Many arguments required by this function are provide by the
+    \code{link[mcglm]{mc_dexpm}}.  The argument dU is the derivative
+    of the U matrix with respect to the models parameters. It should
+    be computed by the user.
+}
+\author{
+Wagner Hugo Bonat
 }
 \seealso{
-\code{\link[Matrix]{expm}}, \code{link[mcglm]{mc_dexp_gold}} and
-\code{link[mcglm]{mc_dexpm}}.
+\code{\link[Matrix]{expm}}, \code{link[mcglm]{mc_dexp_gold}}
+    and \code{link[mcglm]{mc_dexpm}}.
 }
 
diff --git a/man/mc_derivative_sigma_beta.Rd b/man/mc_derivative_sigma_beta.Rd
index 88554679af7970c264a9c8df515fe9b7e51eb5af..a822978e8b20011a0328a5e2df5330310813ce54 100644
--- a/man/mc_derivative_sigma_beta.Rd
+++ b/man/mc_derivative_sigma_beta.Rd
@@ -18,11 +18,14 @@ mc_derivative_sigma_beta(D, D_V_sqrt_mu, Omega, V_sqrt, variance)
 \item{variance}{A string specifying the variance function name.}
 }
 \value{
-A list of matrices, containg the derivatives of V^{1/2} with respect to the regression
-parameters.
+A list of matrices, containg the derivatives of \eqn{V^{1/2}}
+    with respect to the regression parameters.
 }
 \description{
-Compute the derivatives of V^{1/2} matrix with respect to the regression
-parameters beta.
+Compute the derivatives of \eqn{V^{1/2}} matrix with
+    respect to the regression parameters beta.
+}
+\author{
+Wagner Hugo Bonat
 }
 
diff --git a/man/mc_dexp_gold.Rd b/man/mc_dexp_gold.Rd
index 174642287aad7168da3793aea84f59b47173466e..2cda4e26ac3a2507cff2774e134f424a7bd19d5c 100644
--- a/man/mc_dexp_gold.Rd
+++ b/man/mc_dexp_gold.Rd
@@ -15,16 +15,21 @@ mc_dexp_gold(M, dM)
 A list with two elements: \eqn{expm(M)} and its derivatives.
 }
 \description{
-Given a matrix \eqn{M} and its derivative \eqn{dM} the function \code{dexp_gold}
-returns the exponential-matrix \eqn{expm(M)} and its derivative. This function is based on
-the \code{\link[Matrix]{expm}} function. It is not really used in the package, but I keep this
-function to test my own implementation based on eigen values decomposition.
+Given a matrix \eqn{M} and its derivative \eqn{dM} the
+    function \code{dexp_gold} returns the exponential-matrix
+    \eqn{expm(M)} and its derivative. This function is based on the
+    \code{\link[Matrix]{expm}} function. It is not really used in the
+    package, but I keep this function to test my own implementation
+    based on eigen values decomposition.
 }
 \examples{
 M <- matrix(c(1,0.8,0.8,1), 2,2)
 dM <- matrix(c(0,1,1,0),2,2)
 mcglm::mc_dexp_gold(M = M, dM = dM)
 }
+\author{
+Wagner Hugo Bonat
+}
 \seealso{
 \code{\link[Matrix]{expm}}, \code{\link[base]{eigen}}.
 }
diff --git a/man/mc_expm.Rd b/man/mc_expm.Rd
index 0ede4aa9f94ec6c81280554e6e7034d5e83b99cb..b454c0ba57fee272fd3b162f50182f1c0b2266a7 100644
--- a/man/mc_expm.Rd
+++ b/man/mc_expm.Rd
@@ -9,25 +9,32 @@ mc_expm(U, n = dim(U)[1], sparse = FALSE, inverse = FALSE)
 \arguments{
 \item{U}{A matrix.}
 
-\item{n}{A number specifing the dimension of the matrix U. Default \code{n = dim(U)[1]}.}
+\item{n}{A number specifing the dimension of the matrix U. Default
+\code{n = dim(U)[1]}.}
 
-\item{sparse}{Logical defining the class of the output matrix. If \code{sparse = TRUE} the output
-class will be 'dgCMatrix' if \code{sparse = FALSE} the class will be 'dgMatrix'.}
+\item{sparse}{Logical defining the class of the output matrix. If
+\code{sparse = TRUE} the output class will be 'dgCMatrix' if
+\code{sparse = FALSE} the class will be 'dgMatrix'.}
 
-\item{inverse}{Logical defining if the inverse will be computed or not.}
+\item{inverse}{Logical defining if the inverse will be computed or
+not.}
 }
 \value{
-A list with \eqn{\Omega = expm(U)} its inverse (if \code{inverse = TRUE}) and
-auxiliares matrices to compute the derivatives.
+A list with \eqn{\Omega = expm(U)} its inverse (if
+    \code{inverse = TRUE}) and auxiliares matrices to compute the
+    derivatives.
 }
 \description{
 Given a matrix \code{U} the function \code{mc_expm}
-returns the exponential-matrix \eqn{expm(U)} and some auxiliares matrices to compute
-its derivatives. This function is based on the eigen-value decomposition it means that it is
-very slow.
+    returns the exponential-matrix \eqn{expm(U)} and some auxiliares
+    matrices to compute its derivatives. This function is based on
+    the eigen-value decomposition it means that it is very slow.
+}
+\author{
+Wagner Hugo Bonat
 }
 \seealso{
 \code{\link[Matrix]{expm}}, \code{\link[base]{eigen}},
-\code{link[mcglm]{mc_dexp_gold}}.
+    \code{link[mcglm]{mc_dexp_gold}}.
 }
 
diff --git a/man/mc_getInformation.Rd b/man/mc_getInformation.Rd
index e1c04d7335edfafb3ec1e5fb038ed1c8dfb59a7e..866b02de4d0166e17c2ee98c0fde5dc43ae7b081 100644
--- a/man/mc_getInformation.Rd
+++ b/man/mc_getInformation.Rd
@@ -9,14 +9,20 @@ mc_getInformation(list_initial, list_power_fixed, n_resp)
 \arguments{
 \item{list_initial}{A list of initial values.}
 
-\item{list_power_fixed}{A list of logical specyfing if the power parameters should be estimated or not.}
+\item{list_power_fixed}{A list of logical specyfing if the power
+parameters should be estimated or not.}
 
 \item{n_resp}{A number specyfing the nmber of response variables.}
 }
 \value{
-The number of \eqn{\beta}'s, \eqn{\tau}'s, power and correlation parameters.
+The number of \eqn{\beta}'s, \eqn{\tau}'s, power and
+    correlation parameters.
 }
 \description{
-This computes all information required about the number of model parameters.
+This computes all information required about the number
+    of model parameters.
+}
+\author{
+Wagner Hugo Bonat
 }
 
diff --git a/man/mc_link_function.Rd b/man/mc_link_function.Rd
index 1434211d3e8bbc9a9e786fc52a3c2154193624d9..730cedf16e132fe53dc5d4c43280c553ba101585 100644
--- a/man/mc_link_function.Rd
+++ b/man/mc_link_function.Rd
@@ -39,44 +39,57 @@ mc_inverse(beta, X, offset)
 \arguments{
 \item{beta}{A numeric vector of regression parameters.}
 
-\item{X}{A design matrix, see \code{\link[stats]{model.matrix}} for details.}
+\item{X}{A design matrix, see \code{\link[stats]{model.matrix}} for
+details.}
 
-\item{offset}{A numeric vector of offset values. It will be sum up on the linear predictor as a
-covariate with known regression parameter equals one (\eqn{\mu = g^{-1}(X\beta + offset)}).
-If no offset is present in the model, set offset = NULL.}
+\item{offset}{A numeric vector of offset values. It will be sum up on
+the linear predictor as a covariate with known regression
+parameter equals one (\eqn{\mu = g^{-1}(X\beta + offset)}).  If
+no offset is present in the model, set offset = NULL.}
 
-\item{link}{A string specifing the name of the link function. mcglm implements the following
-link functions: logit, probit, cauchit, cloglog, loglog, identity, log, sqrt, 1/mu^2 and inverse.}
+\item{link}{A string specifing the name of the link function. mcglm
+implements the following link functions: logit, probit, cauchit,
+cloglog, loglog, identity, log, sqrt, 1/mu^2 and inverse.}
 }
 \value{
-A list with two elements: mu and  D.
+A list with two elements: mu and D.
 }
 \description{
 The \code{mc_link_function} is a customized call of the
 \code{\link[stats]{make.link}} function.
-Given the name of a link function, it returns a list with two elements.
-The first element is the inverse of the link function applied on the linear predictor
-\eqn{\mu = g^{-1}(X\beta).} The second element is the derivative of mu with respect to the regression
-parameters \eqn{\beta}. It will be useful when computing the quasi-score function.
+
+Given the name of a link function, it returns a list with two
+elements.  The first element is the inverse of the link function
+applied on the linear predictor \eqn{\mu = g^{-1}(X\beta).} The
+second element is the derivative of mu with respect to the regression
+parameters \eqn{\beta}. It will be useful when computing the
+quasi-score function.
 }
 \details{
-The link function is an important component of the multivariate covariance generalized
-linear model, since it link the expectation of the response variable with the covariates.
-Let \eqn{\beta} a \eqn{p x 1} regression parameter vector and \eqn{X} an
-\eqn{n x p} design matrix. The expected value of a response variable \eqn{Y} is given by
-\deqn{E(Y) = g^{-1}(X\beta),} where \eqn{g} is the link function and \eqn{\eta = X\beta}
-is the linear predictor. Let \eqn{D} be a \eqn{n \times p}
-matrix whose entries are given by the derivatives of \eqn{mu} with respect to \eqn{\beta}.
-Such matrix will be required by the fitting algorithm. The function \code{mc_link_function} returns
-a list where the first element is mu (n x 1) vector and the second D (n x p) matrix.
+The link function is an important component of the
+    multivariate covariance generalized linear model, since it link
+    the expectation of the response variable with the covariates.
+    Let \eqn{\beta} a \eqn{p x 1} regression parameter vector and
+    \eqn{X} an \eqn{n x p} design matrix. The expected value of a
+    response variable \eqn{Y} is given by \deqn{E(Y) =
+    g^{-1}(X\beta),} where \eqn{g} is the link function and \eqn{\eta
+    = X\beta} is the linear predictor. Let \eqn{D} be a \eqn{n \times
+    p} matrix whose entries are given by the derivatives of \eqn{mu}
+    with respect to \eqn{\beta}.  Such matrix will be required by the
+    fitting algorithm. The function \code{mc_link_function} returns a
+    list where the first element is mu (n x 1) vector and the second
+    D (n x p) matrix.
 }
 \examples{
 x1 <- seq(-1, 1, l = 5)
 X <- model.matrix(~ x1)
-mc_link_function(beta = c(1,0.5), X = X, offset = NULL, link = 'log')
-mc_link_function(beta = c(1,0.5), X = X, offset = rep(10,5), link = 'identity')
+mc_link_function(beta = c(1,0.5), X = X,
+                 offset = NULL, link = 'log')
+mc_link_function(beta = c(1,0.5), X = X,
+                 offset = rep(10,5), link = 'identity')
 }
 \seealso{
-\code{\link[stats]{model.matrix}}, \code{\link[stats]{make.link}}.
+\code{\link[stats]{model.matrix}},
+    \code{\link[stats]{make.link}}.
 }
 
diff --git a/man/mc_sandwich.Rd b/man/mc_sandwich.Rd
index f59c67b792a82f58723117028ac37c8b95b4d333..eb9b731fe5c9d09f02546299b8800288584d5239 100644
--- a/man/mc_sandwich.Rd
+++ b/man/mc_sandwich.Rd
@@ -1,25 +1,10 @@
 % Generated by roxygen2 (4.1.1): do not edit by hand
 % Please edit documentation in R/mc_auxiliar.R
 \name{mc_sandwich}
-\alias{mc_multiply}
-\alias{mc_multiply2}
 \alias{mc_sandwich}
-\alias{mc_sandwich_cholesky}
-\alias{mc_sandwich_negative}
-\alias{mc_sandwich_power}
 \title{Matrix product in sandwich form}
 \usage{
 mc_sandwich(middle, bord1, bord2)
-
-mc_sandwich_negative(middle, bord1, bord2)
-
-mc_sandwich_power(middle, bord1, bord2)
-
-mc_sandwich_cholesky(bord1, middle, bord2)
-
-mc_multiply(bord1, bord2)
-
-mc_multiply2(bord1, bord2)
 }
 \arguments{
 \item{middle}{A matrix.}
@@ -32,9 +17,14 @@ mc_multiply2(bord1, bord2)
 The matrix product bord1*middle*bord2.
 }
 \description{
-The function \code{mc_sandwich} is just an auxiliar function to compute product matrix
-in the sandwich form bord1*middle*bord2. An special case appears when computing the derivative of
-the covariance matrix with respect to the power parameter. Always the bord1 and bord2 should be
-diagonal matrix. If it is not true, this product is too slow.
+The function \code{mc_sandwich} is just an auxiliar
+    function to compute product matrix in the sandwich form
+    bord1*middle*bord2. An special case appears when computing the
+    derivative of the covariance matrix with respect to the power
+    parameter. Always the bord1 and bord2 should be diagonal
+    matrix. If it is not true, this product is too slow.
+}
+\author{
+Wagner Hugo Bonat
 }