checkRd: (-1) AMME.Rd:10: Lost braces
    10 | The second model is a cross-sectional or longitudinal macro model (i.e., a model where the unit of analysis is a node, subgraph, or network or a combination of nodes, subgraphs, and networks measured collected from multiple settings [such as distinct schools or organizations]) of the form \eqn{g(Y_i|M_{i},X_{i},T_{i})}, where \eqn{Y_i} is the outcome variable, \eqn{M_i} is the mediating macro variable,  \eqn{X_i} is a matrix of control variables that possibly vary as a function of selection process \eqn{T_{ij}}, and \eqn{T_i} is the optional unit-level measure of {T_{ij}}. The AMME is the change in \eqn{Y_i} when \eqn{T_{ij}} allowed to vary versus set to 0 because of an associated change in \eqn{M_i}. The AMME is given by \deqn{AMME=\frac{1}{2n} y_i(T_i(t),M_i(T_{ij}),X_i(t))-y_i(T_i(t),M_i(0),X_i(t))}, where \eqn{n} is the number of observations and \eqn{t=0,T_{ij}}. AMME currently accepts the following micro models: \code{glm}, \code{glmer}, \code{ergm}, \code{btergm}, \code{sienaFit}, \code{rem.dyad}, and \code{netlogit} objects. The following macro model objects are accepted: \code{lm}, \code{glm}, \code{lmer}, \code{glmer}, \code{gam}, \code{plm}, and \code{lnam} objects. Pooled estimation for multiple network models is also implemented for \code{ergm} and \code{sienaFit} micro models. Both parametric and nonparametric estimation are available.
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checkRd: (-1) AMME.Rd:10: Lost braces; missing escapes or markup?
    10 | The second model is a cross-sectional or longitudinal macro model (i.e., a model where the unit of analysis is a node, subgraph, or network or a combination of nodes, subgraphs, and networks measured collected from multiple settings [such as distinct schools or organizations]) of the form \eqn{g(Y_i|M_{i},X_{i},T_{i})}, where \eqn{Y_i} is the outcome variable, \eqn{M_i} is the mediating macro variable,  \eqn{X_i} is a matrix of control variables that possibly vary as a function of selection process \eqn{T_{ij}}, and \eqn{T_i} is the optional unit-level measure of {T_{ij}}. The AMME is the change in \eqn{Y_i} when \eqn{T_{ij}} allowed to vary versus set to 0 because of an associated change in \eqn{M_i}. The AMME is given by \deqn{AMME=\frac{1}{2n} y_i(T_i(t),M_i(T_{ij}),X_i(t))-y_i(T_i(t),M_i(0),X_i(t))}, where \eqn{n} is the number of observations and \eqn{t=0,T_{ij}}. AMME currently accepts the following micro models: \code{glm}, \code{glmer}, \code{ergm}, \code{btergm}, \code{sienaFit}, \code{rem.dyad}, and \code{netlogit} objects. The following macro model objects are accepted: \code{lm}, \code{glm}, \code{lmer}, \code{glmer}, \code{gam}, \code{plm}, and \code{lnam} objects. Pooled estimation for multiple network models is also implemented for \code{ergm} and \code{sienaFit} micro models. Both parametric and nonparametric estimation are available.
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