# This is currently a test

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The statistical model estimated the probability, {tex \pi_{i}}, of capturing dolphins on a tow, {tex i}. A year effect, {tex logit(\pi_i) = \lambda_{j[i]} + \sum_c \beta_c x_{ic}.} was estimated for each year, {tex j}, allowing for annual variation in the capture event rates that was unrelated to the covariates, {tex x}. The contribution of each covariate, indexed by {tex c}, was governed by a regression coefficient, {tex \beta_c}, that was estimated by the model. The logit transform of the capture event probability was defined as the sum of the year effect, {tex \lambda}, and the covariates:

katex {{{
logit(\pi_i) = \lambda_{j[i]} + \sum_c \beta_c x_{ic}.
}}}
determines the result.

# The following is more tests

katex {{{
f(x) = \int_{-\infty}^\infty
    \hat f(\xi)\,e^{2 \pi i \xi x}
    \,d\xi 
}}}

katex {{{
\begin{bmatrix}
    a & b \\
    c & a 
    \end{bmatrix}
}}}

katex {{{
\begin{bmatrix}
    a & c \\
    c & a 
    \end{bmatrix}
}}}