The relation between baseline logit and conditional logit models
In mclogit: Multinomial Logit Models for Categorical Responses and Discrete Choices

Baseline-category logit models can be expressed as particular form of conditional logit models. In a conditional logit model (without random effects) the probability that individual $i$ chooses alternative $j$ from choice set $\mathcal{S}_i$ is

$$ \pi_{ij} = \frac{\exp(\eta_{ij})}{\sum_{k\in\mathcal{S}i}\exp(\eta{ik})} $$

where

$$ \eta_{ij} = \alpha_1x_{1ij}+\cdots+\alpha_qx_{qij} $$

In a baseline-category logit model, the set of alternatives is the same for all individuals $i$ that is $\mathcal{S}_i = {1,\ldots,q}$ and the linear part of the model can be written like:

$$ \eta_{ij} = \beta_{j0}+\beta_{j1}x_{i1}+\cdots+\beta_{jr}x_{ri} $$

where the coefficients in the equation for baseline category $j$ are all zero, i.e.

$$ \beta_{10} = \cdots = \beta_{1r} = 0 $$

After setting

$$ \begin{aligned} x_{(g\times(j-1))ij} = d_{gj}, \quad x_{(g\times(j-1)+h)ij} = d_{gj}x_{hi}, \qquad \text{with }d_{gj}= \begin{cases} 0&\text{for } j\neq g\text{ or } j=g\text{ and } j=0\ 1&\text{for } j=g \text{ and } j\neq0\ \end{cases} \end{aligned} $$

we have for the log-odds:

$$ \begin{aligned} \begin{aligned} \ln\frac{\pi_{ij}}{\pi_{i1}} &=\beta_{j0}+\beta_{ji}x_{1i}+\cdots+\beta_{jr}x_{ri} \ &=\sum_{h}\beta_{jh}x_{hi}=\sum_{g,h}\beta_{jh}d_{gj}x_{hi} =\sum_{g,h}\alpha_{g\times(j-1)+h}(d_{gj}x_{hi}-d_{g1}x_{hi}) =\sum_{g,h}\alpha_{g\times(j-1)+h}(x_{(g\times(j-1)+h)ij}-x_{(g\times(j-1)+h)i1})\ &=\alpha_{1}(x_{1ij}-x_{1i1})+\cdots+\alpha_{s}(x_{sij}-x_{si1}) \end{aligned} \end{aligned} $$

where $\alpha_1=\beta_{21}$, $\alpha_2=\beta_{22}$, etc.

That is, the baseline-category logit model is translated into a conditional logit model where the alternative-specific values of the attribute variables are interaction terms composed of alternativ-specific dummes and individual-specific values of characteristics variables.

Analogously, the random-effects extension of the baseline-logit model can be translated into a random-effects conditional logit model where the random intercepts in the logit equations of the baseline-logit model are translated into random slopes of category-specific dummy variables.