Are all vectors matrices? It's pretty clear to me that all vectors are matrices (either 1 x n or n x 1).  But there is some discussion about this at work.  We're wondering whether the expression "Vectors/Matrices" in a software GUI can be shortened to "Matrices".  (The shorter the expression, the better, because of space restrictions in the GUI.)

So, is every vector a matrix or is there some subtlety in the definitions that I don't know about?
Every finite dimensional vector space of dimension $n$ is isomorphic to a vector space in which the vectors are $n$-tuples (whether written as "row vectors", $1\times n$ 'matrices', or "column vectors", $n\times 1$ matrices). That is, under a suitable interpretation, you can think of them as vectors.

However, 


It is often useful to think of the vectors differently for specific vector spaces; and
For infinite dimensional vector spaces, you cannot do it unless you extend your definition of "matrix" to include matrices that have an infinite number or rows or an infinite number of columns.


For example: consider the vector space of all polynomials of degree at most $3$ with real coefficients, with the usual polynomial addition for vector addition, and usual multiplication by real numbers as the scalar multiplication. This is a vector space in which the vectors are not matrices: they are polynomials of the form $a+bx+cx^2+dx^3$ with $a,b,c,d\in\mathbb{R}$. However, you can "think of them" as tuples $(a,b,c,d)$ because the vector operations occur "componentwise" in any case: when you add two polynomials, $a+bx+cx^2+dx^3$ and $r+sx+tx^2+ux^3$, you get $(a+r) + (b+s)x + (c+t)x^2+(d+u)x^3$; when you multiply by scalars, again each coefficient gets multiplied independently. That is, there is what we call an "isomorphism" between the vector space of all polynomials of degree at most $3$ and the vector space $\mathbb{R}^4$ that makes the polynomial $a+bx+cx^2+dx^3$ correspond to the tuple $(a,b,c,d)$; under this correspondence, the image of a sum is the sum of the images, and the image of a scalar multiple is the scalar multiple of the image (it is what we call a linear transformation, and it is both one-to-one and onto).

However, even though we can think of the polynomials as $4$-tuples (by doing the "translation" described above), there are situations where it is far more useful to think of the vectors as polynomials. For example, the function that takes a polynomial $p(x) = a+bx+cx^2+dx^3$ as an input and returns $p(3)$ as the output is a very nice function: we have that $(p+q)(3) = p(3)+q(3)$ and $(\alpha p)(3) = \alpha(p(3))$ (again, a linear transformation). It is very easy to think of it in terms of polynomials, but it is more annoying to think of it in terms of tuples. Or consider the function that sends a polynomial $p(x)$ to the polynomial you get by taking $p(x) + 3p&#39;(x) + 2p&#39;&#39;(5)$ (derivatives). Again, easy to handle as polynomials, more annoying as tuples (but certainly possible).

Or consider the collection of all continuous, differentiable functions $f\colon \mathbb{R}\to\mathbb{R}$ that satisfy the following differential equation:
$$xf&#39;&#39;(x) + \cos(x)f&#39;(x) + 2f(x) = 0.$$
The collection of all such functions forms a vector space under the usual addition and scalar multiplication of functions; but even though it is isomorphic to a vector space of tuples (in fact, to $\mathbb{R}^2$), it's better to think of it in terms of functions than in terms of tuples for purposes of actually dealing with the differential equation.

Going to point 2 above: consider the vector space of all polynomials with the usual polynomial addition. Unless you want to allow infinite tuples (which you can, but they tend to be difficult to work with), you cannot represent arbitrary polynomials with $1\times n$ matrices and still keep represent all polynomials. Or to make things even worse: consider the vector space $V$ of all functions $f\colon \mathbb{R}\to\mathbb{R}$, with pointwise addition and scalar multiplication; you would need a tuple that has an many entries as there are real numbers to represent it as a "matrix". It is far better to think of the vectors as functions, not as row or column vectors.

So, as long as you stick to finite dimensional vector spaces, the answer is "up to suitable translation, yes, you can think of all vectors as tuples". However, there are in general lots of translations and no good reason to prefer one over the other, so that you would need to be careful to specify in each case how you are translating. And in many situations, it is better to think of the vectors as they are given rather than as tuples, because the kinds of things you want to do with them are easier to do if they are polynomials/functions/other kinds of objects than if they are tuples.
to my knowledge all vectors are either nx1 or 1xm  matrices.