Looking for estimates for my data using cumulative beta distribution I'm trying to find the estimates for $alpha$ and $beta$ of the beta distribution (red curve) that will fit the black curve (empirical) better.

I have a function that finds "non-parametric" inverse cumulative function of my data (boot.mean). I would like to find a distribution that would fit. So far, I think cumulative beta distribution (alpha = beta = 2) could somehow be used. I would love to see a distribution that fits the bill better, though...
# raw data produced by a function (inverse cumulative distribution)
boot.mean <- c(37.021, 35.051, 29.091, 27.094, 22.058, 18.994, 16.944, 12.897, 7.903, 4.926, 3.939, 1.94, 1.94, 0.968)

#"fidge" (not sensu Climategate) boot.mean for comparison to qbeta
scaled <- 1 - (boot.mean/boot.mean[1])
scaled[1] <- 0.01 #dreader zero be gone
range(scaled)
 [1] 0.0100000 0.9738527

# this is the theoretical curve
x <- seq(0, 1, length = 100)
y <- qbeta(x, shape1 = 2, shape2 = 2)

# all along the x axis
x.axis <- seq(from = 0, to = 1, length = length(scaled))

# plot empirical and the theoretical values
plot(x.axis, scaled, type = "l")
lines(y, x, col = "red")

# I'm just an x-con trying to fit a distribution to my data
(beta.fitted <- MASS::fitdistr(x = scaled, densfun = qbeta, start = list(shape1 = 2, shape2 = 2)))
 Error in optim(x = c(0.01, 0.0532130412468599, 0.214202749790659, 0.268145106831258,  : 
   non-finite finite-difference value [2]
 In addition: There were 50 or more warnings (use warnings() to see the first 50)

 A: It's not a good idea to rescale the data in this ad hoc way, because it can result in an inferior fit (and ruins any chance of estimating the sampling variance of the scale parameter): just fit a scaled Beta distribution to the data themselves.
You do have to assign percentage points to the data; below I have used $p(i) = (i-1/2)/n$ for the $i^\text{th}$ smallest of $n$ values, sorted as $x_1 \le x_2 \le \cdots \le x_n$.  Fit a rescaled CDF to the empirical distribution, $\{(x_i, p_i)\}$.  The fit ideally would account for the correlations and heteroscedasticity of the values, but in this case nonlinear least squares does fine:

This particular fit is $F(x/\gamma)$ where $F$ is the CDF of a Beta($\alpha,\beta$) distribution with $\alpha=0.59$, $\beta=0.87$, and $\gamma=39.2$.  This is a U-shaped distribution (i.e., it has modes at both tails).  (The correlations and heteroscedasticity indicate that least squares confidence intervals for the parameters cannot be trusted; bootstrap them instead.  I haven't carried out the calculation and so will only report the untrustworthy standard errors: they're about $0.06$ for $\alpha$, $0.15$ for $\beta$, and $2.4$ for $\gamma$.)
Consider following this up with a goodness of fit test.  Even a simple $\chi^2$ test will give some useful hints about lack of fit.  For these data, the graph indicates this fit works pretty well, regardless.  A residual-vs.-fit plot suggests the fit is a little better at the high end of the data, but otherwise looks sufficiently random with small residuals:

This is consistent with a model in which the data have a little bit of measurement error: that would ruin the fit more where the CDF is steep (at the low values) than at other places (the middle to high values).
A: First remark : your data is nowhere near a distribution, and definitely not the beta function. As I see it, you see your boot.mean as 'density' and your x-axis (the index?) as value. The beta function is limited between 0 and 1, and as the area under the curve of any density function should equal 1, your data doesn't come close. Good point of @whuber: fit a scaled version. Alternatively: Scale to the sum of the data, as @iterator said. Now as the beta function requires scaling twice (both on the X-axis, so the indices and on the Y-axis, being the actual data)
Now you talk about the beta function and you talked about the inverse of the cumulative normal distribution somewhere else. I suppose you mean 'when that distribution is looking in the mirror, it sees what I want to see...' ;-)
So an ad-hoc way of doing this (without any theoretical background, as that background is not the one you need here), is given below. Apart from what everybody else said here, I just want to point out the optim() function, which is doing basically what you're looking for. Regardless whether you fit a scaled and mirrored beta distribution or something that looks close to an inverse normal cumulative distribution for some value of close...
customFit <- function(x, data) {
    d.data <- rev(cumsum(dnorm(1:length(data), x[1], x[2]))) * max(data)
    SS <- sum((d.data - data)^2)
    return(SS)
}

fit.optim <- optim(c(5, 8), customFit, data = boot.mean)

plot(boot.mean)
lines(rev(cumsum(dnorm(1:length(boot.mean), 
         fit.optim$par[1], fit.optim$par[2]))) * max(boot.mean), 
         col = "red")

Note of warning: apart from having a defined function that fits your data, there's little you can do with this result...
