Survival | A 2D survival game written in Unity | Game Engine library

Below you will find my python code for a class assignment I was given a couple weeks ago which I have been unable to successfully debug. The problem is about finding the value at risk (i.e., the p% quantile) for an aggregate loss random variable, using FFT. We are given a clear mathematical procedure by which we can gain an estimation of the discretized CDF of the aggregate loss random variable. My results are, however, seriously off and I am making some kind of mistake which I have been unable to find even after hours of debugging my code.

The aggregate loss random variable S is given such that S=sum(X_i for i in range(N)), where N is negative binomially distributed with r=5, beta=.2, and X_i is exponentially distributed with theta=1. The probability generating function for this parametrization is P(z)=[1-\beta(z-1)]^{-r}.

We were asked to approximate the distribution of S by

1. choosing a grid width h and an integer n such that r=2^n is the number of elements to discretize X on,
2. discretizing X and calculating the probabilities of being in equally spaced intervals of width h,
3. applying the FFT to the discretized X,
4. applying the PGF of N to the elements of the Fourier-transformed X,
5. applying the inverse FFT to this vector.

The resulting vector should be an approximation for the probability masses of each such interval for S. I know from previous methods that the 95% VaR ought to be ~4 and the 99.9% VaR ought to be ~10. But my code returns nonsensical results. Generally speaking, my index where the ECDF reaches levels >0.95 is way too late, and even after hours of debugging I have not managed to find where I am going wrong.

I have also asked this question on the math stackexchange, since this question is very much on the intersection of programming and math and I have no idea at this moment whether the issue is on the implementation side of things or whether I am applying the mathematical ideas wrong.

When I plot the survfit plot of data with two different censoring events, the overall plot (s0) doesnt start at time = 0, pstate = 100%, but jumps to 100% when the first cencoring event occurs.

Here you can see in an example, where the jump occurs at time 1, that is the first cencoring event.

I am interested in retieving machine readable meta information about R packages.

For example, when I go to CRAN I can see a short description about the package, before I download it: https://cran.r-project.org/web/packages/MASS/

I could not find any way to retrieve a different output from the CRAN server than HTML. I would like to avoid parsing HTML and instead somehow retrieve meta information about packages in a more convenient format (e.g., JSON).

I saw that each R package (at least to my knowledge) has a yaml-like (?) description text inside its source code package (the file is called DESCRIPTION). However, so far I could only find this kind of description inside tar archives, which means that I would have to download the package before I can access its description.

Here an example of the DESCRIPTION from the MASS package:

I have event data on a population level, ie for each day there is a sum of individuals with events and individuals censored. I would like to expand this data to a more traditional format for survival analysis, ie each individual get a row. So for each day, a number of rows need to added for the number of events (with events = 1 and censor = 0) and for the number of censor (with events = 0 and censor = 1). Below is an example of an input data frame (dataIn) and of the desired output.

I have a DataFrame with cash, inflows and outflows. I need to create a feature survival that is the maximum number of periods the cash is enough to pay the projection of outflows (excluding the inflows from the computation).

Let's take an example from the table below.

(Again, the inflows do not count in this exercise).
In t=1, from the starting cash = 100, I can add the outflows: -20, -50, -10, -10 e still having a positive cash (100-20-50-10-10 = 10 > 0) while with the outflow in t=5 the cash would be negative. So, as long as I can "survive" 4 periods in t=1 the survival = 4.
In t=2 the survival = 3 and so on.

As it is a big DataFrame, how can I do it efficiently with Pandas?

I am checking a few of my Cox multivariate regression analyses' proportional hazard assumptions using time-dependent co-variates, using the survival package. The question is looking at survival in groups with different ADAMTS13 levels (a type of enzyme).

Could I check if something is wrong with my code itself? It keeps saying Error in tt(TMAdata$ADAMTS13level.f) : could not find function "tt" . Why?

Notably, ADAMTS13level.f is a factor variable.

I'm currently trying to run tbl_regression with an imputed dataset from mice run through a logistic glm. I'm having trouble trying to combine the custom tidiers pool_and_tidy_mice and tidy_standardize in order to get the regression output from the pooled mice results and the standardized odds ratio estimates.

Is there a way to get the standardized odds ratios with pooling imputed data with tbl_regression or possibly another step I can take to get them?

Using the surival package as an example, I can get standardized odds ratio with non-imputed data with this code:

For example, I'm doing some survival analysis and want to fit a model:

I'm trying to create Kaplan-Meier life tables using a dplyr pipeline. I'm having trouble calculating the cumulative probability of survival without using a for loop. Here is some example data.