lgamma | Implementations of polygamma , lgamma , and beta functions | GPU library

A similar question in How to write a double for loop in r with choosing maximal element in one loop?.

The same setup:

If I want to sample theta[j] as first for j=1,2,...,71, then draw replicated( like 1000 times) yrep[k] form Bin(n[j], theta[j]), n[j] is known.

For theta[1], we have yrep[1,1], yrep[1,2], ..., yrep[1,1000]. Then for all theta[j], we will have a matrix of data set of yrep[i,j], i=1,...,71, j=1,..,1000.Then compute mean, max or min of each column yrep[1,1], yrep[1,2], yrep[1,3], ... yrep[1,71], we will get 1000 mean, max or min.

How to write this for loop?

I first try to write a loop to sample theta[j] and yrep. I do not know how to add a code to compute the maximal, mean, and minimal in this loop. I am not sure if this code is right:

I want to sample theta[j] as first for j=1,2,...,71, then draw replicated( like 1000 times) yrep[k] form Bin(n[j], theta[j]), n[j] is known.

Then for each theta[j], I have 1000 yrep. If I want to choose the maximal element among this 1000 yrep. Finally, I will have 71 maximal elements.

My for loop in R is as follows:

I'm new to GNU and I've been trying to compile a package using Autotools on Mac. After executing the command ./configure, I get the following:

I tried asking this question before and rightfully was informed I need to provide a fully working example so others can reproduce my result and then give guidance.

Here is a shortened version of my code for parsing the file. The point of readFileToStrings is to save the file into seperate strings based on the patterns [Atoms], [GTO], and [MO].

I am parsing through individual lines of code in R to get a better understanding of a large function. While I know sapply apples a function over a vector, I am having trouble understanding what it is doing in a specific instance. Unfortunately, I cannot find a clear explanation on this exact question elsewhere.

If you simulate data with the sample below code, the variable Y is a vector with 2000 values and calc_sizes is the number of unique values in Y (87 unique values). When sapply is applied to likelihoods it is done in the context of likelihoods[calc_sizes]<-sapply(calc_sizes, nb.likelihood). This returns a vector of 688 values

What is sapply doing here, and how/why does it return a vector of 688 and not 87?

To be clear, the code is functioning properly - there is no technical issue. I would just prefer to understand and learn as opposed to blindly writing code that eventually worked (and so I don't have to bug this forum so often).

You should be able to copy and paste the code verbatim to get the results mentioned. Thank you for any insight!

I'm trying to use bootstrapping to calculate the confidence interval of MLE of parameter v. But in every iteration, optim() gives me the same result. Why is this happenning?

Here's my code:

I'm writing a Lincoln-Petersen MLE function in R. I have two versions of my distribution function, one that uses the gamma function, and one that uses Rs internal dmultinom (which uses lgamma). Both return identical results when I plug in sample values, however the dmultinom version fails to provide acceptable MLE estimates. I'm curious as to what is causing this.

I am learning Perl6 from Perl5.

In order to make this compile, I'll post the whole program:

i run jq v1.6 on Windows 10,

my input is "2009-12-20 08:00:00" and i want to get the Day of Week.

my test filter is ("2009-12-20 08:00:00" | sub(" "; "T") + "Z" | fromdate | gmtime) but all i get is an error:

jq: error: strptime/1 not implemented on this platform

Tried so many things, but nothing worked.

builtins returns the relevant functions: