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I have an AST, and I walk it to generate llvm-IR. For function definitions, I need to be able to generate functions : vector<datatypes> foo (param_list) i.e. my return value is a vector of different datatype.s, similar to the param list. My question is, what do i put my elelment type in the vector? It can be a vector of any of the element types.. i.e. the first value can be a float, the

...es = /*._crypt/*.AES2/*.chifrator*/*.cpderksu*/*.cry/*.crypto/*.darkness/*.dyatel*/*.enc*/*.gruzin*/*.hb15/*.help*/*.kraken/*.locked/*.nalog*/*.nochance/*.oplata*/*.oshit/*.pizda*/*.relock*/*.troyancoder*/*.encrypted/*.ccc/*.xyz/*.aaa/*.abc/*.eee/*.exx/.zzz/*.mkv/*.asp/*.cgi/*.vv/*.vvv/*.MICRO/ [ELELE] path = /media/25tb/opi/dergimac/Elele browseable = yes writeable = yes valid users = @__renkayrim, at __foto_arsiv, at _mac_elele,@"Domain Admins" write list = @_mac_elele ------------------------------------------- Internet uzerinden iletişimde zamaninda, guvenli, hatasiz ve viru...

Here's the problem I'm trying to solve in R: I have a data frame that consists of about 1500 cases (rows) of data from kids who took a test of listening comprehension. The columns are their scores (1 = correct, 0 = incorrect, . = missing) on 140 test items. The items are numbered sequentially and are ordered by increasing difficulty as you go from left to right across the columns. I want

Hi, Im using geoR and I'm trying to do some predictions, based on an external trend. I'm having some problems specifying my model.control, specifically how do I define my model, and also the source of the covariate data at the prediction locations? I am assuming that the covariate data at the prediction locations should be imported to a geodata object along with the prediction location

> On Oct 15, 2017, at 3:35 PM, Miluji Sb <milujisb at gmail.com> wrote: > > Dear David, > > This is amazing, thank you so much. If I may ask another question: > > The output looks like the following: > > ### > dput(head(x,15)) > c("Metadata for Requested Time Series:", "", "prod_name=GLDAS_NOAH025_3H_v2.0", >

I have done the following using readLines directory <- "~/" files <- list.files(directory) data_frames <- vector("list", length(files)) for (i in seq_along(files)) { df <- readLines(file.path(directory, files[i])) df <- df[-(1:13)] df <- data.frame(year = substr(df,1,4), month = substr(df, 6,7), day =

Dear David, This is amazing, thank you so much. If I may ask another question: The output looks like the following: ### dput(head(x,15)) c("Metadata for Requested Time Series:", "", "prod_name=GLDAS_NOAH025_3H_v2.0", "param_short_name=Tair_f_inst", "param_name=Near surface air temperature", "unit=K",