code stringlengths 1 13.8M |
|---|
vpc_cens <- function(sim = NULL,
obs = NULL,
psn_folder = NULL,
bins = "jenks",
n_bins = 8,
bin_mid = "mean",
obs_cols = NULL,
sim_cols = NULL,
software... |
PV_pre_triang_dis=function(data,years=10){
app=rep(NA,years)
for(i in 1:years) app[i]=triangular_moments_dis_U(data,i)
PV=1+sum(app[1:years-1])
return(PV)
} |
do.dne <- function(X, label, ndim=2, numk=max(ceiling(nrow(X)/10),2),
preprocess=c("center","scale","cscale","decorrelate","whiten")){
aux.typecheck(X)
n = nrow(X)
p = ncol(X)
ndim = as.integer(ndim)
if (!check_ndim(ndim,p)){stop("* do.dne : 'ndim' is a positive integer in [1,
... |
structure <- function (.Data, ...)
{
if(is.null(.Data))
warning("Calling 'structure(NULL, *)' is deprecated, as NULL cannot have attributes.\n Consider 'structure(list(), *)' instead.")
attrib <- list(...)
if(length(attrib)) {
specials <- c(".Dim", ".Dimnames", ".Names", ".Tsp", ".... |
library(bmp)
posey <- c(30, 167, 332, 457, 822, 1016, 1199,
1437, 1621, 1770, 1924, 2101, 2251, 2442, 2594,
2757, 2918, 3072, 3205, 3356, 3526, 3685,
4068, 4217)
susac <- c(751, 1286, 1485, 1666, 2030,
2187)
v <- list.files("buster_posey_catching/")
v <- v[order(as.numeric(unlist(lapply(strsplit(v, split="i"), "[[", 1)... |
QA_Results <- data.table::CJ(
Group = c(0, 1, 2, 3),
xregs = c(0, 1, 2, 3),
Trans = c(TRUE, FALSE),
Training = "Failure",
Forecast = "Failure"
)
for (run in seq_len(QA_Results[, .N])) {
if (QA_Results[run, Group] == 0) {
groupvars <- NULL
ModelData <- data.table::fread(file = file.path("C:/Users/... |
catEffectBootAdaptor<-function (df, index, testFnc = sumSqCat, useResp = TRUE, ...) {
if (useResp) respVal <- df$resp
else respVal <- df$bkg
testFnc(respVal[index], df$cat, ...)
} |
count_levels_num <- function(x) {
.Call('_inspectdf_count_levels_num', PACKAGE = 'inspectdf', x)
}
count_levels_char <- function(x) {
.Call('_inspectdf_count_levels_char', PACKAGE = 'inspectdf', x)
}
na_numeric <- function(x) {
.Call('_inspectdf_na_numeric', PACKAGE = 'inspectdf', x)
}
na_character <- funct... |
T <- 50
m <- 10
P <- 5
H <- 2
N_min <- 20
X <- rnorm(T)
mspe <- MSPE(X, m1 = T - m + 1, m2 = T, P = P, H = H, N = c(0, N_min:(T-m-H)))
N <- mspe$N
M <- mspe$mspe
h <- 1
plot(mspe, h, N_min = N_min, legend = (h == 1))
idx1_s <- which(M[h, , N == 0] == min(M[h, , N == 0]), arr.ind = TRUE)[1]
abline(h = M[h, idx1_s, N == ... |
bhl_getunpublisheditems <- function(...) {
.Defunct(package = "rbhl", msg = "API method removed")
} |
if (requiet("testthat") &&
requiet("insight") &&
requiet("robustbase")) {
data(mtcars)
m1 <- lmrob(mpg ~ gear + wt + cyl, data = mtcars)
test_that("model_info", {
expect_true(model_info(m1)$is_linear)
})
test_that("find_predictors", {
expect_identical(find_predictors(m1), list(conditional = c("gea... |
fit_clutter <- function(df, age, dh, basal_area, volume, site, plot, .groups=NA, model = "full", keep_model = FALSE){
basal_area2<-basal_area1<-I1<-I2<-volume2<-.<-Reg<-NULL
if( missing(df) ){
stop("df not set", call. = F)
}else if(!is.data.frame(df)){
stop("df must be a dataframe", call.=F)
... |
wait_slurm <- function(x, ...) UseMethod("wait_slurm")
wait_slurm.slurm_job <- function(x, ...) {
wait_slurm.integer(get_job_id(x), ...)
}
wait_slurm.integer <- function(x, timeout = -1, freq = 0.1, force = TRUE, ...) {
if (opts_slurmR$get_debug()) {
warning("waiting is not available in debug mode.", call. =... |
test_that("compare state works correctly", {
loc <- tempfile("watcher")
dir.create(loc)
empty <- dir_state(loc)
expect_equal(length(empty), 0)
file.create(file.path(loc, "test-1.txt"))
one <- dir_state(loc)
expect_equal(length(one), 1)
expect_equal(basename(names(one)), "test-1.txt")
diff <- compare_s... |
AutoLightGBMClassifier <- function(
data = NULL,
TrainOnFull = FALSE,
ValidationData = NULL,
TestData = NULL,
TargetColumnName = NULL,
... |
.build_client <-
function(api,
encode,
version = NULL,
progress = NULL,
pat = getOption("osfr.pat")) {
api <- match.arg(api, c("osf", "wb"))
encode <- match.arg(encode, c("form", "multipart", "json", "raw"))
server <- Sys.getenv("OSF_SERVER")
url <- switch(api,
... |
BLOSUM62<-function(seqs,label=c(),outFormat="mat",outputFileDist=""){
path.pack=system.file("extdata",package="ftrCOOL")
if(length(seqs)==1&&file.exists(seqs)){
seqs<-fa.read(seqs,alphabet="aa")
seqs_Lab<-alphabetCheck(seqs,alphabet = "aa",label)
seqs<-seqs_Lab[[1]]
label<-seqs_Lab[[2]]
}
else i... |
list2matrix.bas <- function(x, what, which.models = NULL) {
namesx <- x$namesx
if (is.null(which.models)) which.models <- 1:x$n.models
listobj <- x[[what]][which.models]
which <- x$which[which.models]
n.models <- length(which.models)
p <- length(namesx)
mat <- matrix(0, nrow = n.models, ncol = p)
for (i... |
context("elevation utils")
testthat::skip_on_cran()
raster_poa <- system.file("extdata/poa/poa_elevation.tif", package = "r5r")
data_path <- system.file("extdata/poa", package = "r5r")
r5r_core <- setup_r5(data_path = data_path, temp_dir = TRUE)
test_that("tobler_hiking", {
expect_error( tobler_hiking('bananas') )
... |
bbase.os <-
function(x, K, bdeg = 3, eps = 1e-5, intercept = TRUE) {
B <- bs(x, degree = bdeg, df = K + bdeg, intercept = intercept)
B
} |
tcplot <- function (data, u.range, cmax = FALSE, r = 1,
ulow = -Inf, rlow = 1, nt = 25, which = 1:npar, conf = 0.95,
lty = 1, lwd = 1, type = "b", cilty = 1, ask = nb.fig < length(which) &&
dev.interactive(), ...){
n <- length(data)
data <- sort(data)
if (missing(u.range)) {
u.range... |
library(plyr)
suppressPackageStartupMessages(library(dplyr))
library(ggplot2)
library(readr)
gap_dat <- read_tsv("05_gap-merged-with-china-1952.tsv") %>%
mutate(country = factor(country),
continent = factor(continent))
gap_dat %>% str()
gap_dat %>%
sapply(function(x) x %>% is.na() %>% sum())
gap_dat$year... |
"sdtm_ae"
"sdtm_cm"
"sdtm_dm"
"sdtm_ds"
"sdtm_ex"
"sdtm_lb"
"sdtm_mh"
"sdtm_qs"
"sdtm_relrec"
"sdtm_sc"
"sdtm_se"
"sdtm_suppae"
"sdtm_suppdm"
"sdtm_suppds"
"sdtm_supplb"
"sdtm_sv"
"sdtm_ta"
"sdtm_te"
"sdtm_ti"
"sdtm_ts"
"sdtm_tv"
"sdtm_vs" |
tree_add_dates <- function(dated_tree = NULL,
missing_taxa = NULL,
dating_method = "mrbayes",
adding_criterion = "random",
mrbayes_output_file = "mrbayes_tree_add_dates.nexus") {
dated_tree <- tree_check(tree =... |
"dataAGGR" |
library(amt)
data(amt_fisher)
set.seed(123)
tr <- make_trast(amt_fisher[1:50, ], res = 5)
mini_fisher <- amt_fisher[1:40, ]
mcp <- hr_mcp(mini_fisher)
loc <- hr_locoh(mini_fisher)
kde <- hr_kde(mini_fisher)
mini_fisher1 <- amt_fisher[11:50, ]
mcp1 <- hr_mcp(mini_fisher1)
loc1 <- hr_locoh(mini_fisher1)
kde1 <- hr_kde(mi... |
brute_IDs <- function(total.length, redundancy, alphabet, num.tries = 10, available.colors = NULL) {
if (missing(alphabet)) {
stop("Error: you need to enter an 'alphabet size,' e.g. the number of paint colors you have")
}
if (missing(total.length)) {
stop("Error: you need to enter the total length of the ... |
context("ashr with half-uniform mixture priors")
test_that("mixcompdist=+uniform gives all non-negative values for b and zero for a", {
set.seed(1); z=rnorm(10); z.ash=ash(z,1,mixcompdist="+uniform")
k = length(z.ash$fitted_g$pi)
expect_true(all(z.ash$fitted_g$b >= rep(0,k)))
expect_equal(z.ash$fitted_g$a,rep(0... |
women
names(women)
height
attach(women)
height
weight
women$height
g <- "My First List"
h <- c(25, 26, 18, 39)
j <- matrix(1:10, nrow=5)
k <- c("one", "two", "three")
mylist <- list(title=g, ages=h, j, k, women)
mylist
mylist[[2]]
mylist[[5]]
plot(x=height, y=weight, type='b', lty=5, pch=11, fg='red', bg='green',... |
stratsamp <- function(n, distribution, parameters, p) {
lims <- find_strata(p, distribution, parameters)
outmat <- matrix(data = NA, nrow = n, ncol = length(p)-1)
counts <- rep(0, length(lims)-1)
while (any(counts < n)) {
r <- distribution_sampling(1, distribution, parameters)
intvl <- fi... |
context("mlc")
suppressPackageStartupMessages(library(caret))
set.seed(1)
mat <- matrix(rnorm(300), ncol = 3, nrow = 100)
colnames(mat) <- letters[1:3]
y <- sample(factor(c("a", "b")), 100, replace = TRUE)
test_that("fit mlc",{
expect_is( mr <- mlc(mat,y), "list")
expect_equal(names(mr), c("a", "b",... |
semprobit <- function(formula, W, data, subset, ...) {
cl <- match.call()
mf <- match.call(expand.dots = FALSE)
m <- match(c("formula", "data", "subset"), names(mf), 0L)
mf <- mf[c(1L, m)]
mf$drop.unused.levels <- TRUE
mf[[1L]] <- as.name("model.frame")
mf <- eval(mf, paren... |
tar_resources_url <- function(
handle = NULL
) {
out <- resources_url_init(
handle = handle
)
resources_validate(out)
out
} |
export_amira.path<-function(vertices,filename,Lines=c(1:(dim(vertices)[1]-1)-1,-1),path)
{
while(tolower(paste(filename,".am",sep=""))%in%list.files(path)!=FALSE){
i<-1
filename<-paste(filename,"(",i,")",sep="")
i<-i+1
}
cat(paste("
file = paste(path, "/", filename, ".am", sep = ""),
append =... |
test_that("translations", {
expect_identical(pow(5, 2), 25)
expect_identical(phi(0), 0.5)
expect_equal(phi(2), 0.9772499, tolerance = 0.0000001)
x <- NA
log(x) <- log(5)
expect_equal(x, 5)
expect_equal(logit(0.5), 0)
expect_equal(logit(1), Inf)
x <- NA
logit(x) <- logit(0.75)
expect_equal(x, 0.75)... |
read.cross.csv <-
function(dir, file, na.strings=c("-","NA"),
genotypes=c("A","H","B","D","C"),
estimate.map=TRUE, rotate=FALSE, ...)
{
if(missing(file)) file <- "data.csv"
if(!missing(dir) && dir != "") {
file <- file.path(dir, file)
}
args <- list(...)
if... |
gbm.perf <- function(object,
plot.it=TRUE,
oobag.curve=FALSE,
overlay=TRUE,
method,
main="") {
if(!is.logical(plot.it) || (length(plot.it)) > 1 || is.na(plot.it))
stop("plot.it must be a logical - exclud... |
test_that("gives warning markers are not correct", {
expect_warning(style_text(c(
"1+1",
"
"
)))
})
test_that("trailing spaces are stripped when checking marker and written back", {
expect_equal(
style_text(c(
"
"1+1",
"
)) %>%
as.character(),
c("
)
})
test_that("... |
genscorestat<-function(scores,group,correct=0){
N<-length(group); MV<-table(group)
refg<-names(MV)[1]
if(is.numeric(group)) refg<-as.numeric(refg)
if(length(MV)!=2){
message("genscorestat works only for two groups")
out<-NA
}else{
abar<-mean(scores)
ahat<-mean(scores^2)
vv<-... |
"dccm.nma" <-
function(x, nmodes=NULL, ncore=NULL, progress = NULL, ...) {
nma <- x
if (missing(nma))
stop("dccm.nma: must supply a 'nma' object, i.e. from 'nma'")
if(!"nma" %in% class(nma))
stop("dccm.nma: must supply 'nma' object, i.e. from 'nma'")
ncore <- setup.ncore(ncore, bigmem... |
stat.entropyFunction = function(bitString) {
pT = sum(bitString)/length(bitString)
pF = 1-pT
if (pT==1 || pT==0) {
e = 0
} else {
e = -pT*log2(pT)-pF*log2(pF)
}
return(e)
} |
"ChickWeight" <-
structure(list(
weight = c(42, 51, 59, 64, 76, 93, 106, 125, 149,
171, 199, 205, 40, 49, 58, 72, 84, 103, 122, 138, 162, 187, 209,
215, 43, 39, 55, 67, 84, 99, 115, 138, 163, 187, 198, 202, 42,
49, 56, 67, 74, 87, 102, 108, 136, 154, 160, 157, 41, 42, 48,
60, 79, 106, 141, 164,... |
NULL
dcmp = function(x, lambda, nu, log = FALSE)
{
prep = prep.zicmp(length(x), lambda, nu)
dcmp_cpp(x, prep$lambda, prep$nu, take_log = log)
}
rcmp = function(n, lambda, nu)
{
prep = prep.zicmp(n, lambda, nu)
ymax = getOption("COMPoissonReg.ymax")
rcmp_cpp(n, prep$lambda, prep$nu, ymax = ymax)
}
pcmp = function(x... |
groupAndRename <- function(obj, var, before, after, addNA=FALSE) {
groupAndRenameX(obj=obj, var=var, before=before, after=after, addNA=addNA)
}
setGeneric("groupAndRenameX", function(obj, var, before, after, addNA=FALSE) {
standardGeneric("groupAndRenameX")
})
setMethod(f="groupAndRenameX", signature=c("factor"),
d... |
ans <- pl(amount = c(1, -1),
timestamp = c(2, 3),
price = c(1, 2),
along.timestamp = 3:1,
vprice = 1:3)
expect_equal(ans[[1]]$timestamp , 1:3)
expect_equal(unname(ans[[1]]$pl), c(0,1,1)) |
REND <- function(TPDc = NULL, TPDs = NULL){
if (is.null(TPDc) & is.null(TPDs)) {
stop("At least one of 'TPDc' or 'TPDs' must be supplied")
}
if (!is.null(TPDc) & class(TPDc) != "TPDcomm"){
stop("The class of one object do not match the expectations,
Please, specify if your object is a TPDc or a TP... |
r1sd <- function(x, na = TRUE) {
return((x - mean(x, na.rm = na)) / (1 * sd(x, na.rm = na)))
} |
QRSimul <-
function(VecX, tau, times, subj, X, y, d, kn, degree, lambda, gam){
dim = length(subj)
X = matrix(X, nrow=dim)
H = length(tau)
px = ncol(X)
n = length(unique(subj))
if(px != length(VecX))
stop("the length of VecX and the number of covariate(s) must match")
XX = as.matrix(X)
if(all(X[,1]==1)) VecX[1]=1 else V... |
writeEnvelope <- function(obj, centerfun = mean) {
if(inherits(obj, c("SpatialPoints", "SpatialPointsDataFrame"), which = FALSE)) {
SpatialPointsEnvelope(obj)
} else if(inherits(obj, "list") && length(obj) > 0 &&
all(vapply(
X = obj,
FUN = inherits,
FUN.VALU... |
session <- function(url, ...) {
session <- structure(
list(
handle = httr::handle(url),
config = c(..., httr::config(autoreferer = 1L)),
response = NULL,
url = NULL,
back = character(),
forward = character(),
cache = new_environment()
),
class =... |
context('Test the creation of custom indicators')
data(forestgap)
data(serengeti)
datasets <- list(forestgap[3:4],
forestgap[1:2])
test_methods <- function(teststring, datalength, obj) {
ok_print <- any(grepl(teststring, capture.output(print(obj))))
expect_true(ok_print)
ok_summary <- any... |
testthat::context("H2O AUTOML TEST")
test_that("Fire up H2O", {
testthat::skip_on_cran()
h2o.init(
nthreads = -1,
ip = 'localhost',
port = 54321
)
model_spec <<- automl_reg(mode = 'regression') %>%
set_engine(
engine = 'h2o',
max_runtime_sec... |
library(pcalg)
amat1 <- t(cbind(c(0,1,0,1,0),c(0,0,1,0,1),c(0,0,0,1,1),c(0,0,0,0,1),c(0,0,0,0,0)))
amat2 <- t(cbind(c(0,1,0,1,1),c(0,0,0,0,1),c(0,0,0,0,1),c(0,0,0,0,1),c(0,0,0,0,0)))
g1 <- as(amat1,"graphNEL")
g2 <- as(amat2,"graphNEL")
res <- compareGraphs(g1,g2)
if ((round(res["tpr"],5)!=0.83333) | (round(res["fpr"],... |
skip_tests_for_cran <- TRUE
skip_maxnet <- FALSE
skip_maxent.jar <- TRUE
skip_bioclim <- TRUE
skip_simDiff <- TRUE
library(dplyr)
options(warn=-1)
set.seed(48)
occs <- read.csv(file.path(system.file(package="dismo"), "/ex/bradypus.csv"))[,2:3]
envs.orig <- raster::stack(list.files(path=paste(system.file(package='dismo'... |
MCpriorIntFun <-
function(Nsim=200,
prior,
Hpar,
dimData,
FUN=function(par,...){as.vector(par)},
store=TRUE,
show.progress = floor(seq(1, Nsim, length.out = 20 ) ),
Nsim.min=Nsim,
precision = 0,
...)
{
start.time=proc.time()
no... |
NULL
"ghp100k" |
kkmeans <- function(K, parameters) {
state <- list()
state$time <- system.time({
H <- eigen(K, symmetric = TRUE)$vectors[, 1:parameters$cluster_count]
objective <- sum(diag(t(H) %*% K %*% H)) - sum(diag(K))
H_normalized <- H/matrix(sqrt(rowSums(H^2, 2)),
... |
translogEla <- function( xNames, data, coef, coefCov = NULL,
dataLogged = FALSE ) {
checkNames( c( xNames ), names( data ) )
nExog <- length( xNames )
nCoef <- 1 + nExog + nExog * ( nExog + 1 ) / 2
if( nCoef > length( coef ) ) {
stop( "a translog function with ", nExog, " exogenous variables",
... |
.init_base_test_templ <- function() {
templ_dir <- file.path(get_templ_dir(), "BaseTestProjectTemplate")
if (dir.exists(templ_dir)) {
return(templ_dir)
}
unzip(file.path("data", "BaseTestProjectTemplate.zip"), exdir = get_templ_dir())
build_prj <- RSuite::prj_start("BaseTestProjectBuild", skip_rc = TRUE, ... |
getGVGenotype <- function(ped) {
if (hasGenotype(ped)) {
genotype <- ped[ , c("id", "first", "second")]
} else {
genotype <- NULL
}
genotype
} |
NULL
kernel <- setClass('kernel',
representation(type='character', kernel='matrix',
pathway='pathway'))
setValidity('kernel', function(object){
msg <- NULL
valid <- TRUE
if( !isSymmetric(round(object@kernel),10) ){
... |
LModularity <- function(cor.matrix, method = optimal.community, ...){
if(any(cor.matrix < 0)){
warning("Some correlations are negative. Using squared correlations.")
cor.matrix = cor.matrix^2
}
g = graph.adjacency(cor.matrix, weighted = TRUE, mode = 'undirected')
comm = method(g, ...)
modules = uniq... |
LBRecap.custom.part=function(
data,
last.column.count=FALSE,
partition,
neval=1000,
by.incr=1,
output=c("base","complete")){
output=match.arg(output)
if(!(any(c("data.frame","matrix","array","table") %in% class(data)))){
stop("input data must be a data.frame or a matrix object or an array")
}
data.matrix=... |
plot.ref.grid = function(x, ...) {
plot(x = as(x, "emmGrid"), ...)
}
ref.grid = function(...) {
.Deprecated(new = "ref_grid", old = "ref.grid", package = "emmeans")
ref_grid(...)
}
recover.data = function(object, ...)
UseMethod("recover.data")
recover.data.call = function(...)
recover_data.call(...... |
distNumeric <- function(x, y, method = "mrw", xyequal = TRUE) {
if((is.matrix(x)&&is.matrix(x))==FALSE)
stop("x and y must be a matrix object!")
if(ncol(x)!=ncol(y))
stop(sQuote("x")," and ",sQuote("y"),
" must have the same number of columns")
if (xyequal == TRUE) {
span <- apply(x, 2, funct... |
hullArea <- function (x,y) {
ne <- length(x)
harea <- abs (0.5 * ( (x[1:(ne-1)] %*% y[2:ne]) - ( y[1:(ne-1)] %*% x[2:ne]) ) )
harea
} |
loadNamespace("fields")
loadNamespace("graphics")
loadNamespace("ggplot2")
loadNamespace("hash")
map.build <- function(data,
labels=NULL,
xdim=10,
ydim=5,
alpha=.3,
train=1000,
normalize=F... |
roc_aunu <- function(data, ...) {
UseMethod("roc_aunu")
}
roc_aunu <- new_prob_metric(
roc_aunu,
direction = "maximize"
)
roc_aunu.data.frame <- function(data,
truth,
...,
options = list(),
... |
getAlbumInfo<-function(album_id,token){
req<-httr::GET(paste0("https://api.spotify.com/v1/albums/",album_id),httr::config(token = token))
json1<-httr::content(req)
json2<-jsonlite::fromJSON(jsonlite::toJSON(json1))
df <- data.frame("id" = json2$id, "artist" = as.character(json2$artists$name),"name" = json2$name... |
lsv <- function(data,k1,p = 6,q = 0,interval = c(0.001,0.999)) {
n <- length(data)
k <- 1:k1
sdrunning <- function(kscale) {
sd(running(data,fun = sum,width = kscale,by = kscale))
}
s <- sapply(k,sdrunning)
d1 <- sum((s^4)/(k^p))
g2 <- function(H) {
ckH <-... |
add.column <- function(lprec, x, indices)
{
if(missing(indices)) {
if(length(x) != dim(lprec)[1])
stop("the length of ", sQuote("x"), " is not equal to the number of ",
"constraints in the model")
epsel <- .Call(RlpSolve_get_epsel, lprec)
indices <- which(abs(x) > epsel)
x <- x[indic... |
higherMomentsIV <- function(formula, data, verbose=TRUE){
cl <- match.call()
check_err_msg(checkinput_highermomentsiv_formula(formula=formula))
check_err_msg(checkinput_highermomentsiv_data(data=data))
check_err_msg(checkinput_highermomentsiv_formulaVSdata(formula=formula, data=data))
check_err_msg(checkin... |
NULL
fclust_read <- function(filename = "") {
if (nchar(filename) == 0) stop("'filename' cannot be an empty string")
tmp <- c("nbElt", "nbAss", "nbXpr",
"opt.method", "opt.mean", "opt.model",
"opt.jack", "jack",
"opt.na", "opt.repeat", "affectElt",
"fobs", "mOccur", "xpr"... |
gh_whoami <- function(.token = NULL, .api_url = NULL, .send_headers = NULL) {
.token <- .token %||% gh_token(.api_url)
if (isTRUE(.token == "")) {
message("No personal access token (PAT) available.\n",
"Obtain a PAT from here:\n",
"https://github.com/settings/tokens\n",
"For ... |
.viridisOpts <- function (n, alpha = 1, begin = 0, end = 1, option = "D",...)
{
if (begin < 0 | end < 0 | begin > 1 | end > 1) {
stop("begin and end must be in [0,1]")
}
option <- switch(option, A = "A", magma = "A", B = "B", inferno = "B",
C = "C", plasma = "C", D = "D", viridis = "D", {... |
get_committee_by_name <- function(NAME, cycle=2018, page = 1, myAPI_Key){
API = 'campaign-finance'
if(!validate_cycle(cycle))
stop("Incorrect cycle")
if(is.character(NAME)){
NAME <- gsub(' ', '%20', NAME)
query <- sprintf("%s/committees/search.json?query=%s", cycle, NAME)
pp_query(query, API, page... |
activity_id <- function(x) {
UseMethod("activity_id")
}
activity_id.eventlog <- function(x){
return(attr(x, "activity_id"))
}
activity_id.eventlog_mapping <- function(x) {
return(x$activity_identifier)
}
activity_id.activitylog <- function(x){
return(attr(x, "activity_id"))
}
activity_id.activitylog_mapping <- func... |
SHASH <- function (mu.link="identity", sigma.link="log", nu.link ="log", tau.link="log")
{
mstats <- checklink( "mu.link", "Sinh-Arcsinh", substitute(mu.link),
c("inverse", "log", "identity", "own"))
dstats <- checklink("sigma.link", "Sinh-Arcsinh", substitute(sigma.link),
... |
proximity_builder <- function(im.res, neighborhood = "ar1",
type = c("sparse", "full"),
weight = "binary", phi = 1,
r = NULL, h = NULL, w = NULL,
include.coords = FALSE,
... |
.getPQ <- function(X, V, Lambda, Sigma, Psi) {
p <- NA
q <- NA
if(!.minu(X)) {
if(is.vector(X)) {
p <- 1
q <- 1
} else {
p <- dim(X)[1]
q <- dim(X)[2]
}
}
if(!.minu(V)) {
if(is.na(q)) {
q <- ifelse(is.vector(V), 1, dim(V)[1])
}
}
if(!.minu(Lambda)) {
i... |
round_df_char <- function(df, digits, pad = " ", na_vals = NA) {
nas <- is.na(df)
if (!is.data.frame(df)) {
df <- as.data.frame.matrix(df, stringsAsFactors = FALSE)
}
rn <- rownames(df)
cn <- colnames(df)
df <- as.data.frame(lapply(df, function(col) {
if (suppressWarnings(all(!is.... |
Qstat.reg.sb = function(DATA1, DATA2, vecA, Psize, gamma, Bsize, sigLev)
{
Tsize = nrow(DATA1)
Nsize = Tsize - Psize
vecQ.BP = matrix(0,Psize,1)
vecQ.LB = matrix(0,Psize,1)
vecCRQ = crossqreg.max(DATA1, DATA2, vecA, Psize)
for (k in 1:Psize){
... |
plots <- function(...,
n_rows = NULL,
n_columns = NULL,
guides = NULL,
tags = FALSE,
tag_prefix = NULL,
tag_suffix = NULL,
tag_sep = NULL,
title = NULL,
subti... |
"model.comp.bayes.parobs" <- function(object, type="lpml", verbose=FALSE, ncores=NULL) {
nkeep <- object$mcmc$nkeep
Sigmahat <- apply(object$mcmc.draws$Sigma, c(1,2), mean)
Omegahat <- apply(object$mcmc.draws$Omega, c(1,2), mean)
thetahat <- rowMeans(object$mcmc.draws$theta)
if (!is.null(ncores)) {
ncores_ <- ... |
copy_filt <- function(abund,threshold){
if(is.null(dim(abund)) == FALSE){
sapply(1:ncol(abund), function(colnum){temp = abund[,colnum]
if(threshold == round(threshold)){
rownums = which(temp < threshold)
}else{
rownums = which(temp < sum(temp)*threshold)
}
abund[rownums, colnum] <... |
shiftid <- function (e_ij1, e_ij2, e_i1, e_i2, time1, time2,
industry.names = NULL,
shift.method = "Dunn",
gerfin.shifts = "mean",
print.results = TRUE,
plot.results = FALSE, plot.colours = NULL, plot.title = NULL... |
do.findmain <- function (
ramclustObj = NULL,
cmpd = NULL,
mode = "positive",
mzabs.error = 0.005,
ppm.error = 10,
ads = NULL,
nls = NULL,
scoring = "auto",
plot.findmain = TRUE,
writeMat = TRUE,
writeMS = TRUE,
use.z = TRUE)
{
if(is.null(ramclustObj)) {
stop("must supply ramc... |
PrintToPDF <- function(Path,
OutputName,
ObjectList = NULL,
Tables = FALSE,
MaxPages = 500,
Title = "Model Output",
Width = 12,
Height = 7,
... |
library(testthat)
test_that("it returns correct data", {
r <- ptd_spc_options(
value_field = "value_field",
date_field = "date_field",
facet_field = "facet_field",
rebase = as.Date("2020-01-01"),
fix_after_n_points = NULL,
improvement_direction = "increase",
target = 1,
trajectory = "t... |
ergm.estimate<-function(init, model, statsmatrices, statsmatrices.obs=NULL,
epsilon=1e-10, nr.maxit=1000, nr.reltol=sqrt(.Machine$double.eps),
metric="lognormal",
method="Nelder-Mead",
calc.mcmc.se=TRUE, hessianflag=TRUE,
... |
1:4 %*% 1:4
sum(1:4 * 1:4) |
wod_4hist_k_p <- function( obs_index, surv.object, covariate.data , nruns , m ) {
actual_data <- cbind( covariate.data , surv.object[,1], surv.object[,2] )
time_index <- ncol(actual_data) - 1
status_index <- ncol(actual_data)
data_length <- nrow(actual_data)
obs_influences <- rep(... |
summarizeadd2 <-
function(abo,probe.effects){
inds.all = indexProbes(abo,which="pm")
if(dim(Biobase::exprs(abo))[2] != 1) stop("\n error: to many chips \n\n")
if(sum( names(inds.all) == names(probe.effects)) != length(probe.effects)) {
reorderind <- as.numeric(factor(names(probe.e... |
library(buildmer)
library(testthat)
test_that('build.formula',{
form1 <- Reaction ~ Days + (Days|Subject)
terms <- tabulate.formula(form1)
form2 <- build.formula(dep='Reaction',terms)
library(lme4)
check <- function (f) resid(lmer(f,sleepstudy))
expect_equal(check(form1),check(form2))
}) |
make_true_parameter_MRMC <- function(StanS4class) {
f <- StanS4class
z <- extract_EAP_CI(f,"z",f@dataList$C )$z.EAP
dz <- extract_EAP_CI(f,"dz",f@dataList$C-1 )$dz.EAP
mu <- extract_EAP_by_array(f,mu)
v <- extract_EAP_by_array(f,v)
} |
predictLKrigFixedFunction <- function(object, xnew=NULL, Znew = NULL,
drop.Z = FALSE,
collapseFixedEffect = FALSE){
if( is.null(xnew)){
xnew<- object$x
}
nt<- object$nt
nZ<- object$nZ
ind.drift<- c( rep( TRUE, (nt-nZ) ), rep(... |
setMethodS3("segmentByHaarSeg", "RawGenomicSignals", function(this, ..., cache=FALSE, force=FALSE, verbose=FALSE) {
verbose <- Arguments$getVerbose(verbose)
if (verbose) {
pushState(verbose)
on.exit(popState(verbose))
}
verbose && enter(verbose, "Segmenting")
verbose && cat(verbose, "Chrom... |
options(shiny.trace = F)
require(shiny)
require(shinysky)
shinyServer(function(input, output, session) {
observe({
if (input$id_blank == 0)
return()
showshinyalert(session, "shinyalert1", paste("You have clicked", "blank"))
})
observe({
if (input$id_primary... |
hidden_paths <- function(model){
if(!inherits(model, c("hmm", "mhmm")))
stop("Argument model must be an object of class 'hmm' or 'mhmm.")
if(inherits(model,"mhmm")){
model <- combine_models(model)
mix <- TRUE
} else mix <- FALSE
if(model$n_channels == 1){
model$observations <-... |
fitSpliced=function(cell, body, tail, method,thresh=NULL){
if(method == "BestFit"){
return(fitSplicedBestFit(cell, body, tail, thresh0 = 0.7, thresh.max = 0.98))
} else {
if(method != "Fixed"){
thresh<-fitThreshold(cell, body, tail, method)
}
pars=fitSplicedPar(cell, thresh, body, tail)
... |
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