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jangoreckijangorecki
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frollvar and frollsd
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7 files changed

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‎R/froll.R‎

Lines changed: 6 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -216,3 +216,9 @@ frollprod = function(x, n, fill=NA, algo=c("fast","exact"), align=c("right","lef
216216
frollmedian = function(x, n, fill=NA, algo=c("fast","exact"), align=c("right","left","center"), na.rm=FALSE, has.nf=NA, adaptive=FALSE, partial=FALSE, give.names=FALSE, hasNA) {
217217
froll(fun="median", x=x, n=n, fill=fill, algo=algo, align=align, na.rm=na.rm, has.nf=has.nf, adaptive=adaptive, partial=partial, hasNA=hasNA, give.names=give.names)
218218
}
219+
frollvar = function(x, n, fill=NA, algo=c("fast","exact"), align=c("right","left","center"), na.rm=FALSE, has.nf=NA, adaptive=FALSE, partial=FALSE, give.names=FALSE, hasNA) {
220+
froll(fun="var", x=x, n=n, fill=fill, algo=algo, align=align, na.rm=na.rm, has.nf=has.nf, adaptive=adaptive, partial=partial, hasNA=hasNA, give.names=give.names)
221+
}
222+
frollsd = function(x, n, fill=NA, algo=c("fast","exact"), align=c("right","left","center"), na.rm=FALSE, has.nf=NA, adaptive=FALSE, partial=FALSE, give.names=FALSE, hasNA) {
223+
froll(fun="sd", x=x, n=n, fill=fill, algo=algo, align=align, na.rm=na.rm, has.nf=has.nf, adaptive=adaptive, partial=partial, hasNA=hasNA, give.names=give.names)
224+
}

‎inst/tests/froll.Rraw‎

Lines changed: 70 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -1418,6 +1418,73 @@ test(6001.693, frollapply(FUN=median, adaptive=TRUE, c(1:2,NA), c(2,0,2)), c(NA,
14181418
test(6001.694, frollapply(FUN=median, adaptive=TRUE, c(1:2,NA), c(2,0,2), na.rm=TRUE), c(NA,NA_integer_,2L))
14191419
test(6001.695, frollapply(FUN=median, adaptive=TRUE, c(1:2,NA_real_), c(2,0,2), na.rm=TRUE, partial=TRUE), c(1,NA_real_,2))
14201420

1421+
test(6001.711, frollvar(1:3, 0), c(NA_real_,NA_real_,NA_real_), options=c("datatable.verbose"=TRUE), output="window width of size 0")
1422+
test(6001.712, frollvar(1:3, 0, fill=99), c(NA_real_,NA_real_,NA_real_))
1423+
test(6001.713, frollvar(c(1:2,NA), 0), c(NA_real_,NA_real_,NA_real_))
1424+
test(6001.714, frollvar(c(1:2,NA), 0, na.rm=TRUE), c(NA_real_,NA_real_,NA_real_))
1425+
test(6001.715, frollvar(1:3, 0, algo="exact"), c(NA_real_,NA_real_,NA_real_), options=c("datatable.verbose"=TRUE), output="window width of size 0")
1426+
test(6001.716, frollvar(c(1:2,NA), 0, algo="exact"), c(NA_real_,NA_real_,NA_real_))
1427+
test(6001.717, frollvar(c(1:2,NA), 0, algo="exact", na.rm=TRUE), c(NA_real_,NA_real_,NA_real_))
1428+
test(6001.718, frollvar(c(1:2,NA), 2), c(NA,0.5,NA), options=c("datatable.verbose"=TRUE), output="redirecting to frollvarExact")
1429+
test(6001.721, frollvar(adaptive=TRUE, 1:3, c(2,0,2)), c(NA,NA,0.5))
1430+
test(6001.722, frollvar(adaptive=TRUE, 1:3, c(2,0,2), fill=99), c(99,NA,0.5))
1431+
test(6001.723, frollvar(adaptive=TRUE, c(1:2,NA), c(2,0,2)), c(NA_real_,NA_real_,NA_real_))
1432+
test(6001.724, frollvar(adaptive=TRUE, c(1:2,NA), c(2,0,2), na.rm=TRUE), c(NA_real_,NA_real_,NA_real_))
1433+
test(6001.725, frollvar(adaptive=TRUE, 1:3, c(2,0,2), algo="exact"), c(NA,NA,0.5))
1434+
test(6001.726, frollvar(adaptive=TRUE, 1:3, c(2,0,2), fill=99, algo="exact"), c(99,NA,0.5))
1435+
test(6001.727, frollvar(adaptive=TRUE, c(1:2,NA), c(2,0,2), algo="exact"), c(NA_real_,NA_real_,NA_real_))
1436+
test(6001.728, frollvar(adaptive=TRUE, c(1:2,NA), c(2,0,2), algo="exact", na.rm=TRUE), c(NA_real_,NA_real_,NA_real_))
1437+
test(6001.729, frollvar(adaptive=TRUE, c(1:2,NA), c(2,0,2), algo="exact", na.rm=TRUE, partial=TRUE), c(NA_real_,NA_real_,NA_real_))
1438+
test(6001.730, frollvar(adaptive=TRUE, c(1:2,NA), c(2,0,2), fill=99, algo="exact", na.rm=TRUE), c(99,NA,NA))
1439+
test(6001.781, frollapply(FUN=var, 1:3, 0), c(NA_real_,NA_real_,NA_real_))
1440+
test(6001.782, frollapply(FUN=var, 1:3, 0, fill=99), c(NA_real_,NA_real_,NA_real_))
1441+
test(6001.783, frollapply(FUN=var, c(1:2,NA), 0), c(NA_real_,NA_real_,NA_real_))
1442+
test(6001.784, frollapply(FUN=var, c(1:2,NA), 0, na.rm=TRUE), c(NA_real_,NA_real_,NA_real_))
1443+
test(6001.7910, frollapply(FUN=var, adaptive=TRUE, 1:3, c(2,0,2)), c(NA,NA,0.5))
1444+
test(6001.7911, frollapply(FUN=var, adaptive=TRUE, list(1:3,2:4), c(2,0,2)), list(c(NA,NA,0.5), c(NA,NA,0.5)))
1445+
test(6001.7912, frollapply(FUN=var, adaptive=TRUE, 1:3, list(c(2,0,2), c(0,2,0))), list(c(NA,NA,0.5), c(NA,0.5,NA)))
1446+
test(6001.7913, frollapply(FUN=var, adaptive=TRUE, list(1:3,2:4), list(c(2,0,2), c(0,2,0))), list(c(NA,NA,0.5), c(NA,0.5,NA), c(NA,NA,0.5), c(NA,0.5,NA)))
1447+
test(6001.792, frollapply(FUN=var, adaptive=TRUE, 1:3, c(2,0,2), fill=99), c(99,NA,0.5))
1448+
test(6001.793, frollapply(FUN=var, adaptive=TRUE, c(1:2,NA), c(2,0,2)), c(NA_real_,NA_real_,NA_real_))
1449+
test(6001.794, frollapply(FUN=var, adaptive=TRUE, c(1:2,NA), c(2,0,2), na.rm=TRUE), c(NA_real_,NA_real_,NA_real_))
1450+
test(6001.795, frollapply(FUN=var, adaptive=TRUE, c(1:2,NA_real_), c(2,0,2), na.rm=TRUE, partial=TRUE), c(NA_real_,NA_real_,NA_real_))
1451+
1452+
test(6001.810, frollsd(1:3, 0), c(NA_real_,NA_real_,NA_real_), options=c("datatable.verbose"=TRUE), output="frollsdFast: calling sqrt(frollvarFast(...))")
1453+
test(6001.811, frollsd(1:3, 0), c(NA_real_,NA_real_,NA_real_), options=c("datatable.verbose"=TRUE), output="window width of size 0")
1454+
test(6001.812, frollsd(1:3, 0, fill=99), c(NA_real_,NA_real_,NA_real_))
1455+
test(6001.813, frollsd(c(1:2,NA), 0), c(NA_real_,NA_real_,NA_real_))
1456+
test(6001.814, frollsd(c(1:2,NA), 0, na.rm=TRUE), c(NA_real_,NA_real_,NA_real_))
1457+
test(6001.815, frollsd(1:3, 0, algo="exact"), c(NA_real_,NA_real_,NA_real_), options=c("datatable.verbose"=TRUE), output="window width of size 0")
1458+
test(6001.816, frollsd(c(1:2,NA), 0, algo="exact"), c(NA_real_,NA_real_,NA_real_))
1459+
test(6001.817, frollsd(c(1:2,NA), 0, algo="exact", na.rm=TRUE), c(NA_real_,NA_real_,NA_real_))
1460+
test(6001.818, frollsd(c(1:2,NA), 2), c(NA,sqrt(0.5),NA), options=c("datatable.verbose"=TRUE), output="redirecting to frollvarExact")
1461+
test(6001.8191, frollsd(1:3, 2, fill=99), c(99,sqrt(0.5),sqrt(0.5)))
1462+
test(6001.8192, frollsd(1:3, 2, fill=99, algo="exact"), c(99,sqrt(0.5),sqrt(0.5)))
1463+
test(6001.8201, frollsd(adaptive=TRUE, 1:3, c(2,2,2)), c(NA,sqrt(0.5),sqrt(0.5)), options=c("datatable.verbose"=TRUE), output="frolladaptivefun: algo 0 not implemented, fall back to 1")
1464+
test(6001.8202, frollsd(adaptive=TRUE, 1:3, c(2,2,2)), c(NA,sqrt(0.5),sqrt(0.5)), options=c("datatable.verbose"=TRUE), output="frolladaptivesdExact: calling sqrt(frolladaptivevarExact(...))")
1465+
test(6001.821, frollsd(adaptive=TRUE, 1:3, c(2,0,2)), c(NA,NA,sqrt(0.5)))
1466+
test(6001.822, frollsd(adaptive=TRUE, 1:3, c(2,0,2), fill=99), c(99,NA,sqrt(0.5)))
1467+
test(6001.823, frollsd(adaptive=TRUE, c(1:2,NA), c(2,0,2)), c(NA_real_,NA_real_,NA_real_))
1468+
test(6001.824, frollsd(adaptive=TRUE, c(1:2,NA), c(2,0,2), na.rm=TRUE), c(NA_real_,NA_real_,NA_real_))
1469+
test(6001.825, frollsd(adaptive=TRUE, 1:3, c(2,0,2), algo="exact"), c(NA,NA,sqrt(0.5)))
1470+
test(6001.826, frollsd(adaptive=TRUE, 1:3, c(2,0,2), fill=99, algo="exact"), c(99,NA,sqrt(0.5)))
1471+
test(6001.827, frollsd(adaptive=TRUE, c(1:2,NA), c(2,0,2), algo="exact"), c(NA_real_,NA_real_,NA_real_))
1472+
test(6001.828, frollsd(adaptive=TRUE, c(1:2,NA), c(2,0,2), algo="exact", na.rm=TRUE), c(NA_real_,NA_real_,NA_real_))
1473+
test(6001.829, frollsd(adaptive=TRUE, c(1:2,NA), c(2,0,2), algo="exact", na.rm=TRUE, partial=TRUE), c(NA_real_,NA_real_,NA_real_))
1474+
test(6001.830, frollsd(adaptive=TRUE, c(1:2,NA), c(2,0,2), fill=99, algo="exact", na.rm=TRUE), c(99,NA,NA))
1475+
test(6001.881, frollapply(FUN=sd, 1:3, 0), c(NA_real_,NA_real_,NA_real_))
1476+
test(6001.882, frollapply(FUN=sd, 1:3, 0, fill=99), c(NA_real_,NA_real_,NA_real_))
1477+
test(6001.883, frollapply(FUN=sd, c(1:2,NA), 0), c(NA_real_,NA_real_,NA_real_))
1478+
test(6001.884, frollapply(FUN=sd, c(1:2,NA), 0, na.rm=TRUE), c(NA_real_,NA_real_,NA_real_))
1479+
test(6001.8910, frollapply(FUN=sd, adaptive=TRUE, 1:3, c(2,0,2)), c(NA,NA,sqrt(0.5)))
1480+
test(6001.8911, frollapply(FUN=sd, adaptive=TRUE, list(1:3,2:4), c(2,0,2)), list(c(NA,NA,sqrt(0.5)), c(NA,NA,sqrt(0.5))))
1481+
test(6001.8912, frollapply(FUN=sd, adaptive=TRUE, 1:3, list(c(2,0,2), c(0,2,0))), list(c(NA,NA,sqrt(0.5)), c(NA,sqrt(0.5),NA)))
1482+
test(6001.8913, frollapply(FUN=sd, adaptive=TRUE, list(1:3,2:4), list(c(2,0,2), c(0,2,0))), list(c(NA,NA,sqrt(0.5)), c(NA,sqrt(0.5),NA), c(NA,NA,sqrt(0.5)), c(NA,sqrt(0.5),NA)))
1483+
test(6001.892, frollapply(FUN=sd, adaptive=TRUE, 1:3, c(2,0,2), fill=99), c(99,NA,sqrt(0.5)))
1484+
test(6001.893, frollapply(FUN=sd, adaptive=TRUE, c(1:2,NA), c(2,0,2)), c(NA_real_,NA_real_,NA_real_))
1485+
test(6001.894, frollapply(FUN=sd, adaptive=TRUE, c(1:2,NA), c(2,0,2), na.rm=TRUE), c(NA_real_,NA_real_,NA_real_))
1486+
test(6001.895, frollapply(FUN=sd, adaptive=TRUE, c(1:2,NA_real_), c(2,0,2), na.rm=TRUE, partial=TRUE), c(NA_real_,NA_real_,NA_real_))
1487+
14211488
# frollmedian
14221489
rollmedian = function(x, k, na.rm=FALSE) {
14231490
ans = rep(NA_real_, length(x))
@@ -2250,7 +2317,7 @@ rollfun = function(x, n, FUN, fill=NA_real_, na.rm=FALSE, nf.rm=FALSE, partial=F
22502317
}
22512318
ans
22522319
}
2253-
base_compare = function(x, n, funs=c("mean","sum","max","min","prod","median"), algos=c("fast","exact")) {
2320+
base_compare = function(x, n, funs=c("mean","sum","max","min","prod","median","var","sd"), algos=c("fast","exact")) {
22542321
num.step = 0.0001
22552322
for (fun in funs) {
22562323
for (na.rm in c(FALSE, TRUE)) {
@@ -2334,7 +2401,7 @@ base_compare(x, n)
23342401
#### against zoo
23352402
if (requireNamespace("zoo", quietly=TRUE)) {
23362403
drollapply = function(...) as.double(zoo::rollapply(...)) # rollapply is not consistent in data type of answer, force to double
2337-
zoo_compare = function(x, n, funs=c("mean","sum","max","min","prod","median"), algos=c("fast","exact")) {
2404+
zoo_compare = function(x, n, funs=c("mean","sum","max","min","prod","median","var","sd"), algos=c("fast","exact")) {
23382405
num.step = 0.0001
23392406
#### fun, align, na.rm, fill, algo, partial
23402407
for (fun in funs) {
@@ -2432,7 +2499,7 @@ arollfun = function(FUN, x, n, na.rm=FALSE, align=c("right","left"), fill=NA, nf
24322499
}
24332500
ans
24342501
}
2435-
afun_compare = function(x, n, funs=c("mean","sum","max","min","prod","median"), algos=c("fast","exact")) {
2502+
afun_compare = function(x, n, funs=c("mean","sum","max","min","prod","median","var","sd"), algos=c("fast","exact")) {
24362503
num.step = 0.0001
24372504
#### fun, align, na.rm, fill, algo
24382505
for (fun in funs) {

‎man/froll.Rd‎

Lines changed: 18 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -10,13 +10,17 @@
1010
\alias{frollmin}
1111
\alias{frollprod}
1212
\alias{frollmedian}
13+
\alias{frollvar}
14+
\alias{frollsd}
1315
\alias{roll}
1416
\alias{rollmean}
1517
\alias{rollsum}
1618
\alias{rollmax}
1719
\alias{rollmin}
1820
\alias{rollprod}
1921
\alias{rollmedian}
22+
\alias{rollvar}
23+
\alias{rollsd}
2024
\title{Rolling functions}
2125
\description{
2226
Fast rolling functions to calculate aggregates on a sliding window. For a user-defined rolling function see \code{\link{frollapply}}. For "time-aware" (irregularly spaced time series) rolling function see \code{\link{frolladapt}}.
@@ -34,6 +38,10 @@
3438
na.rm=FALSE, has.nf=NA, adaptive=FALSE, partial=FALSE, give.names=FALSE, hasNA)
3539
frollmedian(x, n, fill=NA, algo=c("fast","exact"), align=c("right","left","center"),
3640
na.rm=FALSE, has.nf=NA, adaptive=FALSE, partial=FALSE, give.names=FALSE, hasNA)
41+
frollvar(x, n, fill=NA, algo=c("fast","exact"), align=c("right","left","center"),
42+
na.rm=FALSE, has.nf=NA, adaptive=FALSE, partial=FALSE, give.names=FALSE, hasNA)
43+
frollsd(x, n, fill=NA, algo=c("fast","exact"), align=c("right","left","center"),
44+
na.rm=FALSE, has.nf=NA, adaptive=FALSE, partial=FALSE, give.names=FALSE, hasNA)
3745
}
3846
\arguments{
3947
\item{x}{ Integer, numeric or logical vector, coerced to numeric, on which sliding window calculates an aggregate function. It supports vectorized input, then it needs to be a \code{data.table}, \code{data.frame} or a \code{list}, in which case a rolling function is applied to each column/vector. }
@@ -85,7 +93,7 @@
8593
\item \code{has.nf=TRUE} uses non-finite aware implementation straightaway.
8694
\item \code{has.nf=FALSE} uses faster implementation that does not support non-finite values. Then depending on the rolling function it will either:
8795
\itemize{
88-
\item (\emph{mean, sum, prod}) detect non-finite, re-run non-finite aware.
96+
\item (\emph{mean, sum, prod, var, sd}) detect non-finite, re-run non-finite aware.
8997
\item (\emph{max, min, median}) does not detect non-finites and may silently produce an incorrect answer.
9098
}
9199
In general \code{has.nf=FALSE && any(!is.finite(x))} should be considered as undefined behavior. Therefore \code{has.nf=FALSE} should be used with care.
@@ -98,12 +106,12 @@
98106
\itemize{
99107
\item \emph{max} and \emph{min} rolling function will not do only a single pass but, on average, they will compute \code{length(x)/n} nested loops. The larger the window, the greater the advantage over the \emph{exact} algorithm, which computes \code{length(x)} nested loops. Note that \emph{exact} uses multiple CPUs so for a small window sizes and many CPUs it may actually be faster than \emph{fast}. However, in such cases the elapsed timings will likely be far below a single second.
100108
\item \emph{median} will use a novel algorithm described by \emph{Jukka Suomela} in his paper \emph{Median Filtering is Equivalent to Sorting (2014)}. See references section for the link. Implementation here is extended to support arbitrary length of input and an even window size. Despite extensive validation of results this function should be considered experimental. When missing values are detected it will fall back to slower \code{algo="exact"} implementation.
101-
\item Not all functions have \emph{fast} implementation available. As of now adaptive \emph{max}, adaptive \emph{min} and adaptive \emph{median} do not have \emph{fast} implementation, therefore it will automatically fall back to \emph{exact} implementation. \code{datatable.verbose} option can be used to check that.
109+
\item Not all functions have \emph{fast} implementation available. As of now, adaptive \emph{max}, \emph{min}, \emph{median}, \emph{var} and \emph{sd} do not have \emph{fast} adaptive implementation, therefore it will automatically fall back to \emph{exact} adaptive implementation. \code{datatable.verbose} option can be used to check that.
102110
}
103111
\item \code{algo="exact"} will make the rolling functions use a more computationally-intensive algorithm. For each observation in the input vector it will compute a function on a rolling window from scratch (complexity \eqn{O(n^2)}).
104112
\itemize{
105113
\item Depeneding on the function, this algorithm may suffers less from floating point rounding error (the same consideration applies to base \code{\link[base]{mean}}).
106-
\item In case of \emph{mean} (and possibly other functions in future), it will additionally make extra pass to perform floating point error correction. Error corrections might not be truly exact on some platforms (like Windows) when using multiple threads.
114+
\item In case of \emph{mean}, it will additionally make an extra pass to perform floating point error correction. Error corrections might not be truly exact on some platforms (like Windows) when using multiple threads.
107115
}
108116
}
109117
}
@@ -158,6 +166,8 @@ frollmax(d, 3:4)
158166
frollmin(d, 3:4)
159167
frollprod(d, 3:4)
160168
frollmedian(d, 3:4)
169+
frollvar(d, 3:4)
170+
frollsd(d, 3:4)
161171
162172
# partial=TRUE
163173
x = 1:6/2
@@ -179,6 +189,11 @@ frollsum(list(x=1:5, y=5:1), c(tiny=2, big=4), give.names=TRUE)
179189
frollmax(c(1,2,NA,4,5), 2)
180190
frollmax(c(1,2,NA,4,5), 2, has.nf=FALSE)
181191
192+
# use verobse=TRUE for extra insight
193+
.op = options(datatable.verbose = TRUE)
194+
frollsd(c(1:5,NA,7:8), 4)
195+
options(.op)
196+
182197
# performance vs exactness
183198
set.seed(108)
184199
x = sample(c(rnorm(1e3, 1e6, 5e5), 5e9, 5e-9))

‎src/data.table.h‎

Lines changed: 11 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -231,7 +231,9 @@ typedef enum { // adding rolling functions here and in frollfunR in frollR.c
231231
MAX = 2,
232232
MIN = 3,
233233
PROD = 4,
234-
MEDIAN = 5
234+
MEDIAN = 5,
235+
VAR = 6,
236+
SD = 7
235237
} rollfun_t;
236238
// froll.c
237239
void frollfun(rollfun_t rfun, unsigned int algo, const double *x, uint64_t nx, ans_t *ans, int k, int align, double fill, bool narm, int hasnf, bool verbose, bool par);
@@ -247,6 +249,10 @@ void frollprodFast(const double *x, uint64_t nx, ans_t *ans, int k, double fill,
247249
void frollprodExact(const double *x, uint64_t nx, ans_t *ans, int k, double fill, bool narm, int hasnf, bool verbose);
248250
void frollmedianFast(const double *x, uint64_t nx, ans_t *ans, int k, double fill, bool narm, int hasnf, bool verbose, bool par);
249251
void frollmedianExact(const double *x, uint64_t nx, ans_t *ans, int k, double fill, bool narm, int hasnf, bool verbose);
252+
void frollvarFast(const double *x, uint64_t nx, ans_t *ans, int k, double fill, bool narm, int hasnf, bool verbose);
253+
void frollvarExact(const double *x, uint64_t nx, ans_t *ans, int k, double fill, bool narm, int hasnf, bool verbose);
254+
void frollsdFast(const double *x, uint64_t nx, ans_t *ans, int k, double fill, bool narm, int hasnf, bool verbose);
255+
void frollsdExact(const double *x, uint64_t nx, ans_t *ans, int k, double fill, bool narm, int hasnf, bool verbose);
250256

251257
// frolladaptive.c
252258
void frolladaptivefun(rollfun_t rfun, unsigned int algo, const double *x, uint64_t nx, ans_t *ans, const int *k, double fill, bool narm, int hasnf, bool verbose);
@@ -262,6 +268,10 @@ void frolladaptiveminExact(const double *x, uint64_t nx, ans_t *ans, const int *
262268
void frolladaptiveprodExact(const double *x, uint64_t nx, ans_t *ans, const int *k, double fill, bool narm, int hasnf, bool verbose);
263269
//void frolladaptivemedianFast(const double *x, uint64_t nx, ans_t *ans, const int *k, double fill, bool narm, int hasnf, bool verbose); // does not exists as of now
264270
void frolladaptivemedianExact(const double *x, uint64_t nx, ans_t *ans, const int *k, double fill, bool narm, int hasnf, bool verbose);
271+
//void frolladaptivevarFast(const double *x, uint64_t nx, ans_t *ans, const int *k, double fill, bool narm, int hasnf, bool verbose); // does not exists as of now
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void frolladaptivevarExact(const double *x, uint64_t nx, ans_t *ans, const int *k, double fill, bool narm, int hasnf, bool verbose);
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//void frolladaptivesdFast(const double *x, uint64_t nx, ans_t *ans, const int *k, double fill, bool narm, int hasnf, bool verbose); // does not exists as of now
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void frolladaptivesdExact(const double *x, uint64_t nx, ans_t *ans, const int *k, double fill, bool narm, int hasnf, bool verbose);
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// frollR.c
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SEXP frollfunR(SEXP fun, SEXP xobj, SEXP kobj, SEXP fill, SEXP algo, SEXP align, SEXP narm, SEXP hasnf, SEXP adaptive);

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