odin supports many functions that you’d expect to see for constructing differential equation models; primarily mathematical functions available through R’s “Rmath” library. These include all mathematical operations, and many more obscure mathematical functions. Special support is provided for working with arrays. A further set of functions is available for working discrete time stochastic models.

Basic operators

  • +Plus: Both infix (a + b) and prefix (+a) versions supported (e.g., 1 + 23)

  • -Minus: Both infix (a - b) and prefix (-a) versions supported (e.g., 10 - 19)

  • *Multiply: Multiply two numbers together (e.g., 2 * 612)

  • /Divide: Divide two numbers (e.g., 12 / 62)

  • ^Power: Raise the first number to the power of the second. Either number may be a floating point number (e.g., 2.3 ^ 1.22.716898)

  • (Parenthesis: Group expressions together (e.g., (1 + 5) * 212)

  • ifConditional: Inline conditional statement. This takes a form slightly different to typically seen in R with the result of the statement directly assigned (e.g., if (9 > 10) 1 else 22)

Because general programming is not supported in odin and because every line must contain an assignment, instead of writing

if (mycondition) {
  a <- true_value
} else {
  a <- false_value

instead write

a <- if (mycondition) true_value else false_value

(this works in normal R too!)

Array support

There are a group of functions for interacting with arrays

  • sumSum: Compute sum over an array, or over some dimension of an array

  • lengthLength: Total length of an array

  • dimLength of one of an array’s dimensions: If an array x has 10 rows and 20 columns, then dim(x, 1) is 10 and dim(x, 2) is 20. Note that this differs from dim in R and dim(x, i) is closer to dim(x)[[i]]

  • [Subset an array: See below

  • interpolateInterpolate an array over time: See below

When working with arrays, use generally implies a “for loop” in the generated C code. For example, in the example in the main package vignette the derivatives are computed as

deriv(y[]) <- r[i] * y[i] * (1 - sum(ay[i, ]))

The indexes on the right hand side can be one of i, j, k, l i5, i6, i7 or i8 corresponding to the index on the left hand side being iterated over (odin supports arrays up to 8 dimensions). The left-hand-side here contains no explicit entry (y[]) which is equivalent to y[1:length(y)], which expands (approximately) to the “for loop”

for (i in 1:length(y)) {
  deriv(y[i]) <- r[i] * y[i] * (1 - sum(ay[i, ]))

(except slightly different, and in C).

Similarly, the expression

ay[, ] <- a[i, j] * y[j]

involves loops over two dimensions (ay[, ] becomes ay[1:dim(ay, 1), 1:dim(ay, 2)] and so the loop becomes

for (i in 1:dim(ay, 1)) {
  for (j in 1:dim(ay, 2)) {
    ay[i, j] <- a[i, j] * y[j]

Due to constraints with using C, few things can be used as an index; in particular the following will not work:

idx <- if (t > 5) 2 else 1
x <- vec[idx]

(or where idx is some general odin variable as the result of a different assignment). You must use as.integer to cast this to integer immediately before indexing:

idx <- if (t > 5) 2 else 1
x <- vec[as.integer(idx)]

This will truncate the value (same behaviour as truncate) so be warned if passing in things that may be approximately integer - you may want to use as.integer(round(x)) in that case.

The interpolation functions are described in more detail in the main package vignette


A number of logical-returning operators exist, primarily to support the if statement; all the usual comparison operators exist (though not vectorised | or &).

  • >Greater than (e.g., 1 > 2FALSE)

  • <Less than (e.g., 1 < 2TRUE)

  • >=Greater than or equal to (e.g., 1 >= 2FALSE)

  • <=Less than or equal to (e.g., 1 <= 2TRUE)

  • ==Is exactly equal to (e.g., 1 == 1TRUE)

  • !=Is not exactly equal to (e.g., 1 != 2TRUE)

  • &&Boolean AND (e.g., (1 == 1) && (2 > 1)TRUE)

  • ||Boolean OR (e.g., (1 == 1) && (2 > 1)TRUE)

Be wary of strict equality with == or != as numbers may be floating point numbers, which have some surprising properties for the uninitiated, for example

sqrt(3)^2 == 3
## [1] FALSE

Mathematical operators

  • %%Modulo: Finds the remainder after division of one number by another (e.g., 123 %% 10023)

  • %/%Integer divide: Different to floating point division, effectively the full number of times one number divides into another (e.g., 20 %/% 72)

  • absAbsolute value (e.g., abs(-1)1)

  • signSign function: Returns the sign of its argument as either -1, 0 or 1, which may be useful for multiplying by another argument (e.g., sign(-100)-1)

  • roundRound a number (e.g., round(1.23)1; round(1.23, 1)1.2)

  • floorFloor of a number: Largest integer not greater than the provided number (e.g., floor(6.5)6)

  • ceilingCeiling of a number: Smallest integer not less than the provided number (e.g., ceiling(6.5)7)

  • truncTruncate a number: Round a number towards zero

  • maxMaximum: Returns maximum of all arguments given (e.g., max(2, 6, 1)6)

  • minMinimum (e.g., min(2, 6, 1)1)

  • expExponential function (e.g., exp(1)2.718282)

  • expm1Computes exp(x) - 1 accurately for small |x| (e.g., exp(1)1.718282)

  • logLogarithmic function (e.g., log(1)0)

  • log2Logarithmic function in base 2 (e.g., log2(1024)10)

  • log10Logarithmic function in base 10 (e.g., log10(1000)3)

  • log1pComputes log(x + 1) accurately for small |x| (e.g., log1p(1)0.6931472)

  • sqrtSquare root function (e.g., sqrt(4)2)

  • betaBeta function (e.g., beta(3, 5)0.00952381)

  • lbetaLog beta function (e.g., lbeta(3, 5)-4.65396)

  • chooseBinomial coefficients (e.g., choose(60, 3)34220)

  • lchooseLog binomial coefficients (e.g., choose(60, 3)10.44057)

  • gammaGamma function (e.g., gamma(10)362880)

  • lgammaLog gamma function (e.g., lgamma(10)12.80183)

The exact for %% and %/% for floating point numbers and signed numbers are complicated - please see ?Arithmetic. The rules for operators in odin are exactly those in R as the same underlying functions are used.

Similarly, for the differences between round, floor, ceiling and truncate, see the help page ?round. Note that R’s behaviour for rounding away from 0.5 is exactly followed and that this slightly changed behaviour at version 4.0.0

All the usual trig functions are also available:

  • cosCosine function

  • sinSine function

  • tanTangent function

  • acosArc-cosine function

  • asinArc-sin function

  • atanArc-tangent function

  • atan2Two-arg arc-tangent function

  • coshHyperbolic cosine function

  • sinhHyperbolic sine function

  • tanhHyperbolic tangent function

  • acoshHyperbolic arc-cosine function

  • asinhHyperbolic arc-sine function

  • atanhHyperbolic arc-tangent function

Stochastic models

For discrete time stochastic models, all of R’s normal stochastic distribution functions are available:

  • unif_randStandard uniform distribution: Sample from the uniform distribution on [0, 1] - more efficient than but equivalent to runif(0, 1)

  • norm_randStandard normal distribution: Sample from the standard normal distribution - more efficient than but equivalent to rnorm(0, 1)

  • exp_randStandard exponential distribution: Sample from the exponential distribution with rate 1 - more efficient than but equivalent to rexp(1)

  • rbetaBeta distribution: With parameters shape1 and shape2 (see ?rbeta for details)

  • rbinomBinomial distribution: With parameters size (number of trials) and prob (probability of success)

  • rcauchyCauchy distribution: With parameters location and scale

  • rchisqChi-Squared distribution: With parameter df

  • rexpExponential distribution: With parameter rate

  • rfF-distribution: With parameter df1 and `df2

  • rgammaGamma distribution: With parameters shape and rate

  • rgeomGeometric distribution: Distribution with parameters prob

  • rhyperHypergeometric distribution: With parameters m (the number of white balls in the urn), n (the number of black balls in the urn) and k (the number of balls drawn from the urn)

  • rlogisLogistic distribution: With parameters location and scale

  • rlnormLog-normal distribution: With parameters meanlog and sdlog

  • rnbinomNegative binomial distribution: With parameters size, prob and mu

  • rnormNormal distribution: With parameters mean and sd

  • rpoisPoisson distribution: With parameter lambda

  • rtStudent’s t distribution: With parameter df

  • runifuniform distribution: With parameters min and max

  • rweibullWeibull distribution: With parameters shape and scale

  • rwilcoxWilcoxon rank sum statistic distribution: With parameters n and m

  • rsignrankWilcoxon signed rank statistic distribution: With parameter n

With random number functions we can write:

x <- runif(10, 20)

which will generate a random number from the uniform distribution. If you write:

x[] <- runif(10, 20)

then each element of x will be filled with a different random number drawn from this distribution (which is generally what you want). Random numbers are considered to be time varying which means they will automatically generate each time step, so if you write

x <- rnorm(0, 10)
update(y[]) <- y[i] + x

then at each time step, each element of y will be updated by the same random number from a normal distribution with a mean of zero and a standard deviation of 10 - the number will change each time step but be the same for each element of y in the example above.

In addition, two functions that are vector returning and require some care to use:

  • rmultinommultinomial distribution: The first parameter is the number of samples and the second is the per-class probability and must be a vector

  • rmhyperMultivariate hypergeometric distribution: The first parameter is the number of samples and the second is the per-class count and must be a vector

Both these functions require a vector input (of probabilities for rmultinom and of counts for rmhyper) and return a vector the same length. So the expression

y[] <- rmultinom(10, p)

will produce a vector y of samples from the multinomial distribution with parameters size = 10 (so after wards sum(y) is 10) and probabilities p. It is very important that y and p have the same size.

At the moment it is not possible to use expressions like

y[1, ] <- rmultinom(10, p[i, ])

but this is planned for implementation in the future. A full example of using rmultinom is given in the discrete models vignette.