General syntax

odin is a “Domain Specific Language”; a mini-language that solves a specific problem; in this case representing systems of difference or differential equations. It is syntactically R (i.e., it can be parsed with R’s parser) but it is not itself R. Only a subset of expressions and syntax are supported.

Every line in odin code must be an assignment or a relationship (there are some minor exceptions below).

An assignment looks like

a <- expression

while a relationship looks like

b ~ Distribution(...)

where b will be an entry from data (introduced by data()), Distribution will be a monty distribution function (see below), and ... will be arguments to this function, which might come from data or from model variables. See vignette("fitting") for a high-level introduction to this interface.

odin2 supports many functions that you’d expect to see for constructing dynamical models. These include most common mathematical operations and some that are quite obscure. The support for stochastic models and comparison to data comes from monty.

Basic operations

• + – Plus: Both infix (a + b) and prefix (+a) versions supported (e.g., 1 + 2 → 3)
• - – Minus: Both infix (a - b) and prefix (-a) versions supported (e.g., 10 - 1 → 9)
• * – Multiply: Multiply two numbers together (e.g., 2 * 6 → 12)
• / – Divide: Divide two numbers (e.g., 12 / 6 → 2)
• ^ – Power: Raise the first number to the power of the second. Either number may be a floating point number (e.g., 2.3 ^ 1.2 → 2.716898)
• ( – Parenthesis: Group expressions together (e.g., (1 + 5) * 2 → 12)

Conditionals

You can only use conditionals with if as an inline expression, for example

a <- if (9 > 10) 1 else 2

would result in a being assigned as 2 (this works in R normally too!).

Because general flow control is not supported, you cannot write:

if (9 > 10) {
  a <- 1
} else {
  a <- 2
}

Operators

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 > 2 → FALSE)
• < – Less than (e.g., 1 < 2 → TRUE)
• >= – Greater than or equal to (e.g., 1 >= 2 → FALSE)
• <= – Less than or equal to (e.g., 1 <= 2 → TRUE)
• == – Is exactly equal to (e.g., 1 == 1 → TRUE)
• != – Is not exactly equal to (e.g., 1 != 2 → TRUE)
• && – 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 functions and constants

• %% – Modulo: Finds the remainder after division of one number by another (e.g., 123 %% 100 → 23)
• %/% – Integer divide: Different to floating point division, effectively the full number of times one number divides into another (e.g., 20 %/% 7 → 2)
• abs – Absolute value (e.g., abs(-1) → 1)
• sign – Sign 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)
• round – Round a number (e.g., round(1.23) → 1; round(1.23, 1) → 1.2)
• floor – Floor of a number: Largest integer not greater than the provided number (e.g., floor(6.5) → 6)
• ceiling – Ceiling of a number: Smallest integer not less than the provided number (e.g., ceiling(6.5) → 7)
• trunc – Truncate a number: Round a number towards zero
• max – Maximum: Returns maximum of two numbers (e.g., max(2, 6) → 6)
• min – Minimum (e.g., min(2, 6) → 2)
• exp – Exponential function (e.g., exp(1) → 2.718282)
• expm1 – Computes exp(x) - 1 accurately for small |x| (e.g., exp(1) → 1.718282)
• log – Logarithmic function in base e (e.g., log(1) → 0)
• log2 – Logarithmic function in base 2 (e.g., log2(1024) → 10)
• log10 – Logarithmic function in base 10 (e.g., log10(1000) → 3)
• log1p – Computes log(x + 1) accurately for small |x| (e.g., log1p(1) → 0.6931472)
• sqrt – Square root function (e.g., sqrt(4) → 2)
• beta – Beta function (e.g., beta(3, 5) → 0.00952381)
• lbeta – Log beta function (e.g., lbeta(3, 5) → -4.65396)
• choose – Binomial coefficients (e.g., choose(60, 3) → 34220)
• lchoose – Log binomial coefficients (e.g., choose(60, 3) → 10.44057)
• gamma – Gamma function (e.g., gamma(10) → 362880)
• lgamma – Log gamma function (e.g., lgamma(10) → 12.80183)

The exact behaviour for %% and %/% for floating point numbers and negative numbers is complicated - please see ?Arithmetic. The rules for operators in odin try to follow those in R as closely as possible.

The constant pi can be used, along with all the usual trig functions:

• cos – Cosine function
• sin – Sine function
• tan – Tangent function
• acos – Arc-cosine function
• asin – Arc-sin function
• atan – Arc-tangent function
• atan2 – Two-argument arc-tangent function
• cosh – Hyperbolic cosine function
• sinh – Hyperbolic sine function
• tanh – Hyperbolic tangent function
• acosh – Hyperbolic arc-cosine function
• asinh – Hyperbolic arc-sine function
• atanh – Hyperbolic arc-tangent function

Arrays

Use of arrays implies a “for-loop” in the generated code. For example, you might write a vectorised version of the logistic map as:

update(y[]) <- r[i] * y[i] * (1 - y[i])

which will expand to code equivalent to

for (i in 1:length(y)) {
  y_next[i] <- r[i] * y[i] * (1 - y[i])
}

The loop extent here (over the entire range of y) is determined by the left hand side expression (y[]). This enables use of i on the right hand side to index as loop progresses.

The indices on the right hand side can be i, j, k, l, i5, i6, i7 or i8 (odin supports arrays up to 8 dimensions: do let us know if you need more for some reason).

Arrays can have more than one dimension, for example the expression:

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

involves loops over two dimensions because we loop over the whole extent of ay which is a matrix. This is roughly equivalent to:

for (i in 1:nrow(ay)) {
  for (j in 1:ncol(ay)) {
     ay[i, j] <- a[i, j] * y[j]
  }
}

Note here that y is accessed using j, even though it is only a vector. This is because the loop extents are generated by the left hand side.

dim(y) <- 10
dim(ay) <- c(nr, nc)

dim(x) <- 10
x_tot <- sum(x)

m1_tot <- sum(m[, 2])

m_col_totals[] <- sum(m[, i])

ax_tmp[, ] <- a[i, j] * x[j]
ax[] <- sum(a[, i])

Distribution functions

We support distribution functions in two places:

First, for discrete-time models we support sampling from a distribution at each time step. For example:

a <- Normal(0, 1)

will assign a to a draw from the standard normal distribution. You cannot use these functions from continuous time models. You cannot sample from stochastic functions in a continuous time (ODE) model.

Second, for comparison to data, for example:

a ~ Normal(0, 1)

will add a log likelihood term looking up the log density of the data element a against a standard normal distribution. This form can be used in both discrete-time and continuous-time models. For more information, see vignette("fitting").

Some distributions have several versions; these are distinguished by the arguments to the functions. For example:

a <- Gamma(2, 0.1)
a <- Gamma(shape = 2, rate = 0.1)

draw from a Gamma distribution with a shape of 2 and a rate of 0.1, while

a <- Gamma(2, scale = 10)
a <- Gamma(shape = 2, scale = 10)

draw from a Gamma distribution with a shape of 2 and a scale of 10.

The currently supported distributions are (alphabetically):

• Beta – the beta distribution with parameters a and b (vs rbeta’s shape1 and shape2)
• BetaBinomial – the beta-binomial distribution with two forms:
  • size, prob (the mean probability of success), rho (dispersion parameter) (default)
  • size, a, b
• Binomial – the binomial distribution with parameters size and prob
• Exponential – the exponential distribution with two forms:
  • rate (default); this is the same parameterisation as rexp
  • mean which is the inverse of rate. NOTE: we may change this to scale soon
• Gamma – the gamma distribution with two forms:
  • shape, rate (default)
  • shape, scale
• Hypergeometric – the hypergeometric distribution with parameters m (number of white balls), n (number of black balls), and k (number of samples), and we return the number of white balls. We may support alternative parametrisations of this distribution in future (this version is the same parametrisation as rhyper)
• NegativeBinomial – the negative binomial distribution with two forms:
  • size, prob (default)
  • size, mu (the mean)`
• Normal – the normal distribution with parameters mean, sd
• Poisson – the Poisson distribution with parameter lambda (the mean)
• Uniform – the uniform distribution with parameters min and max

In the future, we plan support for additional distributions, please let us know if we are missing any that you need. The support for these functions comes from monty and we will link here to the docs in that package once they exist for additional details.

x[] <- Normal(0, 1)

a <- Normal(0, 1)
x[] <- a

Special functions

There are some special odin functions that may appear on the right hand side and which must be the only function used in the expression.

a <- parameter()

n <- parameter(12, constant = TRUE, type = "integer")

a <- parameter()
dim(a) <- parameter(rank = 2)

d <- data()

interpolate(time, value, mode)

Restricted names

You cannot assign to a name that is reserved in:

• C++ - includes useful words such as new and switch
• C - largely a subset of C++’s words, but also excludes restrict
• JavaScript - includes useful words such as default and export
• A few words restricted by odin itself: time, dt, parameter, data, interpolate, delay, initial, deriv, update, output, dim, config, state, state_next, state_deriv, shared, internal, pi. We may reduce this list in future.

In addition, odin restricts a few prefixes; a name cannot start with odin_, interpolate_, delay_ or adjoint_.