Nothing

```
### constants.R: "constants" for the lcmix package
LC_LOG2 <- log(2)
LC_LOGPI <- log(pi)
LC_LOG2PI <- log(2*pi)
LC_LOGSQRT2PI <- log(sqrt(2*pi))
LC_LOGSQRT2 <- log(sqrt(2))
# it's just useful to have these around
LC_EPS <- sqrt(.Machine$double.eps)
LC_LEPS <- log(LC_EPS)
LC_REPS <- 1.0 - LC_EPS
LC_LREPS <- log(LC_REPS)
# LC_EPS and LC_REPS ("reverse LC_EPS") are numbers very close, but not quite equal, to 0 and 1 respectively
LC_MAXSTDNORM <- 8
LC_MINSTDNORM <- -LC_MAXSTDNORM
# maximum and minimum values of standard normal random variables; going outside these bounds with a variable that is supposed to be standard normal (e.g. Phi^-1(F(x)) for any random variable x) may lead to instability
LC_ITER_MAX <- 1000
# default max iterations for EM
LC_ITER_TOL <- 1e-6 # 1e-6 or LC_EPS
# point below which relative changes in log-likelihood must fall to terminate the algorithm
LC_TOPOLOGY <- c("layered", "chained")
# topologies for multi-data models
LC_FAMILY <- list(normal = list(uni="norm", multi="mvnorm"),
pvii = list(uni="pvii", multi="mvpvii"),
weibull = list(uni="weisd", multi="mvweisd"),
gamma = list(uni="gamma", multi="mvgamma"),
exponential = list(uni="exp", multi="mvexp"),
altpvii = list(uni="apvii", multi="amvpvii"))
# names of acceptable model distribution _families_ are names(LC_FAMILY); the $uni and $multi elements of the elements of LC_FAMILY give the names of the applicable univariate and multivariate distributions, respectively, for those families
LC_NONNEGFAM <- c("weibull", "gamma", "exponential")
# distribution families requiring non-negative values
LC_DISTN <- as.vector(unlist(LC_FAMILY))
# names of acceptable model _distributions_
LC_SIMPAR = list(
hidden = list(
prob0 = c(0.03, 0.97),
probz = list(
binding = c(0.03, 0.97),
expression = c(0.05, 0.92, 0.03),
conservation = c(0.25, 0.75)
)
),
observed = list(
binding = list(
mean = c(0.825, -0.825),
var = c(1.50, 0.50),
sd = sqrt(c(1.50, 0.50))
),
expression = list(
mean = matrix(c( 1.5, 1.5, 1.5,
0.00, 0.00, 0.00,
-1.5, -1.5, -1.5),
nrow=3, byrow=TRUE),
cov = list(
matrix(c( 3.00, 1.00, -0.50,
1.00, 3.00, 0.00,
-0.50, 0.00, 3.00),
nrow=3, byrow=TRUE),
diag(3),
matrix(c( 2.00, -0.20, 0.10,
-0.20, 2.00, 0.10,
0.10, 0.10, 2.00),
nrow=3, byrow=TRUE)
)
),
conservation = list(
mean = c(1.25, -1.25),
var = c(1.50, 0.50),
sd = sqrt(c(1.50, 0.50))
)
)
) # marginal distribution parameters for simulating data with similar performance to the Ci data
```

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