library(lme4)
library(lmerTest)
library(MASS)

# Generate data
X <- structure(list(time = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("today", "tomorrow"), class = "factor"), cond = structure(c(1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L), .Label = c("A", "B", "C"), class = "factor"), target = structure(c(1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L), .Label = c("target1", "target2", "target3", "target4", "target5", "target6", "target7", "target8", "target9"), class = "factor"), id = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("s1", "s2"), class = "factor"), rating = c(4L, 1L, 3L, 6L, 6L, 5L, 2L, 4L, 0L, 4L, 0L, 5L, 4L, 4L, 2L, 3L, 6L, 0L)), class = "data.frame", row.names = c(NA, -18L))

# Convert to factors
X$cond <- factor(X$cond)
X$time <- factor(X$time)

# Contrast coding
contrastCond <- rbind(c(1,  -0.5, -0.5), # A vs. (B + C)/2
                      c(0,     1,   -1)) # B vs. C
contrastTime <- c(+1, -1) # today vs. tomorrow

# Generalized inverse matrix
MatCond <- ginv(contrastCond)

# Mixed model analyses
mdl <- lmer(rating ~ cond * time + (1|id) + (1|target),
            contrasts = list(cond = MatCond, time = contrastTime),
            data = X)

summary(mdl)