Are misses in my data distributed completely at random? I have a large data set containing children's scores on arithmetic tasks with a lot of missing values possibly due to the age of the children? My hypothesis is that the missingness is not completely at random but at random. I need to do a CFA and want to know whether I need to condition on age or not? I know SPSS 17 has a missing values analysis but my data is in R and is quite large. I would like to do a missing values analysis in R but have not been able to find a package that can do this. Does anyone know?
 A: As far as I understand your question, you want to investigate if missing values in your data appear due to some pattern. In this case, you don't need any "missing value analysis" -- this is the same problem as checking whether the score is bigger than 0.7 or whatever. Just convert your dataset into two-class factor (missing, not-missing) and look for correlations.
A: Your question is a little difficult to decipher.  One approach for dealing with missing data is imputation -- and there is a substantial literature on this and an already large and growing set of packages at CRAN so you may want to start there.
A: As @Dirk Eddelbuettel already mentioned, your questions is not very clear. In fact, I think you are asking two questions. The first question is related to your M(C)AR assumption. The second question is about (an) appropriate R package(s). 
(1) "Testing" for MAR
To test if age has an effect on the missingness of your score variable, you could run a simple logistic regression model with age as a predictor variable. Your response variable is 0: score is not missing, 1: score is missing (see also @mbq's answer and @Macro's comment). Given the assumption that younger children are more likely to not report math scores, we expect to see a significant negative effect of age . 
## Make up some data
set.seed(2)
## Younger children are more likely to not report math scores, 
## so I use a Poisson distribution to model that behaviour
missData <- rpois(10000, 10)
dfr <- data.frame(score=rnorm(100), age=sample(6:15, 100, replace=TRUE))
dfr <- dfr[order(dfr$age), ]
dfr$agemiss <- sort(sample(missData, 100, replace=TRUE))
dfr$miss <- ifelse(dfr$agemiss == dfr$age, 1, 0)                  

## Run the logistic regression with age as predictor
> summary(glm(miss ~ age, data=dfr, family=binomial))

[...]

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)   5.9729     1.4946   3.996 6.43e-05 ***
age          -0.7997     0.1760  -4.544 5.53e-06 ***
---
[...]

(2) (Some) Missing data related R packages 
Some of these packages also have functions to explore patterns of missingness (e.g., missing.pattern.plot() in the mi package). 


*

*Amelia II: A Program for Missing Data 

*Hmisc: Harrell Miscellaneous 

*mi: Missing Data Imputation and Model Checking 

*mitools: Tools for multiple imputation of missing data
