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I use the package memisc for exporting my regression tables to LaTeX. I now have a three model with different independent variables (IV) and various control variables (CV) that are the same in every model.

Using

mtable("Modell 1"=lm(DV ~ IV1 + CV1 + CV2),
 "Modell 2"=lm(DV ~ IV2 + CV1 + CV2),
 "Modell 3"=lm(DV ~ IV3 + CV1 + CV2))

, I get a this output:

===========================================================================
 Modell 1 Modell 2 Modell 3 Modell 4 
---------------------------------------------------------------------------
 (Intercept) -0.122 -0.112 -0.122 -0.097 
 (0.116) (0.115) (0.116) (0.116) 
 IV1 0.026 -0.083 
 (0.030) (0.064) 
 CV1 0.017 0.015 0.018 0.012 
 (0.019) (0.019) (0.019) (0.019) 
 CV2 0.064*** 0.063*** 0.066*** 0.063*** 
 (0.016) (0.015) (0.016) (0.016) 
 IV3 0.076 0.196* 
 (0.045) (0.099) 
 IV4 -0.016 -0.047 
 (0.076) (0.078) 
---------------------------------------------------------------------------

(I truncated the summary statistics). I would like IV1, IV2 and IV3 to appear first, and afterwards all the control variables. Is there any automatable solution?

This is an intersiting question. Please, consider the following example.

library(memisc)
# independent variables (IV) are dpi, ddpi, log(dpi) and log(ddpi)
# control variables (CV) are pop15 and pop75
mtable("Model 1" = lm(sr ~ dpi + pop15 + pop75, LifeCycleSavings),
 "Model 2" = lm(sr ~ ddpi + pop15 + pop75, LifeCycleSavings),
 "Model 3" = lm(sr ~ log(dpi) + pop15 + pop75, LifeCycleSavings),
 "Model 4" = lm(sr ~ log(ddpi) + pop15 + pop75, LifeCycleSavings))
# looks like mtable appends new coefficients to the list of coefficients defined in the first model
# output
Calls:
Model 1: lm(formula = sr ~ dpi + pop15 + pop75, data = LifeCycleSavings)
Model 2: lm(formula = sr ~ ddpi + pop15 + pop75, data = LifeCycleSavings)
Model 3: lm(formula = sr ~ log(dpi) + pop15 + pop75, data = LifeCycleSavings)
Model 4: lm(formula = sr ~ log(ddpi) + pop15 + pop75, data = LifeCycleSavings)

=====================================================================
 Model 1 Model 2 Model 3 Model 4 
---------------------------------------------------------------------
 (Intercept) 31.457*** 28.125*** 36.304** 26.118*** 
 (7.482) (7.184) (10.511) (7.416) 
 dpi -0.001 
 (0.001) 
 pop15 -0.492** -0.452** -0.506** -0.408** 
 (0.149) (0.141) (0.154) (0.144) 
 pop75 -1.568 -1.835 -1.649 -1.663 
 (1.121) (0.998) (1.110) (1.009) 
 ddpi 0.428* 
 (0.188) 
 log(dpi) -0.779 
 (1.018) 
 log(ddpi) 1.584* 
 (0.722) 
---------------------------------------------------------------------
 R-squared 0.274 0.337 0.271 0.332 
 adj. R-squared 0.227 0.293 0.223 0.288 
 sigma 3.939 3.767 3.948 3.780 
 F 5.797 7.778 5.700 7.608 
 p 0.002 0.000 0.002 0.000 
 Log-likelihood -137.410 -135.171 -137.525 -135.355 
 Deviance 713.767 652.606 717.054 657.424 
 AIC 284.821 280.341 285.050 280.709 
 BIC 294.381 289.902 294.610 290.269 
 N 50 50 50 50 
=====================================================================

We now want the IV (i.e. dpi, ddpi, log(dpi) and log(ddpi)) to appear first.

Option 1

# we will define a fake model which includes all the IVs
mtables <- mtable("Model fake" = lm(sr ~ dpi + ddpi + log(dpi) + log(ddpi), LifeCycleSavings),
 "Model 1" = lm(sr ~ dpi + pop15 + pop75, LifeCycleSavings),
 "Model 2" = lm(sr ~ ddpi + pop15 + pop75, LifeCycleSavings),
 "Model 3" = lm(sr ~ log(dpi) + pop15 + pop75, LifeCycleSavings),
 "Model 4" = lm(sr ~ log(ddpi) + pop15 + pop75, LifeCycleSavings))
# we will display mtables without the fake model
mtables[2:5]
# output
Calls:
Model 1: lm(formula = sr ~ dpi + pop15 + pop75, data = LifeCycleSavings)
Model 2: lm(formula = sr ~ ddpi + pop15 + pop75, data = LifeCycleSavings)
Model 3: lm(formula = sr ~ log(dpi) + pop15 + pop75, data = LifeCycleSavings)
Model 4: lm(formula = sr ~ log(ddpi) + pop15 + pop75, data = LifeCycleSavings)

=====================================================================
 Model 1 Model 2 Model 3 Model 4 
---------------------------------------------------------------------
 (Intercept) 31.457*** 28.125*** 36.304** 26.118*** 
 (7.482) (7.184) (10.511) (7.416) 
 dpi -0.001 
 (0.001) 
 ddpi 0.428* 
 (0.188) 
 log(dpi) -0.779 
 (1.018) 
 log(ddpi) 1.584* 
 (0.722) 
 pop15 -0.492** -0.452** -0.506** -0.408** 
 (0.149) (0.141) (0.154) (0.144) 
 pop75 -1.568 -1.835 -1.649 -1.663 
 (1.121) (0.998) (1.110) (1.009) 
---------------------------------------------------------------------
 R-squared 0.274 0.337 0.271 0.332 
 adj. R-squared 0.227 0.293 0.223 0.288 
 sigma 3.939 3.767 3.948 3.780 
 F 5.797 7.778 5.700 7.608 
 p 0.002 0.000 0.002 0.000 
 Log-likelihood -137.410 -135.171 -137.525 -135.355 
 Deviance 713.767 652.606 717.054 657.424 
 AIC 284.821 280.341 285.050 280.709 
 BIC 294.381 289.902 294.610 290.269 
 N 50 50 50 50 
=====================================================================

Option 2

# stargazer can be an option
library(stargazer)
stargazer(lm(sr ~ dpi + pop15 + pop75, LifeCycleSavings),
 lm(sr ~ ddpi + pop15 + pop75, LifeCycleSavings),
 lm(sr ~ log(dpi) + pop15 + pop75, LifeCycleSavings),
 lm(sr ~ log(ddpi) + pop15 + pop75, LifeCycleSavings),
 type = "text", column.labels = c("Model 1", "Model 2", "Model 3", "Model 4"),
 model.numbers = FALSE)
# output
=====================================================================
 Dependent variable: 
 ---------------------------------------
 sr 
 Model 1 Model 2 Model 3 Model 4 
---------------------------------------------------------------------
dpi -0.001 
 (0.001) 

ddpi 0.428** 
 (0.188) 

log(dpi) -0.779 
 (1.018) 

log(ddpi) 1.584** 
 (0.722) 

pop15 -0.492*** -0.452*** -0.506*** -0.408***
 (0.149) (0.141) (0.154) (0.144) 

pop75 -1.568 -1.835* -1.649 -1.663 
 (1.121) (0.998) (1.110) (1.009) 

Constant 31.457*** 28.125*** 36.304*** 26.118***
 (7.482) (7.184) (10.511) (7.416) 

---------------------------------------------------------------------
Observations 50 50 50 50 
R2 0.274 0.337 0.271 0.332 
Adjusted R2 0.227 0.293 0.223 0.288 
Residual Std. Error (df = 46) 3.939 3.767 3.948 3.780 
F Statistic (df = 3; 46) 5.797*** 7.778*** 5.700*** 7.608*** 
=====================================================================
Note: *p<0.1; **p<0.05; ***p<0.01

Solution based on your data

mtables <- mtable("Modell 0"=lm(DV ~ IV1 + IV2 + IV3),
 "Modell 1"=lm(DV ~ IV1 + CV1 + CV2),
 "Modell 2"=lm(DV ~ IV2 + CV1 + CV2),
 "Modell 3"=lm(DV ~ IV3 + CV1 + CV2))
mtables[2:4]
# or use stargazer() as above