| Title: | Estimator for Optimised Optional Randomised Response Technique |
| Version: | 0.1.0 |
| Description: | Provides functions for estimation under the Randomised Response Technique for sensitive survey data, including Warner's estimator, Optional Randomised Response Technique estimator proposed by Chaudhuri and Mukerjee,and the Optimized Optional Randomised Response Technique estimator proposed by Pushadapu et al. The package also includes Monte Carlo simulation tools for evaluating estimator performance. The implemented methods are based on Warner (1965) <doi:10.1080/01621459.1965.10480775>, Chaudhuri and Mukerjee (1985),and Pushadapu et al. (2025) <doi:10.1111/insr.12581>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2026-07-03 08:01:06 UTC; hp |
| Author: | Safeela Nasrin [aut, cre], Kaustav Aditya [aut], Ritwika Das [aut] |
| Maintainer: | Safeela Nasrin <nasrinkareem315@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2026-07-10 20:10:22 UTC |
Chaudhuri-Mukerjee Estimator
Description
Chaudhuri-Mukerjee Estimator
Usage
cm_estimator(PIAOM1H, p, X2, n, n1)
Arguments
PIAOM1H |
Observed proportion of "yes" responses among respondents who answered directly. |
p |
Warner probability |
X2 |
Number of "yes" responses from the Warner group (group sensitive to question) |
n |
Sample size |
n1 |
Number of respondents answering directly. |
Value
CM estimator of PIA
Examples
## Example 5 from Pushadapu et al. (2025)
## COVID-19 prevalence survey among students
n <- 145
n1 <- 101
x1 <- 42
x2 <- 18
p <- 0.3
PIAOM1H <- x1 / n1
## Chaudhuri and Mukerjee estimator
cm_estimator(
PIAOM1H = PIAOM1H,
p = p,
X2 = x2,
n = n,
n1 = n1
)
OORRT_Pushadapu Estimator
Description
OORRT_Pushadapu Estimator
Usage
oorrt_pushadapu_estimator(PIAOM1H, X2, p, n, n1)
Arguments
PIAOM1H |
Observed proportion of "yes" responses among respondents who answered directly (group 1) |
X2 |
Number of "yes" responses from the Warner group (group sensitive to question= group 2) |
p |
Warner probability |
n |
Sample size |
n1 |
Number of respondents answering directly. |
Value
A list with proposed estimator, ALPHA1H, ALPHA2H, MSE, CI bounds
Examples
## Example 5 from Pushadapu et al. (2025)
## COVID-19 prevalence survey among students
n <- 145
n1 <- 101
x1 <- 42
x2 <- 18
p <- 0.3
PIAOM1H <- x1 / n1
oorrt_pushadapu_estimator(
PIAOM1H = PIAOM1H,
X2 = x2,
p = p,
n = n,
n1 = n1
)
OORRT Simulation Study
Description
OORRT Simulation Study
Usage
orrt_simulation(
nitr = 1000,
p = 0.3,
PIA_seq = seq(0.05, 0.45, 0.05),
PIAOM1_seq = seq(0.2, 0.95, 0.05),
n_seq = seq(150, 500, 50),
W1_seq = seq(0.4, 0.7, 0.1)
)
Arguments
nitr |
Number of Monte Carlo iterations |
p |
Warner probability |
PIA_seq |
Sequence of true proportions to test |
PIAOM1_seq |
Sequence of group 1 proportions |
n_seq |
Sequence of sample sizes |
W1_seq |
Sequence of weights for group 1 |
Value
Data frame with bias, MSE, RE, coverage, etc.
Warner Estimator
Description
Warner Estimator
Usage
warner_estimator(n, p, X)
Arguments
n |
Sample size |
p |
Warner probability |
X |
no. of "yes" response |
Value
Warner estimator of PIA