diff --git a/DESCRIPTION b/DESCRIPTION
index 0c010575..814a8806 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Title: Matching Adjusted Indirect Comparison
 Package: maicplus
 Version: 0.1.0
-Date: 2023-03-03
+Date: 2024-10-29
 Authors@R: c(
    person(
       given = "Gregory",
@@ -36,9 +36,10 @@ Authors@R: c(
    person("MSD, Inc.", role = c("cph", "fnd")),
    person("F. Hoffmann-La Roche AG", role = c("cph", "fnd"))
    )
-Description: The maicplus package facilitates performing matching adjusted indirect comparison
+Description: Facilitates performing matching adjusted indirect comparison
  (MAIC) analysis where the endpoint of interest is either time-to-event
- (e.g. overall survival) or binary (e.g. objective tumor response).
+ (e.g. overall survival) or binary (e.g. objective tumor response). The method
+ is described by Signorovitch et al (2012) <doi:10.1016/j.jval.2012.05.004>.
 License: Apache License 2.0
 URL: https://github.com/hta-pharma/maicplus/, https://hta-pharma.github.io/maicplus/
 BugReports: https://github.com/hta-pharma/maicplus/issues
@@ -75,3 +76,4 @@ Language: en-US
 LazyData: true
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.2
+Config/testthat/edition: 3
diff --git a/NEWS.md b/NEWS.md
index d6a01b00..ce9a6b31 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,13 +1,3 @@
 # maicplus 0.1.0
 
-### New features
-
-* Add an initializer script.
-
-### Enhancements
-
-* Documentation on how to use the initialize a package.
-
-### Bug fixes
-
-* None.
+- first CRAN release
diff --git a/R/binary-helper.R b/R/binary-helper.R
index 2475c514..4f5d729d 100644
--- a/R/binary-helper.R
+++ b/R/binary-helper.R
@@ -91,6 +91,8 @@ get_pseudo_ipd_binary <- function(binary_agd, format = c("stacked", "unstacked")
 #' @param binobj returned object from \code{stats::glm}
 #' @param legend label to indicate the binary fit
 #' @param weighted logical flag indicating whether weights have been applied in the glm fit
+#' @returns A `data.frame` containing a summary of the number of events and subjects in a logistic
+#' regression model.
 #' @examples
 #' data(adrs_sat)
 #' pseudo_adrs <- get_pseudo_ipd_binary(
diff --git a/R/plot_km2.R b/R/plot_km2.R
index 2008578a..d0cbcf18 100644
--- a/R/plot_km2.R
+++ b/R/plot_km2.R
@@ -169,6 +169,7 @@ kmplot2 <- function(weights_object,
 #' @param use_line_types a numeric vector of length up to 4, line type to the KM curves,
 #'   it will be passed to \code{lty} of \code{lines()}
 #' @example inst/examples/basic_kmplot2_ex.R
+#' @returns A Kaplan-Meier plot object created with `survminer::ggsurvplot()`.
 #' @export
 
 basic_kmplot2 <- function(kmlist,
diff --git a/inst/WORDLIST b/inst/WORDLIST
index e0da9f60..7c9bde76 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -15,6 +15,7 @@ binarize
 bootswatch
 Bucher
 bucher
+Chrysostomos
 Comparator
 comparator
 confounders
@@ -59,6 +60,7 @@ maicplus
 medSurv
 meier
 MGG
+Miranta
 MSD
 multivariable
 navbar
diff --git a/inst/examples/maic_unanchored_binary_ex.R b/inst/examples/maic_unanchored_binary_ex.R
index 30e4f6d5..4457863b 100644
--- a/inst/examples/maic_unanchored_binary_ex.R
+++ b/inst/examples/maic_unanchored_binary_ex.R
@@ -1,4 +1,7 @@
+#
 # unanchored example using maic_unanchored for binary outcome
+#
+
 data(centered_ipd_sat)
 data(adrs_sat)
 
@@ -7,7 +10,7 @@ centered_colnames <- grep("_CENTERED$", colnames(centered_ipd_sat), value = TRUE
 weighted_data <- estimate_weights(data = centered_ipd_sat, centered_colnames = centered_colnames)
 weighted_data2 <- estimate_weights(
   data = centered_ipd_sat, centered_colnames = centered_colnames,
-  n_boot_iteration = 500
+  n_boot_iteration = 100
 )
 
 # get dummy binary IPD
@@ -51,3 +54,5 @@ maic_unanchored(
   # binary specific args
   binary_robust_cov_type = "HC3"
 )
+
+#---------------------------------
diff --git a/inst/examples/maic_unanchored_ex.R b/inst/examples/maic_unanchored_ex.R
index c07e23d7..c7740428 100644
--- a/inst/examples/maic_unanchored_ex.R
+++ b/inst/examples/maic_unanchored_ex.R
@@ -1,4 +1,6 @@
+#
 # unanchored example using maic_unanchored for time-to-event data
+#
 data(centered_ipd_sat)
 data(adtte_sat)
 data(pseudo_ipd_sat)
@@ -16,7 +18,7 @@ weighted_data2 <- estimate_weights(
   centered_colnames = grep("_CENTERED$", names(centered_ipd_sat)),
   start_val = 0,
   method = "BFGS",
-  n_boot_iteration = 500,
+  n_boot_iteration = 100,
   set_seed_boot = 1234
 )
 
diff --git a/man/basic_kmplot2.Rd b/man/basic_kmplot2.Rd
index 192c8d5c..7d42148b 100644
--- a/man/basic_kmplot2.Rd
+++ b/man/basic_kmplot2.Rd
@@ -44,6 +44,9 @@ it will be passed to 'col' of \code{lines()}}
 \item{use_line_types}{a numeric vector of length up to 4, line type to the KM curves,
 it will be passed to \code{lty} of \code{lines()}}
 }
+\value{
+A Kaplan-Meier plot object created with \code{survminer::ggsurvplot()}.
+}
 \description{
 This function generates a basic KM plot using ggplot.
 }
diff --git a/man/glm_makeup.Rd b/man/glm_makeup.Rd
index c7ae0c5a..8d7e2a8b 100644
--- a/man/glm_makeup.Rd
+++ b/man/glm_makeup.Rd
@@ -13,6 +13,10 @@ glm_makeup(binobj, legend = "before matching", weighted = FALSE)
 
 \item{weighted}{logical flag indicating whether weights have been applied in the glm fit}
 }
+\value{
+A \code{data.frame} containing a summary of the number of events and subjects in a logistic
+regression model.
+}
 \description{
 Helper function to summarize outputs from glm fit
 }
diff --git a/man/maic_unanchored.Rd b/man/maic_unanchored.Rd
index 65f26357..99180d66 100644
--- a/man/maic_unanchored.Rd
+++ b/man/maic_unanchored.Rd
@@ -78,7 +78,9 @@ columns. This function is not sensitive to upper or lower case of letters in col
 }
 }
 \examples{
+#
 # unanchored example using maic_unanchored for time-to-event data
+#
 data(centered_ipd_sat)
 data(adtte_sat)
 data(pseudo_ipd_sat)
@@ -96,7 +98,7 @@ weighted_data2 <- estimate_weights(
   centered_colnames = grep("_CENTERED$", names(centered_ipd_sat)),
   start_val = 0,
   method = "BFGS",
-  n_boot_iteration = 500,
+  n_boot_iteration = 100,
   set_seed_boot = 1234
 )
 
@@ -134,7 +136,10 @@ result_boot <- maic_unanchored(
 )
 result$descriptive$summary
 result$inferential$summary
+#
 # unanchored example using maic_unanchored for binary outcome
+#
+
 data(centered_ipd_sat)
 data(adrs_sat)
 
@@ -143,7 +148,7 @@ centered_colnames <- grep("_CENTERED$", colnames(centered_ipd_sat), value = TRUE
 weighted_data <- estimate_weights(data = centered_ipd_sat, centered_colnames = centered_colnames)
 weighted_data2 <- estimate_weights(
   data = centered_ipd_sat, centered_colnames = centered_colnames,
-  n_boot_iteration = 500
+  n_boot_iteration = 100
 )
 
 # get dummy binary IPD
@@ -187,4 +192,6 @@ maic_unanchored(
   # binary specific args
   binary_robust_cov_type = "HC3"
 )
+
+#---------------------------------
 }
diff --git a/man/maicplus-package.Rd b/man/maicplus-package.Rd
index ad0e3653..ecd36ef7 100644
--- a/man/maicplus-package.Rd
+++ b/man/maicplus-package.Rd
@@ -8,7 +8,7 @@
 \description{
 \if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}}
 
-The maicplus package facilitates performing matching adjusted indirect comparison (MAIC) analysis where the endpoint of interest is either time-to-event (e.g. overall survival) or binary (e.g. objective tumor response).
+Facilitates performing matching adjusted indirect comparison (MAIC) analysis where the endpoint of interest is either time-to-event (e.g. overall survival) or binary (e.g. objective tumor response). The method is described by Signorovitch et al (2012) \doi{10.1016/j.jval.2012.05.004}.
 }
 \seealso{
 Useful links:
diff --git a/tests/testthat/_snaps/maic_anchored.md b/tests/testthat/_snaps/maic_anchored.md
new file mode 100644
index 00000000..758f5a90
--- /dev/null
+++ b/tests/testthat/_snaps/maic_anchored.md
@@ -0,0 +1,720 @@
+# maic_anchored works for TTE
+
+    Code
+      testout$descriptive$summary
+    Output
+        trt_ind treatment                 type records    n.max  n.start    events
+      1       C         C IPD, before matching     500 500.0000 500.0000 500.00000
+      2       A         A IPD, before matching     500 500.0000 500.0000 190.00000
+      3       C         C  IPD, after matching     500 173.3137 173.3137 173.31374
+      4       A         A  IPD, after matching     500 173.3137 173.3137  55.37398
+      5       C         C        AgD, external     500 500.0000 500.0000 500.00000
+      6       B         B        AgD, external     300 300.0000 300.0000 178.00000
+          events%     rmean  se(rmean)    median   0.95LCL   0.95UCL
+      1 100.00000  2.564797 0.11366994  1.836467  1.644765  2.045808
+      2  38.00000  8.709690 0.35514766  7.587627  6.278691 10.288538
+      3 100.00000  2.679473 0.20670827  1.815795  1.457222  2.292484
+      4  31.95014 10.584609 0.57397937 12.166430 10.244293        NA
+      5 100.00000  2.455272 0.09848888  1.851987  1.670540  2.009650
+      6  59.33333  4.303551 0.33672602  2.746131  2.261125  3.320857
+
+---
+
+    Code
+      testout$inferential$summary
+    Output
+               case        HR       LCL       UCL         pval
+      1          AC 0.2216588 0.1867151 0.2631423 2.136650e-66
+      2 adjusted_AC 0.1631852 0.1113815 0.2390829 1.361531e-20
+      3          BC 0.5718004 0.4811989 0.6794607 2.143660e-10
+      4          AB 0.3876507 0.3039348 0.4944253 2.270430e-14
+      5 adjusted_AB 0.2853885 0.1876867 0.4339497 4.509575e-09
+
+---
+
+    Code
+      testout$inferential$fit
+    Output
+      $km_before_ipd
+      Call: survfit(formula = Surv(TIME, EVENT) ~ ARM, data = ipd, conf.type = km_conf_type)
+      
+              n events median 0.95LCL 0.95UCL
+      ARM=C 500    500   55.9    50.1    62.3
+      ARM=A 500    190  230.9   191.1   313.2
+      
+      $km_after_ipd
+      Call: survfit(formula = Surv(TIME, EVENT) ~ ARM, data = ipd, weights = ipd$weights, 
+          conf.type = km_conf_type)
+      
+            records   n events median 0.95LCL 0.95UCL
+      ARM=C     500 173  173.3   55.3    44.4    69.8
+      ARM=A     500 173   55.4  370.3   311.8      NA
+      
+      $km_agd
+      Call: survfit(formula = Surv(TIME, EVENT) ~ ARM, data = pseudo_ipd, 
+          conf.type = km_conf_type)
+      
+              n events median 0.95LCL 0.95UCL
+      ARM=C 500    500   56.4    50.8    61.2
+      ARM=B 300    178   83.6    68.8   101.1
+      
+      $model_before_ipd
+      Call:
+      coxph(formula = Surv(TIME, EVENT) ~ ARM, data = ipd)
+      
+               coef exp(coef) se(coef)      z      p
+      ARMA -1.50662   0.22166  0.08753 -17.21 <2e-16
+      
+      Likelihood ratio test=341.2  on 1 df, p=< 2.2e-16
+      n= 1000, number of events= 690 
+      
+      $model_after_ipd
+      Call:
+      coxph(formula = Surv(TIME, EVENT) ~ ARM, data = ipd, weights = weights, 
+          robust = TRUE)
+      
+              coef exp(coef) se(coef) robust se      z      p
+      ARMA -1.8129    0.1632   0.1602    0.1949 -9.303 <2e-16
+      
+      Likelihood ratio test=155.2  on 1 df, p=< 2.2e-16
+      n= 1000, number of events= 690 
+      
+      $model_agd
+      Call:
+      coxph(formula = Surv(TIME, EVENT) ~ ARM, data = pseudo_ipd)
+      
+               coef exp(coef) se(coef)      z        p
+      ARMB -0.55897   0.57180  0.08802 -6.351 2.14e-10
+      
+      Likelihood ratio test=43.66  on 1 df, p=3.91e-11
+      n= 800, number of events= 678 
+      
+      $res_AC
+      $res_AC$est
+      [1] 0.1631852
+      
+      $res_AC$se
+      [1] 0.194863
+      
+      $res_AC$ci_l
+      [1] 0.1113815
+      
+      $res_AC$ci_u
+      [1] 0.2390829
+      
+      $res_AC$pval
+      [1] 1.361531e-20
+      
+      
+      $res_AC_unadj
+      $res_AC_unadj$est
+      [1] 0.2216588
+      
+      $res_AC_unadj$se
+      [1] 0.08752989
+      
+      $res_AC_unadj$ci_l
+      [1] 0.1867151
+      
+      $res_AC_unadj$ci_u
+      [1] 0.2631423
+      
+      $res_AC_unadj$pval
+      [1] 2.13665e-66
+      
+      
+      $res_BC
+      $res_BC$est
+      [1] 0.5718004
+      
+      $res_BC$se
+      [1] 0.0880166
+      
+      $res_BC$ci_l
+      [1] 0.4811989
+      
+      $res_BC$ci_u
+      [1] 0.6794607
+      
+      $res_BC$pval
+      [1] 2.14366e-10
+      
+      
+      $res_AB
+                  result             pvalue 
+      "0.29[0.19; 0.43]"           "<0.001" 
+      
+      $res_AB_unadj
+                  result             pvalue 
+      "0.39[0.30; 0.49]"           "<0.001" 
+      
+      $boot_res
+      NULL
+      
+      $boot_res_AC
+      NULL
+      
+      $boot_res_AB_mc
+      NULL
+      
+      $boot_res_AB
+      NULL
+      
+
+---
+
+    Code
+      testout2$descriptive$summary
+    Output
+        trt_ind treatment                 type records    n.max  n.start    events
+      1       C         C IPD, before matching     500 500.0000 500.0000 500.00000
+      2       A         A IPD, before matching     500 500.0000 500.0000 190.00000
+      3       C         C  IPD, after matching     500 173.3137 173.3137 173.31374
+      4       A         A  IPD, after matching     500 173.3137 173.3137  55.37398
+      5       C         C        AgD, external     500 500.0000 500.0000 500.00000
+      6       B         B        AgD, external     300 300.0000 300.0000 178.00000
+          events%     rmean  se(rmean)    median   0.95LCL   0.95UCL
+      1 100.00000  2.564797 0.11366994  1.836467  1.644765  2.045808
+      2  38.00000  8.709690 0.35514766  7.587627  6.278691 10.288538
+      3 100.00000  2.679473 0.20670827  1.815795  1.457222  2.292484
+      4  31.95014 10.584609 0.57397937 12.166430 10.244293        NA
+      5 100.00000  2.455272 0.09848888  1.851987  1.670540  2.009650
+      6  59.33333  4.303551 0.33672602  2.746131  2.261125  3.320857
+
+---
+
+    Code
+      testout2$inferential$summary
+    Output
+               case        HR       LCL       UCL         pval
+      1          AC 0.2216588 0.1867151 0.2631423 2.136650e-66
+      2 adjusted_AC 0.1631852 0.1113815 0.2390829 1.361531e-20
+      3          BC 0.5718004 0.4811989 0.6794607 2.143660e-10
+      4          AB 0.3876507 0.3039348 0.4944253 2.270430e-14
+      5 adjusted_AB 0.2853885 0.1876867 0.4339497 4.509575e-09
+
+---
+
+    Code
+      testout2$inferential$fit
+    Output
+      $km_before_ipd
+      Call: survfit(formula = Surv(TIME, EVENT) ~ ARM, data = ipd, conf.type = km_conf_type)
+      
+              n events median 0.95LCL 0.95UCL
+      ARM=C 500    500   55.9    50.1    62.3
+      ARM=A 500    190  230.9   191.1   313.2
+      
+      $km_after_ipd
+      Call: survfit(formula = Surv(TIME, EVENT) ~ ARM, data = ipd, weights = ipd$weights, 
+          conf.type = km_conf_type)
+      
+            records   n events median 0.95LCL 0.95UCL
+      ARM=C     500 173  173.3   55.3    44.4    69.8
+      ARM=A     500 173   55.4  370.3   311.8      NA
+      
+      $km_agd
+      Call: survfit(formula = Surv(TIME, EVENT) ~ ARM, data = pseudo_ipd, 
+          conf.type = km_conf_type)
+      
+              n events median 0.95LCL 0.95UCL
+      ARM=C 500    500   56.4    50.8    61.2
+      ARM=B 300    178   83.6    68.8   101.1
+      
+      $model_before_ipd
+      Call:
+      coxph(formula = Surv(TIME, EVENT) ~ ARM, data = ipd)
+      
+               coef exp(coef) se(coef)      z      p
+      ARMA -1.50662   0.22166  0.08753 -17.21 <2e-16
+      
+      Likelihood ratio test=341.2  on 1 df, p=< 2.2e-16
+      n= 1000, number of events= 690 
+      
+      $model_after_ipd
+      Call:
+      coxph(formula = Surv(TIME, EVENT) ~ ARM, data = ipd, weights = weights, 
+          robust = TRUE)
+      
+              coef exp(coef) se(coef) robust se      z      p
+      ARMA -1.8129    0.1632   0.1602    0.1949 -9.303 <2e-16
+      
+      Likelihood ratio test=155.2  on 1 df, p=< 2.2e-16
+      n= 1000, number of events= 690 
+      
+      $model_agd
+      Call:
+      coxph(formula = Surv(TIME, EVENT) ~ ARM, data = pseudo_ipd)
+      
+               coef exp(coef) se(coef)      z        p
+      ARMB -0.55897   0.57180  0.08802 -6.351 2.14e-10
+      
+      Likelihood ratio test=43.66  on 1 df, p=3.91e-11
+      n= 800, number of events= 678 
+      
+      $res_AC
+      $res_AC$est
+      [1] 0.1631852
+      
+      $res_AC$se
+      [1] 0.194863
+      
+      $res_AC$ci_l
+      [1] 0.1113815
+      
+      $res_AC$ci_u
+      [1] 0.2390829
+      
+      $res_AC$pval
+      [1] 1.361531e-20
+      
+      
+      $res_AC_unadj
+      $res_AC_unadj$est
+      [1] 0.2216588
+      
+      $res_AC_unadj$se
+      [1] 0.08752989
+      
+      $res_AC_unadj$ci_l
+      [1] 0.1867151
+      
+      $res_AC_unadj$ci_u
+      [1] 0.2631423
+      
+      $res_AC_unadj$pval
+      [1] 2.13665e-66
+      
+      
+      $res_BC
+      $res_BC$est
+      [1] 0.5718004
+      
+      $res_BC$se
+      [1] 0.0880166
+      
+      $res_BC$ci_l
+      [1] 0.4811989
+      
+      $res_BC$ci_u
+      [1] 0.6794607
+      
+      $res_BC$pval
+      [1] 2.14366e-10
+      
+      
+      $res_AB
+                  result             pvalue 
+      "0.29[0.19; 0.43]"           "<0.001" 
+      
+      $res_AB_unadj
+                  result             pvalue 
+      "0.39[0.30; 0.49]"           "<0.001" 
+      
+      $boot_res
+      
+      STRATIFIED BOOTSTRAP
+      
+      
+      Call:
+      boot(data = boot_ipd, statistic = stat_fun, R = R, strata = weights_object$boot_strata, 
+          w_obj = weights_object, normalize = normalize_weights)
+      
+      
+      Bootstrap Statistics :
+             original       bias    std. error
+      t1* -1.24586465 -0.161716794  0.28787014
+      t2*  0.04571853  0.001994459  0.01042480
+      t3*  0.21381891  0.003564636  0.02391077
+      t4* -1.81286926 -0.166912210  0.25058383
+      t5*  0.19486304  0.003675676  0.02618306
+      t6*  0.03797160  0.001994459  0.01042480
+      
+      $boot_res_AC
+      $boot_res_AC$est
+      [1] 0.1631852
+      
+      $boot_res_AC$se
+      [1] NA
+      
+      $boot_res_AC$ci_l
+      [1] 0.1179974
+      
+      $boot_res_AC$ci_u
+      [1] 0.3151137
+      
+      $boot_res_AC$pval
+      [1] NA
+      
+      
+      $boot_res_AB_mc
+      $boot_res_AB_mc$est
+      [1] 0.287692
+      
+      $boot_res_AB_mc$se
+      [1] NA
+      
+      $boot_res_AB_mc$ci_l
+      [1] 0.1923646
+      
+      $boot_res_AB_mc$ci_u
+      [1] 0.5945605
+      
+      $boot_res_AB_mc$pval
+      [1] NA
+      
+      
+      $boot_res_AB
+      $boot_res_AB$est
+      [1] 0.2853885
+      
+      $boot_res_AB$se
+      [1] NA
+      
+      $boot_res_AB$ci_l
+      [1] 0.1695758
+      
+      $boot_res_AB$ci_u
+      [1] 0.4802958
+      
+      $boot_res_AB$pval
+      [1] NA
+      
+      
+
+# maic_anchored for binary case gives the expected result
+
+    Code
+      testout$descriptive$summary
+    Output
+        trt_ind treatment                 type   n   events events_pct
+      1       C         C IPD, before matching 500 338.0000   67.60000
+      2       A         A IPD, before matching 500 390.0000   78.00000
+      3       C         C  IPD, after matching 500 131.2892   26.25784
+      4       A         A  IPD, after matching 500 142.8968   28.57935
+      5       C         C        AgD, external 320 120.0000   37.50000
+      6       B         B        AgD, external 480 280.0000   58.33333
+
+---
+
+    Code
+      testout$inferential$summary
+    Output
+               case        OR       LCL       UCL         pval
+      1          AC 1.3119021 0.8210000 2.0963303 2.562849e-01
+      2 adjusted_AC 1.6993007 1.2809976 2.2541985 2.354448e-04
+      3          BC 2.3333333 1.7458092 3.1185794 1.035032e-08
+      4          AB 0.5622438 0.3239933 0.9756933 4.061296e-02
+      5 adjusted_AB 0.7282717 0.4857575 1.0918611 1.248769e-01
+
+---
+
+    Code
+      testout$inferential$fit
+    Output
+      $model_before_ipd
+      
+      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = ipd)
+      
+      Coefficients:
+      (Intercept)         ARMA  
+           0.7354       0.5302  
+      
+      Degrees of Freedom: 999 Total (i.e. Null);  998 Residual
+      Null Deviance:	    1170 
+      Residual Deviance: 1157 	AIC: 1161
+      
+      $model_after_ipd
+      
+      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = ipd, 
+          weights = weights)
+      
+      Coefficients:
+      (Intercept)         ARMA  
+           0.6559       0.2715  
+      
+      Degrees of Freedom: 999 Total (i.e. Null);  998 Residual
+      Null Deviance:	    495.5 
+      Residual Deviance: 493.9 	AIC: 454.5
+      
+      $model_agd
+      
+      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = pseudo_ipd)
+      
+      Coefficients:
+      (Intercept)         ARMB  
+          -0.5108       0.8473  
+      
+      Degrees of Freedom: 799 Total (i.e. Null);  798 Residual
+      Null Deviance:	    1109 
+      Residual Deviance: 1075 	AIC: 1079
+      
+      $res_AC
+      $res_AC$est
+      [1] 1.311902
+      
+      $res_AC$se
+      [1] 0.3275028
+      
+      $res_AC$ci_l
+      [1] 0.821
+      
+      $res_AC$ci_u
+      [1] 2.09633
+      
+      $res_AC$pval
+      [1] 0.2562849
+      
+      
+      $res_AC_unadj
+      $res_AC_unadj$est
+      [1] 1.699301
+      
+      $res_AC_unadj$se
+      [1] 0.2488482
+      
+      $res_AC_unadj$ci_l
+      [1] 1.280998
+      
+      $res_AC_unadj$ci_u
+      [1] 2.254199
+      
+      $res_AC_unadj$pval
+      [1] 0.0002354448
+      
+      
+      $res_BC
+      $res_BC$est
+      [1] 2.333333
+      
+      $res_BC$se
+      [1] 0.3510631
+      
+      $res_BC$ci_l
+      [1] 1.745809
+      
+      $res_BC$ci_u
+      [1] 3.118579
+      
+      $res_BC$pval
+      [1] 1.035032e-08
+      
+      
+      $res_AB
+                  result             pvalue 
+      "0.56[0.32; 0.98]"            "0.041" 
+      
+      $res_AB_unadj
+                  result             pvalue 
+      "0.73[0.49; 1.09]"            "0.125" 
+      
+      $boot_res
+      NULL
+      
+      $boot_res_AC
+      NULL
+      
+      $boot_res_AB_mc
+      NULL
+      
+      $boot_res_AB
+      NULL
+      
+
+---
+
+    Code
+      testout2$descriptive$summary
+    Output
+        trt_ind treatment                 type   n   events events_pct
+      1       C         C IPD, before matching 500 338.0000   67.60000
+      2       A         A IPD, before matching 500 390.0000   78.00000
+      3       C         C  IPD, after matching 500 131.2892   26.25784
+      4       A         A  IPD, after matching 500 142.8968   28.57935
+      5       C         C        AgD, external 320 120.0000   37.50000
+      6       B         B        AgD, external 480 280.0000   58.33333
+
+---
+
+    Code
+      testout2$inferential$summary
+    Output
+               case        OR       LCL       UCL         pval
+      1          AC 1.3119021 0.8210000 2.0963303 2.562849e-01
+      2 adjusted_AC 1.6993007 1.2809976 2.2541985 2.354448e-04
+      3          BC 2.3333333 1.7458092 3.1185794 1.035032e-08
+      4          AB 0.5622438 0.3239933 0.9756933 4.061296e-02
+      5 adjusted_AB 0.7282717 0.4857575 1.0918611 1.248769e-01
+
+---
+
+    Code
+      testout2$inferential$fit
+    Output
+      $model_before_ipd
+      
+      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = ipd)
+      
+      Coefficients:
+      (Intercept)         ARMA  
+           0.7354       0.5302  
+      
+      Degrees of Freedom: 999 Total (i.e. Null);  998 Residual
+      Null Deviance:	    1170 
+      Residual Deviance: 1157 	AIC: 1161
+      
+      $model_after_ipd
+      
+      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = ipd, 
+          weights = weights)
+      
+      Coefficients:
+      (Intercept)         ARMA  
+           0.6559       0.2715  
+      
+      Degrees of Freedom: 999 Total (i.e. Null);  998 Residual
+      Null Deviance:	    495.5 
+      Residual Deviance: 493.9 	AIC: 454.5
+      
+      $model_agd
+      
+      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = pseudo_ipd)
+      
+      Coefficients:
+      (Intercept)         ARMB  
+          -0.5108       0.8473  
+      
+      Degrees of Freedom: 799 Total (i.e. Null);  798 Residual
+      Null Deviance:	    1109 
+      Residual Deviance: 1075 	AIC: 1079
+      
+      $res_AC
+      $res_AC$est
+      [1] 1.311902
+      
+      $res_AC$se
+      [1] 0.3275028
+      
+      $res_AC$ci_l
+      [1] 0.821
+      
+      $res_AC$ci_u
+      [1] 2.09633
+      
+      $res_AC$pval
+      [1] 0.2562849
+      
+      
+      $res_AC_unadj
+      $res_AC_unadj$est
+      [1] 1.699301
+      
+      $res_AC_unadj$se
+      [1] 0.2488482
+      
+      $res_AC_unadj$ci_l
+      [1] 1.280998
+      
+      $res_AC_unadj$ci_u
+      [1] 2.254199
+      
+      $res_AC_unadj$pval
+      [1] 0.0002354448
+      
+      
+      $res_BC
+      $res_BC$est
+      [1] 2.333333
+      
+      $res_BC$se
+      [1] 0.3510631
+      
+      $res_BC$ci_l
+      [1] 1.745809
+      
+      $res_BC$ci_u
+      [1] 3.118579
+      
+      $res_BC$pval
+      [1] 1.035032e-08
+      
+      
+      $res_AB
+                  result             pvalue 
+      "0.56[0.32; 0.98]"            "0.041" 
+      
+      $res_AB_unadj
+                  result             pvalue 
+      "0.73[0.49; 1.09]"            "0.125" 
+      
+      $boot_res
+      
+      STRATIFIED BOOTSTRAP
+      
+      
+      Call:
+      boot(data = boot_ipd, statistic = stat_fun, R = R, strata = weights_object$boot_strata, 
+          w_obj = weights_object, eff_measure = eff_measure, normalize = normalize_weights)
+      
+      
+      Bootstrap Statistics :
+             original     bias    std. error
+      t1* -0.69289322 0.59931037 0.259160031
+      t2*  0.06888563 0.01114340 0.004040355
+      t3*  0.26246071 0.02034770 0.007139486
+      t4*  0.27147808 0.45352064 0.291583054
+      t5*  0.21675070 0.02420063 0.008380500
+      t6*  0.04698086 0.01114340 0.004040355
+      
+      $boot_res_AC
+      $boot_res_AC$est
+      [1] 1.311902
+      
+      $boot_res_AC$se
+      [1] NA
+      
+      $boot_res_AC$ci_l
+      [1] 0.4706998
+      
+      $boot_res_AC$ci_u
+      [1] 1.476168
+      
+      $boot_res_AC$pval
+      [1] NA
+      
+      
+      $boot_res_AB_mc
+      $boot_res_AB_mc$est
+      [1] 0.500127
+      
+      $boot_res_AB_mc$se
+      [1] NA
+      
+      $boot_res_AB_mc$ci_l
+      [1] 0.1652744
+      
+      $boot_res_AB_mc$ci_u
+      [1] 0.4564577
+      
+      $boot_res_AB_mc$pval
+      [1] NA
+      
+      
+      $boot_res_AB
+      $boot_res_AB$est
+      [1] 0.5622438
+      
+      $boot_res_AB$se
+      [1] NA
+      
+      $boot_res_AB$ci_l
+      [1] 0.2962009
+      
+      $boot_res_AB$ci_u
+      [1] 1.067242
+      
+      $boot_res_AB$pval
+      [1] NA
+      
+      
+
diff --git a/tests/testthat/_snaps/maic_unanchored.md b/tests/testthat/_snaps/maic_unanchored.md
new file mode 100644
index 00000000..2dcbc7b5
--- /dev/null
+++ b/tests/testthat/_snaps/maic_unanchored.md
@@ -0,0 +1,450 @@
+# test binary case
+
+    Code
+      testout$descriptive$summary
+    Output
+        trt_ind treatment            type   n   events events_pct
+      1       B         B Before matching 400 280.0000   70.00000
+      2       A         A Before matching 500 390.0000   78.00000
+      3       B         B  After matching 400 280.0000   70.00000
+      4       A         A  After matching 500 142.8968   28.57935
+
+---
+
+    Code
+      testout$inferential$summary
+    Output
+               case       RR       LCL      UCL      pval
+      1          AB 1.114286 0.9557015 1.299185 0.1671206
+      2 adjusted_AB 1.023627 0.9123647 1.148457 0.6908096
+
+---
+
+    Code
+      testout$inferential$fit
+    Output
+      $model_before
+      
+      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat)
+      
+      Coefficients:
+      (Intercept)         ARMA  
+          -0.3567       0.1082  
+      
+      Degrees of Freedom: 899 Total (i.e. Null);  898 Residual
+      Null Deviance:	    395.5 
+      Residual Deviance: 393.5 	AIC: 1738
+      
+      $model_after
+      
+      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat, 
+          weights = weights)
+      
+      Coefficients:
+      (Intercept)         ARMA  
+         -0.35667      0.02335  
+      
+      Degrees of Freedom: 899 Total (i.e. Null);  898 Residual
+      Null Deviance:	    295.1 
+      Residual Deviance: 295 	AIC: 1145
+      
+      $res_AB
+      $res_AB$est
+      [1] 1.023627
+      
+      $res_AB$se
+      [1] 0.06025155
+      
+      $res_AB$ci_l
+      [1] 0.9123647
+      
+      $res_AB$ci_u
+      [1] 1.148457
+      
+      $res_AB$pval
+      [1] 0.6908096
+      
+      
+      $res_AB_unadj
+      $res_AB_unadj$est
+      [1] 1.114286
+      
+      $res_AB_unadj$se
+      [1] 0.08768422
+      
+      $res_AB_unadj$ci_l
+      [1] 0.9557015
+      
+      $res_AB_unadj$ci_u
+      [1] 1.299185
+      
+      $res_AB_unadj$pval
+      [1] 0.1671206
+      
+      
+      $boot_res
+      NULL
+      
+      $boot_res_AB
+      NULL
+      
+
+---
+
+    Code
+      testout2$descriptive
+    Output
+      $summary
+        trt_ind treatment            type   n   events events_pct
+      1       B         B Before matching 400 280.0000   70.00000
+      2       A         A Before matching 500 390.0000   78.00000
+      3       B         B  After matching 400 280.0000   70.00000
+      4       A         A  After matching 500 142.8968   28.57935
+      
+
+---
+
+    Code
+      testout2$inferential$fit
+    Output
+      $model_before
+      
+      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat)
+      
+      Coefficients:
+      (Intercept)         ARMA  
+          -0.3567       0.1082  
+      
+      Degrees of Freedom: 899 Total (i.e. Null);  898 Residual
+      Null Deviance:	    395.5 
+      Residual Deviance: 393.5 	AIC: 1738
+      
+      $model_after
+      
+      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat, 
+          weights = weights)
+      
+      Coefficients:
+      (Intercept)         ARMA  
+         -0.35667      0.02335  
+      
+      Degrees of Freedom: 899 Total (i.e. Null);  898 Residual
+      Null Deviance:	    295.1 
+      Residual Deviance: 295 	AIC: 1145
+      
+      $res_AB
+      $res_AB$est
+      [1] 1.023627
+      
+      $res_AB$se
+      [1] 0.06025155
+      
+      $res_AB$ci_l
+      [1] 0.9123647
+      
+      $res_AB$ci_u
+      [1] 1.148457
+      
+      $res_AB$pval
+      [1] 0.6908096
+      
+      
+      $res_AB_unadj
+      $res_AB_unadj$est
+      [1] 1.114286
+      
+      $res_AB_unadj$se
+      [1] 0.08768422
+      
+      $res_AB_unadj$ci_l
+      [1] 0.9557015
+      
+      $res_AB_unadj$ci_u
+      [1] 1.299185
+      
+      $res_AB_unadj$pval
+      [1] 0.1671206
+      
+      
+      $boot_res
+      
+      STRATIFIED BOOTSTRAP
+      
+      
+      Call:
+      boot(data = boot_ipd, statistic = stat_fun, R = R, strata = weights_object$boot_strata, 
+          w_obj = weights_object, pseudo_ipd = pseudo_ipd, normalize = normalize_weights)
+      
+      
+      Bootstrap Statistics :
+            original       bias     std. error
+      t1* 0.02335185 1.366708e-02 0.0538052765
+      t2* 0.01056949 8.109601e-05 0.0005263733
+      
+      $boot_res_AB
+      $boot_res_AB$est
+      [1] 1.023627
+      
+      $boot_res_AB$se
+      [1] NA
+      
+      $boot_res_AB$ci_l
+      [1] 0.9086715
+      
+      $boot_res_AB$ci_u
+      [1] 1.122032
+      
+      $boot_res_AB$pval
+      [1] NA
+      
+      
+
+---
+
+    Code
+      testout2$inferential$summary
+    Output
+               case       RR       LCL      UCL      pval
+      1          AB 1.114286 0.9557015 1.299185 0.1671206
+      2 adjusted_AB 1.023627 0.9123647 1.148457 0.6908096
+
+# test time to event case
+
+    Code
+      testout$descriptive$summary
+    Output
+        trt_ind treatment            type records    n.max  n.start    events
+      1       B         B Before matching     300 300.0000 300.0000 178.00000
+      2       A         A Before matching     500 500.0000 500.0000 190.00000
+      3       B         B  After matching     300 300.0000 300.0000 178.00000
+      4       A         A  After matching     500 199.4265 199.4265  65.68878
+            rmean se(rmean)    median  0.95LCL   0.95UCL
+      1  4.303551 0.3367260  2.746131 2.261125  3.320857
+      2  8.709690 0.3551477  7.587627 6.278691 10.288538
+      3  4.303551 0.3367260  2.746131 2.261125  3.320857
+      4 10.166029 0.5499915 11.900015 7.815275 14.873786
+
+---
+
+    Code
+      testout$inferential$summary
+    Output
+               case        HR       LCL       UCL         pval
+      1          AB 0.3748981 0.3039010 0.4624815 5.245204e-20
+      2 adjusted_AB 0.2834780 0.2074664 0.3873387 2.473442e-15
+
+---
+
+    Code
+      testout$inferential$fit
+    Output
+      $km_before
+      Call: survfit(formula = Surv(TIME, EVENT) ~ ARM, data = dat, conf.type = km_conf_type)
+      
+              n events median 0.95LCL 0.95UCL
+      ARM=B 300    178   83.6    68.8     101
+      ARM=A 500    190  230.9   191.1     313
+      
+      $km_after
+      Call: survfit(formula = Surv(TIME, EVENT) ~ ARM, data = dat, weights = dat$weights, 
+          conf.type = km_conf_type)
+      
+            records   n events median 0.95LCL 0.95UCL
+      ARM=B     300 300  178.0   83.6    68.8     101
+      ARM=A     500 199   65.7  362.2   237.9     453
+      
+      $model_before
+      Call:
+      coxph(formula = Surv(TIME, EVENT) ~ ARM, data = dat)
+      
+              coef exp(coef) se(coef)      z      p
+      ARMA -0.9811    0.3749   0.1071 -9.159 <2e-16
+      
+      Likelihood ratio test=80.62  on 1 df, p=< 2.2e-16
+      n= 800, number of events= 368 
+      
+      $model_after
+      Call:
+      coxph(formula = Surv(TIME, EVENT) ~ ARM, data = dat, weights = weights, 
+          robust = TRUE)
+      
+              coef exp(coef) se(coef) robust se      z        p
+      ARMA -1.2606    0.2835   0.1504    0.1593 -7.915 2.47e-15
+      
+      Likelihood ratio test=80.4  on 1 df, p=< 2.2e-16
+      n= 800, number of events= 368 
+      
+      $res_AB
+      $res_AB$est
+      [1] 0.283478
+      
+      $res_AB$se
+      [1] 0.04601759
+      
+      $res_AB$ci_l
+      [1] 0.2074664
+      
+      $res_AB$ci_u
+      [1] 0.3873387
+      
+      $res_AB$pval
+      [1] 2.473442e-15
+      
+      
+      $res_AB_unadj
+      $res_AB_unadj$est
+      [1] 0.3748981
+      
+      $res_AB_unadj$se
+      [1] 0.0405065
+      
+      $res_AB_unadj$ci_l
+      [1] 0.303901
+      
+      $res_AB_unadj$ci_u
+      [1] 0.4624815
+      
+      $res_AB_unadj$pval
+      [1] 5.245204e-20
+      
+      
+      $boot_res
+      NULL
+      
+      $boot_res_AB
+      NULL
+      
+
+---
+
+    Code
+      testout2$descriptive$summary
+    Output
+        trt_ind treatment            type records    n.max  n.start    events
+      1       B         B Before matching     300 300.0000 300.0000 178.00000
+      2       A         A Before matching     500 500.0000 500.0000 190.00000
+      3       B         B  After matching     300 300.0000 300.0000 178.00000
+      4       A         A  After matching     500 199.4265 199.4265  65.68878
+            rmean se(rmean)    median  0.95LCL   0.95UCL
+      1  4.303551 0.3367260  2.746131 2.261125  3.320857
+      2  8.709690 0.3551477  7.587627 6.278691 10.288538
+      3  4.303551 0.3367260  2.746131 2.261125  3.320857
+      4 10.166029 0.5499915 11.900015 7.815275 14.873786
+
+---
+
+    Code
+      testout2$inferential$summary
+    Output
+               case        HR       LCL       UCL         pval
+      1          AB 0.3748981 0.3039010 0.4624815 5.245204e-20
+      2 adjusted_AB 0.2834780 0.2074664 0.3873387 2.473442e-15
+
+---
+
+    Code
+      testout2$inferential$fit
+    Output
+      $km_before
+      Call: survfit(formula = Surv(TIME, EVENT) ~ ARM, data = dat, conf.type = km_conf_type)
+      
+              n events median 0.95LCL 0.95UCL
+      ARM=B 300    178   83.6    68.8     101
+      ARM=A 500    190  230.9   191.1     313
+      
+      $km_after
+      Call: survfit(formula = Surv(TIME, EVENT) ~ ARM, data = dat, weights = dat$weights, 
+          conf.type = km_conf_type)
+      
+            records   n events median 0.95LCL 0.95UCL
+      ARM=B     300 300  178.0   83.6    68.8     101
+      ARM=A     500 199   65.7  362.2   237.9     453
+      
+      $model_before
+      Call:
+      coxph(formula = Surv(TIME, EVENT) ~ ARM, data = dat)
+      
+              coef exp(coef) se(coef)      z      p
+      ARMA -0.9811    0.3749   0.1071 -9.159 <2e-16
+      
+      Likelihood ratio test=80.62  on 1 df, p=< 2.2e-16
+      n= 800, number of events= 368 
+      
+      $model_after
+      Call:
+      coxph(formula = Surv(TIME, EVENT) ~ ARM, data = dat, weights = weights, 
+          robust = TRUE)
+      
+              coef exp(coef) se(coef) robust se      z        p
+      ARMA -1.2606    0.2835   0.1504    0.1593 -7.915 2.47e-15
+      
+      Likelihood ratio test=80.4  on 1 df, p=< 2.2e-16
+      n= 800, number of events= 368 
+      
+      $res_AB
+      $res_AB$est
+      [1] 0.283478
+      
+      $res_AB$se
+      [1] 0.04601759
+      
+      $res_AB$ci_l
+      [1] 0.2074664
+      
+      $res_AB$ci_u
+      [1] 0.3873387
+      
+      $res_AB$pval
+      [1] 2.473442e-15
+      
+      
+      $res_AB_unadj
+      $res_AB_unadj$est
+      [1] 0.3748981
+      
+      $res_AB_unadj$se
+      [1] 0.0405065
+      
+      $res_AB_unadj$ci_l
+      [1] 0.303901
+      
+      $res_AB_unadj$ci_u
+      [1] 0.4624815
+      
+      $res_AB_unadj$pval
+      [1] 5.245204e-20
+      
+      
+      $boot_res
+      
+      STRATIFIED BOOTSTRAP
+      
+      
+      Call:
+      boot(data = boot_ipd, statistic = stat_fun, R = R, strata = weights_object$boot_strata, 
+          w_obj = weights_object, pseudo_ipd = pseudo_ipd, normalize = normalize_weights)
+      
+      
+      Bootstrap Statistics :
+             original       bias    std. error
+      t1* -1.26062079 0.0024513461 0.131388233
+      t2*  0.02536718 0.0005819358 0.002670424
+      
+      $boot_res_AB
+      $boot_res_AB$est
+      [1] 0.283478
+      
+      $boot_res_AB$se
+      [1] NA
+      
+      $boot_res_AB$ci_l
+      [1] 0.2185832
+      
+      $boot_res_AB$ci_u
+      [1] 0.3658412
+      
+      $boot_res_AB$pval
+      [1] NA
+      
+      
+
diff --git a/tests/testthat/data/test_binary_anchored_expected.RData b/tests/testthat/data/test_binary_anchored_expected.RData
deleted file mode 100644
index 8383a644..00000000
Binary files a/tests/testthat/data/test_binary_anchored_expected.RData and /dev/null differ
diff --git a/tests/testthat/data/test_binary_unanchored_expected.RData b/tests/testthat/data/test_binary_unanchored_expected.RData
deleted file mode 100644
index 6e775bc5..00000000
Binary files a/tests/testthat/data/test_binary_unanchored_expected.RData and /dev/null differ
diff --git a/tests/testthat/data/test_tte_anchored_expected.RData b/tests/testthat/data/test_tte_anchored_expected.RData
deleted file mode 100644
index 53f5be06..00000000
Binary files a/tests/testthat/data/test_tte_anchored_expected.RData and /dev/null differ
diff --git a/tests/testthat/data/test_tte_unanchored_expected.RData b/tests/testthat/data/test_tte_unanchored_expected.RData
deleted file mode 100644
index 5d60074a..00000000
Binary files a/tests/testthat/data/test_tte_unanchored_expected.RData and /dev/null differ
diff --git a/tests/testthat/test-maic_anchored.R b/tests/testthat/test-maic_anchored.R
index 7a532924..42e3f0d7 100644
--- a/tests/testthat/test-maic_anchored.R
+++ b/tests/testthat/test-maic_anchored.R
@@ -54,24 +54,15 @@ test_that("maic_anchored works for TTE", {
     km_conf_type = "log-log"
   )
 
-  if (FALSE) {
-    # Manual snapshot of results
-    expectout <- testout
-    expectout2 <- testout2
-    save(list = c("expectout", "expectout2"), file = test_path("data", "test_tte_anchored_expected.RData"))
-  }
-
-  load(test_path("data", "test_tte_anchored_expected.RData"))
   # Compare robust outputs
-  expect_equal(testout$descriptive, expectout$descriptive)
-  expect_equal(testout$inferential$summary, expectout$inferential$summary)
-  expect_equal(testout$inferential$fit$res_AB, expectout$inferential$fit$res_AB)
+  expect_snapshot(testout$descriptive$summary)
+  expect_snapshot(testout$inferential$summary)
+  expect_snapshot(testout$inferential$fit)
 
   # Compare bootstrap outputs
-  expect_equal(testout2$descriptive, expectout2$descriptive)
-  expect_equal(testout2$inferential$fit$boot_est["t"], expectout2$inferential$fit$boot_est["t"])
-  expect_equal(testout2$inferential$fit$boot_est["seed"], expectout2$inferential$fit$boot_est["seed"])
-  expect_equal(testout2$inferential$boot_res_AB, expectout2$inferential$boot_res_AB)
+  expect_snapshot(testout2$descriptive$summary)
+  expect_snapshot(testout2$inferential$summary)
+  expect_snapshot(testout2$inferential$fit)
 })
 
 test_that("maic_anchored for binary case gives the expected result", {
@@ -132,24 +123,13 @@ test_that("maic_anchored for binary case gives the expected result", {
     eff_measure = "OR"
   )
 
-  if (FALSE) {
-    # Manual snapshot of results
-    expectout <- testout
-    expectout2 <- testout2
-    save(list = c("expectout", "expectout2"), file = test_path("data", "test_binary_anchored_expected.RData"))
-  }
-
-  load(test_path("data", "test_binary_anchored_expected.RData"))
-
   # Compare robust outputs
-  expect_equal(testout$descriptive, expectout$descriptive)
-  expect_equal(testout$inferential$summary, expectout$inferential$summary)
-  expect_equal(testout$inferential$fit$res_AB$est, testout$inferential$fit$res_AB$est)
-  expect_equal(testout$inferential$fit$res_AB$pvalue, testout$inferential$fit$res_AB$pvalue)
+  expect_snapshot(testout$descriptive$summary)
+  expect_snapshot(testout$inferential$summary)
+  expect_snapshot(testout$inferential$fit)
 
   # Compare bootstrap outputs
-  expect_equal(testout2$descriptive, expectout2$descriptive)
-  expect_equal(testout2$inferential$fit$boot_est["t"], expectout2$inferential$fit$boot_est["t"])
-  expect_equal(testout2$inferential$fit$boot_est["seed"], expectout2$inferential$fit$boot_est["seed"])
-  expect_equal(testout2$inferential$boot_res_AB, expectout2$inferential$boot_res_AB)
+  expect_snapshot(testout2$descriptive$summary)
+  expect_snapshot(testout2$inferential$summary)
+  expect_snapshot(testout2$inferential$fit)
 })
diff --git a/tests/testthat/test-maic_unanchored.R b/tests/testthat/test-maic_unanchored.R
index dd2ec810..0035c811 100644
--- a/tests/testthat/test-maic_unanchored.R
+++ b/tests/testthat/test-maic_unanchored.R
@@ -60,25 +60,16 @@ test_that("test binary case", {
       binary_robust_cov_type = "HC3"
     )
 
-  if (FALSE) {
-    # Manual snapshot of results
-    expectout <- testout
-    expectout2 <- testout2
-    save(list = c("expectout", "expectout2"), file = test_path("data", "test_binary_unanchored_expected.RData"))
-  }
-
-  load(test_path("data", "test_binary_unanchored_expected.RData"))
 
   # Compare robust outputs
-  expect_equal(testout$descriptive, expectout$descriptive)
-  expect_equal(testout$inferential$summary, expectout$inferential$summary)
-  expect_equal(testout$inferential$fit, expectout$inferential$fit)
+  expect_snapshot(testout$descriptive$summary)
+  expect_snapshot(testout$inferential$summary)
+  expect_snapshot(testout$inferential$fit)
 
   # Compare bootstrap outputs
-  expect_equal(testout2$descriptive, expectout2$descriptive)
-  expect_equal(testout2$inferential$fit$boot_est["t"], expectout2$inferential$fit$boot_est["t"])
-  expect_equal(testout2$inferential$fit$boot_est["seed"], expectout2$inferential$fit$boot_est["seed"])
-  expect_equal(testout2$inferential$boot_res_AB, expectout2$inferential$boot_res_AB)
+  expect_snapshot(testout2$descriptive)
+  expect_snapshot(testout2$inferential$fit)
+  expect_snapshot(testout2$inferential$summary)
 })
 
 
@@ -140,22 +131,14 @@ test_that("test time to event case", {
     km_conf_type = "log-log"
   )
 
-  if (FALSE) {
-    # Manual snapshot of results
-    expectout <- testout
-    expectout2 <- testout2
-    save(list = c("expectout", "expectout2"), file = test_path("data", "test_tte_unanchored_expected.RData"))
-  }
 
-  load(test_path("data", "test_tte_unanchored_expected.RData"))
   # Compare robust outputs
-  expect_equal(testout$descriptive, expectout$descriptive)
-  expect_equal(testout$inferential$summary, expectout$inferential$summary)
-  expect_equal(testout$inferential$fit$res_AB, expectout$inferential$fit$res_AB)
+  expect_snapshot(testout$descriptive$summary)
+  expect_snapshot(testout$inferential$summary)
+  expect_snapshot(testout$inferential$fit)
 
   # Compare bootstrap outputs
-  expect_equal(testout2$descriptive, expectout2$descriptive)
-  expect_equal(testout2$inferential$fit$boot_est["t"], expectout2$inferential$fit$boot_est["t"])
-  expect_equal(testout2$inferential$fit$boot_est["seed"], expectout2$inferential$fit$boot_est["seed"])
-  expect_equal(testout2$inferential$boot_res_AB, expectout2$inferential$boot_res_AB)
+  expect_snapshot(testout2$descriptive$summary)
+  expect_snapshot(testout2$inferential$summary)
+  expect_snapshot(testout2$inferential$fit)
 })
diff --git a/tests/testthat/test-matching.R b/tests/testthat/test-matching.R
index 7aab16b9..1cbdacc1 100644
--- a/tests/testthat/test-matching.R
+++ b/tests/testthat/test-matching.R
@@ -31,7 +31,7 @@ test_that("optimise_weights works as expected", {
     result <- optimise_weights(object, par = c(0, 0), trace = 2),
     "converged"
   )
-  expect_equal(result$alpha, c(-0.07702229, 0.06826333))
+  expect_equal(result$alpha, c(-0.0770222925868337, 0.0682633331364419))
   expect_equal(
     result$wt[, 1],
     c(