Rezidential

Inferential Statistics

1 Load data

## Read
filename <- "Data_Rezidential.RDS"   

Data <- readRDS(filename)  

1.1 Modify ACE Score to take into account that all are institutionalized

Data$CYW <- ifelse(Data$CYW == 0, 0, Data$CYW - 1) 

2 Typo in ASCQ

Data$AAvoid[which(Data$asc_12 == 11)] <- Data$AAvoid[which(Data$asc_12 == 11)] - 10
Data$asc_12[which(Data$asc_12 == 11)] <- 1   # "ASCQ_f" "ASCQ_d" remain uncorrected

3 Typo in Age

Data[Data$ID == 505, ]$varsta <- 15   # age 15 instead of 5

3.1 Minore transformations to Data

4 Desc CESD

library(patchwork)

Data %>%
  dplyr::group_by(CESD) %>%
  dplyr::summarise(n = n()) %>%
  dplyr::mutate(freq = n / sum(n)) %>%
  print(n = Inf)

p_cesd_1 <-
  Data %>%
    ggplot(aes(x = CESD, fill = CESD_d)) +
      geom_histogram(bins = 55, color = "black") +  
      geom_vline(xintercept = 15, linetype = "dashed", color = "black", size = 1.2) +
      scale_y_continuous(
        sec.axis = sec_axis(trans = ~./nrow(Data), labels = percent, 
                            name = "Proportie (%)")) +
      scale_fill_manual(breaks = c("0", "1"), 
                         values = wesanderson::wes_palette("Royal1")[1:2]) +
      ylab("Frecventa") +
      scale_x_continuous(breaks = seq(0, 55, by = 5)) +
      guides(fill = FALSE) +
      facet_wrap(~tip_chestionar2) +
      ggtitle("Niveluri de depresie")

p_cesd_2 <-
  Data_child %>%
    dplyr::mutate(CESD_d = forcats::fct_rev(CESD_d)) %>%
    ggstatsplot::ggpiestats(
      x = CESD_d,
      y = gen,
      type = "parametric", 
      bf.message = FALSE,
      package = "wesanderson",
      palette = "Royal1",
      title = "Copii (5-8 ani)")

p_cesd_3 <-
  Data_adol %>%
    dplyr::mutate(CESD_d = forcats::fct_rev(CESD_d)) %>%
    ggstatsplot::ggpiestats(
      x = CESD_d,
      y = gen,
      type = "parametric", 
      bf.message = FALSE,
      package = "wesanderson",
      palette = "Royal1",
      title = "Preadolescenti si adolescenti (9-18 ani)")



p_cesd_1 / (p_cesd_2 | p_cesd_3)  

# ggsave(plot = p_cesd_1, filename = "p_cesd_1.png", width = 10, height = 8, units = "in", dpi = 500)
# ggsave(plot = p_cesd_2, filename = "p_cesd_2.png", width = 7, height = 7, units = "in", dpi = 500)
# ggsave(plot = p_cesd_3, filename = "p_cesd_3.png", width = 7, height = 7, units = "in", dpi = 500)

5 Desc SCARED

library(patchwork)

Data %>%
  dplyr::group_by(SCARED) %>%
  dplyr::summarise(n = n()) %>%
  dplyr::mutate(freq = n / sum(n)) %>%
  print(n = Inf)

p_scared_1 <-
  Data %>%
  ggplot(aes(x = SCARED, fill = SCARED_d)) +
  geom_histogram(bins = 85, color = "black") +  
  geom_vline(xintercept = 25, linetype = "dashed", color = "black", size = 1.2) +
  scale_y_continuous(
    sec.axis = sec_axis(trans = ~./nrow(Data), labels = percent, 
                        name = "Proportie (%)")) +
  scale_fill_manual(breaks = c("0", "1"), 
                    values = wesanderson::wes_palette("Royal1")[1:2]) +
  ylab("Frecventa") +
  scale_x_continuous(breaks = seq(0, 85, by = 5)) +
  guides(fill = FALSE) +
  facet_wrap(~tip_chestionar2) +
  ggtitle("Niveluri de anxietate")

p_scared_2 <-
  Data_child %>%
  dplyr::mutate(SCARED_d = forcats::fct_rev(SCARED_d)) %>%
  ggstatsplot::ggpiestats(
    x = SCARED_d,
    y = gen,
    type = "parametric", 
    bf.message = FALSE,
    package = "wesanderson",
    palette = "Royal1",
    title = "Copii (5-8 ani)")

p_scared_3 <-
  Data_adol %>%
  dplyr::mutate(SCARED_d = forcats::fct_rev(SCARED_d)) %>%
  ggstatsplot::ggpiestats(
    x = SCARED_d,
    y = gen,
    type = "parametric", 
    bf.message = FALSE,
    package = "wesanderson",
    palette = "Royal1",
    title = "Preadolescenti si adolescenti (9-18 ani)")



p_scared_1 / (p_scared_2 | p_scared_3)  

# ggsave(plot = p_scared_1, filename = "p_scared_1.png", width = 10, height = 8, units = "in", dpi = 500)
# ggsave(plot = p_scared_2, filename = "p_scared_2.png", width = 7, height = 7, units = "in", dpi = 500)
# ggsave(plot = p_scared_3, filename = "p_scared_3.png", width = 7, height = 7, units = "in", dpi = 500)

6 GCIC

Data_gci <- Data[, c(sprintf("gci_%d", 1:14), "OpenC", "CloseC", "centru")]

labels_gci <- 
  c("Foarte neadevărat",
  "Neadevărat",
  "Un pic neadevărat / Un pic adevărat",
  "Adevărat",
  "Foarte adevărat")

itemtext_gci <-
  c("1. Oamenii de la centru se poartă frumos cu mine.",
  "2. Am încredere în oamenii din centru.",
  "3. Oamenii de la centru mă înţeleg.",
  "4. Atunci când mă plâng de ceva, oamenii din centru mă iau în serios.",
  "5. Oamenii de la centru sunt corecți.",
  "6. Simt că aici, la centru, lucrez la îndeplinirea scopurilor mele.",
  "7. În acest centru sunt întotdeauna destui oameni care să mă ajute.",
  "8. Oamenii din centru se țin de cuvânt.",
  "9. Pot să cer ajutor de la oamenii din centru atunci când am nevoie.",
  "10. În acest centru, copiii au încredere unii în alții. (R)",
  "11. Aici, poți să ai încredere în toată lumea. (R)",
  "12. Haosul și gălăgia din centru mă înnebunesc.",
  "13. Sunt prea mulți copii aici.",
  "14. Oamenii de la centru sunt adesea prea ocupați ca să mă ajute.")

Data_gci <-
  Data_gci %>%
  mutate_at(vars(sprintf("gci_%d", 1:14)), ~as.factor(as.character(.))) %>%
  rename_at(vars(sprintf("gci_%d", 1:14)), ~itemtext_gci) %>%
  rename_at(vars("OpenC", "CloseC"), ~c("Climat deschis", "Climat închis")) %>%
  dplyr::rename(Centru = centru)

# Plots  # library(likert)
Likertobj_gci <- likert::likert(Data_gci[, 1:14], nlevels = 5)   # here are percentages

p_gcic_1 <-
  plot(Likertobj_gci, type = "bar", 
       centered = TRUE, center = 3, include.center = TRUE,              # "3" is neutral
       wrap = 40, low.color = 'burlywood', high.color = 'maroon',
       group.order = names(Data_gci[, 1:14])) +
    ylab("Procent") + 
    guides(fill = guide_legend(nrow = 1, title = "Răspuns")) +
    geom_vline(xintercept = 5.51) +
    labs(title = "Climatul de grup din centrul rezidential",
         caption = "Raspunsurile la itemii 10 si 11 au fost cotate invers.")

 
Data_gci %>%
  select("Climat deschis", "Climat închis") %>%
  gather() %>%
  rename_at(vars("key", "value"), ~c("Var", "Scor")) %>%
    ggpubr::ggviolin("Var", "Scor", fill = "Var",
      palette = c("#00AFBB", "#FC4E07"),
      add = "boxplot", add.params = list(fill = "white"),
      xlab = "", legend = "none") +
  stat_summary(fun.data = mean_se,  colour = "darkred")

ggsave(plot = p_gcic_1, filename = "p_gcic_1.png", width = 10, height = 10, units = "in", dpi = 500)

Data_adol %>%
  dplyr::select("OpenC", "CloseC", "CESD", "SCARED", "R_Ind_a", "R_Care_a", "R_Cont_a", "CYRM_a") %>%
  PerformanceAnalytics::chart.Correlation()

7 Mediation

7.1 Adol

Data_adol_standardized <- 
  Data_adol %>%
  dplyr::select("OpenC", "CloseC", "CESD", "SCARED", "CYRM_a", "PD", "GAD",  "SepA", "SAD") %>%
  mutate_all(~(scale(.) %>% as.vector))

# psych::mediate(CESD ~ OpenC + CloseC + (CYRM_a), data = Data_adol, zero = TRUE)
# psych::mediate(SCARED ~ OpenC + CloseC + (CYRM_a), data = Data_adol)

# psych::mediate(CESD + SCARED ~ OpenC + CloseC + (CYRM_a), data = Data_adol) # %>% summary()

# SCARED
# mod_adol <- psych::mediate(CESD + SCARED ~ OpenC + CloseC + (CYRM_a), data = Data_adol_standardized) 
# mod_adol %>% summary()
# mod_adol$boot$ci.ab

# GAD
summary(lm(CESD ~ OpenC + CloseC, data = Data_adol_standardized)); # ggstatsplot::ggcoefstats(lm(CESD ~ OpenC + CloseC, data = Data_adol_standardized))

Call:
lm(formula = CESD ~ OpenC + CloseC, data = Data_adol_standardized)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2953 -0.6849 -0.0908  0.5795  3.2962 

Coefficients:
             Estimate Std. Error t value             Pr(>|t|)    
(Intercept)  0.000231   0.028182   0.008                0.993    
OpenC       -0.177514   0.033525  -5.295          0.000000146 ***
CloseC       0.309068   0.033000   9.366 < 0.0000000000000002 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9044 on 1027 degrees of freedom
  (15 observations deleted due to missingness)
Multiple R-squared:  0.1839,    Adjusted R-squared:  0.1824 
F-statistic: 115.7 on 2 and 1027 DF,  p-value: < 0.00000000000000022

summary(lm(GAD ~ OpenC + CloseC, data = Data_adol_standardized)); # ggstatsplot::ggcoefstats(lm(GAD ~ OpenC + CloseC, data = Data_adol_standardized))

Call:
lm(formula = GAD ~ OpenC + CloseC, data = Data_adol_standardized)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3076 -0.7273 -0.1250  0.5807  3.0347 

Coefficients:
            Estimate Std. Error t value            Pr(>|t|)    
(Intercept)  0.00046    0.03018   0.015               0.988    
OpenC        0.05007    0.03587   1.396               0.163    
CloseC       0.35479    0.03521  10.077 <0.0000000000000002 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9436 on 975 degrees of freedom
  (67 observations deleted due to missingness)
Multiple R-squared:  0.1121,    Adjusted R-squared:  0.1102 
F-statistic: 61.53 on 2 and 975 DF,  p-value: < 0.00000000000000022

# QuantPsyc::lm.beta(lm(GAD ~ OpenC + CloseC, data = Data_adol_standardized))

mod_adol <- psych::mediate(CESD + GAD ~ OpenC + CloseC + (CYRM_a), data = Data_adol_standardized) 

mod_adol %>% summary()

Call: psych::mediate(y = CESD + GAD ~ OpenC + CloseC + (CYRM_a), data = Data_adol_standardized)

Direct effect estimates (traditional regression)    (c') 

R = 0.49 R2 = 0.24   F = 109.68 on 3 and 1041 DF   p-value:  0.000000000000000000000000000000000000000000000000000000000000103 

R = 0.35 R2 = 0.12   F = 47.09 on 3 and 1041 DF   p-value:  0.000000000000000000000000000153 

 Total effect estimates (c) 

 'a'  effect estimates 

 'b'  effect estimates 

 'ab'  effect estimates (through mediators)

mod_adol$boot$ci.ab

             [,1]       [,2]         [,3]         [,4]        [,5]       [,6]         [,7]         [,8]
2.5%  -0.14548215 0.01104065 -0.066631435 0.0007907931 -0.14548215 0.01104065 -0.066631435 0.0007907931
97.5% -0.08305958 0.04997896 -0.003989529 0.0198466558 -0.08305958 0.04997896 -0.003989529 0.0198466558

7.2 Child

Data_child_standardized <- 
  Data_child %>%
  dplyr::select("OpenC", "CloseC", "CESD", "SCARED", "CYRM_k", "PD", "GAD",  "SepA", "SAD") %>%
  mutate_all(~(scale(.) %>% as.vector))

# SCARED
# mod_child <- psych::mediate(CESD + SCARED ~ OpenC + CloseC + (CYRM_k), data = Data_child_standardized)  
# mod_child %>% summary() 
# mod_child$boot$ci.ab

# GAD
# Data_child_standardized_gad <-
#   Data_child_standardized %>%
#   dplyr::select("OpenC", "CloseC", "CESD", "GAD", "CYRM_k") %>%
#   tidyr::drop_na()

summary(lm(CESD ~ OpenC + CloseC, data = Data_child_standardized)); # ggstatsplot::ggcoefstats(lm(CESD ~ OpenC + CloseC, data = Data_child_standardized))

Call:
lm(formula = CESD ~ OpenC + CloseC, data = Data_child_standardized)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6203 -0.5811 -0.1272  0.5067  2.7556 

Coefficients:
             Estimate Std. Error t value        Pr(>|t|)    
(Intercept) -0.002541   0.053237  -0.048        0.961968    
OpenC       -0.232169   0.063279  -3.669        0.000304 ***
CloseC       0.438460   0.062916   6.969 0.0000000000349 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8038 on 225 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.3561,    Adjusted R-squared:  0.3504 
F-statistic: 62.23 on 2 and 225 DF,  p-value: < 0.00000000000000022

summary(lm(GAD ~ OpenC + CloseC, data = Data_child_standardized)); # ggstatsplot::ggcoefstats(lm(GAD ~ OpenC + CloseC, data = Data_child_standardized))

Call:
lm(formula = GAD ~ OpenC + CloseC, data = Data_child_standardized)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5765 -0.6724 -0.1850  0.4993  4.0651 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.002605   0.062437   0.042 0.966760    
OpenC       -0.173376   0.074503  -2.327 0.020869 *  
CloseC       0.253914   0.075269   3.373 0.000877 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9321 on 220 degrees of freedom
  (7 observations deleted due to missingness)
Multiple R-squared:  0.1343,    Adjusted R-squared:  0.1265 
F-statistic: 17.07 on 2 and 220 DF,  p-value: 0.0000001286

mod_child <- psych::mediate(CESD + GAD ~ OpenC + CloseC + (CYRM_k), data = Data_child_standardized)  

mod_child %>% summary() 

Call: psych::mediate(y = CESD + GAD ~ OpenC + CloseC + (CYRM_k), data = Data_child_standardized)

Direct effect estimates (traditional regression)    (c') 

R = 0.65 R2 = 0.42   F = 54.97 on 3 and 226 DF   p-value:  0.0000000000000000000000000101 

R = 0.36 R2 = 0.13   F = 11.12 on 3 and 226 DF   p-value:  0.000000781 

 Total effect estimates (c) 

 'a'  effect estimates 

 'b'  effect estimates 

 'ab'  effect estimates (through mediators)

mod_child$boot$ci.ab

            [,1]      [,2]        [,3]        [,4]       [,5]      [,6]        [,7]        [,8]
2.5%  -0.1268604 0.0356824 -0.03366688 -0.04257002 -0.1268604 0.0356824 -0.03366688 -0.04257002
97.5% -0.0267708 0.1430005  0.04075252  0.04015818 -0.0267708 0.1430005  0.04075252  0.04015818

Data_standardized <- 
  Data %>%
  dplyr::select("CYW", "OpenC", "CloseC", "CESD", "SCARED", "CYRM_a", "CYRM_k", "PD", "GAD",  "SepA", "SAD") %>%
  dplyr::mutate(CYRM = dplyr::coalesce(CYRM_a, CYRM_k)) %>%
  dplyr::select(-CYRM_a, -CYRM_k) %>% 
  mutate_all(~(scale(.) %>% as.vector))
Data_standardized <- cbind(Data_standardized, gen = Data$gen)


#psych::mediate(CESD + SCARED ~ CYW + CloseC + (CYRM), data = Data_standardized) 
mod_adolchild <- psych::mediate(CESD + SCARED ~ OpenC + CloseC + (CYRM), data = Data_standardized) 
mod_adolchild %>% summary()
mod_adolchild$boot$ci.ab

bla <- 
  Data_standardized %>%
  dplyr::select("OpenC", "CloseC", "CESD", CYRM, GAD) %>%
  drop_na()

–>

8 lavvan Adol

path_model <- '
OpenC ~~ CloseC
CESD ~~ SCARED
# CYRM_a ~ gen

# direct effect
 CESD ~ c1*OpenC
 CESD ~ c2*CloseC
 
 SCARED ~ c3*OpenC
 SCARED ~ c4*CloseC
 
# mediator
 CYRM_a ~ a1*OpenC
 CYRM_a ~ a2*CloseC
 
 CESD ~ b1*CYRM_a
 SCARED ~ b2*CYRM_a
 
# indirect effect (a*b)
 ab1 := a1*b1   # for c1: Open-CESD
 ab2 := a2*b1   # for c2: Close-CESD
 
 ab3 := a1*b2   # for c3: Open-SCARED
 ab4 := a2*b2   # for c4: Close-SCARED
 
# total effect
 total1 := c1 + (a1*b1)
 total2 := c2 + (a2*b1)
 
 total3 := c3 + (a1*b2)
 total4 := c4 + (a2*b2)
'

fit_path_model <- lavaan::sem(model = path_model, data = Data_adol, estimator = "ml", se = "bootstrap") # se = "bootstrap"
summary(fit_path_model, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)
semPaths(fit_path_model, what = "std", title = TRUE, curvePivot = TRUE, layout = "tree")

# significant standardized paths only
lavaanPlot::lavaanPlot(model = fit_path_model, 
                       graph_options = list(layout = "dot"), 
                       node_options = list(shape = "box", fontname = "Helvetica"), 
                       edge_options = list(color = "grey"), 
                       coefs = TRUE, stand = TRUE, covs = TRUE, 
                       sig = 0.05, stars = c("regress", "cov"))  # unstandardized: sig = 1.00

9 lavvan Adol & Child

path_model <- '
OpenC ~~ CloseC
CESD ~~ SCARED

# CYRM ~ CYW
# CESD ~ CYW
# SCARED ~ CYW

# direct effect
 CESD ~ c1*OpenC
 CESD ~ c2*CloseC
 
 SCARED ~ c3*OpenC
 SCARED ~ c4*CloseC
 
# mediator
 CYRM ~ a1*OpenC
 CYRM ~ a2*CloseC
 
 CESD ~ b1*CYRM
 SCARED ~ b2*CYRM
 
# indirect effect (a*b)
 ab1 := a1*b1   # for c1: Open-CESD
 ab2 := a2*b1   # for c2: Close-CESD
 
 ab3 := a1*b2   # for c3: Open-SCARED
 ab4 := a2*b2   # for c4: Close-SCARED
 
# total effect
 total1 := c1 + (a1*b1)
 total2 := c2 + (a2*b1)
 
 total3 := c3 + (a1*b2)
 total4 := c4 + (a2*b2)
'

fit_path_model <- lavaan::sem(model = path_model, data = Data_standardized, estimator = "ml", se = "bootstrap", test = "boot") # se = "bootstrap"
summary(fit_path_model, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)
parameterEstimates(fit_path_model, ci = TRUE, level = 0.95, boot.ci.type = "bca.simple", standardized = TRUE)
                   

# significant standardized paths only
lavaanPlot::lavaanPlot(model = fit_path_model, 
                       graph_options = list(layout = "dot"), 
                       node_options = list(shape = "box", fontname = "Helvetica"), 
                       edge_options = list(color = "grey"), 
                       coefs = TRUE, stand = TRUE, covs = TRUE, 
                       sig = 0.05, stars = c("regress", "cov"))  # unstandardized: sig = 1.00

–>

10 Session Info

R version 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 8.1 x64 (build 9600)

Matrix products: default

locale:
[1] LC_COLLATE=Romanian_Romania.1250  LC_CTYPE=Romanian_Romania.1250    LC_MONETARY=Romanian_Romania.1250 LC_NUMERIC=C                     
[5] LC_TIME=Romanian_Romania.1250    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] patchwork_1.1.1            semPlot_1.1                semTools_0.5-4             lavaan_0.6-8               medmod_1.0.0              
 [6] car_3.0-10                 carData_3.0-2              RColorBrewer_1.1-2         corrplot_0.84              GGally_1.4.0              
[11] Hmisc_4.1-1                Formula_1.2-3              survival_2.44-1.1          lattice_0.20-38            rio_0.5.26                
[16] scales_1.1.1               ggpubr_0.4.0               PerformanceAnalytics_1.5.2 xts_0.11-2                 zoo_1.8-4                 
[21] tadaatoolbox_0.16.1        summarytools_0.8.8         broom_0.7.6                psycho_0.6.1               psych_2.0.12              
[26] plyr_1.8.6                 forcats_0.5.1              stringr_1.4.0              dplyr_1.0.6                purrr_0.3.4               
[31] readr_1.4.0                tidyr_1.1.3                tibble_3.1.1               ggplot2_3.3.3              tidyverse_1.3.1           
[36] papaja_0.1.0.9997          kableExtra_1.3.4           knitr_1.31                 pacman_0.5.1              

loaded via a namespace (and not attached):
  [1] estimability_1.3          coda_0.19-2               acepack_1.4.1             multcomp_1.4-8            wesanderson_0.3.6        
  [6] data.table_1.14.0         rpart_4.1-15              RCurl_1.95-4.11           generics_0.1.0            TH.data_1.0-9            
 [11] correlation_0.6.1         webshot_0.5.1             xml2_1.3.2                lubridate_1.7.10          assertthat_0.2.1         
 [16] d3Network_0.5.2.1         viridis_0.5.1             WRS2_1.1-1                xfun_0.22                 hms_1.0.0                
 [21] evaluate_0.14             fansi_0.4.2               dbplyr_2.1.1              readxl_1.3.1              igraph_1.2.6             
 [26] DBI_1.0.0                 tmvnsim_1.0-2             htmlwidgets_1.5.3         reshape_0.8.8             kSamples_1.2-9           
 [31] stats4_3.6.1              paletteer_1.3.0           Rmpfr_0.7-1               ellipsis_0.3.2            backports_1.2.1          
 [36] pbivnorm_0.6.0            insight_0.14.2            prismatic_1.0.0           rapportools_1.0           pwr_1.2-2                
 [41] jmvcore_1.2.23            vctrs_0.3.8               abind_1.4-5               withr_2.4.1               pryr_0.1.4               
 [46] checkmate_1.8.5           emmeans_1.5.4             sna_2.4                   fdrtool_1.2.15            mnormt_2.0.2             
 [51] svglite_1.2.1             cluster_2.1.1             mi_1.0                    crayon_1.4.1              ellipse_0.4.1            
 [56] labeling_0.4.2            SuppDists_1.1-9.4         pkgconfig_2.0.3           statsExpressions_1.1.0    nlme_3.1-140             
 [61] ggm_2.3                   nnet_7.3-12               rlang_0.4.11              lifecycle_1.0.0           MatrixModels_0.4-1       
 [66] sandwich_2.5-0            kutils_1.70               modelr_0.1.8              cellranger_1.1.0          matrixStats_0.54.0       
 [71] Matrix_1.2-17             mc2d_0.1-18               boot_1.3-22               reprex_2.0.0              base64enc_0.1-3          
 [76] whisker_0.3-2             png_0.1-7                 viridisLite_0.3.0         rjson_0.2.20              PMCMRplus_1.9.0          
 [81] parameters_0.14.0         rootSolve_1.8.2.1         bitops_1.0-6              pander_0.6.3              multcompView_0.1-7       
 [86] arm_1.10-1                jpeg_0.1-8                rockchalk_1.8.129         rstatix_0.7.0             ggsignif_0.6.1           
 [91] memoise_1.1.0             magrittr_2.0.1            compiler_3.6.1            lme4_1.1-26               cli_2.5.0                
 [96] pbapply_1.3-4             htmlTable_1.12            MASS_7.3-51.4             tidyselect_1.1.0          stringi_1.5.3            
[101] lisrelToR_0.1.4           sem_3.1-9                 pixiedust_0.9.1           OpenMx_2.11.5             latticeExtra_0.6-28      
[106] ggrepel_0.9.1             grid_3.6.1                tools_3.6.1               lmom_2.8                  parallel_3.6.1           
[111] matrixcalc_1.0-3          rstudioapi_0.13           foreign_0.8-71            gridExtra_2.3             ipmisc_6.0.2             
[116] gld_2.6.2                 pairwiseComparisons_3.1.6 farver_2.1.0              BDgraph_2.53              digest_0.6.27            
[121] BWStest_0.2.2             nortest_1.0-4             quadprog_1.5-5            Rcpp_1.0.6                BayesFactor_0.9.12-4.2   
[126] performance_0.7.2         httr_1.4.2                gdtools_0.1.7             likert_1.3.5              effectsize_0.4.5         
[131] colorspace_2.0-0          rvest_1.0.0               XML_3.98-1.16             fs_1.5.0                  splines_3.6.1            
[136] statmod_1.4.35            rematch2_2.1.2            expm_0.999-3              Exact_2.1                 xtable_1.8-4             
[141] gmp_0.5-13.2              jsonlite_1.7.2            nloptr_1.2.2.2            corpcor_1.6.9             glasso_1.10              
[146] zeallot_0.1.0             R6_2.5.0                  pillar_1.6.1              htmltools_0.5.1.1         glue_1.4.2               
[151] minqa_1.2.4               class_7.3-15              codetools_0.2-16          mvtnorm_1.1-1             utf8_1.2.1               
[156] network_1.13.0.1          huge_1.2.7                curl_4.3                  DescTools_0.99.40         gtools_3.8.1             
[161] zip_1.0.0                 openxlsx_4.1.0            rmarkdown_2.7             qgraph_1.5                statnet.common_4.1.4     
[166] munsell_0.5.0             e1071_1.7-0               ggstatsplot_0.8.0         haven_2.4.1               reshape2_1.4.4           
[171] gtable_0.3.0              bayestestR_0.10.0        

 

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A work by Claudiu Papasteri

claudiu.papasteri@gmail.com